An artificial intelligence algorithm used newborn blood samples to shed light on the biological complexity of what can go wrong after preterm birth, a Stanford Medicine-led study found.
Category: robotics/AI – Page 5
Conscious AI: Conflations and Comparisons
Consciousness, like intelligence, is multi-faceted. This makes the future of AI more unpredictable and potentially even more hazardous.
Well, no. Of course not. There’s no fundamental necessity for these two characteristics to be tightly bound together. A chatbot can provide a human companion with sparkling conversation without having its own inner sparkle of feeling. Like an actor, it can mimic expressions of emotional highs and lows whilst lacking any interior passion. It can talk persuasively about having an inner life without there being any inside inside.
What babies can teach AI
Researchers at Google DeepMind tried to teach an AI system to have that same sense of “intuitive physics” by training a model that learns how things move by focusing on objects in videos instead of individual pixels. They trained the model on hundreds of thousands of videos to learn how an object behaves. If babies are surprised by something like a ball suddenly flying out of the window, the theory goes, it is because the object is moving in a way that violates the baby’s understanding of physics. The researchers at Google DeepMind managed to get their AI system, too, to show “surprise” when an object moved differently from the way it had learned that objects move.
Yann LeCun, a Turing Prize winner and Meta’s chief AI scientist, has argued that teaching AI systems to observe like children might be the way forward to more intelligent systems. He says humans have a simulation of the world, or a “world model,” in our brains, allowing us to know intuitively that the world is three-dimensional and that objects don’t actually disappear when they go out of view. It lets us predict where a bouncing ball or a speeding bike will be in a few seconds’ time. He’s busy building entirely new architectures for AI that take inspiration from how humans learn. We covered his big bet for the future of AI here.
The AI systems of today excel at narrow tasks, such as playing chess or generating text that sounds like something written by a human. But compared with the human brain—the most powerful machine we know of—these systems are brittle. They lack the sort of common sense that would allow them to operate seamlessly in a messy world, do more sophisticated reasoning, and be more helpful to humans. Studying how babies learn could help us unlock those abilities.
AI House Davos
Embodied AI refers to AI integrated into physical systems that can perceive, reason, and act in the real world through sensors and actuators, like robots and autonomous vehicles. This fireside conversation explores how advances in AI like vision–language–action models are redefining what machines can understand and do, especially as we move from navigation to mobile manipulation. The speakers discuss how quickly today’s rapid progress in AI might transfer to robotics and embodied systems, and how soon we can expect to see these technologies making a tangible impact on our daily lives.
Speakers.
Yann LeCun (Advanced Machine Intelligence, Founder and Executive Chairman)
Marc Pollefeys (ETH Zürich and Faculty, ETH AI Center, Professor)
© AI House Davos 2026
Founders & Strategic Partners:
ETH AI Center, Merantix, G42, Hewlett Packard Enterprise, EPFL AI Center, The University of Tokyo.
Presenting Partners:
KPMG.
Human-Centric Intelligence: A New Paradigm For AI Decision Making
In my latest Forbes article, I explore one of the most critical questions facing leaders today:
How do we use AI to augment human intelligence rather than diminish it?
AI’s true power isn’t about automation alone—it’s about amplifying human judgment, creativity, and decision-making.
#AI #HumanCentricAI #artificialintelligence #tech #AugmentedIntelligence #Forbes #Leadership #Cybersecurity #EmergingTechnology #DigitalTransformation
Human-centric AI is the new frontier; it is not AI against human intelligence, but AI with human intelligence.
AI in Charge: Large-Scale Experimental Evidence on Electric Vehicle Charging Demand
Asynchronous firing and off states in working memory maintenance.
Mozumder, Wang et al. use high-density recordings in macaque prefrontal and parietal cortex to show that working memory is sustained by asynchronous spiking activity without prolonged silent periods. Off states are characterized by relatively decreased information decoding and are synchronized between areas. The balance between asynchronous firing and off states determines memory maintenance.
Elon Musk Holds Surprise Talk At The World Economic Forum In Davos
The musk blueprint: navigating the supersonic tsunami to hyperabundance when exponential curves multiply: understanding the triple acceleration.
On January 22, 2026, Elon Musk sat down with BlackRock CEO Larry Fink at the World Economic Forum in Davos and delivered what may be the most important articulation of humanity’s near-term trajectory since the invention of the internet.
Not because Musk said anything fundamentally new—his companies have been demonstrating this reality for years—but because he connected the dots in a way that makes the path to hyperabundance undeniable.
[Watch Elon Musk’s full WEF interview]
This is not visionary speculation.
This is engineering analysis from someone building the physical infrastructure of abundance in real-time.
Neutrophil extracellular trapping network-associated biomarkers in liver fibrosis: machine learning and experimental validation
The diagnostic and therapeutic potential of neutrophil extracellular traps (NETs) in liver fibrosis (LF) has not been fully explored. We aim to screen and verify NETs-related liver fibrosis biomarkers through machine learning.
In order to obtain NETs-related differentially expressed genes (NETs-DEGs), differential analysis and WGCNA analysis were performed on the GEO dataset (GSE84044, GSE49541) and the NETs dataset. Enrichment analysis and protein interaction analysis were used to reveal the candidate genes and potential mechanisms of NETs-related liver fibrosis. Biomarkers were screened using SVM-RFE and Boruta machine learning algorithms, followed by immune infiltration analysis. A multi-stage model of fibrosis in mice was constructed, and neutrophil infiltration, NETs accumulation and NETs-related biomarkers were characterized by immunohistochemistry, immunofluorescence, flow cytometry and qPCR. Finally, the molecular regulatory network and potential drugs of biomarkers were predicted.
A total of 166 NETs-DEGs were identified. Through enrichment analysis, these genes were mainly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction pathway. Machine learning screened CCL2 as a NETs-related liver fibrosis biomarker, involved in ribosome-related processes, cell cycle regulation and allograft rejection pathways. Immune infiltration analysis showed that there were significant differences in 22 immune cell subtypes between fibrotic samples and healthy samples, including neutrophils mainly related to NETs production. The results of in vivo experiments showed that neutrophil infiltration, NETs accumulation and CCL2 level were up-regulated during fibrosis. A total of 5 miRNAs, 2 lncRNAs, 20 function-related genes and 6 potential drugs were identified based on CCL2.
AI-powered intelligent 6G radio access technology significantly enhances wireless communication performance
Korea’s research community has reached an important milestone on the path toward next-generation mobile communications with the development of a technology platform that brings the 6G era closer. Researchers expect that AI-Native mobile networks, in which artificial intelligence autonomously controls and optimizes the communication system, could achieve transmission efficiencies up to 10 times higher than those of 5G.
Breakthroughs in AI-based wireless access ETRI has completed the development of AI-based wireless access technology (AI-RAN), a core foundational technology for the 6G era, and has achieved significant results in paving the way for the AI-based next-generation mobile communication era.
The biggest feature of this technology is that it has applied AI to wireless transmission, network control, and edge computing throughout the network to reliably handle large volumes of data even in ultra-dense network environments.