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Category: robotics/AI

AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning

The problem? Human brains (and animal brains, too) are incredibly complex. While these handcrafted models are great starting points, they often oversimplify things and miss the messy, rich reality of actual behavior. On the flip side, using powerful, flexible AI to analyze data can capture that richness, but AI usually gives us a “black box”—it finds patterns but can’t explain *why* or *how* it found them, leaving scientists to do the heavy lifting of figuring out the rules.

Scientific models are widely used across the natural sciences as an interface between scientific theories and empirical data [1]. Such models play a key role, for example, in the study of human and animal learning, where they express algorithmic hypotheses and relate them to psychology and neuroscience data [2, 3]. These models are traditionally handcrafted by expert researchers based on existing theory or new insights. Such handcrafted models, however, are now known to fall short of capturing the full richness of behavior, even in their narrow domains [4– 7]. An alternative data-driven approach has emerged, seeking to discover new insights by fitting and interpreting flexible models [8– 11]. However, these tools require substantial human effort to derive insight from data, and it has been unclear how to discover new ideas from data efficiently. Here, we present DataDIVER, a general approach for automatically discovering computational models from data, and demonstrate that these models surface novel mechanistic insights into human and animal learning. Our approach delivers models that take the form of short computer programs, which are optimized both to fit data well and to be simple. These programs explicitly connect with existing theoretical frameworks and are readily understandable by human scientists. They can also be used to make novel predictions, some of which we show are borne out in re-analysis of existing data. General-purpose tools for surfacing new ideas from data, especially in combination with the large datasets that are increasingly available in many fields, stand to dramatically accelerate scientific discovery.

The authors have declared no competing interest.

AI system automates scientific software design, outperforming human-written code in key benchmarks

A research team at Google co-led by Michael Brenner, Catalyst Professor of Applied Mathematics and Physics at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and Google research scientist, has produced a new artificial intelligence system that can automatically write scientific software programs that surpass the performance of human-written programs. The paper is published in the journal Nature.

How the ERA system came together The system is called Empirical Research Assistance (ERA), and the project was co-led by Brenner and Shibl Mourad from Google DeepMind. Harvard Ph.D. students Qian-Ze Zhu, Ryan Krueger, and Sarah Martinson contributed as Google student researchers while working in Brenner’s group. The research was done in Brenner’s capacity as a Catalyst Professor, a position established by the University to enhance relationships between academia and the private sector by supporting senior faculty in research roles at external companies.

Across modern science, customized software is constantly used to test specific hypotheses or interpret complex data. The authors refer to this type of computer program as “empirical software”—a program whose sole purpose is to maximize how well it does on a scientific task, like making weather predictions or forecasting hospitalizations during a disease outbreak. Any problem that can be expressed as a numerical value—its “score”—is called a scorable task.

Building a hill-climbing machine: Launching seven new MAI models

The title’s “hill-climbing machine” refers to Microsoft’s iterative, scientifically rigorous engineering framework. By tightly linking clean data pipelines, specialized training infrastructure, and reinforcement learning environments, they have created an optimization loop designed to steadily “climb” toward higher capabilities as compute scales.

Today we are announcing a family of seven new models developed in-house at Microsoft AI. Beyond these models, we’re building a superintelligence lab – a system and an approach we believe will define the next phase of AI.

This is an extraordinary time in technology. The compute used to train frontier models has increased by a factor of one trillion. Now we expect another thousand-fold increase over the next three years, which in turn means more advanced capabilities, and the continued rollout of ever more effective AI.

This epic compute ramp will change the nature of work, business and daily life. We all have to prepare for this reality. Our job at MAI is to help you do this – to push the frontier, and to build a hill-climbing machine to keep you at the frontier.

LLMs help robots understand vague instructions and focus on key details

Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell”—that is, physically showing how to do something a few different ways, while also explaining what you’re doing.

Let’s say you asked the robot to place some coffee on your desk without disturbing you during a Zoom call. You’ll prefer that the robot doesn’t get close to you and the laptop so that it doesn’t interrupt your meeting. To enable this behavior, the robot should be trained with data that clearly demonstrates the full task. Computer scientists have attempted to explain manipulation tasks to robots by recording lots of physical demonstrations or writing extensive directions. But if you don’t have both, the machine is likely to misunderstand what it needs to do.

It’s laborious for humans to do all that showing and telling, so researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have automated the process of teaching a robot, while clarifying instructions automatically and using nearly five times less demonstration data.

Deep brain stimulation boosts myelination and shifts brain networks linked to depression

Researchers from the Icahn School of Medicine at Mount Sinai have uncovered the first direct evidence that deep brain stimulation (DBS) can remodel white matter pathways in the brain and alter communication across large-scale neural networks, revealing a previously unrecognized mechanism that may explain how the therapy helps patients recover from severe depression. The study, published June 1 in Nature Neuroscience, provides critical insight into the biological basis of DBS, an emerging therapy for treatment-resistant depression and other neuropsychiatric disorders.

Deep brain stimulation, approved by the U.S. Food and Drug Administration to treat essential tremor, Parkinson’s disease, epilepsy, and obsessive-compulsive disorder, is a neurosurgical procedure involving placement of a neurostimulator (sometimes referred to as a “brain pacemaker”), which sends high-frequency electrical impulses through implanted electrodes deep in the brain to specific areas responsible for the symptoms of each disorder.

Although DBS has shown sustained clinical benefit for many patients with severe depression who do not respond to medications, psychotherapy, and electroconvulsive therapy, the mechanisms underlying its therapeutic effects have remained poorly understood.

Instagram users locked out after Meta AI abused to steal accounts

Multiple Instagram users had their accounts hijacked after attackers convinced Meta’s AI-powered support tools that they were the legitimate owners.

In many cases, impacted users are unable to recover access due to the platform’s use of automated assistance that involves only AI/chatbot loops and no human support agents.

On Monday, multiple holders of rare and high-value accounts reported suddenly losing access to their accounts, claiming that their identities had been verified via facial scans and that they had enabled safeguards such as two-factor authentication (2FA).

Authorities struggle to stop AI tools generating nude images without consent

There has been a sharp rise in so-called “nudification” technology. These AI-powered tools can generate realistic fake images and videos that depict people as undressed, often without their knowledge or consent. William Brangham reports on the growing concern over the technology and the efforts underway to rein it in.

Notice: Transcripts are machine and human generated and lightly edited for accuracy. They may contain errors.

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