The brain dynamically transforms cognitive information. Here the authors build task-performing, functioning neural network models of sensorimotor transformations constrained by human brain data without the use of typical deep learning techniques.
To expand its GPT capabilities, OpenAI released its long-anticipated o1 model, in addition to a smaller, cheaper o1-mini version. Previously known as Strawberry, the company says these releases can “reason through complex tasks and solve harder problems than previous models in science, coding, and math.”
Although it’s still a preview, OpenAI states this is the first of this series in ChatGPT and on its API, with more to come.
The company says these models have been training to “spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes.”
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AI’s language can intrigue, but mistaking its patterns for deeper wisdom blurs the line between thoughtful insight and mere digital mimicry.
In today’s fast-paced world, speed is celebrated. Instant messaging outpaces thoughtful letters, and rapid-fire tweets replace reflective essays. We’ve become conditioned to believe that faster is better. But what if the next great leap in artificial intelligence challenges that notion? What if slowing down is the key to making AI think more like us—and in doing so, accelerating progress?
OpenAI’s new o1 model, built on the transformative concept of the hidden Chain of Thought, offers an interesting glimpse into this future. Unlike traditional AI systems that rush to deliver answers by scanning data at breakneck speeds, o1 takes a more human-like approach. It generates internal chains of reasoning, mimicking the kind of reflective thought humans use when tackling complex problems. This evolution not only marks a shift in how AI operates but also brings us closer to understanding how our own brains work.
This concept of AI thinking more like humans is not just a technical accomplishment—it taps into fascinating ideas about how we experience reality. In his book The User Illusion, Tor Nørretranders reveals a startling truth about our consciousness: only a tiny fraction of the sensory input we receive reaches conscious awareness. He argues that our brains process vast amounts of information—up to a million times more than we are consciously aware of. Our minds act as functional filters, allowing only the most relevant information to “bubble up” into our conscious experience.
This conversation between Max Tegmark and Joel Hellermark was recorded in April 2024 at Max Tegmark’s MIT office. An edited version was premiered at Sana AI Summit on May 15 2024 in Stockholm, Sweden.
Max Tegmark is a professor doing AI and physics research at MIT as part of the Institute for Artificial Intelligence \& Fundamental Interactions and the Center for Brains, Minds, and Machines. He is also the president of the Future of Life Institute and the author of the New York Times bestselling books Life 3.0 and Our Mathematical Universe. Max’s unorthodox ideas have earned him the nickname “Mad Max.”
In this episode of The Cognitive Revolution, Nathan interviews Samo Burja, founder of Bismarck Analysis, on the strategic dynamics of artificial intelligence through a geopolitical lens. They discuss AI’s trajectory, the chip supply chain, US-China relations, and the challenges of AI safety and militarization. Samo brings both geopolitical expertise and technological sophistication to these critical topics, offering insights on balancing innovation, security, and international cooperation.
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In this special crossover episode of The Cognitive Revolution, Nathan Labenz joins Robert Wright of the Nonzero newsletter and podcast to explore pressing questions about AI development. They discuss the nature of understanding in large language models, multimodal AI systems, reasoning capabilities, and the potential for AI to accelerate scientific discovery. The conversation also covers AI interpretability, ethics, open-sourcing models, and the implications of US-China relations on AI development.
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The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.
Keywords: spiking neural networks, biological neural network, autonomous robot, robotics, computer vision, neuromorphic hardware, toolkits, survey, review.
