AI’s Math Tricks Don’t Work for Scientific Computing

AI has driven an explosion of new number formats—the ways in which numbers are represented digitally. Engineers are looking at every possible way to save computation time and energy, including shortening the number of bits used to represent data. But what works for AI doesn’t necessarily work for scientific computing, be it for computational physics, biology, fluid dynamics, or engineering simulations. IEEE Spectrum spoke with Laslo Hunhold, who recently joined Barcelona-based Openchip as an AI engineer, about his efforts to develop a bespoke number format for scientific computing.

LASLO HUNHOLD

Laslo Hunhold is a senior AI accelerator engineer at Barcelona-based startup Openchip. He recently completed a Ph.D. in computer science from the University of Cologne, in Germany.

What makes number formats interesting to you?

Laslo Hunhold: I don’t know another example of a field that so few are interested in but has such a high impact. If you make a number format that’s 10 percent more [energy] efficient, it can translate to all applications being 10 percent more efficient, and you can save a lot of energy.

Why are there so many new number formats?

Hunhold: For decades, computer users had it really easy. They could just buy new systems every few years, and they would have performance benefits for free. But this hasn’t been the case for the last 10 years. In computers, you have a certain number of bits used to represent a single number, and for years the default was 64 bits. And for AI, companies noticed that they don’t need 64 bits for each number. So they had a strong incentive to go down to 16, 8, or even 2 bits [to save energy]. The problem is, the dominating standard for representing numbers in 64 bits is not well designed for lower bit counts. So in the AI field, they came up with new formats which are more tailored toward AI.

Why does AI need different number formats than scientific computing?

Hunhold: Scientific computing needs high dynamic range: You need very large numbers, or very small numbers, and very high accuracy in both cases. The 64-bit standard has an excessive dynamic range, and it is many more bits than you need most of the time. It’s different with AI. The numbers usually follow a specific distribution, and you don’t need as much accuracy.

What makes a number format “good”?

Hunhold: You have infinite numbers but only finite bit representations. So you need to decide how you assign numbers. The most important part is to represent numbers that you’re actually going to use. Because if you represent a number that you don’t use, you’ve wasted a representation. The simplest thing to look at is the dynamic range. The next is distribution: How do you assign your bits to certain values? Do you have a uniform distribution, or something else? There are infinite possibilities.

What motivated you to introduce the takum number format?

Hunhold: Takums are based on posits. In posits, the numbers that get used more frequently can be represented with more density. But posits don’t work for scientific computing, and this is a huge issue. They have a high density for [numbers close to one], which is great for AI, but the density falls off sharply once you look at larger or smaller values. People have been proposing dozens of number formats in the last few years, but takums are the only number format that’s actually tailored for scientific computing. I found the dynamic range of values you use in scientific computations, if you look at all the fields, and designed takums such that when you take away bits, you don’t reduce that dynamic range

This article appears in the March 2026 print issue as “Laslo Hunhold.”

Reference: https://ift.tt/BDlCua2

The Age Verification Trap

Social media is going the way of alcohol, gambling, and other social sins: societies are deciding it’s no longer kids’ stuff. Lawmakers point to compulsive use, exposure to harmful content, and mounting concerns about adolescent mental health. So, many propose to set a minimum age, usually 13 or 16.

In cases when regulators demand real enforcement rather than symbolic rules, platforms run into a basic technical problem. The only way to prove that someone is old enough to use a site is to collect personal data about who they are. And the only way to prove that you checked is to keep the data indefinitely. Age-restriction laws push platforms toward intrusive verification systems that often directly conflict with modern data-privacy law.

This is the age-verification trap. Strong enforcement of age rules undermines data privacy.

How Does Age Enforcement Actually Work?

Most age-restriction laws follow a familiar pattern. They set a minimum age and require platforms to take “reasonable steps” or “effective measures” to prevent underage access. What these laws rarely spell out is how platforms are supposed to tell who is actually over the line. At the technical level, companies have only two tools.

The first is identity-based verification. Companies ask users to upload a government ID, link a digital identity, or provide documents that prove their age. Yet in many jurisdictions, 16-year-olds do not have IDs. In others, IDs exist but are not digital, not widely held, or not trustworthy. Storing copies of identity documents also creates security and misuse risks.

