Lifeboat Foundation

Princeton researchers have trained an AI to predict and prevent a common problem arising during nuclear fusion reactions — and they think it might be able to solve other problems, too.

The challenge: If the Spice Girls were physicists, their song “2 Become 1” might have been about nuclear fusion, a reaction that occurs when two atoms merge.

Fusion releases a tremendous amount of energy in the form of heat — it’s what powers the sun and other stars — and if we could harness the reaction here on Earth, we would have a near limitless source of clean energy.

Stanford and McMaster University researchers created an artificial intelligence (AI) model to design molecules that inhibit the growth of Acinetobacter baumannii, a common drug-resistant bacteria. They synthesized and validated six structurally novel molecules that demonstrated antibacterial activity against A. baumannii and other phylogenetically diverse bacterial pathogens. This study represents a significant step toward the practical application of generative AI approaches for antibiotic discovery and drug discovery in general.

The research article, “Generative AI for designing and validating easily synthesizable and structurally novel antibiotics,” was published in Nature Machine Intelligence.

Among the most critical issues in contemporary medicine is the worldwide spread of factors contributing to antibiotic resistance. In 2019, drug-resistant infections were responsible for an estimated 4.95 million deaths. As new antibiotics are being developed slower than the spread of antimicrobial resistance determinants, this figure is expected to reach 10 million annually by 2050.

A UCL-led research team has used artificial intelligence (AI) techniques to infer the influence and properties of dark energy more precisely from a map of dark and visible matter in the universe covering the last 7 billion years.

The study, submitted to the Monthly Notices of the Royal Astronomical Society and available on the arXiv preprint server, was carried out by the Dark Energy Survey collaboration. The researchers doubled the precision at which key characteristics of the universe, including the overall density of dark energy, could be inferred from the map.

This increased precision allows researchers to rule out models of the universe that might previously have been conceivable.

A small team of AI researchers at Microsoft reports that the company’s Orca-Math small language model outperforms other, larger models on standardized math tests. The group has published a paper on the arXiv preprint server describing their testing of Orca-Math on the Grade School Math 8K (GSM8K) benchmark and how it fared compared to well-known LLMs.

Many popular LLMs such as ChatGPT are known for their impressive conversational skills—less well known is that most of them can also solve math word problems. AI researchers have tested their abilities at such tasks by pitting them against the GSM8K, a dataset of 8,500 grade-school math word problems that require multistep reasoning to solve, along with their correct answers.

In this new study, the research team at Microsoft tested Orca-Math, an AI application developed by another team at Microsoft specifically designed to tackle math word problems, and compared the results with larger AI models.

Scientists modeled human-like communication skills and the transfer of knowledge between AIs — so they can teach each other to perform tasks without a huge amount of training data.

Tests aim to see if AI models can determine when you’re speaking to your phone without needing a trigger phrase.

AI technology developed by Johns Hopkins University to detect COVID-19 in lung ultrasound images, revolutionizing medical diagnostics.

Researchers develop skyrmion-based microelectronic device for sustainable, high-performance AI computing with energy-efficient technology.

This post is also available in: עברית (Hebrew)

Recent advancements in artificial intelligence make it increasingly harder to detect deepfake voices, and the solution might actually come from AI itself.

Scientists at Klick Labs were inspired by their clinical studies using vocal biomarkers to help enhance health outcomes and created an audio deepfake detection method that taps into signs of life like breathing patterns and micropauses in speech.