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Odor pleasantness shapes approach and avoidance. Here, the authors show that the piriform cortex and amygdala encode the pleasantness of appetitive and aversive odors separately, while ventral prefrontal cortex represents a continuous salience code.

Privacy in distributed quantum sensing with Gaussian quantum networks

James Webb Space Telescope reveals weather patterns from how planet WASP-94 A b filters the light of its parent star.

Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually, help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.

In 2025, total investments in robotics companies reached a record $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality.

The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe.

The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term, and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough.

As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics.

1. The YouTube-to-Reality Gap Is Real

For years we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon.

Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work.

2. Data Is An Unsolved Challenge

Large Language Models like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training.

The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics

Giving AI a body (in the form of a robot) so that it can engage with people in the physical world continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world.

Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a Herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill, to get to a similar level of capability, which is not yet human level.

3. There Will Be No Single Robot AI

We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us.

General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is, You don’t. At least not for quite some time.

We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their on-board agentic AI models.

AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies.

4. Hardware Is Still Very Hard

Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems, to the computer controlling it, all the way down to its individual actuators.

Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things.

Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people.

5. Real Value Comes From “Easy” Tasks

There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots.

Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done, while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control.

Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models.

This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience.

AI Robots Are Coming, One Step at a Time

Researchers achieve a high secret key rate for quantum communication over fiber optics. By combining advanced signal modulation with new security analysis tools, they have made highly secure, high-speed quantum networks closer to practical implementation.

Local quantum nondemolition measurements and optical manipulation of long-lived circular Rydberg atoms are demonstrated by coupling them to an auxiliary array of low-angular-momentum Rydberg atoms.

The study builds a digital twin of a power substation and combines topology, alarms, waveforms, and measurements using attention-based graph models to diagnose fault location, fault type, and protection failures with robust performance under noise and missing data.

PDS5B, a cohesin regulatory protein, is shown to bind DNA and enhance the RAD51 recombinase in the promotion of DNA strand exchange and protection of DNA from MRE11 RAD50-NBS1. Here the authors use biochemical and cellular analyses to reveal that DNA binding by PDS5B is essential for DNA damage repair and the preservation of stressed DNA replication forks.

The authors report a resonant inelastic x-ray scattering study of superconducting Sm-based infinite-layer nickelate thin films. Despite the two-fold enhancement of Tc in the Sm-based nickelates compared to their Pr-based counterparts, they find that the effective in-plane exchange coupling strength is reduced by approximately 20%.

That author's affiliation: Karlsruhe Institute of Technology Institution (first & last author): Karlsruhe Institute of Technology

Mapping the positions of Two-Level-Systems on the surface of a superconducting transmon qubit

That author's affiliation: Hunan Institute of Technology First author institution: Hunan Institute of Technology Last author institution: Unknown

GNN enhanced reinforcement learning for robot navigation in complex topological networks

By using singularity physics to enable cubic-root scaling of frequency and phase modulations induced by the Coriolis effect to enhance the performance of chip-scale Coriolis vibratory gyroscopes, substantial improvements in signal-to-noise ratio and precision are demonstrated.

A genome-wide association study combining data from the Estonian Biobank and the UK Biobank identifies many common and low-frequency locus–metabolic trait associations, enabling the identification of putative causal links with disease outcomes.

Bright squeezed vacuum light boosts nonlinear atomic tunnelling ionization more than 20-fold compared with coherent light, enabling quantum control of strong-field processes without increasing classical intensity.

A reconstruction method based on Gaussian-apodized single-sideband electron ptychography removes artefacts to enable the high-fidelity identification of guest species in porous materials.

A multimodal analysis reveals a ‘U-shaped’ association between sleep duration and biological ageing across various organ systems, with too much or not enough sleep being linked with accelerated organ ageing. The study also shows that biological ageing differentially mediates the relationship between extremes in sleep duration and late-life depression.

Ionization experiment shows that quantum light can behave like a conventional laser that has a higher intensity.

Quantum learning with tunable loss functions

That author's affiliation: Centre National de la Recherche Scientifique Institution (first & last author): Centre National de la Recherche Scientifique

Classical approaches to imaging through complex media do not account for the quantum nature of the incident field. Now, images encoded on an entangled two-photon state are shown to transmit through a scattering medium whereas scattered by classical light.

This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.

Key learnings

• Provides an introduction to LLM-based AI Agents
• Describes an approach to applying LLM-based AI Agents to robotic teams
• Provides demonstrations of the approach running in hardware with a heterogeneous team of robots
• Presents lessons learned and future work in this area

Clinical success of fecal microbiota transplantation depends on donor microbe engraftment. Here, the authors show that inorganic nitrogen utilization capacity drives gut microbiome remodeling and that boosting this function enhances donor microbe engraftment and improved colitis treatment in mice.

Single-atom catalysts offer high efficiency for hydrogen evolution, but control of metal–support interactions remains challenging. Here, the authors report a rhodium single-atom catalyst platform enabling continuous tuning of metal–support frontier orbital interactions via anion-engineered supports.

Decoherence is a central obstacle to scalable quantum technologies across diverse physical platforms. Here the authors develop an anticipatory framework for real-time evolution of decoherence in quantum systems, demonstrating its internal-prediction component using machine learning, and apply it to the problem of spectral diffusion in solid-state quantum emitters.

That author's affiliation: Zhejiang University First author institution: Southern University of Science and Technology Last author institution: Zhejiang University

Research on dualities is commonly restricted to one-to-one mappings. Here, by incorporating artificial gauge fields, the authors demonstrate 2D and 3D acoustic metamaterials enabling ℤ2 × ℤ2 and (ℤ2)6 duality groups, respectively, where distinct structures share identical band structures, and self-duality gives rise to symmetry-protected high-order degeneracies.

More spin current can be produced with less energy lost at the source, thanks to inter-magnet pumping that rebalances angular momentum dissipation between sublattices in a ferrimagnetic multilayer.

In neurons, the mapping from inputs to output involves complex biophysical processes. Despite this complexity, it is now shown that simple artificial models explain a large fraction of the variability in neuronal activity.

A miniature bio-inspired antenna for sub-6 GHz consumer wireless and biomedical diagnostic applications

That author's affiliation: ETH Zurich Institution (first & last author): ETH Zurich

Combining high-throughput molecular simulations, machine learning, and mixed-integer linear programming, the authors design peptides that localize to condensate interfaces, revealing surfactant-like, charge-dependent sequence rules.

That author's affiliation: Southern University of Science and Technology Institution (first & last author): Southern University of Science and Technology

Fu and colleagues present LUNAR, a self-supervised neural-physics method that reconstructs 3D molecular positions and optical aberrations directly from raw microscopy data, enabling calibration-free super-resolution imaging in complex biological samples.

Here, the authors show that the interplay of oxygen vacancies and erbium emitters in bismuth oxyhalides enables reversible, high-contrast switching of upconversion emission, offering a new strategy for high-level anticounterfeiting.

Near-optimal discrimination of displaced squeezed binary signals using displacement, inverse-squeezing, and photon-number-resolving detection

Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence

That author's affiliation: National Taiwan University Institution (first & last author): National Taiwan University

Generalized Toffoli gates with customizable single-step multiple-qubit control

That author's affiliation: University of Lethbridge Institution (first & last author): University of Lethbridge

Max Planck introduced units of length, time and mass defined solely in terms of fundamental constants. As Saurya Das explains, these units define a system in which quantum mechanics, relativity, gravity and thermodynamics meet on equal footing.

That author's affiliation: Institut Català de Nanociència i Nanotecnologia First author institution: Eindhoven University of Technology Last author institution: Institut Català de Nanociència i Nanotecnologia

The combination of viscous heat flow and thermoelastic effects leads to a non-diffusive heat transport regime in MoSe2 and MoS2. Moreover, it can be controlled through the variation in sample thickness and by choosing between continuous and pulsed heating.

How magnetic impurities influence superconductivity and electronic order in kagome metals remains unclear. Now anisotropic Kondo resonances intertwined with the superconducting gap are observed in a magnetically doped kagome superconductor.

A comprehensive review of how spectrum congestion, dynamic sharing, and cognitive radio systems are reshaping RF coexistence testing for military and commercial applications.

What Attendees will Learn

1. Why spectrum congestion threatens wireless reliability — Explore how over 30 billion connected devices, more than 4,000 allocation changes worldwide, and the expansion from 11 to over 80 cellular bands are intensifying contention for finite RF spectrum resources.
   
2. How real-world coexistence failures affect safety-critical systems — Understand the interference risks between 5G C band transmitters and aircraft radar altimeters, and between terrestrial L band networks and GPS receivers that were not designed for adjacent high-power signals.
3. Why tiered spectrum sharing frameworks are essential — Examine how CBRS uses a cloud-based Spectrum Access System (SAS) and environmental sensing to dynamically protect incumbent Navy radar while enabling commercial cellular services across three priority tiers.
4. What coexistence test architectures look like in practice — Learn how controlled environment testing with anechoic chambers, over-the-air signal generation, and standards such as ANSI C63.27 enable repeatable evaluation of RF device performance under real-world interference conditions.

This sponsored article is brought to you by Applied Materials.

At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs — such as those achieved under the Human Genome Project — required a new operating paradigm: Concentrate the world’s best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace.

Today’s AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much — or more — energy than compute itself. As a result, reducing energy per bit can extend system‑level performance alongside gains in peak compute.

The path to energy‑efficient AI therefore runs through system‑level engineering, spanning three tightly interconnected domains:

• Logic, where performance per watt depends on efficient transistor switching, low‑loss power, and signal delivery through dense wiring stacks.
• Memory, where surging bandwidth and capacity demands expose the memory wall, with processor capability advancing faster than memory access.
• Advanced packaging, where 3D integration, chiplet architectures, and high‑density interconnects bring compute and memory closer together — enabling system designs monolithic scaling can no longer sustain.

These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both front‑end device fabrication and back‑end integration processes.

In the angstrom era, the hardest problems arise at the boundaries — between compute and memory in the package, front‑end and back‑end integration, and the tightly coupled process steps needed for precise 3D fabrication. And it is precisely this boundary‑driven complexity where the traditional innovation model breaks down.

The Traditional R&D Workflow Is Too Slow for Angstrom‑Era AI

For decades, the semiconductor industry’s R&D model has resembled a relay race. Capabilities are developed in one part of the ecosystem, handed off downstream through integration and manufacturing, evaluated by chip and system designers, and only then fed back for the next iteration. That model worked when progress was dominated by relatively modular steps that could be scaled independently and simply dropped into the manufacturing flow.

But the AI timeline has upended these rules. At angstrom‑scale dimensions, the physics enforces inescapable coupling across the entire stack: materials choices shape integration schemes; integration defines design rules; design rules dictate power delivery; wiring sets thermal budgets; and thermals ultimately constrain packaging scaling. System architects simply cannot wait 10–15 years for each major semiconductor technology inflection to mature.

Representing a roughly $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history.

A long‑term perspective is essential to align materials innovation with emerging device architectures — and to develop the tools and processes required to integrate both with manufacturable precision. At Applied Materials, together with our customers, we are charting a course across the next 3–4 generations, extending as far as 10 years down the roadmap.

The angstrom era demands that we break down silos and bring together the industry’s best minds — from leading companies to leading academic institutions. If the problem is coupled, the solution must be coupled. If the timeline is compressed, the learning loop must be compressed. It’s not enough to just innovate — we must innovate how we innovate.

EPIC: A Center and Platform for High‑Velocity Co‑Innovation

This is the challenge that Applied Materials EPIC Center is designed to solve.

Representing a roughly US $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. When it opens in 2026, it will deliver state‑of‑the‑art cleanroom capabilities built from the ground up to shorten the path from early‑stage research to full‑scale manufacturing. But the facilities are only one component of the model. EPIC is also a platform, an operating system for high-velocity co‑innovation that revolutionizes how ideas move from the lab to the fab.

EPIC is a platform, an operating system for high-velocity co‑innovation that revolutionizes how ideas move from the lab to the fab.Applied Materials

The EPIC model compresses the traditional workflow. Customer engineers work side‑by‑side with Applied technologists from day one — moving beyond isolated process optimization and downstream handoffs. Within a shared, secure environment, EPIC tightly integrates atomistic modeling, test vehicles, process development, validation, and metrology feedback. Constraints that once surfaced late in development are identified and addressed early.

The result is a potentially 2x faster path that benefits the entire ecosystem under one roof:

• Chipmakers gain earlier access to Applied’s R&D portfolio, faster learning cycles, and accelerated transfer of next‑generation technologies into high‑volume manufacturing.
• Ecosystem partners gain earlier access to advanced manufacturing technology and collaboration opportunities that expand what is possible through materials innovation.
• Academic institutions gain opportunities to strengthen the lab‑to‑fab pipeline and help develop future semiconductor talent.

Building on decades of co‑development, we are reinventing the innovation pipeline with our partners across logic, memory, and advanced packaging to deliver the next leap in energy‑efficient AI.

Accelerating Advanced Logic

Logic remains the engine of AI compute. In the angstrom era, however, system‑level gains are increasingly constrained by power and energy. Extending AI performance now depends on architectures that deliver more performance per watt — accelerating the move to 3D devices such as gate‑all‑around (GAA) transistors, which boost density within a compact footprint while preserving power efficiency.

These architectural shifts are unfolding at unprecedented scale, with the logic roadmap already extending beyond first‑generation GAA toward more advanced designs. One key example is GAA with backside power delivery, which relocates thick power lines to the backside of the wafer, reducing resistive losses and freeing front‑side routing for tighter logic cell integration. Another example brings adjacent GAA PMOS and NMOS transistors closer together while inserting a dielectric isolation wall between them to minimize electrical interference. Further out, complementary FETs (CFETs) push density scaling even more by stacking PMOS and NMOS devices directly atop one another.

While these architectures deliver compelling gains in performance per watt and logic density without relying solely on tighter lithography, they significantly raise integration complexity. Manufacturing a single GAA device today can involve more than 2,000 tightly interdependent process steps. At the same time, wiring stacks continue to grow taller and denser to connect these advanced logic devices. Modern leading‑edge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring.

At this level of complexity, the process steps used to create these precise 3D devices and wiring stacks cannot be optimized independently. Design and process must evolve in lockstep, and materials innovation and fabrication methods must advance alongside device architecture. EPIC’s co‑innovation model is designed to accelerate exactly this convergence — enabling logic compute to continue advancing the frontiers of AI at the pace the roadmap demands.

Powering the Memory Roadmap

At the same time, the AI computing era is fundamentally reshaping how data is generated, moved, and processed — making memory technologies, especially DRAM, central to delivering the energy‑efficient performance AI systems require. As models grow larger and more data‑hungry, the DRAM roadmap is shifting toward architectures that deliver higher density, greater bandwidth, and faster access per watt.

At the DRAM cell level, this shift is driving a transition from 6F² buried‑channel array transistors (BCAT) to more compact 4F² architectures, which orient the transistor vertically to boost density and reduce chip area. Looking beyond 4F², sustaining gains in performance per watt will require moving past what 2D scaling alone can deliver. The industry is therefore turning to 3D DRAM, stacking memory cells vertically to add capacity within a constrained footprint. As these structures grow taller and aspect ratios intensify, high-mobility materials engineering in three dimensions becomes increasingly critical to performance and reliability.

Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring. One emerging approach places select periphery functions beneath the DRAM array by bonding two wafers — one optimized for the DRAM cells and the other for CMOS logic — using multiple wiring layers.

In parallel, DRAM performance is being extended by leveraging logic‑proven enhancers in the memory periphery. These include mobility boosters such as embedded silicon germanium and stress films, along with wiring upgrades like improved low‑k dielectrics and advanced copper interconnects. Memory manufacturers are also transitioning periphery transistors from planar devices to FinFET architectures, following the logic roadmap to further improve I/O speed. These valuable inflections are central to EPIC’s mission — where they can be co-developed and rapidly validated for next‑generation memory systems.

Driving System Scaling With Advanced Packaging

As data movement becomes the dominant energy cost in AI systems, advanced packaging has emerged as a critical lever for improving system‑level efficiency—shortening interconnect distances, increasing bandwidth density, and reducing the power required to move data between logic and memory.

High‑bandwidth memory (HBM) marks a major inflection along this path. By stacking DRAM dies — scaling to 16 layers and beyond — and placing memory much closer to the processor, HBM enables rapid access to ever‑larger working datasets. This delivers step‑function gains in both bandwidth and energy efficiency.