The second option is inference. Platforms try to guess age based on behavior, device signals, or biometric analysis, most commonly facial age estimation from selfies or videos. This avoids formal ID collection, but it replaces certainty with probability and error.

In practice, companies combine both. Self-declared ages are backed by inference systems. When confidence drops, or regulators ask for proof of effort, inference escalates to ID checks. What starts as a light-touch checkpoint turns into layered verification that follows users over time.

What Are Platforms Doing Right Now?

This pattern is already visible on major platforms.

Meta has deployed facial age estimation on Instagram in multiple markets, using video-selfie checks through third-party partners. When the system flags users as possibly underaged, it prompts them to record a short selfie video. An AI system estimates their age and, if it decides they are under the threshold, restricts or locks the account. Appeals often trigger additional checks, and misclassifications are common.

TikTok has confirmed that it also scans public videos to infer users’ ages. Google and YouTube rely heavily on behavioral signals tied to viewing history and account activity to infer age, then ask for government ID or a credit card when the system is unsure. A credit card functions as a proxy for adulthood, even though it says nothing about who is actually using the account. The Roblox games site, which recently launched a new age-estimate system, is already suffering from users selling child-aged accounts to adult predators seeking entry to age-restricted areas, Wired reports.

For a typical user, age is no longer a one-time declaration. It becomes a recurring test. A new phone, a change in behavior, or a false signal can trigger another check. Passing once does not end the process.

How Do Age Verification Systems Fail?

These systems fail in predictable ways.

False positives are common. Platforms identify as minors adults with youthful faces, or who are sharing family devices, or have otherwise unusual usage. They lock accounts, sometimes for days. False negatives also persist. Teenagers learn quickly how to evade checks by borrowing IDs, cycling accounts, or using VPNs.

The appeal process itself creates new privacy risks. Platforms must store biometric data, ID images, and verification logs long enough to defend their decisions to regulators. So if an adult who is tired of submitting selfies to verify their age finally uploads an ID, the system must now secure that stored ID. Each retained record becomes a potential breach target.

Scale that experience across millions of users, and you bake the privacy risk into how platforms work.

Is Age Verification Compatible with Privacy Law?

This is where emerging age-restriction policy collides with existing privacy law.

Modern data-protection regimes all rest on similar ideas: collect only what you need, use it only for a defined purpose, and keep it only as long as necessary.

Age enforcement undermines all three.

To prove they are following age verification rules, platforms must log verification attempts, retain evidence, and monitor users over time. When regulators or courts ask whether a platform took reasonable steps, “we collected less data” is rarely persuasive. For companies, defending themselves against accusations of neglecting to properly verify age supersedes defending themselves against accusations of inappropriate data collection.

It is not an explicit choice by voters or policymakers, but instead a reaction to enforcement pressure and how companies perceive their litigation risk.

Less Developed Countries, Deeper Surveillance

Outside wealthy democracies, the tradeoff is even starker.

Brazil’s Statute of Child-rearing and Adolescents (ECA in Portuguese) imposes strong child-protection duties online, while its data protection law restricts data collection and processing. Now providers operating in Brazil must adopt effective age-verification mechanisms and can no longer rely on self-declaration alone for high-risk services. Yet they also face uneven identity infrastructure and widespread device sharing. To compensate, they rely more heavily on facial estimation and third-party verification vendors.

In Nigeria many users lack formal IDs. Digital service providers fill the gap with behavioral analysis, biometric inference, and offshore verification services, often with limited oversight. Audit logs grow, data flows expand, and the practical ability of users to understand or contest how companies infer their age shrinks accordingly. Where identity systems are weak, companies do not protect privacy. They bypass it.

The paradox is clear. In countries with less administrative capacity, age enforcement often produces more surveillance, not less, because inference fills the void of missing documents.

How Do Enforcement Priorities Change Expectations?

Some policymakers assume that vague standards preserve flexibility. In the U.K., then–Digital Secretary Michelle Donelan, argued in 2023 that requiring certain online safety outcomes without specifying the means would avoid mandating particular technologies. Experience suggests the opposite.

When disputes reach regulators or courts, the question is simple: can minors still access the platform easily or not? If the answer is yes, authorities tell companies to do more. Over time, “reasonable steps” become more invasive.

