Chinese companies showcased A.I. gains in Shanghai this week. More chips will only help them improve faster, industry insiders say.
Category: robotics/AI – Page 4
Memories Go Where?
How does your brain decide where to store a brand-new piece of information—like a new face, word, or concept? In this video, we’ll explore a working neural circuit that demonstrates how cortical columns could be allocated dynamically and efficiently—using real spikes, real timing, and biologically realistic learning rules. Instead of vague theories or abstract algorithms, we’ll show a testable mechanism that selects the first available cortical column in just 5 milliseconds, highlighting the incredible speed and parallelism of the brain. This is a crucial first step in building intelligence from the ground up—one circuit at a time.
Useful links:
The Future AI Society: https://futureaisociety.org.
The Brain Simulator III (UKS) project: https://github.com/FutureAIGuru/BrainSimIII
The Brain Simulator II (Neural Simulator) project: https://github.com/FutureAIGuru/BrainSimII
Overview Video: https://youtu.be/W2uauk2bFjs.
More Details Video: https://youtu.be/6po1rMFZkik.
How the UKS Learns Video: https://youtu.be/Rv0lrem3lVs.
Synthetic data boosts gait analysis: AI trained on simulations rivals existing models
Gait assessment is critical for diagnosing and monitoring neurological disorders, yet current clinical standards remain largely subjective and qualitative. Recent advances in AI have enabled more quantitative and accessible gait analysis using widely available sensors such as smartphone cameras.
However, most existing AI models are designed for specific patient populations and sensor configurations, primarily due to the scarcity of diverse clinical datasets—a constraint often driven by privacy concerns. As a result, these models tend to underperform when applied to populations or settings not well represented in the training data, limiting their broader clinical applicability.
In a study published in Nature Communications, researchers from IBM Research, the Cleveland Clinic, and the University of Tsukuba propose a novel framework to overcome this limitation. Their approach involves generating synthetic gait data using generative AI trained on physics-based musculoskeletal simulations.
State of the art in fault-tolerant quantum computing
This report reviews the construction and potential use of FTQC (Fault Tolerant Quantum Computing) computers to reliably perform complex calculations by overcoming the problems posed by the errors and noise inherent in quantum systems.
After recalling the reality of the quantum advantage and its needs, the report describes the use of error-correcting codes in the design of FTQCi computers. It then reports on the progress of the five most advanced physical technologies in the world for building such computers and the obstacles they will have to face in order to achieve the transition to scale necessary for the execution of useful applications. Finally, it discusses the technical and economic environment for quantum computers, how their performance can be compared and evaluated, and their future coexistence with other computing technologies (3D silicon, AI) or with supercomputers.
AI agent autonomously solves complex cybersecurity challenges using text-based tools
Artificial intelligence agents—AI systems that can work independently toward specific goals without constant human guidance—have demonstrated strong capabilities in software development and web navigation. Their effectiveness in cybersecurity has remained limited, however.
That may soon change, thanks to a research team from NYU Tandon School of Engineering, NYU Abu Dhabi and other universities that developed an AI agent capable of autonomously solving complex cybersecurity challenges.
The system, called EnIGMA, was presented this month at the International Conference on Machine Learning (ICML) 2025 in Vancouver, Canada.
Nanotechnology in AI: Building Faster, Smaller, and Smarter Systems
As artificial intelligence (AI) rapidly advances, the physical limitations of conventional semiconductor hardware have become increasingly apparent. AI models today demand vast computational resources, high-speed processing, and extreme energy efficiency—requirements that traditional silicon-based systems struggle to meet. However, nanotechnology is stepping in to reshape the future of AI by offering solutions that are faster, smaller, and smarter at the atomic scale.
The recent article published by AZoNano provides a compelling overview of how nanotechnology is revolutionizing the design and operation of AI systems, pushing beyond the constraints of Moore’s Law and Dennard scaling. Through breakthroughs in neuromorphic computing, advanced memory devices, spintronics, and thermal management, nanomaterials are enabling the next generation of intelligent systems.
AI can evolve to feel guilt—but only in certain social environments
Guilt is a highly advantageous quality for society as a whole. It might not prevent initial wrongdoings, but guilt allows humans to judge their own prior judgments as harmful and prevents them from happening again. The internal distress caused by feelings of guilt often—but not always—results in the person taking on some kind of penance to relieve themselves from internal turmoil. This might be something as simple as admitting their wrongdoing to others and taking on a slight stigma of someone who is morally corrupt. This upfront cost might be initially painful, but can relieve further guilt and lead to better cooperation for the group in the future.
As we interact more and more with artificial intelligence and use it in almost every aspect of our modern society, finding ways to instill ethical decision-making becomes more critical. In a recent study, published in the Journal of the Royal Society Interface, researchers used game theory to explore how and when guilt evolves in multi-agent systems.
The researchers used the “prisoners’ dilemma”—a game where two players must choose between cooperating and defecting. Defecting provides an agent with a higher payoff, but they must betray their partner. This, in turn, makes it more likely that the partner will also defect. However, if the game is repeated over and over, cooperation results in a better payoff for both agents.
Liquid droplets trained to play tic-tac-toe
Artificial intelligence and high-performance computing are driving up the demand for massive sources of energy. But neuromorphic computing, which aims to mimic the structure and function of the human brain, could present a new paradigm for energy-efficient computing.
To this end, researchers at Lawrence Livermore National Laboratory (LLNL) created a droplet-based platform that uses ions to perform simple neuromorphic computations. Using its ability to retain short-term memory, the team trained the droplet system to recognize handwritten digits and play tic-tac-toe. The work was published in Science Advances.
The authors were inspired by the human brain, which computes with ions instead of electrons. Ions move through fluids, and moving them may require less energy than moving electrons in solid-state devices.