More broadly, the rise of 3D packages such as HBM underscores why advanced packaging is becoming central to the AI era. Packaging now addresses system‑level constraints that logic and memory device scaling alone can no longer overcome. It also enables a move away from monolithic systems‑on‑chip toward chiplet‑based architectures, as AI workloads increasingly demand flexible designs that combine logic, memory, and specialized accelerators optimized for specific tasks.

A vital technology powering this roadmap is hybrid bonding. With interconnect pitches approaching those of on‑chip wiring, conventional bumps and microbumps run into fundamental limits in density, power, and signal integrity. Hybrid bonding removes these barriers by allowing dramatically higher interconnect and I/O density, supporting a broad range of chiplet architectures — from memory stacking to tighter compute‑memory integration.

As bonded structures like HBM stacks grow larger and more complex, warpage control, die placement, stack alignment, and thermal management become first‑order challenges. EPIC tackles these and other high‑value advanced‑packaging challenges through early, parallel co‑innovation across materials, integration, and manufacturing.

Bringing It All Together

Across logic, memory, and advanced packaging, our industry faces an ambitious roadmap that promises significant gains in energy efficiency for AI systems. But realizing that potential demands breakthrough materials innovation at a time when feature sizes are shrinking, interfaces are multiplying, and process interdependencies are escalating. These challenges cannot be solved on 10–15‑year timelines under the traditional relay‑race model. We must break down silos, align earlier across the ecosystem, and parallelize learning to keep pace with AI’s demands.

In the AI era, progress will be defined by the speed at which lightbulb moments turn into manufacturing and commercialization reality. The only viable path forward is a new innovation model — and EPIC is how we are driving it.

Squeezed light can improve optical phase measurements but usually needs careful calibration. Here, authors demonstrate a self-calibrating adaptive method that jointly estimates phase and squeezing, achieving quantum-enhanced precision from scratch across the full phase range.

Quantum magic dynamics in random circuits

High-performance continuous-variable quantum secret sharing using a state-discrimination detector

Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering

Practical blueprint for low-depth photonic quantum computing with quantum dots

An ultrafast imaging technique captured the propagation of charge-decoupled excitations in twisted bilayer WSe2. Two spin–valley modes with distinct propagation behaviours were revealed, consistent with the phase and amplitude modes of a spin–valley superfluid.

That author's affiliation: New York University First author institution: University of Oregon Last author institution: New York University

Government-controlled media influences the output of large language models via their training data, and models queried in the languages of countries with lower media freedom show a stronger pro-regime valence than models queried in the languages of countries with higher media freedom.

That author's affiliation: The University of Texas MD Anderson Cancer Center Institution (first & last author): The University of Texas MD Anderson Cancer Center

Treatment data for triple-negative breast cancer show the importance of macrophage subtypes and cancer-cell metaprograms for interferon signalling, HLA expression and cell cycle activity that are associated with a good response to neoadjuvant chemotherapy.

That author's affiliation: University of Science and Technology of China Institution (first & last author): University of Science and Technology of China

A programmable photonic quantum processor, Jiuzhang 4.0, incorporates 1,024 high-efficiency squeezed states into a hybrid spatial–temporal encoded 8,176-mode circuit.

That author's affiliation: Massachusetts Institute of Technology Institution (first & last author): Massachusetts Institute of Technology

Electron-beam control enables deterministic placement of tens of thousands of atomic defects in three-dimensional crystals, creating stable, programmable artificial matter for scalable quantum and nanoscale technologies.

That author's affiliation: University of Geneva Institution (first & last author): University of Geneva

Machine learning analysis of cell-type-specific gene expression in mouse and human neocortex and human cortical organoids reveals human-specific cell-type and temporal variations in expression controlled by JUNB.

That author's affiliation: Duke University Institution (first & last author): Duke University

Combining a computational framework and optogenetic and chemogenetic manipulations within and downstream of the cortico-basal ganglia circuit identifies the specific cortico-basal ganglia synapses that drive the acquisition and expression of rapid vocal changes during juvenile song learning.

That author's affiliation: University of North Texas Institution (first & last author): Vanderbilt University

Integration of data representing 35,120 brain scans from diverse global studies enables construction of reference charts that define normative microstructural and macrostructural properties across the human lifespan for research and clinical diagnosis.

That author's affiliation: University of Vienna Institution (first & last author): University of Vienna

An electron-beam technique that can precisely create thousands of atomic defects in a crystal could be used to build quantum devices.

Deviations from the textbook current–phase relationship of a Josephson junction can arise from the intrinsic physics of the junction, but also from the inductance of metallic traces. Now a scheme has been developed to distinguish these cases.

Non-Hermitian systems support non-trivial topological effects, yet eigenvalue braiding remains difficult to control and observe. Now, active tuning of laser modes enables programmable and directly observable braiding on an integrated photonic chip.

How angular momentum is exchanged and conserved among lattice modes has been difficult to measure experimentally, but has now been observed via a coherent three-phonon scattering process in a topological insulator.

Fe-based polyanionic cathodes are promising for large-scale Na-ion batteries but are limited by incomplete Na utilization. Now, research shows that tuning the local Na coordination via V substitution in phosphate-based cathode allows additional Na sites to participate, enabling near-complete Na utilization, enhancing energy density and cycling stability.

Fe-based polyanionic cathodes are promising for Na-ion batteries but are limited by inactive Na sites and irreversible Na loss. Here the authors employ targeted V3+ substitution to tune the Na+ coordination environment, activate inert sites and stabilize high-voltage redox for high-performance Na-ion batteries.

Electrocatalytic CO2 reduction is often hindered by the competing hydrogen evolution reaction, reducing selectivity for the desired products. Here the authors demonstrate that helical chiral copper electrodes can suppress hydrogen evolution by generating spin-polarized carriers through the chiral-induced spin selectivity effect.

UK Biobank breach prompts the field of genomics to rethink open science

Genomics and experimental data suggest that an evolutionary arms race between cholera-causing bacteria and their viral predators shapes the disease in humans.

Trust is no longer enough: secure data sharing requires international collaboration across institutions and governments.

That author's affiliation: Simon Fraser University Institution (first & last author): Simon Fraser University

Ancestral state inference methods are used to reconstruct host species transmission histories over time; however, these methods are biased by uneven sampling. The authors develop SAASI, a new ancestral state inference method that accounts for sampling bias.

That author's affiliation: Newcastle University Institution (first & last author): Newcastle University

Human methionine synthase, a cobalamin-dependent enzyme linking the methionine and folate cycles, shows a flexible architecture by cryo-EM. Computational and biophysical data reveal partner interactions key for cofactor loading and activation.

That author's affiliation: Soochow University Institution (first & last author): Soochow University

Soft tactile chips embed in-situ sensing in pneumatic actuators, improving teleoperation via adaptive haptic feedback. Liquid-metal pressure and pectin temperature sensors enable precise manipulation and better human-robot interaction.

That author's affiliation: Southern University of Science and Technology Institution (first & last author): Southern University of Science and Technology

Massive spatial superpositions are a resource for quantum interferometry, but it has been hard to generate them beyond single atoms. Now spatially entangled massive states are realized through the tunnelling of atomic clusters in optical lattices.

That author's affiliation: UNSW Sydney First author institution: UNSW Sydney Last author institution: University of Canberra

A tunable artificial crystal in a shallow GaAs quantum well is shown to enable interaction-driven insulating behaviour. Electrostatic control tunes the band structure from graphene-like to kagome-like bands.

That author's affiliation: University of Washington First author institution: Tata Institute of Fundamental Research Last author institution: University of Washington

Multidimensional spectroscopy probes both the electronic and nuclear degrees of freedom during and following ultrafast proton transfer, revealing vibronic dynamics that govern reaction coordinates and intramolecular vibrational redistribution.

That author's affiliation: Department of Chemistry First author institution: Department of Chemistry Last author institution: Chemical Biology and Biological Chemistry

Graph neural networks can predict ketosynthase substrate specificity

Authors show how voltage-gated sodium channels influence each other’s gating kinetics when localized in sufficient proximity to each other as part of clusters in the membrane. These nanoscale effects critically shape macroscopic physiology and drug response.

That author's affiliation: Nanyang Technological University Institution (first & last author): Nanyang Technological University

Researchers demonstrate field-free magnetisation switching in perpendicular magnetic anisotropy systems using spin reorientation and orbital Hall effects. This approach enables low power operation and scalable spintronic memory devices.

That author's affiliation: University of Cambridge Institution (first & last author): University of Cambridge

Precise characterisation of short tandem repeat expansions remains technically challenging. Here, Patiño-Guillén and colleagues present a single-molecule nanopore-based strategy that enables direct quantification of tandem repeats in native RNA.

The authors report that maternal RSV vaccination induces robust affinity-matured neutralizing antibodies in mothers and infants. Antibody transfer increased after 36 weeks, supporting current vaccination recommendations, and suggesting that early vaccination may benefit preterm-risk pregnancies.

Non-Markovianity and memory enhancement in quantum reservoir computing

Publisher Correction: Lifetime of the singly charged ²²⁹Th nuclear isomer

Efficient simulation of low-entanglement bosonic Gaussian states in polynomial time

Transport of charges has been widely studied in two-dimensional moiré materials. However, charge-neutral collective excitations are difficult to access, especially when they are decoupled from charged quasiparticles. Now they are observed in a moiré homobilayer.

The post What Causes Lightning? The Answer Keeps Getting More Interesting. first appeared on Quanta Magazine

That author's affiliation: School of Astronomy and Space Science, Nanjing University, Nanjing, China First author institution: School of Astronomy and Space Science, Nanjing University, Nanjing, China Last author institution: Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, China

Formation of the Solar System’s comet reservoirs remain uncertain. Here, the authors show that AI-based analysis of comet activity reveals a far more populated Oort Cloud than previously thought.

That author's affiliation: College of Chemistry, Zhengzhou University, Zhengzhou, Henan, 450001, China Institution (first & last author): College of Chemistry, Zhengzhou University, Zhengzhou, Henan, 450001, China

Traditional electrolytes are electrochemically unstable on the positive electrode surface. Here, the author designed a solvent locked electrolyte to form a stable boride/fluoride interface. The assembled Na||Na2.26Fe1.87(SO4)3 battery maintained 88.2% capacity after 16500 cycles at 1 A g-1.

That author's affiliation: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, China First author institution: Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, China Last author institution: School of Materials Science and Engineering, Taizhou University, Taizhou, China

Self-assembled monolayers struggle to uniformly coat textured surfaces, limiting high-efficiency perovskite and silicon tandem solar cells. Du et al. increase surface hydroxyl groups by incorporating carbon into polysilicon, enabling stable monolayer attachment and record device efficiency.

That author's affiliation: Paderborn University, Integrated Quantum Optics, Institute for Photonic Quantum Systems (PhoQS), Paderborn, Germany Institution (first & last author): Paderborn University, Integrated Quantum Optics, Institute for Photonic Quantum Systems (PhoQS), Paderborn, Germany

Bridging chemistry and Gaussian boson sampling: a photonic hierarchy of approximations for molecular vibronic spectra

That author's affiliation: Shanghai Key Laboratory of Superconductor Integrated Circuit Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China First author institution: Shanghai Key Laboratory of Superconductor Integrated Circuit Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China Last author institution: Shanghai Research Center for Quantum Sciences, Shanghai, China

Multiuser entanglement distribution network across cryogenic nodes enabled by integrated photonic chips

That author's affiliation: Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy First author institution: Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy Last author institution: Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY, USA

Large-scale quantum reservoir computing using a Gaussian Boson Sampler

Helicity-resolved Raman spectroscopy reveals dynamical coupling between charge-density-wave amplitude fluctuations and symmetry-distinct phonons in a ferroaxial van der Waals crystal. This resonant dressing amplifies the material’s planar chiral lattice response through the underlying electronic order.

That author's affiliation: AWS Center for Quantum Computing, Pasadena, CA, USA Institution (first & last author): AWS Center for Quantum Computing, Pasadena, CA, USA

Specialized quantum memories will be required to achieve quantum speedups for data-intensive problems. Now, a proof-of-principle demonstration of such a quantum memory has been performed with a superconducting processor.

A method applied to a single trapped ion combines two linear spin-dependent interactions to generate nonlinear couplings in the ion’s motion: squeezing, trisqueezing and quadsqueezing interactions are demonstrated. The approach can be applied to any spin–oscillator system, produces stronger unitary interactions with the flexibility to switch quickly between orders, and scales seamlessly to higher orders and multiple oscillators.

More than 30 years ago, in the mountain village of Mbem in northwest Cameroon, the moon and stars in the night sky were the only light young Jude Numfor knew after the sunset. Electricity had not yet reached his rural community.

“There was one person in the village with a petrol generator and a small television,” Numfor says. “When he turned it on, all the children would run to his house and peep through the window.”

That memory became the spark for Numfor’s mission: to bring electricity to rural communities like his hometown. To accomplish his goal, in 2006 he cofounded Wireless Light and Power, since renamed Renewable Energy Innovators Cameroon, and he serves as its CEO.

REI Cameroon designs, installs, and maintains solar minigrids for rural electrification. The minigrids use photovoltaic technology and battery-energy storage systems to generate electricity at 50 hertz. The electricity is distributed through smart meters.

In 2017 the company received a grant from IEEE Smart Village to fund the expansion of REI’s minigrid operations and refine its business model. Smart Village supports projects and organizations bringing electricity and educational and employment opportunities to remote communities worldwide. The program is supported by IEEE societies and donations to the IEEE Foundation.

The partnership has led to a collaboration developing open source metering, a free, community-driven way of tracking energy usage. Unlike proprietary utility meters, the system allows users, researchers, and utilities to view, customize, and verify how data is collected, ensuring transparency in billing, consumption tracking, and grid management.

Smart Village’s support has been pivotal, Numfor says: “It’s not just about money. We share ideas, we get advice, and we have made friends. Entrepreneurship is lonely, but with the [Smart Village] community, it is different.”

From teenage tinkerer to entrepreneur

Numfor’s first experience of life with electricity was in 2001, after moving in with a missionary family in the small village of Allat. They used solar panels to power their whole home—an unimaginable luxury in Mbem. “I could watch TV, eat ice cream, and turn on lights,” he says. “It made me wish my brothers in Mbem had the same opportunity.”

Numfor’s curiosity about electricity was ignited when a motion-sensor solar light in the family’s home stopped working. He tinkered with the device to find out why. “My missionary family told me to play with it like a toy,” he says, laughingly. “I replaced the dead battery with a motorcycle battery and was able to bring the power back for the night.”

Jude Numfor [right] testing a rechargeable solar lantern, which aimed to replace hazardous kerosene lamps—known locally as “bush lamps.”REI Cameroon

His missionary parents encouraged Numfor to study technology and engineering on his own, as none of the country’s universities offered solar energy educational programs at the time. They built him a library and stocked it with books on engineering, management, and entrepreneurship.

In 2006, armed with his new knowledge, Numfor launched Wireless Light and Power with a friend, Ludwig Teichgraber. The nonprofit aimed to replace hazardous kerosene lamps—known locally as “bush lamps”—with rechargeable solar lanterns.

These solar lanterns—called “light packs”—were built locally by Numfor and a team of 11 young Cameroonians using PVC pipes, nickel-metal hydride batteries, and LED bulbs. Families rented the lamps for a small fee, swapping discharged lamps for fully charged ones at solar-powered charging kiosks when they ran out of power. The kiosks then recharged the depleted lamps, making them available for the next swap. “The solar lantern was safer and cleaner, plus it gave children a chance to read at night,” Numfor explains. “People loved them.”

Between 2006 and 2010, his team replicated the model across several villages. But when the global financial crisis hit in 2008, donor support dwindled, forcing the organization to evolve. “We pivoted from being an NGO to a commercial venture,” he says. “That’s how REI was born.”

Building solar minigrids to serve community needs

The new company’s goal was to move away from the lanterns and toward full electrification of communities. Villagers’ aspirations changed, Numfor says, as they now wanted to power their TVs, music systems, and mobile phones. In response, in 2010, REI developed one of the first solar minigrids in West Africa. Using locally procured components, the prototype supplied steady power to six households. The minigrid system used 12 123-watt solar photovoltaic panels manufactured by Sharp, 16 12-volt 100 ampere-hour automatic gain control lead acid batteries, and a Xantrex charge controller and inverter. Locally sourced wooden light poles were erected to distribute electricity throughout the village. REI charged each household a fee for the electricity.

