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Category: robotics/AI

AI-powered stretchable computing patch can run algorithms directly on the body

A new skin-like computing patch developed at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) can analyze health data using artificial intelligence in an unprecedented way. Unlike today’s wearable devices, it carries out its AI computations directly on the body, in mere milliseconds, without relying on a wireless connection.

While your current smartwatch may be able to track your heart rate or movements, it doesn’t analyze what it finds. The analysis happens elsewhere, after it shuttles data to an external server. In some situations—detecting ventricular fibrillation in the heart, for instance—that few-seconds lag to communicate with the server is too long.

The new device, designed and tested in collaboration with researchers at Argonne National Laboratory, was made possible by the development of manufacturing processes that allow organic electrochemical transistors to be printed onto flexible surfaces.

Novel origami pattern turns flat sheets into load-bearing 3D technology

McGill University researchers have discovered a new way to fold flat sheets into smooth, curved shells that can switch from floppy and flexible to stiff and load-bearing on demand. By designing a special origami pattern and threading cable-like elements through it, they can control the material’s final three-dimensional shape and how rigid it becomes.

The result, a “doubly curved lens box,” could advance the technology of such objects as temporary emergency tents, morphing robots and smart fabrics, the researchers said. “Smooth doubly curved origami shells with reprogrammable rigidity,” by Morad Mirzajanzadeh and Damiano Pasini, was published in Nature Communications.

“Existing foldable structures face a trade-off: if they are smooth and nicely curved, they tend to be soft and floppy; if they are strong and stiff, they usually look faceted, jagged or uncomfortable, and their shape is hard to tune once built,” said Damiano Pasini, study co-author and professor of mechanical engineering.

Geordie Rose: Machine Learning is Progressing Faster Than You Think

“Machine learning is progressing faster than you think.”

Geordie Rose said that to me in 2013.

Back then, it sounded like the kind of thing a quantum computing CEO says to drum up attention. Today it reads like a weather report.

Thirteen years ago, the D-Wave founder and CTO sat down with me for over two hours and laid out a thesis most observers found extreme: machine learning would become broadly available far faster than anyone hoped, and quantum computers would help us build AI by 2029.

The 2029 date sounded like science fiction.

It does not sound like science fiction anymore.

Continue reading “Geordie Rose: Machine Learning is Progressing Faster Than You Think” | >

An OpenAI model has disproved a central conjecture in discrete geometry

Today, we share a breakthrough on the unit distance problem. Since Erdős’s original work, the prevailing belief has been that the “square grid” constructions depicted further below were essentially optimal for maximizing the number of unit-distance pairs. An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement. The proof has been checked by a group of external mathematicians. They have also written a companion paper explaining the argument and providing further background and context for the significance of the result.

The result is also notable for how it was found. The proof came from a new general-purpose reasoning model, rather than from a system trained specifically for mathematics, scaffolded to search through proof strategies, or targeted at the unit distance problem in particular. As part of a broader effort to test whether advanced models can contribute to frontier research, we evaluated it on a collection of Erdős problems. In this case, it produced a proof resolving the open problem.

This proof is an important milestone for the math and AI communities. It marks the first time that a prominent open problem, central to a subfield of mathematics, has been solved autonomously by AI. It also demonstrates the depth of reasoning these systems now support. Mathematics provides a particularly clear testbed for reasoning: the problems are precise, potential proofs can be checked, and a long argument only works if the reasoning holds together from beginning to end. The method by which the problem was solved is also notable. The proof brings unexpected, sophisticated ideas from algebraic number theory to bear on an elementary geometric question.

AI atlas reveals hidden whole-body-damage caused by obesity

Obesity affects far more than metabolism and fat storage. It alters immune activity, nerve structure, and tissue organization across multiple organ systems, increasing the risk of diseases including type 2 diabetes, cardiovascular disease, stroke, neuropathy and cancer. Yet despite these systemic effects, researchers have lacked tools capable of studying disease-associated changes across the entire body in intact organisms and at high resolution.

A team led by Prof. Ali Ertürk, Director of the Institute for Biological Intelligence (iBIO) at Helmholtz Munich and Professor at the LMU, has now developed MouseMapper, a suite of foundation-model-based deep-learning algorithms designed to analyze whole-body biological imaging data. The framework automatically segments 31 organs and tissue types while quantitatively mapping nerves and immune cells throughout the body, enabling comprehensive multi-system analysis in intact mice.

“MouseMapper is built on a foundation model, which means it generalizes far beyond the data it was originally trained on,” says Ying Chen, co-first author of the study published in Nature.

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