Lifeboat Foundation

Toyota’s latest electric LQ concept car satisfies the “huмan need to Ƅe engaged eмotionally” with an on-Ƅoard artificial intelligence agent naмed Yui.

The Toyota LQ is Ƅoth fully electric and equipped with an SAE leʋel four equiʋalent autoмated driʋing systeм, мeaning no huмan interʋention is needed to driʋe the ʋehicle.

Its мain feature howeʋer is the on-Ƅoard AI-powered, interactiʋe agent, called Yui, which proʋides a personalised мoƄility experience Ƅy learning and responding to the driʋer’s eмotional and physical state.

Helmeted and harnessed directly behind my pilot, I prepare for takeoff as the cockpit canopy shuts over us. It could be a scene from Top Gun: Maverick save for the fact that we’re not launching from an aircraft carrier but pulling out of pit lane at the Thermal Club’s track in a final prototype of the Czinger 21C hypercar.

The $2 million, carbon-fiber-bodied, tandem-seat Czinger 21C astounds with specs—1,250 hp, zero to 62 mph in 1.9 seconds, a claimed top speed of 253 mph—and recently blew away the McLaren P1’s production-car track record at Circuit of the Americas by six seconds. But more impressive—seriously—is the hybrid’s build process: The main structural components are designed by Czinger’s proprietary AI software and then 3D-printed. “These structures cannot be made more perfect for the requirements inputted,” says Kevin Czinger, who, along with his son Lukas, cofounded Los Angeles–based Czinger Vehicles. “You could have 1,000 engineers and they would never get to this solution.”

Researchers at the University of Toronto have developed an artificial intelligence system that can create proteins not found in nature using generative diffusion, the same technology behind popular image-creation platforms such as DALL-E and Midjourney.

The system will help advance the field of generative biology, which promises to speed drug development by making the design and testing of entirely new therapeutic proteins more efficient and flexible.

“Our model learns from image representations to generate fully new proteins, at a very high rate,” says Philip M. Kim, a professor in the Donnelly Centre for Cellular and Biomolecular Research at U of T’s Temerty Faculty of Medicine. “All our proteins appear to be biophysically real, meaning they fold into configurations that enable them to carry out specific functions within cells.”

AMD is the latest manufacturer to commit to AI, joining forces with Microsoft to challenge Nvidia’s market dominance.

A small team of chemists at the Russian Academy of Sciences, has found that metal atoms, not nanoparticles, play the key role in catalysts used in fine organic synthesis. In the study, reported in the Journal of the American Chemical Society, the group used multiple types of electron microscopy to track a region of a catalyst during a reaction to learn more about how it was proceeding.

Prior research has shown that there are two main methods for studying a reaction. The first is the most basic: As ingredients are added, the reaction is simply observed and/or measured. This can be facilitated through use of high-speed cameras. This approach will not work with nanoscale reactions, of course. In such cases, chemists use a second method: They attempt to capture the state of all the components before and after the reaction and then compare them to learn more about what happened.

This second approach leaves much to be desired, however, as there is no way to prove that the objects under study correspond with one another. In recent years, chemists have been working on a new approach: Following the action of a single particle during the reaction. This new method has proven to have merit but it has limitations as well—it also cannot be used for reactions that occur in the nanoworld. In this new effort, the researchers used multiple types of electron microscopy coupled with machine-learning algorithms.

An artificial intelligence system enables robots to conduct autonomous scientific experiments—as many as 10,000 per day—potentially driving a drastic leap forward in the pace of discovery in areas from medicine to agriculture to environmental science.

Reported today in Nature Microbiology, the research was led by a professor now at the University of Michigan.

Continue reading “AI could run a million microbial experiments per year, says study” »

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With the risks of hallucinations, private data information leakage and regulatory compliance that face AI, there is a growing chorus of experts and vendors saying there is a clear need for some kind of protection.

One such organization that is now building technology to protect against AI data risks is New York City based Arthur AI. The company, founded in 2018, has raised over $60 million to date, largely to fund machine learning monitoring and observability technology. Among the companies that Arthur AI claims as customers are three of the top-five U.S. banks, Humana, John Deere and the U.S. Department of Defense (DoD).

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Microsoft announced a major expansion of its artificial intelligence-based search tools today, opening up new features that allow visual and multimodal searches, as well as persistent chat tools. The updates significantly expand the capabilities of Bing, the company’s search engine, and Edge, its web browser.

Only select users have been able to test the new AI search features in a limited preview over the last three months. But the company announced today it is now moving Bing and Edge into an open preview, allowing anyone to test the new tools by signing in with a Microsoft account. The move suggests Microsoft believes the new features are ready for wider use and feedback.

Discover the latest exciting, breaking research news for artificial intelligence and machine learning.