“It was a product-market-fit moment,” Numfor says. “People immediately asked, ‘When can we get this, too?’” The word-of-mouth, grassroots growth caught the attention of global partners. Numfor connected with Smart Village and in 2017, REI Cameroon received its first seed grant from the program.

With that funding, Numfor was able to grow organically and attract additional grants, including one from the U.S. Trade Development Agency (USTDA), in partnership with the U.S. Department of Energy’s National Renewable Energy Laboratory. REI has since expanded to six villages, providing power to more than 1,000 households and businesses. With a dedicated team of 16 people, the company operates in multiple regions of the country, each with unique terrain, languages, and cultural dynamics.

“It wasn’t easy,” he acknowledges. “I’m not an academic person—I had to learn everything by doing. [Smart Village] helped me structure the project and grow as an entrepreneur.”

Today, Numfor pays it forward by sharing his Smart Village experience and mentoring new entrepreneurs.

Launching a coalition for smart metering

Minigrids can’t operate efficiently without clarifying operating rules to ensure quality service requirements and consumer protection, while also enabling reliable and effective monitoring of the system, Numfor says. “We need to know how power is being used, detect problems early, and manage the minigrid from a distance,” he explains.

Existing commercial smart-meter providers offer limited and proprietary solutions. One major provider left the market, making their technology infrastructure obsolete. “It’s risky for an entire sector to depend on a few companies for such a critical technology,” Numfor says.

In 2025, with the help of the Smart Village technical community, Numfor convened a consortium of open-source power advocates, including the Africa Mini-Grid Developers Association, EnAccess, Energy IOT, and NESL. The goal was to develop an open smart metering system that is accessible, transparent, and sustainable for all energy providers.

“These organizations are collaborating as Open Advanced Metering Infrastructure [OpenAMI], which is about giving control back to the people who deliver the energy,” he says.

Scaling for impact

Numfor’s passion has grown from bringing light to local rural communities to bringing light to his entire country. Just 54 percent of Cameroon’s citizens have access to electricity, according to the International Energy Agency. For Numfor, the challenge is not just technological—it’s social and economic as well. “Electricity is the most important enabler of education and economic growth today,” he says. “When you have power, you unlock everything else.”

“Electricity changed my life. Now I want to make sure every child can grow up with that same light.” —Jude Numfor

Across the villages where REI has installed sustainable electricity solutions, small businesses are flourishing. Barbershops hum with community chatter, food vendors can preserve perishables, and entrepreneurs run companies such as phone-charging stations and small mills. “Some villages even have laundromats now,” Numfor says proudly. “Electricity creates jobs and changes mindsets.”

Still, it has been a bumpy journey. It wasn’t until 2025 that REI obtained its official authorization (license) from Cameroon’s government to produce and distribute electricity in off-grid areas using solar minigrids. This was a major milestone because REI is one of the first private enterprises in the country to receive such authorization. “We were stuck between pilot projects and growth,” he explains. “Our projects were successful, and there was community demand for more, but to grow, we needed investors who require legal guarantees before committing funds. Now we can scale up and attract investors.”

REI plans to expand its reach dramatically, beginning with 134 new villages identified through a feasibility study supported by the USTDA. Their long-term goal is to electrify 760 villages across Cameroon by 2031.

While authorization opens doors, financing remains one of REI’s biggest challenges. “The minigrid space doesn’t attract venture capitalists easily,” Numfor notes. “Our return on investment is under 15 percent, so it’s not a typical tech startup model. The real return here is the impact” on the community.

He hopes to attract investors who understand that access to electricity drives education, health care, and entrepreneurship. “There are people out there who want to make meaningful change,” he says. “We just need to connect with them. When you electrify a village, you never know who the next innovator will be. Maybe it’s another kid like me, looking through a window, dreaming.”

Finding skilled staff is another challenge, Numfor says. To address this, REI developed an intensive recruitment and training process. “It used to take years to find the right people,” he says. “Now, we can identify who fits our company culture within six months.” Numfor’s wife, Angela Taliklong, who joined the venture in 2010, now oversees administration and human resources.

A brighter Cameroon and beyond

Numfor offers simple words of advice to other impact-driven entrepreneurs: Keep moving.

“One of my mistakes early on was trying to be perfect,” he says. “I was spending time improving prototypes instead of increasing the number of our project installations and scaling how many communities we could electrify. You must keep momentum. Don’t wait until everything is perfect before you move forward.”

That mindset, rooted in resilience and experimentation, has defined his journey. Rajan Kapur, president of Smart Village, says Numfor is a “shining example” of the program’s vision: “scalable and enduring impact through local entrepreneurs, local procurement, and community engagement based on the use of IEEE technology in underserved communities.”

With the ongoing Smart Village partnership, Numfor is determined to bring light and opportunity to every corner of Cameroon, and beyond. He already has launched REI Nigeria.

“Electricity changed my life,” he says. “Now I want to make sure every child can grow up with that same light.”

This article is brought to you by DAIMON Robotics.

This April, Hong Kong-based DAIMON Robotics has released Daimon-Infinity, which it describes as the largest omni-modal robotic dataset for physical AI, featuring high resolution tactile sensing and spanning a wide range of tasks from folding laundry at home to manufacturing on factory assembly lines. The project is supported by collaborative efforts of partners across China and the globe, including Google DeepMind, Northwestern University, and the National University of Singapore.

The move signals a key strategic initiative for DAIMON, a two-and-a-half-year-old company known for its advanced tactile sensor hardware, most notably a monochromatic, vision-based tactile sensor that packs over 110,000 effective sensing units into a fingertip-sized module. Drawing on its high-resolution tactile sensing technology and a distributed out-of-lab collection network capable of generating millions of hours of data annually, DAIMON is building large-scale robot manipulation datasets that include vast amounts of tactile sensing data. To accelerate the real-world deployment of embodied AI, the company has also open-sourced 10,000 hours of its data.

Prof. Michael Yu Wang, co-founder and chief scientist at DAIMON Robotics, has pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.DAIMON Robotics

Behind the strategy is Prof. Michael Yu Wang, DAIMON’s co-founder and chief scientist. Prof. Wang earned his PhD at Carnegie Mellon — studying manipulation under Matt Mason — and went on to found the Robotics Institute at the Hong Kong University of Science and Technology. An IEEE Fellow and former Editor-in-Chief of IEEE Transactions on Automation Science and Engineering, he has spent roughly four decades in the field. His objective is to address the missing “insensitivity” of robot manipulation, which practically relies on the dominant Vision-Language-Action (VLA) model. He and his team have pioneered Vision-Tactile-Language-Action (VTLA) architecture, elevating the tactile to a modality on par with vision.

We spoke with Prof. Wang about how tactile feedback aims to change dexterous manipulation, how the dataset initiative is foreseen to improve our understanding of robotic hands in natural environments, and where — from hotels to convenience stores in China — he sees touch-enabled robots making their first real-world inroads.

Daimon-Infinity is the world’s largest omni-modal dataset for Physical AI, featuring million-hour scale multimodal data, ultra-high-res tactile feedback, data from 80+ real scenarios and 2,000+ human skills, and more.DAIMON Robotics

The Dataset Initiative

This month, DAIMON Robotics released the largest and most comprehensive robotic manipulation dataset with multiple leading academic institutions and enterprises. Why releasing the dataset now, rather than continuing to focus on product development? What impact will this have on the embodied intelligence industry?

DAIMON Robotics has been around for almost two and a half years. We have been committed to developing high-resolution, multimodal tactile sensing devices to perceive the interaction between a robot’s hand (particularly its fingertips) and objects. Our devices have become quite robust. They are now accepted and used by a large segment of users, including academic and research institutes as well as leading humanoid robotics companies.

As embodied AI continues to advance, the critical role of data has been clearer. Data scarcity remains a primary bottleneck in robot learning, particularly the lack of physical interaction data, which is essential for robots to operate effectively in the real world. Consequently, data quality, reliability, and cost have become major concerns in both research and commercial development.

This is exactly where DAIMON excels. Our vision-based tactile technology captures high-quality, multimodal tactile data. Beyond basic contact forces, it records deformation, slip and friction, material properties and surface textures — enabling a comprehensive reconstruction of physical interactions. Building on our expertise in multimodal fusion, we have developed a robust data processing pipeline that seamlessly integrates tactile feedback with vision, motion trajectories, and natural language, transforming raw inputs into training-ready dataset for machine learning models.

Recognizing the industry-wide data gap, we view large-scale data collection not only as our unique competitive advantage, but as a responsibility to the broader community.

By building and open-sourcing the dataset, we aim to provide the high-quality “fuel” needed to power embodied AI, ultimately accelerating the real-world deployment of general-purpose robotic foundation models.

The robotics industry is highly competitive, and many teams have chosen to focus on data. DAIMON is releasing a large and highly comprehensive cross-embodiment, vision-based tactile multimodal robotic manipulation dataset. How were you able to achieve this?

We have a dedicated in-house team focused on expanding our capabilities, including building hardware devices and developing our own large-scale model. Although we are a relatively small company, our core tactile sensing technology and innovative data collection paradigm enable us to build large-scale dataset.

Our approach is to broaden our offering. We have built the world’s largest distributed out-of-lab data collection network. Rather than relying on centralized data factories, this lightweight and scalable system allows data to be gathered across diverse real-world environments, enabling us to generate millions of hours of data per year.

“To drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.” —Prof. Michael Yu Wang, DAIMON Robotics

This dataset is being jointly developed with several institutions worldwide. What roles did they play in its development, and how will the dataset benefit their research and products?

Besides China based teams, our partners include leading research groups from universities, such as Northwestern University and the National University of Singapore, as well as top global enterprises like Google DeepMind and China Mobile. Their decision to partner with DAIMON is a strong testament to the value of our tactile-rich dataset.

Among the companies involved there are some that have already built their own models but are now incorporating tactile information. By deploying our data collection devices across research, manufacturing and other real-world scenarios, they help us to gather highly practical, application-driven data. In turn, our partners leverage the data to train models tailored to their specific use cases. Furthermore, to drive the advancement of the entire embodied AI field, we have open-sourced 10,000 hours of the dataset for the broader community.

Equipped with Daimon’s visuotactile sensor, the gripper delicately senses contact and precisely controls force to pick up a fragile eggshell.Daimon Robotics

From VLA to VTLA: Why Tactile Sensing Changes the Equation

The mainstream paradigm in robotics is currently the Vision-Language-Action (VLA) model, but your team has proposed a Vision-Tactile-Language-Action (VTLA) model. Why is it necessary to incorporate tactile sensing? What does it enable robots to achieve, and which tasks are likely to fail without tactile feedback?

Over these years of working to make generalist robots capable of performing manipulation tasks, especially dexterous manipulation — not just power grasping or holding an object, but manipulating objects and using tools to impart forces and motion onto parts — we see these robots being used in household as well as industrial assembly settings.

It is well established that tactile information is essential for providing feedback about contact states so that robots can guide their hands and fingers to perform reliable manipulation. Without tactile sensing, robots are severely limited. They struggle to locate objects in dark environments, and without slip detection, they can easily drop fragile items like glass. Furthermore, the inability to precisely control force often leads to failed manipulation tasks or, in severe cases, physical damage. Naturally, the VLA approach needs to be enhanced to incorporate tactile information. We expanded the VLA framework to incorporate tactile data, creating the VTLA model.

An additional benefit of our tactile sensor is that it is vision-based: We capture visual images of the deformation on the fingertip surface. We capture multiple images in a time sequence that encodes contact information, from which we can infer forces and other contact states. This aligns well with the visual framework that VLA is based upon. Having tactile information in a visual image format makes it naturally suitable for integration into the VLA framework, transforming it into a VTLA system. That is the key advantage: Vision-based tactile sensors provide very high resolution at the pixel level, and this data can be incorporated into the framework, whether it is an end-to-end model or another type of architecture.

DAIMON has been known for its vision-based tactile sensors that can pack over 110,000 effective sensing units.DAIMON Robotics

The Technology: Monochromatic Vision-based Tactile Sensing

You and your team have spent many years deeply engaged in vision-based tactile sensing and have developed the world’s first monochromatic vision-based tactile sensing technology. Why did you choose this technical path?

Once we started investigating tactile sensors, we understood our needs. We wanted sensors that closely mimic what we have under our fingertip skin. Physiological studies have well documented the capabilities humans have at their fingertips — knowing what we touch, what kind of material it is, how forces are distributed, and whether it is moving into the right position as our brain controls our hands. We knew that replicating these capabilities on a robot hand’s fingertips would help considerably.

When we surveyed existing technologies, we found many types, including vision-based tactile sensors with tri-color optics and other simpler designs. We decided to integrate the best of these into an engineering-robust solution that works well without being overly complicated, keeping cost, reliability, and sensitivity within a satisfactory range, thus ultimately developing a monochromatic vision-based tactile sensing technique. This is fundamentally an engineering approach rather than a purely scientific one, since a great deal of foundational research already existed. With the growing realization of the necessity of tactile data, all of this will advance hand in hand.

DAIMON vision-based tactile sensor captures high-quality, multimodal tactile data.DAIMON Robotics

Last year, DAIMON launched a multi-dimensional, high-resolution, high-frequency vision-based tactile sensor. Compared with traditional tactile sensors, where does its core advantage lie? Which industries could it potentially transform?

The key features of our sensors are the density of distributed force measurement and the deformation we can capture over the area of a fingertip. I believe we have the highest density in terms of sensing units. That is one very important metric. The other is dynamics: the frequency and bandwidth — how quickly we can detect force changes, transmit signals, and process them in real time. Other important aspects are largely engineering-related, such as reliability, drift, durability of the soft surface, and resistance to interference from magnetic, optical, or environmental factors.

A growing number of researchers and companies are recognizing the importance of tactile sensing and adopting our technology. I believe the advances in tactile sensing will elevate the entire community and industry to a higher level. One of our potential customers is deploying humanoid robots in a small convenience store, with densely packed shelves where shelf space is at a premium. The robot needs to reach into very tight spaces — tighter than books on a shelf — to pick out an object. Current two-jaw parallel grippers cannot fit into most of these spaces. Observing how humans pick up objects, you clearly need at least three slim fingers to touch and roll the object toward you and secure it. Thus, we are starting to see very specific needs where tactile sensing capabilities are essential.

From Academia to Startup

After 40 years in academia — founding the HKUST Robotics Institute, earning prestigious honors including IEEE Fellow, and serving as Editor-in-Chief of IEEE TASE — what motivated you to found DAIMON Robotics?

I have come a long way. I started learning robotics during my PhD at Carnegie Mellon, where there were truly remarkable groups working on locomotion under Marc Raibert, who founded Boston Dynamics, and on manipulation under my advisor, Matt Mason, a leader in the field. We have been working on dexterous manipulation, not only at Carnegie Mellon, but globally for many years.

However, progress has been limited for a long time, especially in building dexterous hands and making them work. Only recently have locomotion robots truly taken off, and only in the last few years have we begun to see major advancements in robot hands. There is clearly room for advancing manipulation capabilities, which would enable robots to do work like humans. While at Hong Kong University of Science and Technology, I saw increasingly greater people entering this area in the form of students and postdoctoral researchers. We wanted to jumpstart our effort by leveraging the available capital and talent resources.

Fortunately, one of my postdocs, Dr. Duan Jianghua, has a strong sense for commercial opportunities. Recognizing the rapid growth of robotics market and the unique value that our vision-based tactile sensing technology could bring, together we started DAIMON Robotics, and it has progressed well. The community has grown tremendously in China, Japan, Korea, the U.S., and Europe.

Robots equipped with DAIMON technology have been deployed in factory settings. The company aims to enable robots to achieve “embodied intelligence” and close the gap between what they can see and what they can feel.DAIMON Robotics

Business Model and Commercial Strategy

What is DAIMON’s current business model and strategic focus? What role does the dataset release play in your commercial strategy?

We started as a device company focused on making highly capable tactile sensors, especially for robot hands. But as technology and business developed, everyone realized it is not just about one component, rather the entire technology chain: devices, data of adequate quality and quantity, and finally the right framework to build, train, and deploy models on robots in real application environments.

