“We are excited to be joining Google DeepMind along with some of the Windsurf team,” Mohan and Chen said in a statement. “We are proud of what Windsurf has built over the last four years and are excited to see it move forward with their world class team and kick-start the next phase.”
Google didn’t share how much it was paying to bring on the team. OpenAI was previously reported to be buying Windsurf for $3 billion.
IN A NUTSHELL 🌐 Realbotix’s humanoid robot can fluently speak 15 languages and access 147 additional languages via cloud support. 🏨 The robot aims to enhance communication in healthcare and hospitality by eliminating language barriers and improving user experiences. 📈 The global market for humanoid robots is projected to grow from $2.93 billion in 2025
Artificial intelligence (AI) is infamous for its resource-heavy training, but a new study may have found a solution in a novel communications system, called ZEN, that markedly improves the way large language models (LLMs) train.
The research team at Rice University was helmed by doctoral graduate Zhuang Wang and computer science professor T.S. Eugene Ng with contributions from two other computer science faculty members: assistant professor Yuke Wang and professor Anshumali Shrivastava. Stevens University’s Zhaozhuo Xu and Jingyi Xi of Zhejiang University also contributed to the project.
American researchers from the University of Michigan have developed a new text recognition test, generated by AI and the one created by man.
Recognizing AI-generated content from human-generated content is not an easy task. There are no so many tools, that can effectively distinguish between the two, generated by AI from the human-made and avoid false accusations.
The new test by American researchers may be especially useful for scientists and students, who are increasingly faced with the fact that the works they create are perceived as generated by artificial intelligence. The developers have named their tool «Liketropy», as the theoretical basis of the method includes the statistical ideas of likelihood and entropy.
As AI infiltrates every aspect of our lives, who are some of the people behind this huge inflection point? In this special three-part series, you’ll hear from the people predicting and shaping our tech future. Host Manoush Zomorodi reports on the latest and revisits her favorite conversations with the minds crafting the digital world we live in today: what they’ve gotten right — and wrong — and where they think we’re headed next. Part 1 features futurist Ray Kurzweil and counterculture icon Stewart Brand. TED Radio Hour+ subscribers now get access to bonus episodes, with more ideas from TED speakers and a behind the scenes look with our producers. A Plus subscription also lets you listen to regular episodes (like this one!) without sponsors. Sign-up at plus.npr.org/ted.
A newly developed generative AI model is helping researchers explore protein dynamics with increased speed. The deep learning system, called BioEmu, predicts the full range of conformations a protein can adopt, modeling the structural ensembles that underlie protein function.
The work, in a paper titled “Scalable emulation of protein equilibrium ensembles with generative deep learning,” was published in Science. Researchers developed BioEmu as a high-speed emulator of protein motion, capable of generating thousands of conformational states in just one GPU-hour, significantly outperforming traditional molecular dynamics (MD) simulations.
Understanding protein function has been a challenge, often hinging not on a single structural component of the protein, but on the combined ensemble of shapes within the protein. Proteins frequently shift between different conformations depending on their interactions or environment, which has been a challenge for other methods to capture accurately.
A new cancer drug candidate developed by Lawrence Livermore National Laboratory (LLNL), BBOT (BridgeBio Oncology Therapeutics) and the Frederick National Laboratory for Cancer Research (FNLCR) has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect. In early clinical trials, the compound, known as BBO-10203, has shown promise in disrupting a key interaction between two cancer-driving proteins — RAS and PI3Kα — without causing hyperglycemia (high blood-sugar levels), which has historically hindered similar treatments. Published in Science
Can artificial intelligence (AI) potentially transform health care for the better?
Now, rising to the challenge, an Arizona State University team of researchers has built a powerful new AI tool, called Ark+, to help doctors read chest X‑rays better and improve health care outcomes.
“Ark+ is designed to be an open, reliable and ultimately useful tool in real‑world health care systems,” said Jianming “Jimmy” Liang, an ASU professor from the College of Health Solutions, and lead author of the study recently published in Nature.