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Category: robotics/AI – Page 158

The Futurists — EPS_286: The Meaning Economy with David Shapiro

In this week’s episode we interview author, AI theorist and researcher David Shapiro is part philosopher, part theorist with a fair bit of practical wisdom thrown in. With a hit YouTube channel Shapiro travels the globe as a speaker and advisor musing on the longer-term impacts of AI, technology and human adaptability. In this deep conversation with host Brett King, we delve into the ways in which advanced AI might completely transform our way of life, including economics, politics and what it means to be human itself. This is not one you’ll want to miss.

Follow David Shapiro: ‪@DaveShap‬

ABOUT SHOW
Subscribe and listen to TheFuturists.com Podcast where hosts Brett King and Robert TerceK interview the worlds foremost super-forecasters, thought leaders, technologists, entrepreneurs and futurists building the world of tomorrow. Together we will explore how our world will radically change as AI, bioscience, energy, food and agriculture, computing, the metaverse, the space industry, crypto, resource management, supply chain and climate will reshape our world over the next 100 years. Join us on The Futurists and we will see you in the future!

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AI and brain activity reveal how we perceive faces from other racial groups differently

University of Toronto Scarborough researchers have harnessed artificial intelligence (AI) and brain activity to shed new light on why we struggle to accurately recognize faces of people from different races.

Across a pair of studies, researchers explored the Other-Race-Effect (ORE), a well-known phenomenon in which people recognize faces of their own race more easily than others. They combined AI and collected through EEG (electroencephalography) to reveal new insights into how we perceive other-race faces, including visual distortions more deeply ingrained in our brain than previously thought.

“What we found was striking—people are so much better at seeing the facial details of people from their own race,” says Adrian Nestor, associate professor in the Department of Psychology and co-author of the studies.

Physicists use machine learning to find out how layered gases and metals melt

In physics, a phase transition is a transformation of a substance from one form to another. They happen everywhere, from beneath the Earth’s crust to the cores of distant stars, but the classic example is water transitioning from liquid to gas by boiling.

Things get much more complex when physicists zoom in on the minuscule quantum realm or work with exotic matter. Understanding phase transitions rewards both increased knowledge of fundamental physics and future technological applications.

Now researchers have found out how thin layers of noble gases like helium and metals like aluminum melt in confined spaces by topological excitations. In the study, the layers were confined between two graphene sheets at high pressures.

As AI Becomes More Powerful, Simulation Theory Becomes Compelling: Epic Games CEO Tim Sweeney

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.”

New tool evaluates progress in reinforcement learning

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.

Structurally reprogrammable magnetic metamaterials hold promise for biomedicine, soft robotics

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.

Nvidia, ServiceNow engineer open-source model to create AI agents

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.

FutureHouse Platform: Superintelligent AI Agents for Scientific Discovery

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.

Using MRI, researchers chart brain growth and development during early childhood

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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