Our business strategy is best described as “3D”: Devices, Data, and Deployment. We build devices for data collection, our own ecosystem, and for deploying them in our partners’ potential application domains. This enables the collection of real-world tactile-rich data and complete closed-loop validation. This will become an integral part of the 3D business model. Most startups in this space are following a similar path until eventually some may become more specialized or more tightly integrated with other companies. For now, it is mostly vertical integration.

Embodied Skills and the Convergence Moment

You’ve introduced the concept of “embodied skills” as essential for humanoid robots to move beyond having just an advanced AI “brain.” What prompted this insight? What new capabilities could embodied skills enable? After the rapid evolution of models and hardware over the past two years, has your definition or roadmap for embodied skills evolved?

We have come a long way now see a convergence point where electrical, electronic, and mechatronic hardware technologies have advanced tremendously in last two decades. Robots are now fully electric, do not require hydraulics, because hardware has evolved rapidly. Modern electronics provide tremendous bandwidth with high torques. If we can build intelligence into these systems, we can create truly humanoid robots with the ability to operate in unstructured environments, make decisions, and take actions autonomously.

“Our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans.” —Prof. Michael Yu Wang, DAIMON Robotics

AI has arrived at exactly the right time. Enormous resources have been invested in AI development, especially large language models, which are now being generalized into world models that enable physical AI capabilities. We would like to see these manifested in real-world systems.

While both AI and core hardware technologies continue to evolve, the focus is much clearer now. For example, human-sized robots are preferred in a home environment. This is an exciting domain with a promise of great societal benefit if we can eventually achieve safe, reliable, and cost-effective robots.

The Road to Real-World Deployment

Today, many robots can deliver impressive demos, yet there remains a gap before they truly enter real-world applications. What could be a potential trigger for real-world deployment? Which scenarios are most likely to achieve large-scale deployment first?

I think the road toward large-scale deployment of generalist robots is still long, but we are starting to see signs of feasibility within specific domains. It is very similar to autonomous vehicles, where we are yet to see full deployment of robo-taxis, while we have already started to find mobile robots and smaller vehicles widely deployed in the hospitality industry. Virtually every major hotel in China now has a delivery robot — no arms, just a vehicle that picks up items from the hotel lobby (e.g., food deliveries). The delivery person just loads the food and selects the room number. It is up to the robot thereafter to navigate and reach the guest’s room, which includes using the elevator, to deliver the food. This is already nearly 100 percent deployed in major Chinese hotels.

Hotel and restaurant robots are viewed as a model for deploying humanoid robots in specific domains like overnight drugstores and convenience stores. I expect complete deployment in such settings within a short timeframe, followed by other applications. Overall, we can expect autonomous robots, including humanoids, to progressively penetrate specific sectors, delivering value in each and expanding into others.

Ultimately, our vision is for robots to achieve robust manipulation capabilities and evolve into reliable partners for humans. By seamlessly integrating into our homes and daily lives, they will genuinely benefit and serve humanity.

This interview has been edited for length and clarity.

That author's affiliation: The Chinese University of Hong Kong, Shenzhen First author institution: The Chinese University of Hong Kong, Shenzhen Last author institution: Unknown

In quantum information processing, distilling pure quantum states from noisy ones in the practical one-shot setting imposes a high overhead. Here, Fang and Liu propose a one-shot distillation scheme that exploits catalysis to arbitrarily reduce distillation overhead, and apply this to magic state distillation to improve over existing results.

That author's affiliation: Technical Institute of Physics and Chemistry, Chinese Academy of Sciences First author institution: Technical Institute of Physics and Chemistry, Chinese Academy of Sciences Last author institution: Monash University

Structurally engineered graphene oxide membranes fabricated by heterogeneous co-assembly enable rapid, selective solvent dehydration through coupled water adsorption and diffusion in sp3 /sp2 nanochannels, showing high flux with precise molecular sieving.

That author's affiliation: Newcastle University First author institution: University of Oxford Last author institution: Newcastle University

Applying the right to be forgotten to electronic health records that have been used to train artificial intelligence models could compromise model accuracy and fairness. Here, the authors develop a machine unlearning model that aims to remove data whilst preserving algorithmic fairness across subgroups.

That author's affiliation: Zhejiang University Institution (first & last author): Zhejiang University

Plasmonic nanoparticles show promise for photocatalysis, yet the dynamics of charge transfer remain poorly understood. Here, the authors demonstrate the light-driven growth of a CdS shell on Au nanocubes, correlating shell thickness with charge transfer efficiency at the single-particle level.

That author's affiliation: Penn State University Institution (first & last author): U.S. Department of Energy, Ames National Laboratory and Iowa State University

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

That author's affiliation: National University of Singapore Institution (first & last author): National University of Singapore

Photodynamic therapy (PDT) is often limited by its reliance on specialized photosources, the need for clinic-based treatment, and risks of phototoxicity. Here, the authors report mild-sunlight-activated PDT microneedle patches incorporating polymeric photosensitizers with an intrinsic “on-off” reactive oxygen species-generating mechanism, enabling bio-safe, deep-tissue, and self-administered PDT.

That author's affiliation: Stanford University Institution (first & last author): Stanford University

The tumor suppressor protein p53 is commonly mutated in cancer and has been challenging for therapeutic reactivation. Here, the authors developed a small molecule chemical inducer of proximity to selectively activate p53 transcription and induce cellular senescence and apoptosis in p53Y220C cells.

That author's affiliation: Shandong University First author institution: Shandong University Last author institution: Shanghai University

Overcoming parasitic reactions between the electrolyte and electrodes is essential for realizing energy-dense lithium metal batteries. Here, the authors develop an oxygen distal fluorinated diluent that promotes a diluent- and anion-rich solvation structure, forming a stable inorganic-rich interphase and stable cycling in pouch cells at 500 Wh kg–1 level.

That author's affiliation: University of New Haven Institution (first & last author): University of New Haven

Li, Tao, and colleagues present epi-Patho-DBiT to spatially map chromatin accessibility and histone modifications in archived human lymphoma tissues, revealing epigenetic drivers of lymphoma development, progression and transformation.

That author's affiliation: University of Grenoble Alpes Institution (first & last author): University of Grenoble Alpes

Coupling semiconductor qubit devices to microwave resonators provides a way to transfer quantum information over long distances. A flopping-mode qubit that combines strong coupling to photons with good coherence properties has now been demonstrated.

Higher-order interactions in quantum harmonic oscillator systems can result in useful effects, but they are hard to engineer. An experiment on a single trapped ion now demonstrates how spin can mediate higher-order nonlinear bosonic interactions.

Internal self-discharge can compromise the shelf life of solid-state batteries. Now, physico-chemical analysis of charge loss shows that the internal self-discharge over time is not solely determined by the electronic conductivity of the solid separator but also by its electrochemical stability. This model could help guide separator and cell design.

That author's affiliation: Beihang University Institution (first & last author): Beihang University

High-temperature polymer electrolyte membrane fuel cells tend to use relatively thick membranes to counteract H3PO4-induced degradation, limiting performance. Here the authors introduce a dynamic metal-ion crosslinking strategy to create thin, robust membranes, achieving promising power density and durability.

That author's affiliation: Dalhousie University Institution (first & last author): University of Shanghai for Science and Technology

The authors propose and experimentally demonstrate a scheme for weaving optical topological knots and links that are fully localized in space–time, thereby breaking the conventional constraint of longitudinal space-filling via Milnor polynomials.

That author's affiliation: Centre de Nanosciences et de Nanotechnologies First author institution: Unknown Last author institution: Centre de Nanosciences et de Nanotechnologies

Multi-dimensional frequency-bin entanglement-based quantum key distribution network

That author's affiliation: University of Michigan First author institution: University of Michigan Last author institution: Texas Tech University

Tuning symmetry breaking in magnetic transitions via twist-angle engineering is challenging, as twisted two-dimensional magnets often inherit the magnetic ground states of their constituent parts. Now this tunability is achieved in a double-bilayer moiré magnet.

When it comes to AI models, size matters.

Even though some artificial-intelligence experts warn that scaling up large language models (LLMs) is hitting diminishing performance returns, companies are still coming out with ever larger AI tools. Meta’s latest Llama release had a staggering 2 trillion parameters that define the model.

As models grow in size, their capabilities increase. But so do the energy demands and the time it takes to run the models, which increases their carbon footprint. To mitigate these issues, people have turned to smaller, less capable models and using lower-precision numbers whenever possible for the model parameters.

But there is another path that may retain a staggeringly large model’s high performance while reducing the time it takes to run an energy footprint. This approach involves befriending the zeros inside large AI models.

For many models, most of the parameters—the weights and activations—are actually zero, or so close to zero that they could be treated as such without losing accuracy. This quality is known as sparsity. Sparsity offers a significant opportunity for computational savings: Instead of wasting time and energy adding or multiplying zeros, these calculations could simply be skipped; rather than storing lots of zeros in memory, one need only store the nonzero parameters.

Unfortunately, today’s popular hardware, like multicore CPUs and GPUs, do not naturally take full advantage of sparsity. To fully leverage sparsity, researchers and engineers need to rethink and re-architect each piece of the design stack, including the hardware, low-level firmware, and application software.

In our research group at Stanford University, we have developed the first (to our knowledge) piece of hardware that’s capable of calculating all kinds of sparse and traditional workloads efficiently. The energy savings varied widely over the workloads, but on average our chip consumed one-seventieth the energy of a CPU, and performed the computation on average eight times as fast. To do this, we had to engineer the hardware, low-level firmware, and software from the ground up to take advantage of sparsity. We hope this is just the beginning of hardware and model development that will allow for more energy-efficient AI.

What is sparsity?

Neural networks, and the data that feeds into them, are represented as arrays of numbers. These arrays can be one-dimensional (vectors), two-dimensional (matrices), or more (tensors). A sparse vector, matrix, or tensor has mostly zero elements. The level of sparsity varies, but when zeroes make up more than 50 percent of any type of array, it can stand to benefit from sparsity-specific computational methods. In contrast, an object that is not sparse—that is, it has few zeros compared with the total number of elements—is called dense.

Sparsity can be naturally present, or it can be induced. For example, a social-network graph will be naturally sparse. Imagine a graph where each node (point) represents a person, and each edge (a line segment connecting the points) represents a friendship. Since most people are not friends with one another, a matrix representing all possible edges will be mostly zeros. Other popular applications of AI, such as other forms of graph learning and recommendation models, contain naturally occurring sparsity as well.

Beyond naturally occurring sparsity, sparsity can also be induced within an AI model in several ways. Two years ago, a team at Cerebras showed that one can set up to 70 to 80 percent of parameters in an LLM to zero without losing any accuracy. Cerebras demonstrated these results specifically on Meta’s open-source Llama 7B model, but the ideas extend to other LLM models like ChatGPT and Claude.

The case for sparsity

Sparse computation’s efficiency stems from two fundamental properties: the ability to compress away zeros and the convenient mathematical properties of zeros. Both the algorithms used in sparse computation and the hardware dedicated to them leverage these two basic ideas.

First, sparse data can be compressed, making it more memory efficient to store “sparsely”—that is, in something called a sparse data type. Compression also makes it more energy efficient to move data when dealing with large amounts of it. This is best understood by an example. Take a four-by-four matrix with three nonzero elements. Traditionally, this matrix would be stored in memory as is, taking up 16 spaces. This matrix can also be compressed into a sparse data type, getting rid of the zeros and saving only the nonzero elements. In our example, this results in 13 memory spaces as opposed to 16 for the dense, uncompressed version. These savings in memory increase with increased sparsity and matrix size.

In addition to the actual data values, compressed data also requires metadata. The row and column locations of the nonzero elements also must be stored. This is usually thought of as a “fibertree”: The row labels containing nonzero elements are listed and linked to the column labels of the nonzero elements, which are then linked to the values stored in those elements.

In memory, things get a bit more complicated still: The row and column labels for each nonzero value must be stored as well as the “segments” that indicate how many such labels to expect, so the metadata and data can be clearly delineated from one another.

In a dense, noncompressed matrix data type, values can be accessed either one at a time or in parallel, and their locations can be calculated directly with a simple equation. However, accessing values in sparse, compressed data requires looking up the coordinates of the row index and using that information to “indirectly” look up the coordinates of the column index before finally reaching the value. Depending on the actual locations of the sparse data values, these indirect lookups can be extremely random, making the computation data-dependent and requiring the allocation of memory lookups on the fly.

Second, two mathematical properties of zero let software and hardware skip a lot of computation. Multiplying any number by zero will result in a zero, so there’s no need to actually do the multiplication. Adding zero to any number will always return that number, so there’s no need to do the addition either.

In matrix-vector multiplication, one of the most common operations in AI workloads, all computations except those involving two nonzero elements can simply be skipped. Take, for example, the four-by-four matrix from the previous example and a vector of four numbers. In dense computation, each element of the vector must be multiplied by the corresponding element in each row and then added together to compute the final vector. In this case, that would take 16 multiplication operations and 16 additions (or four accumulations).

In sparse computation, only the nonzero elements of the vector need be considered. For each nonzero vector element, indirect lookup can be used to find any corresponding nonzero matrix element, and only those need to be multiplied and added. In the example shown here, only two multiplication steps will be performed, instead of 16.

The trouble with GPUs and CPUs

Unfortunately, modern hardware is not well suited to accelerating sparse computation. For example, say we want to perform a matrix-vector multiplication. In the simplest case, in a single CPU core, each element in the vector would be multiplied sequentially and then written to memory. This is slow, because we can do only one multiplication at a time. So instead people use CPUs with vector support or GPUs. With this hardware, all elements would be multiplied in parallel, greatly speeding up the application. Now, imagine that both the matrix and vector contain extremely sparse data. The vectorized CPU and GPU would spend most of their efforts multiplying by zero, performing completely ineffectual computations.

Newer generations of GPUs are capable of taking some advantage of sparsity in their hardware, but only a particular kind, called structured sparsity. Structured sparsity assumes that two out of every four adjacent parameters are zero. However, some models benefit more from unstructured sparsity—the ability for any parameter (weight or activation) to be zero and compressed away, regardless of where it is and what it is adjacent to. GPUs can run unstructured sparse computation in software, for example, through the use of the cuSparse GPU library. However, the support for sparse computations is often limited, and the GPU hardware gets underutilized, wasting energy-intensive computations on overhead.

Petra Péterffy

When doing sparse computations in software, modern CPUs may be a better alternative to GPU computation, because they are designed to be more flexible. Yet, sparse computations on the CPU are often bottlenecked by the indirect lookups used to find nonzero data. CPUs are designed to “prefetch” data based on what they expect they’ll need from memory, but for randomly sparse data, that process often fails to pull in the right stuff from memory. When that happens, the CPU must waste cycles calling for the right data.

Apple was the first to speed up these indirect lookups by supporting a method called an array-of-pointers access pattern in the prefetcher of their A14 and M1 chips. Although innovations in prefetching make Apple CPUs more competitive for sparse computation, CPU architectures still have fundamental overheads that a dedicated sparse computing architecture would not, because they need to handle general-purpose computation.

Other companies have been developing hardware that accelerates sparse machine learning as well. These include Cerebras’s Wafer Scale Engine and Meta’s Training and Inference Accelerator (MTIA). The Wafer Scale Engine, and its corresponding sparse programming framework, have shown incredibly sparse results of up to 70 percent sparsity on LLMs. However, the company’s hardware and software solutions support only weight sparsity, not activation sparsity, which is important for many applications. The second version of the MTIA claims a sevenfold sparse compute performance boost over the MTIA v1. However, the only publicly available information regarding sparsity support in the MTIA v2 is for matrix multiplication, not for vectors or tensors.

Although matrix multiplications take up the majority of computation time in most modern ML models, it’s important to have sparsity support for other parts of the process. To avoid switching back and forth between sparse and dense data types, all of the operations should be sparse.

Onyx

Instead of these halfway solutions, our team at Stanford has developed a hardware accelerator, Onyx, that can take advantage of sparsity from the ground up, whether it’s structured or unstructured. Onyx is the first programmable accelerator to support both sparse and dense computation; it’s capable of accelerating key operations in both domains.

CPUs, CGRAs, and FPGAs represent a trade-off between efficiency and flexibility. Each individual logic unit of a CPU is designed for a specific function that it performs efficiently. On the other hand, since each individual bit of an FPGA is configurable, these arrays are extremely flexible, but very inefficient. The goal of CGRAs is to achieve the flexibility of FPGAs with the efficiency of CPUs.