Repeated facial scans, escalating ID checks, and long-term logging become the norm. Platforms that collect less data start to look reckless by comparison. Privacy-preserving designs lose out to defensible ones.

This pattern is familiar, including online sales tax enforcement. After courts settled that large platforms had an obligation to collect and remit sales taxes, companies began continuous tracking and storage of transaction destinations and customer location signals. That tracking is not abusive, but once enforcement requires proof over time, companies build systems to log, retain, and correlate more data. Age verification is moving the same way. What begins as a one-time check becomes an ongoing evidentiary system, with pressure to monitor, retain, and justify user-level data.

The Choice We Are Avoiding

None of this is an argument against protecting children online. It is an argument against pretending there is no tradeoff.

Some observers present privacy-preserving age proofs involving a third party, such as the government, as a solution, but they inherit the same structural flaw: many users who are legally old enough to use a platform do not have government ID. In countries where the minimum age for social media is lower than the age at which ID is issued, platforms face a choice between excluding lawful users and monitoring everyone. Right now, companies are making that choice quietly, after building systems and normalizing behavior that protects them from the greater legal risks. Age-restriction laws are not just about kids and screens. They are reshaping how identity, privacy, and access work on the Internet for everyone.

The age-verification trap is not a glitch. It is what you get when regulators treat age enforcement as mandatory and privacy as optional.

Reference: https://ift.tt/WTQId71

Poem: The Attraction of Blackberries

The first time she tried to seduce me,
(atoms falling in a vacuum)
she asked about blackberries—
(every mass exerts some gravity)

Did I know their season, where they grow?
(galvanometers, gravimeters)
I could answer both easily—
(tools to measure small attractions)

down the dirt road in September.
(devices that report, don’t interfere)
She eagerly went there with me,
(variations in readings occur)

We ate more berries than we kept.
(electron exchange may explain this)
The sweet dark juice painted our lips.
(equilibrium then entropy)

Reference: https://ift.tt/VFDYNuB

AI Data Centers Turn to High-Temperature Superconductors

Data centers for AI are turning the world of power generation on its head. There isn’t enough power capacity on the grid to even come close to how much energy is needed for the number being built. And traditional transmission and distribution networks aren’t efficient enough to take full advantage of all the power available. According to the U.S. Energy Information Administration (EIA), annual transmission and distribution losses average about 5 percent. The rate is much higher in some other parts of the world. Hence, hyperscalers such as Amazon Web Services, Google Cloud and Microsoft Azure are investigating every avenue to gain more power and raise efficiency.

Microsoft, for example, is extolling the potential virtues of high-temperature superconductors (HTS) as a replacement for copper wiring. According to the company, HTS can improve energy efficiency by reducing transmission losses, increasing the resiliency of electrical grids, and limiting the impact of data centers on communities by reducing the amount of space required to move power.

“Because superconductors take up less space to move large amounts of power, they could help us build cleaner, more compact systems,” Alastair Speirs, the general manager of global infrastructure at Microsoft wrote in a blog post.

Superconductors Revolutionize Power Efficiency

Copper is a good conductor, but current encounters resistance as it moves along the line. This generates heat, lowers efficiency, and restricts how much current can be moved. HTS largely eliminates this resistance factor, as it’s made of superconducting materials that are cooled to cryogenic temperatures. (Despite the name, high-temperature superconductors still rely on frigid temperatures—albeit significantly warmer than those required by traditional superconductors.)

The resulting cables are smaller and lighter than copper wiring, don’t lower voltage as they transmit current, and don’t produce heat. This fits nicely into the needs of AI data centers that are trying to cram massive electrical loads into a tiny footprint. Fewer substations would also be needed. According to Speirs, next-gen superconducting transmission lines deliver capacity that is an order of magnitude higher than conventional lines at the same voltage level.

Microsoft is working with partners on the advancement of this technology including an investment of US $75 million into Veir, a superconducting power technology developer. Veir’s conductors use HTS tape, most commonly based on a class of materials known as rare-earth barium copper oxide (REBCO). REBCO is a ceramic superconducting layer deposited as a thin film on a metal substrate, then engineered into a rugged conductor that can be assembled into power cables.

“The key distinction from copper or aluminum is that, at operating temperature, the superconducting layer carries current with almost no electrical resistance, enabling very high current density in a much more compact form factor,” says Tim Heidel, Veir’s CEO and co-founder.

