Evo 2 now includes information from humans, plants, and other eukaryotic species to expand its capabilities in generative functional genomics.
Category: robotics/AI – Page 174
Deep Nanometry: Deep learning system detects disease-related nanoparticles
Researchers, including those from the University of Tokyo, developed Deep Nanometry, an analytical technique combining advanced optical equipment with a noise removal algorithm based on unsupervised deep learning.
Deep Nanometry can analyze nanoparticles in medical samples at high speed, making it possible to accurately detect even trace amounts of rare particles. This has proven its potential for detecting extracellular vesicles indicating early signs of colon cancer, and it is hoped that it can be applied to other medical and industrial fields.
The body is full of microscopic particles smaller than cells. These include extracellular vesicles (EVs), which can be useful in early disease detection and also in drug delivery.
Generative AI tool marks a milestone in biology
Imagine being able to speed up evolution – hypothetically – to learn which genes might have a harmful or beneficial effect on human health. Imagine, further, being able to rapidly generate new genetic sequences that could help cure disease or solve environmental challenges.
Now, scientists have developed a generative AI tool that can predict the form and function of proteins coded in the DNA of all domains of life, identify molecules that could be useful for bioengineering and medicine, and allow labs to run dozens of other standard experiments with a virtual query – in minutes or hours instead of years (or millennia).
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life.
Self-driving lab transforms electronic polymers discovery
Plastic that conducts electricity might sound impossible. But there is a special class of materials known as “electronic polymers” that combines the flexibility of plastic with the functionality of metal. This type of material opens the door for breakthroughs in wearable devices, printable electronics and advanced energy storage systems.
Yet, making thin films from electronic polymers has always been a difficult task. It takes a lot of fine-tuning to achieve the right balance of physical and electronic properties. Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have created an innovative solution to this challenge with artificial intelligence (AI).
They used an AI-driven, automated materials laboratory, a tool called Polybot, to explore processing methods and produce high-quality films. Polybot is located at the Center for Nanoscale Materials, a DOE Office of Science user facility at Argonne.
Elon Musk Just Turned On The World’s Most Powerful Artificial Intelligence
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AI-Coding Startup Codeium In Talks To Raise Funds At Almost $3 Billion Valuation
In today’s AI news, Codeium, an AI-powered coding startup, is raising a new round of funding at a $2.85 billion valuation. The round is being led by returning investor Kleiner Perkins, the people said. The new round comes just six months after Silicon Valley-based Codeium announced that it had closed a $150 million Series C at a $1.25 billion post-money valuation.
In other advancements, a couple of weeks after the initial release of Mistral’s AI assistant, Le Chat, the company told Le Parisien that it has reached one million downloads. “Go and download Le Chat, which is made by Mistral, rather than ChatGPT by OpenAI — or something else,” French president Emmanuel Macron said in a TV interview ahead of the recent AI Action Summit in Paris.
And, Google is launching a new experiment that uses AI to help people explore more career possibilities. The company announced in a blog post on Wednesday that a new “Career Dreamer” tool can find patterns between your experiences, educational background, skills, and interests to connect you with careers that might be a good fit.
Meanwhile, Forbes’ Lance Eliot analyzes a popular mantra right now. The recent AI-industry groupthink that says we merely need to increase the so-called “thinking time” of generative AI and LLMs to get better responses. AI makers are allowing users to stipulate that the AI can expend more time and effort doing various processing before displaying a generated answer.
In videos, Microsoft’s Satya Nadella sits down with Dwarkesh Patel to talk about their new Majorana Quantum chip breakthrough, plans for artificial general intelligence, topological qubits, gaming world models, and whether Microsoft Office commoditizes LLMs, or the other way around.
Then, dive into the world of Model Context Protocol and learn how to seamlessly connect AI agents to databases, APIs, and more. IBM’s Roy Derks breaks down its components, from hosts to servers, and showcases real-world applications. Gain the knowledge to revolutionize your AI projects.
Are Biological Systems More Intelligent Than Artificial Intelligence?. Are Biological Systems More Intelligent Than Artificial Intelligence?
W/ michael timothy bennett australian national university.
Speakers: Michael Timothy Bennett, Cecile Tamura
How topology drives complexity in brain, climate and AI
A study led by Professor Ginestra Bianconi from Queen Mary University of London, in collaboration with international researchers, has unveiled a transformative framework for understanding complex systems.
Published in Nature Physics, this paper establishes the new field of higher-order topological dynamics, revealing how the hidden geometry of networks shapes everything from brain activity to artificial intelligence.
“Complex systems like the brain, climate, and next-generation artificial intelligence rely on interactions that extend beyond simple pairwise relationships. Our study reveals the critical role of higher-order networks, structures that capture multi-body interactions, in shaping the dynamics of such systems,” said Professor Bianconi.