CGRAs are composed of efficient and configurable units, typically memory and compute, that are specialized for a particular application domain. This is the key benefit of this type of array: Programmers can reconfigure the internals of a CGRA at a high level, making it more efficient than an FPGA but more flexible than a CPU.

The Onyx chip, built on a coarse-grained reconfigurable array (CGRA), is the first (to our knowledge) to support both sparse and dense computations. Olivia Hsu

Onyx is composed of flexible, programmable processing element (PE) tiles and memory (MEM) tiles. The memory tiles store compressed matrices and other data formats. The processing element tiles operate on compressed matrices, eliminating all unnecessary and ineffectual computation.

The Onyx compiler handles conversion from software instructions to CGRA configuration. First, the input expression—for instance, a sparse vector multiplication—is translated into a graph of abstract memory and compute nodes. In this example, there are memories for the input vectors and output vectors, a compute node for finding the intersection between nonzero elements, and a compute node for the multiplication. The compiler figures out how to map the abstract memory and compute nodes onto MEMs and PEs on the CGRA, and then how to route them together so that they can transfer data between them. Finally, the compiler produces the instruction set needed to configure the CGRA for the desired purpose.

Since Onyx is programmable, engineers can map many different operations, such as vector-vector element multiplication, or the key tasks in AI, like matrix-vector or matrix-matrix multiplication, onto the accelerator.

We evaluated the efficiency gains of our hardware by looking at the product of energy used and the time it took to compute, called the energy-delay product (EDP). This metric captures the trade-off of speed and energy. Minimizing just energy would lead to very slow devices, and minimizing speed would lead to high-area, high-power devices.

Onyx achieves up to 565 times as much energy-delay product over CPUs (we used a 12-core Intel Xeon CPU) that utilize dedicated sparse libraries. Onyx can also be configured to accelerate regular, dense applications, similar to the way a GPU or TPU would. If the computation is sparse, Onyx is configured to use sparse primitives, and if the computation is dense, Onyx is reconfigured to take advantage of parallelism, similar to how GPUs function. This architecture is a step toward a single system that can accelerate both sparse and dense computations on the same silicon.

Just as important, Onyx enables new algorithmic thinking. Sparse acceleration hardware will not only make AI more performance- and energy efficient but also enable researchers and engineers to explore new algorithms that have the potential to dramatically improve AI.

The future with sparsity

Our team is already working on next-generation chips built off of Onyx. Beyond matrix multiplication operations, machine learning models perform other types of math, like nonlinear layers, normalization, the softmax function, and more. We are adding support for the full range of computations on our next-gen accelerator and within the compiler. Since sparse machine learning models may have both sparse and dense layers, we are also working on integrating the dense and sparse accelerator architecture more efficiently on the chip, allowing for fast transformation between the different data types. We’re also looking at ways to manage memory constraints by breaking up the sparse data more effectively so we can run computations on several sparse accelerator chips.

We are also working on systems that can predict the performance of accelerators such as ours, which will help in designing better hardware for sparse AI. Longer term, we’re interested in seeing whether high degrees of sparsity throughout AI computation will catch on with more model types, and whether sparse accelerators become adopted at a larger scale.

Building the hardware to unstructured sparsity and optimally take advantage of zeros is just the beginning. With this hardware in hand, AI researchers and engineers will have the opportunity to explore new models and algorithms that leverage sparsity in novel and creative ways. We see this as a crucial research area for managing the ever-increasing runtime, costs, and environmental impact of AI.

That author's affiliation: Rensselaer Polytechnic Institute Institution (first & last author): Rensselaer Polytechnic Institute

AI foundation models (FMs) are transforming cancer management and research. Here, we analyze the state-of-the-art FMs, discuss their impact on cancer management, and argue that the next generation of cancer AI FMs will be defined by multimodality, enhanced reasoning, maximized openness, and sustained human guidance.

That author's affiliation: Stanford University Institution (first & last author): Stanford University

Adverse events from chemotherapy are common; however, identifying related symptoms following these events from clinical documentation can be challenging. Here, the authors develop a natural language processing model to extract symptoms from clinical notes.

That author's affiliation: University of Zurich Institution (first & last author): University of Zurich

Flexible electrodes offer higher biocompatibility but remain difficult to locate inside the brain, limiting data interpretation and targeting. Here, the authors demonstrate electrodes with magnetic tags for identification and localization.

That author's affiliation: University of California, Santa Barbara First author institution: Institute of Physics, Academia Sinica Last author institution: Research Center for Applied Science, Academia Sinica

Scaffold-assisted window junctions for superconducting qubit fabrication

Sodium is not lithium

That author's affiliation: Massachusetts Institute of Technology Institution (first & last author): Massachusetts Institute of Technology

The authors show that electron crystals compete closely with non-Abelian fractional Chern insulators in the half-full second moiré band of twisted bilayer MoTe2. In particular, they find an “antitopological” electron crystal with zero Chern number C arising because contributions to C from the full first band and half-full second band cancel.

That author's affiliation: University College Lahore Institution (first & last author): University College Lahore

Perseveration – repeating one choice when others would generate larger rewards – is a common behavior, but neither its purpose nor neuronal mechanisms are understood. Here the authors demonstrate a neural correlate and causal role of dorsal prefrontal cortex, specifically anterior supplementary motor cortex, in perseveration in mice performing a dynamic reward learning task.

When Yong Wang recently received one of the highest honors for early-career data visualization researchers, it marked a milestone in an extraordinary journey that began far from the world’s technology hubs.

Wang was born in a small farming village in southwestern China to parents with little formal education and few electronic devices. Today the IEEE member and associate editor of IEEE Transactions on Visualization and Computer Graphics is an assistant professor of computing and data science at Nanyang Technological University, in Singapore. He studies how people can employ data visualization techniques to get more out of artificial intelligence tools.

YONG WANG

EMPLOYER

Nanyang Technological University, in Singapore

POSITION

Assistant professor of computing and data science

IEEE MEMBER GRADE

Member

ALMA MATERS

Harbin Institute of Technology in China; Huazhong University of Science and Technology in Wuhan, China; Hong Kong University of Science and Technology

“Visualization helps people understand complex ideas,” Wang says. “If we design these tools well, they can make advanced technologies accessible to everyone.”

For his work in the field, the IEEE Computer Society visualization and graphics technical committee presented him with its 2025 Significant New Researcher Award. The recognition highlights his growing influence in fields including human-computer interaction and human-AI collaboration—areas becoming more important as the world generates more data than humans can easily interpret.

Growing up in rural Hunan

Wang was born in southwestern Hunan Province. China’s economy was still developing, and life in his village was modest. Most families in Hunan grew rice, vegetables, and fruit to support themselves.

Wang’s parents worked in agriculture too, and his father often traveled to cities to earn money working in a factory or on construction jobs. The extra income helped support the family and made it possible for Wang to attend college.

“I’m very grateful to my parents,” Wang says. “They never attended university, but they strongly supported my education.”

“If we build tools that help people understand information, then more people can participate in science and innovation. That’s the real power of visualization.”

Technology was scarce in the village, he says. Computers were almost nonexistent, and televisions were considered precious, expensive household possessions.

One childhood memory still makes him laugh: During a summer vacation, he and his brother spent so many hours playing video games on a simple console connected to the family’s television that the TV screen eventually burned out.

“My mother was very angry,” he recalls. “At that time, a TV was a very valuable thing.”

He says that despite never having used a laptop or experimenting with electronic equipment, he was fascinated by the technologies he saw on TV shows.

Discovering robotics and engineering

His parents encouraged a practical career such as medicine or civil engineering, but he felt drawn to robotics and computing, he says.

“I didn’t really understand what computer science involved,” he says. “But from what I saw on TV, it looked exciting and advanced.”

He enrolled at Harbin Institute of Technology, in northeastern China. The esteemed university is known for its engineering programs. His major—automation— combined elements of electrical engineering, robotics, and control systems.

One of the defining experiences of his undergraduate years, he says, was a university robotics competition. Wang and his teammates designed a robot capable of autonomously navigating around obstacles.

The design was simple compared with professional systems, he acknowledges. But, he says, the experience was exhilarating. His team placed second, and Wang began to see engineering as both creative and collaborative.

He graduated with a bachelor’s degree in 2011 and briefly worked as an assistant at the Research Institute of Intelligent Control and Systems at Harbin.

In 2014 he took a position as a research intern working at Da Jiang Innovation in Shenzhen, China.

That experience helped him clarify his future, he says: “I realized I didn’t enjoy doing repetitive work or simply following instructions. I wanted to explore ideas that interested me, and I wanted to conduct research.” The realization pushed him toward graduate school, he says.

Building tools that help humans work with AI

Wang received a master’s degree in pattern recognition and image processing from the Huazhong University of Science and Technology, in Wuhan, China, in 2016.

He then enrolled in the computer science Ph.D. program at the Hong Kong University of Science and Technology and earned the degree in 2018. He remained there as a postdoctoral researcher until 2020, when he moved to Singapore to join Singapore Management University as an assistant professor of computing and information systems. He moved over to Nanyang Technological University as an assistant professor in 2024.

His research focuses on a challenge facing nearly every business: how to make sense of the enormous amounts of data being generated.

“We live in an era of information explosions,” Wang says. “Huge amounts of data are generated, and it’s difficult for people to interpret all of it to make better business decisions.”

Data visualization offers a solution by turning complex information into images, patterns, and diagrams that people can more readily understand.

But many visualizations still must be designed manually by experts, Wang notes. It’s a time-consuming process that creates a bottleneck, he says.

His solution is to use large language models and multimodal systems that can generate text, images, video, and sensor data simultaneously and automate parts of the process.

One system developed by his research group lets users design complex infographics through natural-language instructions combined with simple interactions such as drawing on a touchscreen with a finger. It allows nontechnical people to generate visualizations instead of hiring professional designers.

Another focus of Wang’s research is human-AI collaboration. AI systems can analyze data at enormous scale, but people still need to be the final decision-makers, he says.

Visualization helps bridge the gap between human intention and AI’s complex calculations by making the process an AI system uses to reach a result more transparent and understandable.

“If people understand how the AI system works,” Wang says, “they can collaborate with it more effectively.”

He recently explored how visualization techniques could help researchers understand quantum computing, a field where core concepts—such as superposition, where a bit can be in more than one state at a time—are abstract. In classical computing, the bit state is binary: It’s either 1 or 0. A quantum bit, or qubit, can be 1, 0, or both. The differences get more dizzying from there.

Visualization tools could help scientists monitor quantum systems and interpret quantum machine-learning models, he says.

The importance of IEEE communities

Teaching and mentoring students remain among the most meaningful parts of Wang’s career, he says.

Professional communities such as the IEEE Computer Society, he says, play a major role in helping him transform early-stage graduate students unsure of which lines of inquiry they will pursue into independent researchers with a solid technical focus. Through conferences, publications, and technical committees, IEEE connects Wang with other researchers working in visualization, AI, and human-computer interactions, he says.

Those connections have helped him share ideas, collaborate, and stay up to date on innovations in the research community.

Receiving the Significant New Researcher award motivates him to continue pushing the field forward, he says.

Looking back, he says, the distance between his rural village in Hunan and an international research career still feels remarkable. But, he says, the journey reflects something larger about his chosen field: “If we build tools that help people understand information, then more people can participate in science and innovation.

“That’s the real power of visualization.”

The post A New Type of Neuroplasticity Rewires the Brain After a Single Experience first appeared on Quanta Magazine

That author's affiliation: The Ohio State University First author institution: The Ohio State University Last author institution: Medical College of Wisconsin

Single-cell Hi-C contact maps reveal diverse DNA folding across cells, but data are sparse due to biological mechanism and low sequencing depth. Here, authors introduce structural-zero-aware dissimilarity measures that separate true contact absence from missing data, enhancing cell type clustering.

That author's affiliation: IQM Quantum Computers First author institution: IQM (Germany) Last author institution: University of Würzburg

Near-term fermionic simulation with subspace noise tailored quantum error mitigation

That author's affiliation: Southern University of Science and Technology Institution (first & last author): Southern University of Science and Technology

Understanding dynamic heterogeneity in hydrogen evolution electrocatalysts is essential but difficult with limited spatio-temporal resolution. Here the authors use interferometric electro-optical microscopy to achieve nanometre–millisecond imaging of hydrogen evolution activity on MoS2.

That author's affiliation: California Institute of Technology Institution (first & last author): California Institute of Technology

Magnetic fields usually weaken superconductivity. By contrast, a material platform is demonstrated where applying a moderate field induces superconductivity.

Author Correction: Combination of PARP and KRAS^G12D inhibitors enhances therapeutic efficacy by exploiting vulnerabilities in PDAC

The authors introduce controlled compositional heterogeneity to broaden polarization vector distributions while preserving the antiferroelectric modulation in PbZrO3-based ceramics. They reduce polarization hysteresis while maintaining high polarization strength.

In this study, authors screen a 3.2 million member FKBP scaffold-directed DNA-encoded library and identify FKBP12-binding molecular glues for both bromodomain-containing protein 9 (BRD9) and quinoid dihydropteridine reductase (QDPR).

A room-temperature ferroelectricity with out-of-plane polarization is disclosed in chemical vapor deposition synthesized two-dimensional α-MnS, which exhibits large tunneling electroresistance, high endurance, and long retention time.

This work shows that the effective near-field range of an aperture can be much smaller than the Fraunhofer limit. It introduces distributed beam shaping, coordinating multiple transmitting apertures to extend the effective near-field region.

Surface-code hardware Hamiltonian

Distributed quantum inner product estimation with structured random circuits

Two-qubit gates using on-demand single-photons from ordered shape and size controlled large-volume superradiant quantum dots

That author's affiliation: South China University of Technology First author institution: South China University of Technology Last author institution: Nankai University

The creation of stable and isolated magnetic hopfions—three-dimensional topological solitons—has remained experimentally challenging. Now the laser-induced nucleation of hopfions has been achieved in a chiral magnet.

That author's affiliation: Technion – Israel Institute of Technology First author institution: Technion – Israel Institute of Technology Last author institution: University of Konstanz

Entanglement between particles offers insights into quantum behaviour, but methods for studying it in free-electron systems are lacking. Now a two-electron quantum walk is used to probe decoherence of free electrons inside an electron microscope.

When the robotics engineering field that Maja Matarić wanted to work in didn’t exist, she helped create it. In 2005 she helped define the new area of socially assistive robotics.

As an associate professor of computer science, neuroscience, and pediatrics at the University of Southern California, in Los Angeles, she developed robots to provide personalized therapy and care through social interactions.

Maja Matarić

Employer

University of Southern California, Los Angeles

Job Title

Professor of computer science, neuroscience, and pediatrics

Member grade

Fellow

Alma maters

University of Kansas and MIT

The robots could have conversations, play games, and respond to emotions.

Today the IEEE Fellow is a professor at USC. She studies how robots can help students with anxiety and depression undergo cognitive behavioral therapy. CBT focuses on changing a person’s negative thought patterns, behaviors, and emotional responses.

For her work, she received a 2025 Robotics Medal from MassRobotics, which recognizes female researchers advancing robotics. The Boston-based nonprofit provides robotics startups with a workspace, prototyping facilities, mentorship, and networking opportunities.

When receiving the award at the ceremony in Boston, Matarić was overcome with joy, she says.

“I’ve been very fortunate to be honored with several awards, which I am grateful for. But there was something very special about getting the MassRobotics medal, because I knew at least half the people in the room,” she says. “Everyone was just smiling, and there was a great sense of love.”

Seeing herself as an engineer

Matarić grew up in Belgrade, Serbia. Her father was an engineer, and her mother was a writer. After her father died when she was 16, Matarić and her mother moved to the United States.

She credits her father for igniting her interest in engineering, and her uncle who worked as an aerospace engineer for introducing her to computer science.

Matarić says she didn’t consider herself an engineer until she joined USC’s faculty, since she always had worked in computer science.

“In retrospect, I’ve always been an engineer,” Matarić says. “But I didn’t set out specifically thinking of myself as one—which is just one of the many things I like to convey to young people: You don’t always have to know exactly everything in advance.”

Maja Matarić and her lab are exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. National Science Foundation News

While pursuing her bachelor’s degree in computer science at the University of Kansas in Lawrence, she was introduced to industrial robotics through a textbook. After earning her degree in 1987, she had an opportunity to continue her education as a graduate student at MIT’s AI Lab (now the Computer Science and Artificial Intelligence Lab). During her first year, she explored the different research projects being conducted by faculty members, she said in a 2010 oral history conducted by the IEEE History Center. She met IEEE Life Fellow Rodney Brooks, who was working on novel reactive and behavior-based robotic systems. His work so excited her that she joined his lab and conducted her master’s thesis under his tutelage.