Liquid Nitrogen Cooling in Data Centers

Ruslan Nagimov, the principal infrastructure engineer for Cloud Operations and Innovation at Microsoft, stands near the world’s first HTS-powered rack prototype.Microsoft

HTS cables still operate at cryogenic temperatures, so cooling must be integrated into the power delivery system design. Veir maintains a low operating temperature using a closed-loop liquid nitrogen system: The nitrogen circulates through the length of the cable, exits at the far end, is re-cooled, and then recirculated back to the start.

“Liquid nitrogen is a plentiful, low cost, safe material used in numerous critical commercial and industrial applications at enormous scale,” says Heidel. “We are leveraging the experience and standards for working with liquid nitrogen proven in other industries to design stable, data center solutions designed for continuous operation, with monitoring and controls that fit critical infrastructure expectations rather than lab conditions.”

HTS cable cooling can either be done within the data center or externally. Heidel favors the latter as that minimizes footprint and operational complexity indoors. Liquid nitrogen lines are fed into the facility to serve the superconductors. They deliver power to where it’s needed and the cooling system is managed like other facility subsystem.

Rare earth materials, cooling loops, cryogenic temperatures—all of this adds considerably to costs. Thus, HTS isn’t going to replace copper in the vast majority of applications. Heidel says the economics are most compelling where power delivery is constrained by space, weight, voltage drop, and heat.

“In those cases, the value shows up at the system level: smaller footprints, reduced resistive losses, and more flexibility in how you route power,” says Heidel. “As the technology scales, costs should improve through higher-volume HTS tape manufacturing and better yields, and also through standardization of the surrounding system hardware, installation practices, and operating playbooks that reduce design complexity and deployment risk.”

AI data centers are becoming the perfect proving ground for this approach. Hyperscalers are willing to spend to develop higher-efficiency systems. They can balance spending on development against the revenue they might make by delivering AI services broadly.

“HTS manufacturing has matured—particularly on the tape side—which improves cost and supply availability,” says Husam Alissa, Microsoft’s director of systems technology. “Our focus currently is on validating and derisking this technology with our partners with focus on systems design and integration.”

Reference: https://ift.tt/fhUlaAu

IEEE Plays a Pivotal Role In Climate Mitigation Talks

IEEE has enhanced its standing as a trusted, neutral authority on the role of technology in climate change mitigation and adaption. Last year it became the first technical association to be invited to a U.N. Conference of the Parties on Climate Change.

IEEE representatives participated in several sessions at COP30, held from 11 to 20 November in Belém, Brazil. More than 56,000 delegates attended, including policymakers, technologists, and representatives from industry, finance, and development agencies.

Following the conference, IEEE helped host the selective International Symposium on Achieving a Sustainable Climate. The International Telecommunication Union and IEEE hosted ISASC on 16 and 17 December at ITU’s headquarters in Geneva. Among the more than 100 people who attended were U.N. agency representatives, diplomats, senior leaders from academia, and experts from government, industry, nongovernment organizations, and standards development bodies.

Power and energy expert Saifur Rahman, the 2023 IEEE president, led IEEE’s delegation at both events. Rahman is the immediate past chair of IEEE’s Technology for a Sustainable Climate Matrix Organization, which coordinates, communicates, and amplifies the organization’s efforts.

IEEE’s evolving role at COP

IEEE first attended a COP in 2021.

“Over successive COPs, IEEE’s role has evolved from contributing individual technical sessions to being recognized as a trusted partner in climate action,” Rahman noted in a summary of COP30. “There is [a] growing demand for engineering insight, not just to discuss technologies but [also] to help design pathways for deployment, capacity-building, and long-term resilience.”

Joining Rahman at COP30 were IEEE Fellow Claudio Canizares and IEEE Member Filipe Emídio Tôrres.

Canizares is a professor of electrical and computer engineering at the University of Waterloo, in Ontario, Canada, and the executive director of the university’s sustainable energy institute.

Tôrres chairs the IEEE Centro-Norte Brasil Section (Brazil Chapter). An entrepreneur and a former professor, he is pursuing a Ph.D. in biomedical engineering at the University of Brasilia. He also represented the IEEE Young Professionals group while attending the conference.

