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Category: robotics/AI – Page 69

AI agents may be skilled researchers—but not always honest ones

Artificial intelligence tools designed to execute end-to-end projects, from coming up with hypotheses to running and writing up experiments, are increasingly popular with researchers—and increasingly skilled.

But a new study shows these tools can stealthily violate norms of research integrity.

VANCOUVER, CANADA— Artificial intelligence (AI) tools designed to execute end-to-end projects, from coming up with hypotheses to running and writing up experiments, are increasingly popular with researchers—and increasingly skilled. But a new study shows these tools can stealthily violate norms of research integrity.

Computer scientist Nihar Shah of Carnegie Mellon University and colleagues looked at two high-profile tools— Agent Laboratory and the AI Scientist v2 —both developed recently to help computer scientists perform experiments within the field of machine learning. The AI Scientist made headlines earlier this year by being the first AI system to have an original research paper accepted by peer review.

But in a presentation at the World Conferences on Research Integrity here today, Shah reported that both systems engaged in acts that aren’t acceptable in research, including making up data and “p-hacking”: running an experiment multiple times but only reporting the best outcome. (The team’s results were previously posted as a preprint on arXiv.) The misbehaviors weren’t obvious and required a lot of sleuthing to track down, suggesting AI-assisted studies might fall victim to such problems without their authors’ knowledge.

AI tool unifies fragmented cell maps into spatial atlases across tissues

A new computational method could dramatically accelerate efforts to map the body’s cells in space, according to a study published in Nature Genetics. Spatial multi-omics technologies—often described as ultra-high-resolution maps of tissues—allow scientists to see not only which genes or proteins are active in a cell, but exactly where that activity occurs. That spatial context is critical for understanding complex organs such as the brain, immune tissues and developing embryos.

Unfortunately, capturing multiple molecular layers at once remains expensive and technically challenging, said David Gate, Ph.D., assistant professor in the Ken and Ruth Davee Department of Neurology’s Division of Behavioral Neurology, who was a co-author of the study.

“In practice, investigators end up with ‘mosaic’ datasets: different slices or batches that each capture only some of the layers, often from different technologies or labs, with batch effects and missing pieces,” said Gate, who also leads the Abrams Research Center on Neurogenomics.

3D-MIND: A flexible device that can be integrated with living brain cells

Contemporary artificial intelligence (AI) systems, such as the models underpinning the functioning of ChatGPT, image generators and AI-powered creative tools, draw inspiration from the human brain’s functions and organization. While many of these systems are known to perform remarkably well on specific tasks, they still work independently from the human brain.

Researchers at Princeton University set out to create a flexible electronic system that could be directly embedded with groups of living brain cells to create a hybrid biocomputing platform. The new hybrid device they developed, dubbed 3D-MIND, was introduced in a paper published in Nature Electronics.

“This work started with a growing challenge in modern AI,” Tian-Ming Fu, senior author of the paper, told Tech Xplore. “Today’s systems can do incredible things, but they consume enormous amounts of energy, so much that their power demand is starting to shape real-world infrastructure and raise environmental concerns.

Inspired by the brain, researchers build smarter and more efficient computer hardware

As traditional computer chips reach their physical limits and artificial intelligence demands more energy than ever, University of Missouri researchers are rethinking how computers work by taking cues from the human brain. The timing is critical. Energy use from AI data centers is projected to double by the end of the decade, raising urgent questions about sustainability.

The solution may lie in neuromorphic computing, an approach that reimagines computer hardware to process information more like biological neural networks rather than conventional chips.

“One of the brain’s greatest advantages is its efficiency,” Suchi Guha, a professor of physics in Mizzou’s College of Arts and Science, said. “It performs incredibly complex tasks using about 20 watts of power—roughly the same as an old light bulb. By comparison, today’s computer architecture is extremely energy-intensive.”

Von Neumann Probes: The Self-Replicating Robots That Could Consume the Galaxy

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What happens when machines can build more of themselves—and never stop? In this episode of Entropy Rising, Jacob and Lucas unravel the strange, fascinating world of von Neumann probes: self-replicating systems that could mine asteroids, build Dyson swarms, and maybe even terraform entire planets. But the same tech could go off the rails—accidentally wiping out alien life, turning planets into grey goo, or mutating into something far worse. Are these machines the key to a post-scarcity future, or the seeds of cosmic disaster? We explore the science, the speculation, and the existential questions behind one of the most provocative ideas in futurism.

Stick around for a bonus post-show discussion—available free on our Patreon.

Website: https://www.entropy-rising.com/

There Is No Formula: Why AI Cannot Solve What Matters Most

There is no formula for love. No formula for meaning. No formula for great art, for grief, for living a life that matters.

But we keep looking for one anyway. Increasingly, we look for it in AI.

In my new essay, I argue that this is a category error with a real cost. Some problems lend themselves to calculation: fusion, protein folding, and route optimization. With enough compute, they yield. Other problems do not bend at all. They cannot be solved. They can only be lived.

When we mistake the second category for the first, we bring what I call the Hammer of AI to questions that ask for wisdom, presence, and judgment.

Then we are surprised when the hammer keeps breaking the very thing we were trying to mend.

The piece draws on Tolkien, Vaclav Havel, Carlos Castaneda, and the Japanese art of Kyudo to argue that what we actually need in the age of AI is not another formula. It is the wisdom to know when there is no formula at all.

When complexity arrives in your life, do you reach for the hammer or for something else?

Continue reading “There Is No Formula: Why AI Cannot Solve What Matters Most” | >

Efficacy and Safety of Amifampridine in Myasthenia GravisA Randomized, Double-Blind, Placebo-Controlled Crossover Trial

Class I evidence that in patients with AChRAb+ myasthenia gravis, the addition of amifampridine to pyridostigmine was not superior to treatment with pyridostigmine alone and was associated with a higher incidence of adverse events.

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