#reasoning-focused-models

#reasoning-focused-models

The savings disappear the moment you hit real-world complexity. Disparate data sources and messy inputs, ambiguous situations without clear rule sets, or actually any domain where the rules aren't already obvious. And someone still has to write all those rules.

The team, which is being led by Jülich neurophysics professor Markus Diesmann, will leverage the Joint Undertaking Pioneer for Innovative and Transformative Exascale Research (JUPITER) supercomputer for their simulation. JUPITER is currently the fourth most powerful supercomputer in the world according to the TOP500 list, and features thousands of graphical processing units. The team demonstrated last month that a " spiking neural network " could be scaled up and run on JUPITER, effectively matching the cerebral cortex's 20 billion neurons and 100 trillion connections.

I am a worrier, and have been for most of my life. At some point, someone dear and smart teased me that I worry about the wrong things. The things that hit me, she noted, were never the things I worried about. For a while that left me feeling like an incompetent worrier-until my research caught up. I realized that the things I worry about often don't end up hurting me precisely because worrying helps me diffuse them ahead of time.

A dyad has three parts, not two: Partner A, Partner B, and the relationship or agreements between them. A dyad of two experts who cannot communicate clearly will often lose to a dyad of less-skilled individuals who coordinate effectively.

Autonomous agents take the first part of their names very seriously and don't necessarily do what their humans tell them to do - or not to do. But the situation is more complicated than that. Generative (genAI) and agentic systems operate quite differently than other systems - including older AI systems - and humans. That means that how tech users and decision-makers phrase instructions, and where those instructions are placed, can make a major difference in outcomes.

This process, becoming aware of something not working and then changing what you're doing, is the essence of metacognition, or thinking about thinking. It's your brain monitoring its own thinking, recognizing a problem, and controlling or adjusting your approach. In fact, metacognition is fundamental to human intelligence and, until recently, has been understudied in artificial intelligence systems. My colleagues Charles Courchaine, Hefei Qiu, Joshua Iacoboni, and I are working to change that.

Each of these achievements would have been a remarkable breakthrough on its own. Solving them all with a single technique is like discovering a master key that unlocks every door at once. Why now? Three pieces converged: algorithms, computing power, and massive amounts of data. We can even put faces to them, because behind each element is a person who took a gamble.

For the past three years, the conversation around artificial intelligence has been dominated by a single, anxious question: What will be left for us to do? As large language models began writing code, drafting legal briefs, and composing poetry, the prevailing assumption was that human cognitive labor was being commoditized. We braced for a world where thinking was outsourced to the cloud, rendering our hard-won mental skills, writing, logic, and structural reasoning relics of a pre-automated past.

When a scientist feeds a data set into a bot and says "give me hypotheses to test", they are asking the bot to be the creator, not a creative partner. Humans tend to defer to ideas produced by bots, assuming that the bot's knowledge exceeds their own. And, when they do, they end up exploring fewer avenues for possible solutions to their problem.