Lifeboat Foundation

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.

In a new paper in the journal Nature Machine Intelligence, leading computer scientists from around the world review recent machine learning advances converging towards creating a collective machine-learned intelligence.

An emerging research area in AI is developing multi-agent capabilities with collections of interacting AI systems. Andrea Soltoggio and colleagues develop a vision for combining such approaches with current edge computing technology and lifelong learning advances. The envisioned network of AI agents could quickly learn new tasks in open-ended applications, with individual AI agents independently learning and contributing to and benefiting from collective knowledge.