In the Engineering for Climate Resilience: Water Planning, Energy Transition, Biodiversity session, Rahman showed a video from his 2024 visit to Shennongjia, China, where he monitored a clean energy project designed to protect endangered snub-nosed monkeys from human encroachment. The project integrates renewable energy, which helps preserve the forest and its wildlife.

Rahman also chaired a session at the Sustainable Development Goal Pavilion on balancing decarbonization efforts between industrialized and emerging economies.

Additionally, he participated in a joint panel discussion hosted by IEEE and the World Federation of Engineering Organizations on engineering strategies for climate resilience, including energy transition and biodiversity.

Rahman, Canizares, and Tôrres took part in a session on clean-tech solutions for a sustainable climate, hosted by the International Youth Nuclear Congress. The topics included fossil fuel–free electricity for communications in remote areas and affordable electricity solutions for off-grid areas.

The three also joined several panels organized by the IYNC that addressed climate resilience, career pathways in sustainability, and a mentoring program.

“Over successive COPs, IEEE’s role has evolved from contributing individual technical sessions to being recognized as a trusted partner in climate action.” —Saifur Rahman, 2023 IEEE president

The IYNC hosted the Voices of Transition: Including Pathways to a Clean Energy Future session, for which Tôrres and Rahman were panelists. They discussed the need to include underrepresented and marginalized groups, which often get overlooked in projects that convert communities to renewable energy.

Rahman, Canizares, and Tôrres visited the COP Village, where they met several of the 5,000 Indigenous leaders participating in the conference and discussed potential partnerships and collaborations. Climate change has made the land where the Indigenous people live more susceptible to severe droughts and wildfires, particularly in the Amazon region.

Rahman and Tôrres took a field trip to the Federal University of Para, where they met several faculty members and students and toured the LASSE engineering lab.

A meaningful experience

Tôrres, who says representing IEEE at COP30 was transformative, wrote a detailed report about the event.

“The experience reaffirmed my belief that engineering and technology, when combined with respect for cultural diversity, can play a critical role in shaping a more sustainable and equitable world,” he wrote. “It highlighted the importance of combining cutting-edge technological solutions with Indigenous wisdom and cultural knowledge to address the climate crisis.”

COP30 webinar

Rahman and Canizares give an overview of their COP30 experiences in an IEEE webinar.

“IEEE has a place at the table,” Rahman says in the video. “We want to showcase outside our comfort zone what IEEE can do. We go to all these global events so that our name becomes a familiar term. We are the first technical association organization ever to go to COP and talk about engineering.”

Canizares added that IEEE is now collaborating closely with the United Nations.

“This is an important interaction. And I think, moving forward, IEEE will become more relevant, particularly in the context of technology deployment,” he said. “As governments start technology deployments, they will see IEEE as a provider of solutions.”

ISASC takeaways

Rahman was the general chair of the ISASC event, which focused on the delivery and deployment of clean energy. Among the presenters were IEEE members including Canizares, Paulina Chan, Surekha Deshmukh, Ashutosh Dutta, Tariq Durrani, Samina Husain, Bruce Kraemer, Bruno Meyer, Carlo Alberto Nucci, and Seizo Onoe.

Sessions were organized around six themes: energy transition, information and communication technology, financing, case studies, technical standards, and public-private collaborations. A detailed report includes the discussions, insights, and opportunities identified throughout ISASC.

Here are some key takeaways.

• Although the technology exists to transition to renewable energy, most power grid systems are not ready. Deployment is increasingly constrained by transmission bottlenecks, interconnection delays, permitting challenges, and system flexibility. There’s also a skills shortage.
• Energy transition pathways must be region-specific and should consider local resources, social conditions, funding opportunities, and development priorities.
• Information and communication technologies are central to climate mitigation solutions, despite growing concerns about their environmental impact. Even though the technologies are used in beneficial ways, such as early-warning systems for natural disasters and smart water management, they also are driving the rapid growth of data centers for artificial intelligence applications—which has increased energy prices and driven up water demand.
• Technical standards are a means of accelerating adoption, interoperability, and trust in green technology. There needs to be greater coordination among standards development organizations, particularly at the convergence of energy systems, information technologies, and AI. Fragmented standards hinder interoperability. The lack of technical standards is a major constraint on project financing, limiting investors’ confidence and slowing technology deployment.
• Training and outreach efforts are important for successfully implementing standards, especially in developing regions. IEEE’s global membership and regional sections can be critical channels to address the needs.