Inspired by the way animals use landmarks to navigate, Matarić developed Toto, the first navigating behavior-based robot. Toto used distributed models to map the AI Lab building where Matarić worked and plan its path to different rooms. Toto used sonar to detect walls, doors, and furniture, according to Matarić’s paper, “The Robotics Primer.”

After earning her master’s degree in AI and robotics in 1990, she continued to work under Brooks as a doctoral student, pioneering distributed algorithms that allowed a team of up to 20 robots to execute complex tasks in tandem, including searching for objects and exploring their environment.

Matarić earned her Ph.D. in AI and robotics in 1994 and joined Brandeis University, in Waltham, Mass., as an assistant professor of computer science. There she founded the Interaction Lab, where she developed autonomous robots that work together to accomplish tasks.

Three years later, she relocated to California and joined USC’s Viterbi School of Engineering as an assistant professor in computer science and neuroscience.

In 2002 she helped to found the Center for Robotics and Embedded Systems (now the Robotics and Autonomous Systems Center). The RASC focuses on research into human-centric and scalable robotic systems and promotes interdisciplinary partnerships across USC.

Matarić’s shift in her research came after she gave birth to her first child in 1998. When her daughter was a bit older and asked Matarić why she worked with robots, she wanted to be able to “say something better than ‘I publish a lot of research papers,’ or ‘it’s well-recognized,’” she says.

“In academia, you can be in a leadership role and still do research. It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.”

“Kids don’t consider those good answers, and they’re probably right,” she says. “This made me realize I was in a position to do something different. And I really wanted the answer to my daughter’s future question to be, ‘Mommy’s robots help people.’”

Matarić and her doctoral student David Feil-Seifer presented a paper defining socially assistive robotics at the 2005 International Conference on Rehabilitation Robotics. It was the only paper that talked about helping people complete tasks and learn skills by speaking with them rather than by performing physical jobs, she says.

Feil-Seifer is now a professor of computer science and engineering at the University of Nevada in Reno.

At the same time, she founded the Interaction Lab at USC and made its focus creating robots that provide social, rather than physical, support.

“At this point in my career journey, I’ve matured to a place where I don’t want to do just curiosity-driven research alone,” she says. “Plenty of what my team and I do today is still driven by curiosity, but it is answering the question: ‘How can we help someone live a better life?’”

In 2006 she was promoted to full professor and made the senior associate dean for research in USC’s Viterbi School of Engineering. In 2012 she became vice dean for research.

“In academia, you can be in a leadership role and still do research,” she says. “It’s a wonderful and important opportunity that lets academics be on top of our field and also train the next generation of students and help the next generation of faculty colleagues.”

Research in socially assistive robotics

One of the longest research projects Matarić has led at her Interaction Lab is exploring how socially assistive robots can help improve the communication skills of children with autism spectrum disorder. ASD is a lifelong neurological condition that affects the way people interact with others, and the way they learn. Children with ASD often struggle with social behaviors such as reading nonverbal cues, playing with others, and making eye contact.

Matarić and her team developed a robot, Bandit, that can play games with a child and give the youngster words of affirmation. Bandit is 56 centimeters tall and has a humanlike head, torso, and arms. Its head can pan and tilt. The robot uses two FireWire cameras as its eyes, and it has a movable mouth and eyebrows, allowing it to exhibit a variety of facial expressions, according to the IEEE Spectrum’s robots guide. Its torso is attached to a wheeled base.

The study showed that when interacting with Bandit, children with ASD exhibited social behaviors that were out of the ordinary for them, such as initiating play and imitating the robot.

Matarić and her team also studied how the robot could serve as a social and cognitive aid for elderly people and stroke patients. Bandit was programmed to instruct and motivate users to perform daily movement exercises such as seated aerobics.

Maja Matarić and doctoral student Amy O’Connell testing Blossom, which is being used to study how it can aid students with anxiety or depression.University of Southern California

Over the years, Matarić’s lab developed other robots including Kiwi and Blossom. Kiwi, which looked like an owl, helped children with ASD learn social and cognitive skills, helped motivate elderly people living alone to be more physically active, and mediated discussions among family members. Blossom, originally developed at Cornell, was adapted by the Interaction Lab to make it less expensive and personalizable for individuals. The robot is being used to study how it can aid students with anxiety or depression to practice cognitive behavioral therapy.

Matarić’s line of research began when she learned that large language model (LLM) chatbots were being promoted to help people with mental health struggles, she said in an episode of the AMA Medical News podcast.

“It is generally not easy to get [an appointment with a] therapist, or there might not be insurance coverage,” she said. “These, combined with the rates of anxiety and depression, created a real need.”

That made the chatbot idea appealing, she says, but she was interested to see if they were effective compared with a friendly robot such as Blossom.

Matarić and her team used the same LLMs to power CBT practice with a chatbot and with Blossom. They ran a two-week study in the USC dorms, where students were randomly assigned to complete CBT exercises daily with either a chatbot or the robot. Participants filled out a clinical assessment to measure their psychiatric distress before and after each session.

The study showed that students who interacted with the robot experienced a significant decrease in their mental state, Matarić said in the podcast, and students who interacted with the chatbot did not.

“Joining an [IEEE] society has an impact, and it can be personal. That’s why I recommend my students join the organization—because it’s important to get out there and get connected.”

She and her team also reviewed transcripts of conversations between the students and the robot to evaluate how well the LLM responded to the participants. They found the robot was more effective than the chatbot, even though both were using the same model.

Based on those findings, in 2024 Matarić received a grant from the U.S. National Institute of Mental Health to conduct a six-week clinical trial to explore how effective a socially assistive robot could be at delivering CBT practice. The trial, currently underway, also is expected to study how Blossom can be personalized to adapt to each user’s preferences and progress, including the way the robot moves, which exercises it recommends, and what feedback it gives.

During the trial, the 120 students participating are wearing Fitbits to study their physiologic responses. The participants fill out a clinical assessment to measure their psychiatric distress before and after each session.

Data including the participants’ feelings of relating to the robot, intrinsic motivation, engagement, and adherence will be assessed by the research team, Matarić says.

She says she’s proud of the graduate students working on this project, and seeing them grow as engineers is one of the most rewarding parts of working in academia.

“Engineers generally don’t anticipate having to work with human study participants and needing to understand psychology in addition to the hardcore engineering,” she says. “So the students who choose to do this research are just wonderful, caring people.”

Finding a community at IEEE

Matarić joined IEEE as a graduate student in 1992, the year she published her first paper in IEEE Transactions on Robotics and Automation. The paper, “Integration of Representation Into Goal-Driven Behavior-Based Robots,” described her work on Toto.

As a member of the IEEE Robotics and Automation Society, she says she has gained a community of like-minded people. She enjoys attending conferences including the IEEE International Conference on Robotics and Automation, the IEEE/RSJ International Conference on Intelligent Robots and Systems, and the ACM/IEEE International Conference on Human-Robot Interaction, which is closest to her field of research.

Matarić credits IEEE Life Fellow George Bekey, the founding editor in chief of the IEEE Transactions on Robotics, for recruiting her for the USC engineering faculty position. He knew of her work through her graduate advisor Brooks, who published a paper in the journal that introduced reactive control and the subsumption architecture, which became the foundation of a new way to control robots. It is his most cited paper. Bekey, who was editor in chief at the time, helped guide Brooks through the challenging review process. Matarić joined Brooks’s lab at MIT two years after its publication, and her work on Toto built on that foundation.

“Joining a society has an impact, and it can be personal,” she says. “That’s why I recommend my students join the organization—because it’s important to get out there and get connected.”

That author's affiliation: Weizmann Institute of Science Institution (first & last author): Weizmann Institute of Science

Subcellular RNA localization plays a central role in post-transcriptional regulation. Using high-resolution spatial transcriptomics and a computational approach, the study maps intracellular mRNA localization across tissues, revealing conserved localization patterns and enabling routine analysis of RNA distribution in cells.

That author's affiliation: University of Washington Institution (first & last author): University of Washington

Here the authors present TwinC, a CNN that predicts trans-chromosomal contacts with high accuracy. Trained on Hi-C and validated with DNA SPRITE, it reveals that compartments, chromatin accessibility, transcription factor clusters, and G-quadruplexes drive these interactions.

In this study, the authors find that SHANK3’s large intrinsically disordered region mediates phase separation to support postsynaptic density remodeling during long-term potentiation, providing an insight into how autism spectrum disorder-linked SHANK3 mutations disrupt synaptic plasticity.

Radiotherapy (RT) is standard-of-care in cancer management; however, RT efficacy remains limited. Here, the authors test whether membrane camouflaged BCG bacteria (MBCG) enhance response to RT in preclinical models of glioblastoma. MBCG efficiently targets tumor tissues, induces trained immunity in tumor-associated macrophages, and enhances the RT-induced anti-tumor responses.

Charge-transfer excitations, which define the optical bandgap in many insulators, also contribute to magnetic exchange in antiferromagnets. Femtosecond optical pumping of these transitions in canted antiferromagnet DyFeO3 reshapes the spin-wave spectrum — the set of collective spin excitations that define the dynamics of the antiferromagnet — without destroying the long-range order.

In addition to strongly protected topological phases that rely on exact symmetries, theory predicts that disorder can stabilize weakly protected phases in mixed quantum states, and an example of the latter is now observed in a Rydberg atom array.

Optical forces and torques on nanoparticles are difficult to measure due to the diffraction limit of light. Now, transverse optical torque is observed through the optical trapping and spatial tracking of a designed microscale structure.

Here they show that BMP signalling is the ancestral pathway that patterns the dorsoventral (DV) axis in Annelida and Spiralia. The shift to unequal cleavage involved alternative pathways for patterning the DV axis, leading to a unique case of developmental system drift.

Neural mechanisms underlying conditioned taste aversion are not fully understood. Here authors identified a brain circuit that drives learned aversion to high-fat food by associating it with nausea. This circuit’s learning and memory components offer insight into how the brain forms food avoidance behaviors.

In zebrafish, cholecystokinin drives gonadal development via two parallel pathways: direct FSH stimulation in the pituitary and local regulation of germ cell proliferation and survival within the gonad.

The post Quantum ‘Jamming’ Explores the Truly Fundamental Principles of Nature first appeared on Quanta Magazine

A practical guide to designing log-periodic dipole array fed parabolic reflector antennas using advanced 3D MoM simulation — from parametric modeling to electrically large structures.

What Attendees will Learn

1. How to set design requirements for LPDA-fed reflector antennas — Understand the key specifications including bandwidth ratio, gain targets, and VSWR matching constraints across the full operating range from 100 MHz to 1 GHz.
2. Why advanced 3D EM solvers enable simulation of electrically large multiscale structures — Learn how higher order basis functions, quadrilateral meshing, geometrical symmetry, and CPU/GPU parallelization extend MoM simulation capability by an order of magnitude.
3. How to apply a systematic three-step design strategy with proven workflow starting with first optimizing the stand-alone LPDA for VSWR and gain, then integrating the reflector, and finally tuning parameters to satisfy all performance requests including gain and impedance matching.
4. How parametric CAD modeling accelerates LPDA design — Discover how self-scaling geometry, automated wire-to-solid conversion, and multiple-copy-with-scaling features enable fully parametrized antenna models that streamline optimization across dozens of design variants.

Author Correction: A Bayesian decision support system for automated insulin doses in adults with type 1 diabetes on multiple daily injections: a randomized controlled trial

Author Correction: Replay without sharp wave ripples in a spatial memory task

Author Correction: PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics

This study shows that reduced Mll5 impairs microglial phagocytosis via TREM2- SGK3-GSK3β signaling, causing ASD-like behaviors in mice, while lithium chloride was shown to rescue deficits.

Risk profiling based on how BMI interacts with cardiovascular markers was useful in the general population. In type 1 diabetes— where cardiovascular risk is already high— these profiles are notably valuable for tailored approaches as they reveal how high glucose may hide other risk factors

Poly(A) tails are critical to gene regulation, yet their organism-wide patterns remain unmapped. Here, the authors provide an 18-organ mouse atlas, revealing that poly(A) dynamics is fundamentally orthogonal to mRNA abundance.

Random telegraph noise (RTN) reveals charge trapping dynamics in nanoscale electronic devices. Here, authors demonstrate optically controlled RTN in a CuScP2S6/MoS₂ heterostructure, enabling field and light-tunable defect activity and noise resilient neuromorphic image encoding.

The authors demonstrate that an autoencoder-based neuromorphic architecture combined with Fowler-Nordheim annealing, is sufficient to implement scalable higher-order Ising machines. They show that these machines can consistently produce state-of-the-art solutions with high reliability and competitive time-to-solution metrics.

CRISPR-Cas9-based genome editing is powerful but limited by target range, specificity, and delivery constraints. Here, authors engineer a compact SaCas9 that recognizes NNG PAMs for efficient genome and base editing in cells and mice, and reveal its activation mechanism via cryo-EM structural analysis.

The race to transition online security protocols to ones that can’t be cracked by a quantum computer is already on. The algorithms that are commonly used today to protect data online—RSA and elliptic curve cryptography—are uncrackable by supercomputers, but a large enough quantum computer would make quick work of them. There are algorithms secure enough to be out of reach for both classical and future quantum machines, called post-quantum cryptography, but transitioning to these is a work in progress.

Late last month, the team at Google Quantum AI published a whitepaper that added significant urgency to this race. In it, the team showed that the size of a quantum computer that would pose a cryptographic threat is approximately twenty times smaller than previously thought. This is still far from accessible to the quantum computers that exist today: the largest machines currently consist of approximately 1,000 quantum bits, or qubits, and the whitepaper estimated that about 500 times as much is needed. Nonetheless, this shortens the timeline to switch over to post-quantum algorithms.

The news had a surprising beneficiary: obscure cryptocurrency Algorand jumped 44% in price in response. The whitepaper called out Algorand specifically for implementing post-quantum cryptography on their blockchain. We caught up with Algorand’s chief scientific officer and professor of computer science and engineering at the University of Michigan, Chris Peikert, to understand how this announcement is impacting cryptography, why cryptocurrencies are feeling the effects, and what the future might hold. Peikert’s early work on a particular type of algorithm known as lattice cryptography underlies most post-quantum security today.

IEEE Spectrum: What is the significance of this Google Quantum AI whitepaper?

Peikert: The upshot of this paper is that it shows that a quantum computer would be able to break some of the cryptography that is most widely used, especially in blockchains and cryptocurrencies, with much, much fewer resources than had previously been established. Those resources include the time that it would take to do so and the number of qubits (or quantum bits) that it would have to use.

This cryptography is very central to not just cryptocurrencies but more broadly, to cryptography on the internet. It is also used for secure web connections between web browsers and web servers. Versions of elliptic curve cryptography are used in national security systems and military encryption. It’s very prevalent and pervasive in all modern networks and protocols.

And not only was this paper improving the algorithms, but there was also a concurrent paper showing that the hardware itself was substantially improved. The claim here was that the number of physical qubits needed to achieve a certain kind of logical qubit was also greatly reduced. These two kinds of improvements are compounding upon each other. It’s a kind of a win-win situation from the quantum computing perspective, but a lose-lose situation for cryptography.

IEEE Spectrum: What do Google AI’s findings mean for cryptocurrencies and the broader cybersecurity ecosystem?

Peikert: There’s always been this looming threat in the distance of quantum computers breaking a large fraction of the cryptography that’s used throughout the cryptocurrency ecosystem. And I think what this paper did was really the loudest alarm yet that these kinds of quantum attacks might not be as far off as some have suspected, or hoped, in recent years. It’s caused a re-evaluation across the industry, and a moving up of the timeline for when quantum computers might be capable of breaking this cryptography.

When we think about the timelines and when it’s important to have completed these transitions [to post-quantum cryptography], we also need to factor in the unknown improvements that we should expect to see in the coming years. The science of quantum computing will not stay static, and there will be these further breakthroughs. We can’t say exactly what they will be or when they will come, but you can bet that they will be coming.

IEEE Spectrum: What is your guess on if or when quantum computers will be able to break cryptography in the real world?

