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Category: robotics/AI – Page 3

AI isn’t just changing how we work and play, but it’s also helping us rethink our underlying reality itself.

Tim Sweeney, CEO of Epic Games, the company behind the wildly popular Fortnite and Unreal Engine, recently delved into a philosophical discussion sparked by the rapid advancements in AI. His musings touch upon the age-old simulation hypothesis, questioning not just the nature of our own reality, but also the reality of our potential creators. What’s particularly intriguing is how Sweeney links the increasing sophistication of AI with the growing plausibility of such thought experiments.

“I don’t know,” Sweeney pondered on the Lex Fridman podcast, “The question of whether we are living in a simulation ourselves always boils down to: if we are living in a simulation, where are *they* living? Because at some point there has to be some base reality.”

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Eco-driving involves making small adjustments to minimize unnecessary fuel consumption. For example, as cars approach a traffic light that has turned red, “there’s no point in me driving as fast as possible to the red light,” she says. By just coasting, “I am not burning gas or electricity in the meantime.” If one car, such as an automated vehicle, slows down at the approach to an intersection, then the conventional, non-automated cars behind it will also be forced to slow down, so the impact of such efficient driving can extend far beyond just the car that is doing it.

That’s the basic idea behind eco-driving, Wu says. But to figure out the impact of such measures, “these are challenging optimization problems” involving many different factors and parameters, “so there is a wave of interest right now in how to solve hard control problems using AI.”

The new benchmark system that Wu and her collaborators developed based on urban eco-driving, which they call “IntersectionZoo,” is intended to help address part of that need. The benchmark was described in detail in a paper presented at the 2025 International Conference on Learning Representation in Singapore.

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Scientists from Universidad Carlos III de Madrid (UC3M) and Harvard University have experimentally demonstrated that it is possible to reprogram the mechanical and structural behavior of innovative artificial materials with magnetic properties, known as metamaterials, without the need to modify their composition. This technology opens the door to innovations in fields such as biomedicine and soft robotics, among others.

The study, recently published in the journal Advanced Materials, details how to reprogram these by using flexible magnets distributed throughout their structure.

What is innovative about our proposal is the incorporation of small flexible magnets integrated into a rotating rhomboid matrix that allows the stiffness and energy absorption capacity of the structure to be modified by simply changing the distribution of these magnets or applying an . This confers unique properties that are not present in conventional materials or in nature.

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Revolutionary AI-driven 3D heart scans cut the need for invasive tests and have already saved millions of pounds, according to new analysis. Now rolled out across 56 NHS hospitals in England, the clever tech enables doctors to diagnose and treat patients with suspected heart disease much faster by turning a CT scan of their heart […]

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Nvidia and ServiceNow have created an AI model that can help companies create learning AI agents to automate corporate workloads.

The open-source Apriel model, available generally in the second quarter on HuggingFace, will help create AI agents that can make decisions around IT, human resources and customer-service functions.

“If you look at the foundation models, they’re very big, very slow,” Dorit Zilbershot, ServiceNow’s group vice president of AI experiences and innovation, said in an interview. “This is only a 15-billion-parameters model, it’s highly trained on reasoning. We expect the reasoning to be very, very important.

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This AI superintelligence can help replace the need for tons of research hurdles such as time constraints finding items of knowledge to make what would take weeks or years into seconds of time.

Science is bottlenecked by data. The 38 million papers on PubMed, 500,000+ clinical trials, and thousands of specialized tools have created an information bottleneck that even the most brilliant scientists can’t navigate. At FutureHouse, our mission is to solve this problem by building an AI Scientist. Today, we are taking a significant step forward by releasing the first publicly available superintelligent scientific agents accessible to researchers everywhere, with benchmarked superhuman literature search & synthesis capabilities.

Crow is a general-purpose agent that can search the literature and provide concise, scholarly answers to questions, and is perfect for use via API.

Falcon is specialized for deep literature reviews. It can search and synthesize more scientific literature than any other agent we are aware of, and also has access to several specialized scientific databases, like OpenTargets.

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University of North Carolina-led researchers have used brain connectivity charts built from functional MRI data as a tool for tracking early childhood brain development.

Charts mapped the maturation of brain networks from birth to age six and identified key transitions in how regions of the brain interact. Deviations from these developmental patterns were significantly associated with differences in early cognitive ability, involving primary, default, control, and attention networks.

Early childhood marks a critical period in brain growth, during which undergo rapid, variable changes that shape . While physical growth charts are well-established tools for monitoring parameters such as height and weight, comparable standards for assessing the development of brain function, with timing that differs across children, remain elusive.

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Investors hoping to back former OpenAI chief technology officer Mira Murati’s buzzy new AI startup are being asked to commit a minimum of $50 million, according to two sources with knowledge of the deal. Murati is raising around $2 billion of capital at a $10 billion valuation for Thinking Machines Lab, BI previously reported.

Multiple sources say the mega-round, led by Andreessen Horowitz, is nearing the final stages of fundraising.

A spokesperson for Thinking Machines Lab declined to comment. A spokesperson for A16z did not respond to a request for comment. The round is not finalized, and the details could change. The financing would almost certainly rank as one of the largest seed rounds in history, which typically range in the low to mid-single digits.

A $50 million check size is beyond the scope of most traditional seed investors because it would represent a substantial percentage, if not their entire fund.

The minimum requirement and rich valuation reflect feverish investor enthusiasm for generative AI and the reality that there are a very limited number of technical founders with Murati’s expertise and the team she has assembled. It’s also enormously expensive to train AI models and recruit and retain top talent.

Murati spent over six years at OpenAI, where she worked on the development of ChatGPT and other AI research initiatives. She was briefly appointed interim CEO in November 2023 after OpenAI’s board abruptly fired Sam Altman, a move that sparked turmoil within the company. After Altman’s reinstatement as CEO, Murati resumed her role as CTO.

It has been a widely discussed mystery what exactly Thinking Machines will do to distinguish itself in a crowded and well-funded field that includes not only OpenAI but also Anthropic, Elon Musk’s xAI, and Google’s Gemini.

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If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving.

One approach to counter this is known as eco-driving, which can be installed as a control system in to improve their efficiency.

How much of a difference could that make? Would the impact of such systems in reducing emissions be worth the investment in the technology? Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing.

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