A technology assessment tool

As part of ISASC, IEEE presented a technology assessment tool prototype. The web-based platform is designed to help policymakers, practitioners, and investors compare technology options against climate goals.

The tool can run a comparative analysis of sustainable climate technologies and integrate publicly available, expert-validated data.

IEEE can help the world meet its goals

The ISASC report concluded that by connecting engineering expertise with real-world deployment challenges, IEEE is working to translate global climate goals into measurable actions.

The discussions highlighted that the path forward lies less in inventing new technologies and more in aligning systems to deliver ones that already exist.

Summaries of COP30 and ISASC are available on the IEEE Technology for a Sustainable Climate website.

Reference: https://ift.tt/bIeEuvS

Video Friday: Humanoid Robots Celebrate Spring

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

ICRA 2026: 1–5 June 2026, VIENNA

Enjoy today’s videos!

So, humanoid robots are nearing peak human performance. I would point out, though, that this is likely very far from peak robot performance, which has yet to be effectively exploited, because it requires more than just copying humans.

[ Unitree ]

“The Street Dance of China” Turning lightness into gravity, and rhythm into impact.This is a head-on collision between metal and beats. This Chinese New Year, watch PNDbotics Adam bring the heat with a difference.

[ PNDbotics ]

You had me at robot pandas.

[ MagicLab ]

NASA’s Perseverance rover can now precisely determine its own location on Mars without waiting for human help from Earth. This is possible thanks to a new technology called Mars Global Localization. This technology rapidly compares panoramic images from the rover’s navigation cameras with onboard orbital terrain maps. It’s done with an algorithm that runs on the rover’s Helicopter Base Station processor, which was originally used to communicate with the Ingenuity Mars Helicopter. In a few minutes, the algorithm can pinpoint Perseverance’s position to within about 10 inches (25 centimeters). The technology will help the rover drive farther autonomously and keep exploring.

[ NASA Jet Propulsion Laboratory ]

Legs? Where we’re going, we don’t need legs!

[ Paper ]

This is a bit of a tangent to robotics, but it gets a pass because of the cute jumping spider footage.

[ Berkeley Lab ]

Corvus One for Cold Chain is engineered to live and operate in freezer environments permanently, down to -20°F, while maintaining full flight and barcode scanning performance.

I am sure there is an excellent reason for putting a cold storage facility in the Mojave desert.

[ Corvus Robotics ]

The video documents the current progress made in the picking rate of the Shiva robot when picking strawberries. It first shows the previous status, then the further development, and finally the field test.

[ DFKI ]

Data powers an organization’s digital transformation, and ST Engineering MRAS is leveraging Spot to get a full view of critical equipment and facility. Working autonomously, Spot collects information about machine health - and now, thanks to an integration of the Leica BLK ARC for reality capture, detailed and accurate point cloud data for their digital twin.

[ Boston Dynamics ]

The title of this video is “Get out and have fun!” Is that mostly what humanoid robots are good for right now, pretty much...?

[ Engine AI ]

ASTORINO is a modern 6-axis robot based on 3D printing technology. Programmable in AS-language, it facilitates the preparation of classes with ready-made teaching materials, is easy both to use and to repair, and gives the opportunity to learn and make mistakes without fear of breaking it.

[ Kawasaki ]

Can I get this in my living room?

[ Yaskawa ]

What does it mean to build a humanoid robot in seven months, and the next one in just five? This documentary takes you behind the scenes at Humanoid, a UK-based AI and robotics company building reliable, safe, and helpful humanoid robots. You’ll hear directly from our engineering, hardware, product, and other teams as they share their perspectives on the journey of turning physical AI into reality.

[ Humanoid ]

This IROS 2025 keynote is from Tim Chung who is now at Microsoft, on “Catalyzing the Future of Human, Robot, and AI Agent Teams in the Physical World.”