Peikert: Instead of thinking about a specific date when we expect them to come, we have to think about the probabilities and the risks as time goes on. There have been huge breakthrough developments, including not only this paper, but also some last year. But even with these, I think that the chance of a cryptographic attack by quantum computers being successful in the next three years is extremely low, maybe less than a percent. But then, as you get out to several years, like 5, 6, or 10 years, one has to seriously consider a probability, maybe 5% or 10% or more. So it’s still rather small, but significant enough that we have to worry about the risk, because the value that is protected by this kind of cryptography is really enormous.

The US government has put 2035 as its target for migrating all of the national security systems to post quantum cryptography. That seems like a prudent date, given the timelines that it takes to upgrade cryptography. It’s a slow process. It has to be done very deliberately and carefully to make sure that you’re not introducing new vulnerabilities, that you’re not making mistakes, that everything still works properly. So, you know, given the outlook for quantum computers on the horizon, it’s really important that we prepare now, or ideally, yesterday, or a few years ago, for that kind of transition.

IEEE Spectrum: Are there significant roadblocks you see to industrial adoption of post-quantum cryptography going forward?

Peikert: Cryptography is very hard to change. We’ve only had one or maybe two major transitions in cryptography since the early 1980s or late 1970s when the field first was invented. We don’t really have a systematic way of transitioning cryptography.

An additional challenge is that the performance tradeoffs are very different in post-quantum cryptography than they are in the legacy systems. Keys and cipher texts and digital signatures are all significantly larger in post-quantum cryptography, but the computations are actually faster, typically. People have optimized cryptography for speed in the past, and we have very good fast speeds now for post-quantum cryptography, but the sizes of the keys are a challenge.

Especially in blockchain applications, like cryptocurrencies, space on the blockchain is at a premium. So it calls for a reevaluation in many applications of how we integrate the cryptography into the system, and that work is ongoing. And, the blockchain ecosystem uses a lot of advanced cryptography, exotic things like zero-knowledge proofs. In many cases, we have rudimentary constructions of these fancy cryptography tools from post-quantum type mathematics, but they’re not nearly as mature and industry ready as the legacy systems that have been deployed. It continues to be an important technical challenge to develop post-quantum versions of these very fancy cryptographic schemes that are used in cutting edge applications.

IEEE Spectrum: As an academic cryptography researcher, what attracted you to work with a cryptocurrency, and Algorand in particular?

Peikert: My former PhD advisor is Silvio Micali, the inventor of Algorand. The system is very elegant. It is a very high performing blockchain system and it uses very little energy, has fast transaction finalization, and a number of other great features. And Silvio appreciated that this quantum threat was real and was coming, and the team approached me about helping to improve the Algorand protocol at the basic levels to become more post-quantum secure in 2021. That was a very exciting opportunity, because it was a difficult engineering and scientific challenge to integrate post-quantum cryptography into all the different technical and cryptographic mechanisms that were underlying the protocol.

IEEE Spectrum: What is the current status of post-quantum cryptography in Algorand, and blockchains in general?

Peikert: We’ve identified some of the most pressing issues and worked our way through some of them, but it’s a many-faceted problem overall. We started with the integrity of the chain itself, which is the transaction history that everybody has to agree upon.

Our first major project was developing a system that would add post-quantum security to the history of the chain. We developed a system called state proofs for that, which is a mixture of ordinary post-quantum cryptography and also some more fancy cryptography: It’s a way of taking a large number of signatures and digesting them down into a much smaller number of signatures, while still being confident that these large number of signatures actually exist and are properly formed. We also followed it with other papers and projects that are about adding post-quantum cryptography and security to other aspects of the blockchain in the Algorand ecosystem.

It’s not a complete project yet. We don’t claim to be fully post-quantum secure. That’s a very challenging target to hit, and there are aspects that we will continue to work on into the near future.

IEEE Spectrum: In your view, will we adopt post-quantum cryptography before the risks actually catch up with us?

Peikert: I tend to be an optimist about these things. I think that it’s a very good thing that more people in decision making roles are recognizing that this is an important topic, and that these kinds of migrations have to be done. I think that we can’t be complacent about it, and we can’t kick the can down the road much longer. But I do see that the focus is being put on this important problem, so I’m optimistic that most important systems will eventually have good either mitigations or full migrations in place.

But it’s also a point on the horizon that we don’t know exactly when it will come. So, there is the possibility that there is a huge breakthrough, and we have many fewer years than we might have hoped for, and that we don’t get all the systems upgraded that we would like to have fixed by the time quantum computers arrive.

Author Correction: Fc-engineered large molecules targeting blood-brain barrier transferrin receptor and CD98hc have distinct central nervous system and peripheral biodistribution

Measures of persistent organic pollutant concentrations in human pancreas remain limited; additionally, no studies have correlated pollutant concentrations with direct measures of beta cell function in humans. Here the authors show that lipophilic pollutants—including dioxins/furans, polychlorinated biphenyls, and organochlorine pesticides— accumulate in human pancreas and positively correlate with markers of beta cell dysfunction.

Human Suv3 is a mitochondrial helicase essential for RNA decay. Here, the authors present cryo-EM structures of Suv3 in multiple functional states, revealing an asymmetric dimeric architecture that coordinates ATP hydrolysis for processive RNA unwinding.

Author Correction: A proteogenomic atlas of 1032 brain metastases identifies molecular subtypes, immune landscapes, and therapeutic vulnerabilities

Like many engineers, Sarang Gupta spent his childhood tinkering with everyday items around the house. From a young age he gravitated to projects that could make a difference in someone’s everyday life.

When the family’s microwave plug broke, Gupta and his father figured out how to fix it. When a drawer handle started jiggling annoyingly, the youngster made sure it didn’t do so for long.

Sarang Gupta

Employer

OpenAI in San Francisco

Job

Data science staff member

Member grade

Senior member

Alma maters

The Hong Kong University of Science and Technology; Columbia

By age 11, his interest expanded from nuts and bolts to software. He learned programming languages such as Basic and Logo and designed simple programs including one that helped a local restaurant automate online ordering and billing.

Gupta, an IEEE senior member, brings his mix of curiosity, hands-on problem-solving, and a desire to make things work better to his role as member of the data science staff at OpenAI in San Francisco. He works with the go-to-market (GTM) team to help businesses adopt ChatGPT and other products. He builds data-driven models and systems that support the sales and marketing divisions.

Gupta says he tries to ensure his work has an impact. When making decisions about his career, he says, he thinks about what AI solutions he can unlock to improve people’s lives.

“If I were to sum up my overall goal in one sentence,” he says, “it’s that I want AI’s benefits to reach as many people as possible.”

Pursuing engineering through a business lens

Gupta’s early interest in tinkering and programming led him to choose physics, chemistry, and math as his higher-level subjects at Chinmaya International Residential School, in Tamil Nadu, India. As part of the high school’s International Baccalaureate chapter, students select three subjects in which to specialize.

“I was interested in engineering, including the theoretical part of it,” Gupta says, “But I was always more interested in the applications: how to sell that technology or how it ties to the real world.”

After graduating in 2012, he moved overseas to attend the Hong Kong University of Science and Technology. The university offered a dual bachelor’s program that allowed him to earn one degree in industrial engineering and another in business management in just four years.

In his spare time, Gupta built a smartphone app that let students upload their class schedules and find classmates to eat lunch with. The app didn’t take off, he says, but he enjoyed developing it. He also launched Pulp Ads, a business that printed advertisements for student groups on tissues and paper napkins, which were distributed in the school’s cafeterias. He made some money, he says, but shuttered the business after about a year.

After graduating from the university in 2016, he decided to work in Hong Kong’s financial hub and joined Goldman Sachs as an analyst in the bank’s operations division.

From finance to process optimization at scale

After two parties agree on securities transactions, the bank’s operations division ensures that the trade details are recorded correctly, the securities and payments are ready to transfer, and the transaction settles accurately and on time.

As an analyst, Gupta’s task was to find bottlenecks in the bank’s workflows and fix them. He identified an opportunity to automate trade reconciliation: when analysts would manually compare data across spreadsheets and systems to make sure a transaction’s details were consistent. The process helped ensure financial transactions were recorded accurately and settled correctly.

Gupta built internal automation tools that pulled trade data from different systems, ran validation checks, and generated reports highlighting any discrepancies.

“Instead of analysts manually checking large datasets, the tools automatically flagged only the cases that required investigation,” he says. “This helped the team spend less time on repetitive verification tasks and more time resolving complex issues. It was also my first real exposure to how software and data systems could dramatically improve operational workflows.”

“Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.”

The experience made him realize he wanted to work more deeply in technology and data-driven systems, he says. He decided to return to school in 2018 to study data science and AI, when the fields were just beginning to surge into broader awareness.

He discovered that Columbia offered a dedicated master’s degree program in data science with a focus on AI. After being accepted in 2019, he moved to New York City.

Throughout the program, he gravitated to the applied side of machine learning, taking courses in applied deep learning and neural networks.

One of his major academic highlights, he says, was a project he did in 2019 with the Brown Institute, a joint research lab between Columbia and Stanford focused on using technology to improve journalism. The team worked with The Philadelphia Inquirer to help the newsroom staff better understand their coverage from a geographic and social standpoint. The project highlighted “news deserts”—underserved communities for which the newspaper was not providing much coverage—so the publication could redirect its reporting resources.

To identify those areas, Gupta and his team built tools that extracted locations such as street names and neighborhoods from news articles and mapped them to visualize where most of the coverage was concentrated. The Inquirer implemented the tool in several ways including a new web page that aggregated stories about COVID-19 by county.

“Journalism was an interesting problem set for me, because I really like to read the news every day,” Gupta says. “It was an opportunity to work with a real newsroom on a problem that felt really impactful for both the business and the local community.”

The GenAI inflection point

After earning his master’s degree in 2020, Gupta moved to San Francisco to join Asana, the company that developed the work management platform by the same name. He was drawn to the opportunity to work for a relatively small company where he could have end-to-end ownership of projects. He joined the organization as a product data scientist, focusing on A/B testing for new platform features.

Two years later, a new opportunity emerged: He was asked to lead the launch of Asana Intelligence, an internal machine learning team building AI-powered features into the company’s products.

“I felt I didn’t have enough experience to be the founding data scientist,” he says. “But I was also really interested in the space, and spinning up a whole machine learning program was an opportunity I couldn’t turn down.”

The Asana Intelligence team was given six months to build several machine learning–powered features to help customers work more efficiently. They included automatic summaries of project updates, insights about potential risks or delays, and recommendations for next steps.

The team met that goal and launched several other features including Smart Status, an AI tool that analyzes a project’s tasks, deadlines, and activity, then generates a status update.

“When you finally launch the thing you’ve been working on, and you see the usage go up, it’s exhilarating,” he says. “You feel like that’s what you were building toward: users actually seeing and benefiting from what you made.”

Gupta and his team also translated that first wave of work into reusable frameworks and documentation to make it easier to create machine learning features at Asana. He and his colleagues filed several U.S. patents.

At the time he took on that role, OpenAI launched ChatGPT. The mainstreaming of generative AI and large language models shifted much of his work at Asana from model development to assessing LLMs.

OpenAI captured the attention of people around the world, including Gupta. In September 2025 he left Asana to join OpenAI’s data science team.

The transition has been both energizing and humbling, he says. At OpenAI, he works closely with the marketing team to help guide strategic decisions. His work focuses on developing models to understand the efficiency of different marketing channels, to measure what’s driving impact, and to help the company better reach and serve its customers.

“The pace is very different from my previous work. Things move quickly,” he says. “The industry is extremely competitive, and there’s a strong expectation to deliver fast. It’s been a great learning experience.”

Gupta says he plans to stay in the AI space. With technology evolving so rapidly, he says, he sees enormous potential for task automation across industries. AI has already transformed his core software engineering work, he says, and it’s helped him enhance areas that aren’t natural strengths.

“I’m not a good writer, and AI has been huge in helping me frame my words better and present my work more clearly,” he says. “Whether it’s helping a person improve a trait like that or driving efficiencies at a business, AI just has so much potential to help. I’m excited to be a little part of that.”

Exploring IEEE publications and connections

Gupta has been an IEEE member since 2024, and he values the organization as both a technical resource and a professional network.

He regularly turns to IEEE publications and the IEEE Xplore Digital Library to read articles that keep him abreast of the evolution of AI, data science, and the engineering profession.

IEEE’s member directory tools are another valuable resource that he uses often, he says.

“It’s been a great way to connect with other engineers in the same or similar fields,” he says. “I love sharing and hearing about what folks are working on. It brings me outside of what I’m doing day to day.

“It inspires me, and it’s something I really enjoy and cherish.”

Scott Imbrie vividly remembers the first time he used a robotic arm to shake someone’s hand and felt the robotic limb as if it were his own. “I still get goosebumps when I think about that initial contact,” he says. “It’s just unexplainable.” The moment came courtesy of a brain implant: an array of electrodes that let him control a robotic arm and receive tactile sensations back to the brain.

Getting there took decades. In 1985, Imbrie had woken up in the hospital after a car accident with a broken neck and a doctor telling him he’d never use his hands or legs again. His response was an expletive, he says—and a decision. “I’m not going to allow someone to tell me what I can and can’t do.” With the determination of a head-strong 22-year-old, Imbrie gradually regained the ability to walk and some limited arm movement. Aware of how unusual his recovery was, the Illinois-native wanted to help others in similar situations and began looking for research projects related to spinal cord injuries. For decades, though, he wasn’t the right fit, until in 2020 he was finally accepted into a University of Chicago trial.

Scott Imbrie has shaken hands with a robotic arm controlled by a brain implant. The electrodes record neural signals that enable him to move the device and receive tactile feedback. Top: 60 Minutes/CBS News; Bottom: University of Chicago

Imbrie is part of a rarefied group: More people have gone to space than have received advanced brain-computer interfaces (BCI) like his. But a growing number of companies are now attempting to move the devices out of neuroscience labs and into mainstream medical care, where they could help millions of people with paralysis and other neurological conditions. Some companies even hope that BCIs will eventually become a consumer technology.

None of that will be possible without people like Imbrie. He’s a member of the BCI Pioneers Coalition, an advocacy group founded in 2018 by Ian Burkhart, the first quadriplegic to regain hand movement using a brain implant.

That life-changing experience convinced Burkhart that BCIs will make the leap from lab to real world only if users help shape the technology by sharing their perspectives on what works, what doesn’t, and how the devices fit into daily life. The coalition aims to ensure that companies, clinicians, and regulators hear directly from trial participants.

Ian Burkhart founded the BCI Pioneers Coalition to ensure that companies developing brain implants hear directly from the people using them. Left: Andrew Spear/Redux; Right: Ian Burkhart

The group also serves as a peer-support network for trial participants. That’s crucial, because despite the steady drumbeat of miraculous results from BCI trials, receiving a brain implant comes with significant risks. Surgical complications, such as bleeding or infection in the brain, are possible. Even more concerning is the potential psychological toll if the implant fails to work as expected or if life-changing improvements are eventually withdrawn.

Researchers spell this out upfront, and many are put off, says John Downey, an assistant professor of neurological surgery at the University of Chicago and the lead on Imbrie’s clinical trial. “I would say, the number of people I talk to about doing it is probably 10 to 20 times the number of people that actually end up doing it,” he says.

What Happens in a BCI Trial?

BCI pioneers arrive at their unique status via a number of paths, including spinal cord injuries, stroke-induced paralysis, and amyotrophic lateral sclerosis (ALS). The implants they receive come from Blackrock Neurotech, Neuralink, Synchron, and other companies, and are being tested for restoring limb function, controlling computers and robotic arms, and even restoring speech.

Many of the implants record signals from the motor cortex—the part of the brain that controls voluntary movements—to move external devices. Some others target the somatosensory cortex, which processes sensory signals from the body, including touch, pain, temperature, and limb position, to re-create tactile sensation.

BCI Designs Used by Today’s Pioneers

Ease of use depends heavily on the application. Restoring function to a user’s own limbs or controlling robotic arms involves the most difficult learning curve. In early sessions, participants watch a virtual arm reach for objects while they imagine or attempt the same movement. Researchers record related brain signals and use them to train “decoder” software, which translates neural activity into control signals for a robotic arm or stimulation patterns for the user’s nerves or muscles.

Paralyzed in a 2010 swimming accident, Burkhart took part in a trial conducted by Battelle Memorial Institute and Ohio State University from 2014 to 2021. His implant recorded signals from his motor cortex as he attempted to move his hand, and the system relayed those commands to electrodes in his arm that stimulated the muscles controlling his fingers.

