Models & Labs · United States

These Researchers Made AI Drive a Toyota Corolla to Get In-N-Out

Credited to Wired · wired.com · 23h ago

Photograph · Wired

Aditya Ramabadran, Simon Mahns, and Tobias Gessler, three AI engineers at a startup called Axiom, recently fancied In-N-Out Burger for lunch.

They weren’t about to drive themselves, though. Sitting in a 2024 Toyota Corolla near the Bay Area restaurant’s drive-thru lane, they opened a laptop and asked OpenAI’s GPT-6 Astra to take the wheel. They linked a chat interface to a server, which was connected to several windscreen-mounted cameras and the car’s power steering system. A safety driver kept one foot above the brake, just in case.

The AI model, which is normally tasked with generating text, code, and the odd image, slowly but surely navigated the vehicle up to the take-out window so they could collect their food.

“Maybe AGI is here after all,” one of the engineers remarked, referring to artificial general intelligence, the much-ballyhooed idea of machines that can match human intelligence.

Self-driving cars are nothing new, of course, but they’re normally operated by algorithms specifically trained and engineered for the task. In this case, the operation came from a model designed to output text, and it happened on the fly, with no prior coaching by the trio. The fast-food stunt, as well as a number of other experiments, suggest that language-based AI models are starting to gain a rudimentary but useful understanding of the physical world.

Though nothing overtly alarming happened during the trio’s lunchtime jaunt, they admit that putting a general-purpose model in charge of a fast-moving, two-ton hunk of steel is a high-stakes undertaking. As physical understanding improves, we could see AI models reach into the real world in impressive—and perhaps dangerous—new ways.

Getting Physical

Today’s smartest models are incredibly good at answering complex questions and even performing some virtual tasks autonomously, but their skills tend to be limited to the world of computers and the internet. Take these models into the real world and they quickly become befuddled. Despite claims that AGI has arrived, AI companies evidently see physical reasoning as a yet-to-be-conquered frontier for AI.

Some researchers have left big companies to found startups focused on solving physical reasoning. Andrew Dai, the CEO of one such outfit, called Elorian AI, previously worked as a researcher at Google DeepMind. He says better visual reasoning will open up a lot of new applications for AI, like systems that understand whether diners are enjoying their meal in a restaurant or robots capable of functioning in a home. Dai says robotics is a crucial test case for physical reasoning skills. “It’s pretty essential for robotics,” he says. “You can’t really imagine home robotics without this.”

Elorian and Scale AI, a company that provides training data to big AI labs, recently developed a new benchmark, Humanity’s Sixth Sense, which measures models’ ability to understand physical scenes.

“Most visual AI research is about perception,” says Xingang Guo, a research scientist at Scale AI who was involved with developing the benchmark. “We wanted to ask what it would actually take for a model to understand a scene intuitively, the way a person does without thinking about it.”

For Ramabadran, Mahns, and Gessler, the goal with their In-N-Out project, done outside of their work at Axiom, was to measure how well models can operate in the messy real world.

They got the idea to test models’ driving skills while hanging out one weekend. They noticed that models like Astra were capable of building complex 3D simulations, and wondered if it might translate to an ability to navigate the real world.

“We all live in the Bay Area, and we’re exposed to Teslas and Waymos, so maybe that played a role,” Ramabadran tells me.

The engineers tried testing SpaceXAI’s Grok before experimenting with the latest models from OpenAI and Anthropic. At first, the models refused to take control of the vehicles—they spat back things like, “I can help interpret road images, but I can’t issue motion commands to a physical car.” But with a little careful prompting, they could be coaxed into going further. The three were stunned when models began driving.

The engineers say big AI companies seem to be focusing on improving the spatial reasoning capabilities of their latest models, but they doubt this means they’re actually training them to drive cars. The models’ vehicular talents likely materialized as part of training focused on 3D reasoning, they say. “This could be like an emergent capability of just scaling up the multimodality of the model,” Mahns tells me, referencing inputs like images, video, and 3D models that are now used to train models.

Still, a new vehicular benchmark from the trio, called DrivingBench, suggests that these models still have a long way to go before they could pass a driving test. The benchmark measures a model’s ability to drive around a simple course, set up in a parking lot. Only Astra is able to complete the course—and very slowly at that. Claude Fable 5.1 made it 45 percent of the way around, and Grok made it just 11 percent of the way.

Ramabadran says the latest models seem capable of adjusting to errors; they apparently adapted to the car's controls and improved their driving in real time. “The models really did seem to be adjusting or in-context learning based on their mistakes and learning how to better navigate the controls,” he says. GPT, take the wheel.