The convergence of technologies—from foundation AI models to diverse sensors and actuators to ubiquitous connectivity—is transforming the nature of interactions in the physical and digital world. People have accelerated their collaborative connections and productivity through digital and immersive technologies, no longer limited by geography or language or access. Humans have also leveraged and interacted with AI in many different forms, with the advent of hyperscale AI models (i.e., large language models) forever changing (and at an ever-astonishing pace) the nature of human-AI teams, realized in this era of the AI “copilot.” Similarly, robotics and automation technologies now afford greater opportunities to work with and/or near humans, allowing for increasingly collaborative physical robots to dramatically impact real-world activities. It is the compounding effect of enabling all three capabilities, each complementary to one another in valuable ways, and we envision the triad formed by human-robot-AI teams as revolutionizing the future of society, the economy, and of technology.

[ IROS 2025 ]

This GRASP SFI talk is by Chris Paxton at Agility Robotics, on “How Close Are We To Generalist Humanoid Robots?”

With billions of dollars of funding pouring into robotics, general-purpose humanoid robots seem closer than ever. And certainly it feels like the pace of robotics is faster than ever, with multiple companies beginning large-scale deployments of humanoid robots. In this talk, I’ll go over the challenges still facing scaling robot learning, looking at insights from a year of discussions with researchers all over the world.

[ University of Pennsylvania GRASP Laboratory ]

This week’s CMU RI Seminar is from Jitendra Malik at UC Berkeley, on “Robot Learning, With Inspiration From Child Development.”

For intelligent robots to become ubiquitous, we need to “solve” locomotion, navigation and manipulation at sufficient reliability in widely varying environments. In locomotion, we now have demonstrations of humanoid walking in a variety of challenging environments. In navigation, we pursued the task of “Go to Any Thing” – a robot, on entering a newly rented Airbnb, should be able to find objects such as TV sets or potted plants. RL in simulation and sim-to-real have been workhorse technologies for us, assisted by a few technical innovations. I will sketch promising directions for future work.

[ Carnegie Mellon University Robotics Institute ]

Reference: https://ift.tt/OrnbfsG

The U.S. and China Are Pursuing Different AI Futures

More money has been invested in AI than it took to land on the moon. Spending on the technology this year is projected to reach up to $700 billion, almost double last year’s spending. Part of the impetus for this frantic outlay is a conviction among investors and policymakers in the United States that it needs to “beat China.” Indeed, headlines have long cast AI development as a zero-sum rivalry between the U.S. and China, framing the technology’s advance as an arms race with a defined finish line. The narrative implies speed, symmetry, and a common objective.

But a closer look at AI development in the two countries shows they’re not only not racing toward the same finish line: “The U.S. and China are running in very different lanes,” says Selina Xu, who leads China and AI policy research for Eric Schmidt, the tech investor, philanthropist and former Google chief, in New York City. “The U.S. is doubling down on scaling,” in pursuit of artificial general intelligence (AGI) Xu says, “while for China it’s more about boosting economic productivity and real-world impact.”

Lumping the U.S. and China onto a single AI scoreboard isn’t just inaccurate, it can impact policy and business decisions in a harmful way. “An arms race can become a self-fulfilling prophecy,” Xu says. “If companies and governments all embrace a ‘race to the bottom’ mentality, they will eschew necessary security and safety guardrails for the sake of being ahead. That increases the odds of AI-related crises.”

Where’s the Real Finish Line?

As machine learning advanced in the 2010s, prominent public figures such as Stephen Hawking and Elon Musk warned that it would be impossible to separate AI’s general-purpose potential from its military and economic implications, echoing Cold War–era frameworks for strategic competition. “An arms race is an easy way to think about this situation even if it’s not exactly right,” says Karson Elmgren, a China researcher at the Institute for AI Policy and Strategy, a think tank in San Francisco. Frontier labs, investors, and media benefit from simple, comparable progress metrics, like larger models, better benchmarks, and more computing power, so they favor and compound the arms race framing.

Artificial general intelligence is the implied “finish line” if AI is an arms race. But one of the many problems with an AGI finish line is that by its very nature, a machine superintelligence would be smarter than humans and therefore impossible to control. “If superintelligence were to emerge in a particular country, there’s no guarantee that that country’s interests are going to win,” says Graham Webster, a China researcher at Stanford University, in Palo Alto, California.

An AGI finish line also assumes the U.S. and China are both optimizing for this goal and putting the majority of their resources towards it. This isn’t the case, as the two countries have starkly different economic landscapes.

When Is the Payoff?