Ian Burkhart, who is paralyzed from the chest down, received a brain implant that routed neural signals through a computer to his paralyzed muscles, enabling him to play a video game. Battelle

Getting the system to work seamlessly took time, says Burkhart, and initially required intense concentration. Eventually, he could shift his focus from each individual finger movement to the overall task, allowing him to swipe a credit card, pour from a bottle, and even play Guitar Hero.

Training a decoder is also not a one-and-done process. Systems must be regularly recalibrated to account for “neural drift”—the gradual shift in a person’s neural activity patterns over time. For complex tasks like robotic arm control, researchers may have to essentially train an entirely new decoder before each session, which can take up to an hour.

Austin Beggin says that testing a BCI is hard work, but he adds that moments like petting his dog make it all worth it. Daniel Lozada/The New York Times/Redux

Even after the system is ready, using the device can be taxing, says Austin Beggin, who was paralyzed in a swimming accident in 2015 and now participates in a Case Western Reserve University trial aimed at restoring hand movement. “The mental work of just trying to do something like shaking hands or feeding yourself is 100-fold versus you guys that don’t even think about it,” he says.

It’s also a serious time commitment. Beggin travels more than 2 hours from his home in Lima, Ohio, to Cleveland for two weeks every month to take part in experiments. All the equipment is set up in the house he stays in, and he typically works with the researchers for 3 to 4 hours a day. The majority of the experiments are not actually task-focused, he says, and instead are aimed at adjusting the control software or better understanding his neural responses to different stimuli.

But the BCI users say the hard work is worth it. Beyond the hope of restoring lost function, many feel a strong moral obligation to advance a technology that could help others. Beggin compares the pioneers to the early astronauts who laid the groundwork for the lunar landings. “We’re some of the first astronauts just to get shot up for a couple of hours and come back down to earth,” he says.

The Emotional Impact of BCIs

Speak to BCI early adopters and a pattern emerges: The biggest benefits are often more emotional than practical. Using a robotic arm to feed oneself or control a computer is clearly useful, but many pioneers say the most meaningful moments are the ones the experiment wasn’t even trying to produce. Beggin counts shaking his parents’ hands for the first time since his injury and stroking his pet dachshund as among his favorite moments. “That stuff is absolutely incredible,” he says.

Neuralink participant Alex Conley, who broke his neck in a car accident in 2021, uses his implant to control both a robotic arm and computers, enabling him to open doors, feed himself, and handle a smartphone. But he says the biggest boost has come from using computer-aided design software.

A former mechanic, Conley began using the software within days of receiving his implant to design parts that could be fabricated on a 3D printer. He has designed everything from replacement parts for his uncle’s power tools to bumpers for his brother-in-law’s truck. “I was a very big problem solver before my accident, I was able to fix people’s things,” he says. “This gives me that same little burst of joy.”

BCI user Nathan Copeland used a robotic arm to get a fist bump from then-President Barack Obama in 2016. Jim Watson/AFP/Getty Images

The outside world often underestimates those little wins, says Nathan Copeland, who holds the record for the longest functional brain implant. After breaking his neck in a car accident in 2004, he joined a University of Pittsburgh BCI trial in 2015 and has since used the device to control both computers and a robotic arm.

After he uploaded a video to Reddit of himself playing Final Fantasy XIV, one commenter criticized him for not using his device for more practical tasks. Copeland says people don’t understand that those lighthearted activities also matter. “A lot of tasks that people think are mundane or frivolous are probably the tasks that have the most impact on someone that can’t do them,” he says. “Agency and freedom of expression, I think, are the things that impact a person’s life the most.”

Nathan Copeland plays Final Fantasy XIV using his brain implant to control the game character.

When Brain Implants Become Life-Changing

This perspective resonates with Neuralink’s first user, Noland Arbaugh—paralyzed from the neck down after a swimming accident in 2016. After receiving his implant in January 2024, he was able to control a cursor within minutes of the device being switched on. A few days later, the engineers let him play the video game Civilisation VI, and the technology’s potential suddenly felt real. “I played it for 8 hours or 12 hours straight,” he says. “It made me feel so independent and so free.”

Before receiving his Neuralink implant, Noland Arbaugh used mouth-operated devices to control a computer. He says the BCI is more reliable and enables him to do many more things on his own. Rebecca Noble/The New York Times/Redux

But the technology is also providing more practical benefits. Before his implant, Arbaugh relied on a mouth-held typing stick and a mouth-controlled joystick called a quadstick, which uses sip-or-puff sensors to issue commands. But the fiddliness of this equipment required constant caregiver support. The Neuralink implant has dramatically increased the number of things he can do independently. He says he finds great value in not needing his family “to come in and help me 100 times a day.”

For Casey Harrell, the technology has been even more transformative. Diagnosed with ALS in 2020, the climate activist had just welcomed a baby daughter and was in the midst of a major campaign, pressuring a financial firm to divest from companies that had poor environmental records.

Casey Harrell was able to communicate again within 30 minutes of his BCI being switched on. The device translates his neural signals quickly enough for him to hold conversations. Ian Bates/The New York Times/Redux

“Every morning we’d wake up and there’d be a new thing he couldn’t do, a new part of his body that didn’t work,” says his wife, Levana Saxon. Most alarming was his rapid loss of speech, which, among other things, left him unable to indicate when he was in pain. Then a relative alerted him to a clinical trial at the University of California, Davis, using BCIs to restore speech. He immediately signed up.

The device, implanted in July 2023, records from the brain region that controls muscles involved in talking and translates these signals into instructions for a voice synthesizer. Within 30 minutes of it being switched on, Harrell could communicate again. “I was absolutely overwhelmed with the thought of how this would impact my life and allow me to talk to my family and friends and better interact with my daughter,” he says. “It just was so overwhelming that I began to cry.”

While earlier assistive technology limited him to short, direct commands, Harrell says the BCI is fast enough that he can hold a proper conversation, and he’s been able to resume work part-time.

What’s Holding BCI Technology Back?

BCI technology still has limits. Most trial participants using Blackrock Neurotech implants can operate their devices only in the lab because the systems rely on wired connections and racks of computer hardware. Some users, including Copeland and Harrell, have had the equipment installed at home, but they still can’t leave the house with it. “That would be a big unlock if I was able to do so,” says Harrell.

The academic nature of many trials creates additional constraints. Pressure to publish and secure funding pushes researchers to demonstrate peak performance on narrow tasks rather than build more versatile and reliable systems, says Mariska Vansteensel, who runs BCI studies at the University Medical Center Utrecht in the Netherlands. She says that investigating the technology’s limits or repeating an experiment in new patients is “less rewarded in terms of funding.”

In a clinical trial, Scott Imbrie uses a BCI to control a robotic arm, using signals from his motor cortex to make it move a block. University of Chicago

One of Imbrie’s biggest frustrations is the rapid turnover in experiments. Just as he begins to get proficient at one task, he’s asked to switch to the next task. Study designs also mean that much of the users’ time is spent on mundane tasks required to fine-tune the system.

Perhaps the biggest issue is that trials are often time-limited. That’s partly because scar tissue from the body’s immune response to the implant can gradually degrade signal quality. But constraints on funding and researcher availability can also make it impossible for users to keep using their BCIs after their trials end, even when the technology is still functional.

Ian Burkhart’s BCI enables him to grasp objects, pour from a bottle, and swipe a credit card.

Burkhart has firsthand experience. His trial was extended, but the implant was eventually removed after he got an infection. He always knew the trial would end, but it was nonetheless challenging. “It was a little bit of a tease where I got to see the capability of the restoration of function,” he says. “Now I’m just back to where I was.”

The Push to Commercialize BCIs

Progress is being made in transitioning the technology from experimental research devices to fully-fledged medical products that could help users in their everyday lives. Most academic BCI research has relied on Blackrock Neurotech’s Utah Arrays, which typically feature 96 needlelike electrodes that penetrate the brain’s surface. The implant is connected to a skull-mounted pedestal that’s wired to external hardware. But some of the newer devices are sleeker and less invasive.

Neuralink’s implant houses its electronics and rechargeable battery in a coin-size unit connected to flexible electrode threads inserted into the brain by a robotic “sewing machine.” The implant, which is roughly the size of a quarter or a euro, is mounted in a hole cut into the skull and charges and transfers data wirelessly. Synchron takes a different approach, threading a stent-like implant through blood vessels into the motor cortex. This “stentrode” connects by wire to a unit in the chest that powers the implant and transmits data wirelessly.

Rodney Gorham can use his Synchron implant to control not just a computer, but also smart devices in his home like an air conditioner, fan, and smart speaker. Rodney Decker

Neuralink’s decoder runs on a laptop, while Synchron deploys a smartphone-size signal processing unit as a wireless bridge to the user’s devices, which allows them to use their implants at home and on the move. The companies have also developed adaptive decoders that use machine learning to adjust to neural drift on the fly, reducing the need for recalibration.

Making these devices truly user-friendly will require technology that can interpret user context, says Kurt Haggstrom, Synchron’s chief commercial officer—including mood, attention levels, and environmental factors like background noise and location. This approach will require AI that analyzes neural signals alongside other data streams such as audio and visual input.

Last year, Synchron took a first step by pairing its implant with an Apple Vision Pro headset. When trial participant Rodney Gorham looked at devices such as a fan, a smart speaker, and an air conditioner, the headset overlaid a menu that enabled him to adjust the device’s settings using his implant.

Rodney Gorham uses his Synchron implant to turn on music, feed his dog, and more. Synchron BCI

Another way to reduce cognitive load is to detect high-order signals of intent in neural data rather than low-level motor commands, says Florian Solzbacher, cofounder and chief scientific officer of Blackrock Neurotech. For instance, rather than manually navigating to an email app and typing, the user could simply think about sending an email and the system would then open it with content already prepopulated, he says.

Durability may prove a thornier problem to solve, UChicago’s Downey says. Current implants last around a decade—well short of a lifelong solution. And with limited real estate in the brain, replacement is only possible once or twice, he says.

Rapid technological progress also raises difficult decisions about whether to get a BCI implant now or wait for a more advanced device. This was a major concern for Gorham’s wife, Caroline. “I was hesitant. I didn’t want him to go on the trial but maybe a future one,” she says. “It was my fear of missing out on future upgrades.”

Will Brain Implants Ever Become Consumer Tech?

Some executives have raised the prospect of BCIs eventually becoming consumer devices. Neuralink founder Elon Musk has been particularly vocal, suggesting that the company’s implants could replace smartphones, let people save and replay memories, or even achieve “symbiosis” with AI.

This kind of talk inspires mixed feelings in users. The hype brings visibility and funding, says Beggin, but could divert attention from medical users’ needs. Copeland worries that consumer branding could strip the devices of insurance coverage and that rising demand may make it harder to access qualified surgeons.

Noland Arbaugh, the first recipient of Neuralink’s BCI, says that using the implant to control a computer made him feel independent and free. Steve Craft/Guardian/eyevine/Redux

There are also concerns about how data collected by BCI companies will be handled if the devices go mainstream. As a trial participant, Arbaugh says he’s comfortable signing away his data rights to advance the technology, but he thinks stronger legal protections will be needed in the future. “Does that data still belong to Neuralink? Does it belong to each person? And can that data be sold?” he asks.

Blackrock’s Solzbacher says the company remains focused on the medical applications of the technology. But he also believes it is building a “universal interface to any kind of a computerized system” that may have broader applications in the future. And he says the company owes it to users not to limit them to a bare-bones assistive technology. “Why would somebody who’s got a medical condition want to get less than something that somebody who’s able-bodied would possibly also take?” says Solzbacher.

The ever-optimistic Imbrie heartily agrees. Medical devices are invariably expensive, he says, but targeting consumer applications could push companies to keep devices simple and affordable while continuing to add features. “I truly believe that making it a consumer-available product will just enhance the product’s capabilities for the medical field,” he says.

Imbrie is on a mission to refocus the conversation around BCIs on the positives. While concerns about risks are valid, he worries that the alarming language often used to describe brain implants discourages people from volunteering for trials that could help them.

“I remember laying there in the bed and not being able to move,” he says, “and it was really dehumanizing having to ask someone to do everything for you. As humans, we want to be independent.”

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Photonic devices, which rely on light instead of electricity, have the potential to be faster and more energy efficient than today’s electronics. They also present a unique opportunity to develop devices using soft materials, such as polymers and gels, which are poor conductors of electricity, but are easier to manufacture and more environmentally friendly. The development of these potentially squishy, flexible photonics, however, requires the ability to manipulate light using only light, not electricity.

In soft matter, that’s been done primarily by changing the physical properties of optical materials or by using intense light pulses to change the direction of light. Now, an international team of scientists has developed a new way of controlling light with light using very low light intensities and without changing any of the physical properties of materials.

Igor Muševič, a professor of physics at the University of Ljubljana who led the project, says that he first got the idea for the device while at a conference in San Francisco, listening to a talk by Stefan W. Hell about stimulated emission depletion (STED) microscopy. The imaging technique, for which Hell won a Nobel Prize in Chemistry in 2014, uses two lasers to produce an extremely small light beam to scan objects. “When I saw this, I said, this is manipulation light by light, right?” Muševič recalls.

His realization inspired a device into which a laser pulse is fired. Whether or not this beam makes it out of the device depends on whether or not a second pulse is fired less than a nanosecond afterwards.

A liquid crystal photonic switch

The device consists of a spherically-shaped bead of liquid crystal, held in shape by its elastic material properties and the forces between its molecules, infused with a fluorescent dye and trapped between four upright cone-shaped polymer structures that guide light in and out of the device. When a laser pulse is sent through one of the four polymer waveguides, the light is quickly transferred into the liquid crystal, exciting the fluorescent dye. In a process known as whispering gallery mode resonance, the photons inside the liquid crystal are reflected back inside each time they hit the liquid’s spherical surface. The result is that light circulates inside the cavity until it is eventually reflected into one of the waveguides, which then emits the photons out in a laser beam.

The team realized that sending a second laser pulse of a different color into the waveguides before the liquid crystal started emitting light from the first laser pulse resulted in stimulated emission of the excited dye molecules. The photons from the second laser pulse, which had to be fired into the waveguides after the first laser pulse, interact with the already-excited dye molecules. The interaction causes the dye to emit photons identical to those in the second pulse while depleting the energy from the first pulse. The second laser beam, called the STED beam, is amplified by the process, while the light from the first pulse is so diminished that it isn’t emitted at all. Because the outcome of the first laser pulse could be controlled using the second laser pulse, the team had successfully demonstrated the control of light by light.

Vandna Sharma, Jaka Zaplotnik, et al.

According to the Ljubljana team, the energy efficiency of the liquid crystal approach is much better than previous soft-matter techniques, which had typically involved using intense light fields to change material properties of the soft matter, such as the index of refraction. The new method reduces the energy needed by more than a factor of a hundred. Because the STED laser pulse circulates repeatedly in the crystal, a single photon can deplete many dye molecules of the energy from the first laser pulse.

Miha Ravnik, a theoretical physicist also at the University of Ljubljana who worked on the project, explains that control of light by light is essential in soft-matter photonic logic gates. “You can very much control when [light] is generated and in which direction,” Ravnik says of the light shined into the polymer waveguides. “And this gives you, then, this capability that you create logical operations with light.”

Aside from its potential in photonic logical circuits, the team’s approach presents several technical advantages over photonics made from silicon or other hard materials, Muševič says. For example, using soft matter greatly simplifies the manufacturing process. The liquid crystal in the team’s device can be inserted in less than a second, but manufacturing a similar structure with hard materials is difficult. Additionally, soft matter devices can be manufactured at much lower temperatures than silicon and other hard materials. Muševič also points out that soft matter presents an opportunity to experiment with the geometry of the device. With liquid crystals “you can make many different kinds of cavities,” says Muševič. “You have, I would say, a lot of engineering space.”

Ravnik is excited for the potential of the team’s breakthrough, particularly as a step towards photonic computing and even photonic neural networks. But, he recognizes that these developments are far down the line. “There’s no way this technology can compete with current neural network implementation at all,” he admits. Still, the possibilities are tantalizing. “The energy losses are predicted to be extremely low, the speeds for calculation extremely high.”

Author Correction: Coordination-tailored atomic interfaces for selective CH₄-to-C₂ conversion in aqueous solution

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