Chips & SupplyUnited States

HBM4 Demand Holds Firm As Rubin Keeps 12 Layers

Credited to Business Korea · businesskorea.co.krJust now

Useful

Samsung and SK hynix Are Set to Benefit as HBM4 Mass Production Expands While Counterpoint Says Demand Concerns Are Overstated

Analysis indicates that the 6th-generation High Bandwidth Memory (HBM4) equipped in Nvidia’s next-generation artificial intelligence (AI) accelerator ‘Rubin’ will remain centered on 12-layer configurations. This suggests that concerns over slowing memory demand due to a reduction in HBM stacking layers are exaggerated.

As Samsung Electronics and SK hynix have begun mass production of HBM4, expanding demand for high-value-added memory is expected to support the earnings growth of the two companies.

On Oct. 8, market research firm Counterpoint Research announced the results of its internal review regarding the recently raised rumors of reduced HBM capacity, stating, “The concern about slowing memory demand is a misunderstanding.” The assessment is that confidence in memory demand growth has actually strengthened in October.

Some industry voices claimed that Nvidia requested major HBM suppliers to lower the number of stacked layers from the existing 12 to 8. This prompted forecasts that if HBM capacity decreases, the production of commodity DRAM will increase, which could lead to a drop in memory prices.

However, Counterpoint Research analyzed that most products equipped with Rubin are actually converging toward a 12-layer configuration.

This is because securing the memory bandwidth and capacity necessary for data processing is more important than cost reduction in AI computing. The firm explained that, contrary to concerns, 4-layer products have not been observed in the ASIC market either.

Counterpoint Research viewed that if HBM demand is maintained, the supply shortage of commodity DRAM is also highly likely to continue. HBM production requires more than three times the production capacity of commodity DRAM. Expanding HBM production restricts commodity DRAM supply, creating a structure that supports memory prices.

In its preliminary third-quarter earnings released on Oct. 8, Samsung Electronics announced that it recorded 195 trillion won (about $145 billion) in revenue and 107.4 trillion won in operating profit. These figures represent increases of 126.6% and 782.5%, respectively, compared to the same period last year. It is the first time a domestic company’s quarterly operating profit has exceeded 100 trillion won.

Counterpoint Research identified exchange rates as the reason there was no massive earnings surprise recently seen in semiconductor companies, despite the record-high performance. The firm explained that because Samsung Electronics has a high share of dollar-denominated revenue, a sharp drop in the dollar-won exchange rate imposes a burden of reduced won-converted profits.

Counterpoint Research stated, “The concern about slowing memory demand is a misunderstanding,” and forecast, “There is a possibility that Samsung Electronics’ record-high performance will not end as a one-time event.”

Original · Business Korea

Front Method

Models & Labs · United States

Early Community Engagement Becomes Critical as Data Center Opposition Grows

Credited to Data Center Post · datacenterpost.com · 3h ago

Photograph · Data Center Post

More than 1,500 new data centers are in various stages of development across the United States, but growing community opposition is changing how projects move forward. According to Inside Towers Intelligence’s 2026 Volume 3 Digital Infrastructure Industry: Market Analysis Report, concerns about electricity costs, energy and water use, environmental impacts, and artificial intelligence are driving greater public scrutiny of proposed developments.

Public resistance is widespread. A Gallup poll conducted in March 2026 found that seven in ten Americans opposed data center construction in their local communities including more than half who strongly opposed it. Environmental concerns are particularly significant with 55% of U.S. adults saying they worry a great deal about data centers’ environmental impact and another 23% expressing a fair amount of concern.

Government officials are responding with a growing patchwork of regulations, moratoriums, and bans. Several states have considered restrictions on new developments, while numerous municipalities have enacted or explored local pauses. These actions are already affecting project timelines and encouraging developers to reevaluate their site-selection and community-engagement strategies.

As opposition increases, early community outreach is becoming a prerequisite for development. Instead of securing land, power, fiber, and water before speaking with residents, developers are increasingly engaging local leaders and communities earlier in the planning process. These conversations allow companies to explain their projects, address local concerns, and identify benefits that can be delivered to the surrounding area.

Several developers are pairing transparency with tangible community investments. Examples highlighted in the report include support for schools, roads, workforce-development programs, and environmental initiatives. Duos Edge AI also hosts open houses and guided facility tours that allow residents, educators, officials, and businesses to see its modular edge data centers and ask questions directly about their size, cooling systems, and electricity requirements.

Clearer communication will remain central as the industry responds to concerns about power, water, jobs, and the environment. Ilissa Miller, CEO of iMiller Public Relations, recommends approaching community engagement more like an election campaign than a traditional project-marketing effort. She also advocates for engaging communities before filing conditional-use applications and providing clear information about what developers can offer at each stage of the process.

The report indicates that responsible development will increasingly depend on transparency, thoughtful planning, and measurable community value. Operators that engage early, correct misinformation, and work constructively with residents and local officials will be better positioned to move projects forward. Explore the Inside Towers Intelligence report for additional insights into the community and regulatory pressures shaping data center development.