After decades of rapid growth, China is now facing a grimmer reality. “China has been suffering through an economic slowdown for a mixture of reasons, from real estate to credit to consumption and youth unemployment,” says Xu, adding that the country’s leaders have been “trying to figure out what is the next economic driver that can get China to sustain its growth.”

Enter AI. Rather than pouring resources into speculative frontier models, Beijing has a pressing incentive to use the technology as a more immediate productivity engine. “In China we define AI as an enabler to improve existing industry, like healthcare, energy, or agriculture,” says AI policy researcher Liang Zheng, of Tsinghua University in Beijing, China. “The first priority is to use it to benefit ordinary people.”

To that end, AI investment in China is focused on embedding the technology into manufacturing, logistics, energy, finance, and public services. “It’s a long-term structural change, and companies must invest more in machines, software, and digitalization,” Liang says. “Even very small and medium enterprises are exploring use of AI to improve their productivity.”

China’s AI Plus initiative encourages using AI to boost efficiency. “Having a frontier technology doesn’t really move China towards an innovation-led developed economy,” says Kristy Loke, a fellow at MATS Research who focuses on China’s AI innovation and governance strategies. Instead, she says, “It’s really important to make sure that [these tools] are able to meet the demands of the Chinese economy, which are to industrialize faster, to do more smart manufacturing, to make sure they’re producing things in competitive processes.”

Automakers have embraced intelligent robots in “dark factories” with minimal human intervention; as of 2024, China had around five times more factory robots in use than the U.S. “We used to use human eyes for quality control and it was very inefficient,” says Liang. Now, computer vision systems detect errors and software predicts equipment failures, pausing production and scheduling just-in-time maintenance. Agricultural models advise farmers on crop selection, planting schedules, and pest control.

In healthcare, AI tools triage patients, interpret medical images, and assist diagnoses; Tsinghua is even piloting an AI “Agent Hospital” where physicians work alongside virtual clinical assistants. “In hospitals you used to have to wait a long time, but now you can use your agent to make a precise appointment,” Liang says. Many such applications use simpler “narrow AI” designed for specific tasks.

AI is also increasingly embedded across industries in the U.S., but the focus tends toward service-oriented and data-driven applications, leveraging large language models (LLMs) to handle unstructured data and automate communication. For example, banks use LLM-based assistants to help users manage accounts, find transactions, and handle routine requests; LLMs help healthcare professionals extract information from medical notes and clinical documentation.

“LLMs as a technology naturally fit the U.S. service-sector-based economy more so than the Chinese manufacturing economy,” Elmgren says.

Competition and cooperation

The U.S. and China do compete more or less head-to-head in some AI-related areas, such as the underlying chips. The two have grappled to gain enough control over their supply chains to ensure national security, as recent tariff and export control fights have shown. “I think the main competitive element from a top level [for China] is to wriggle their way out of U.S. coercion over semiconductors. They want to have an independent capability to design, build, and package advanced semiconductors,” Webster says.

Military applications of AI are also a significant arena of U.S.–China competition, with both governments aiming to speed decision-making, improve intelligence, and increase autonomy in weapons systems. The U.S. Department of Defense launched its AI Acceleration Strategy last month, and China has explicitly integrated AI into its military modernization strategy under its policy of military-civil fusion. “From the perspective of specific military systems, there are incremental advantages that one side or the other can gain,” Webster says.

Despite China’s commitment to military and industrial applications, it has not yet picked an AI national champion. “After Deepseek in early 2025 the government could have easily said, ‘You guys are the winners, I’ll give you all the money, please build AGI,’ but they didn’t. They see being ‘close enough’ to the technological frontier as important, but putting all eggs in the AGI basket as a gamble,” Loke says.

American companies are also still working with Chinese technology and workers, despite a slow uncoupling of the two economies. Though it may seem counterintuitive, more cooperation—and less emphasis on cutthroat competition—could yield better results for all. “For building more secure, trustworthy AI, you need both U.S. and Chinese labs and policymakers to talk to each other, to reach consensus on what’s off limits, then compete within those boundaries,” Xu says. “The arms race narrative also just misses the actual on-the-ground reality of companies co-opting each other’s approaches, the amount of research that gets exchanged in academic communities, the supply chains and talent that permeates across borders, and just how intertwined the two ecosystems are.”

Reference: https://ift.tt/OuJoXt5