AI researchers at Meta say they have developed the largest protein-folding model of its kind to date, and that it is capable of predicting the structure of more than 600 million proteins.… The team released the 15-billion-parameter ESM-2 transformer-based model and a database of its protein structure predictions, dubbed the ESM Metagenomic Atlas, on Tuesday. This database includes protein shapes that haven't been observed yet by scientists. Proteins are complex biological molecules containing up of 20 types of amino acids, and perform all sorts of biological functions in organisms. Crucially, they fold up into intricate 3D structures, the shape of which is vital to how they operate; knowing their shape helps scientists understand how they function, and from that, helps them figure out ways to mimic, alter, or counter that behavior. Unfortunately, you can't just take the amino acid formula and immediately work out the eventual structure.

The problem with banking's use of artificial intelligence technology is not always in a bank's inability or unwillingness to invest in it. In fact, more financial institutions are spending on AI and related technologies as banking leaders strive to build out customer personalization. The problem instead can lie in a financial institution's failure to align the technology with the bank's strategy. As a result, a bank or credit union might buy (or build) the technology, but then abandon it shortly after or fail to follow through with it, a McKinsey report points out. The report uses an unnamed large retail bank as an example, one that has set aside resources in the budget for machine-learning (ML) technology -- one part of AI -- to automate marketing and customer personalization campaigns.

How much is too much? These are questions that cut to the heart of a complex issue currently preoccupying senior medical physicists when it comes to the training and continuing professional development (CPD) of the radiotherapy physics workforce. What's exercising management and educators specifically is the extent to which the core expertise and domain knowledge of radiotherapy physicists should evolve to reflect – and, in so doing, best support – the relentless progress of artificial intelligence (AI) and machine-learning technologies within the radiation oncology workflow. In an effort to bring a degree of clarity and consensus to the collective conversation, the ESTRO 2022 Annual Congress in Copenhagen last month featured a dedicated workshop session entitled "Every radiotherapy physicist should know about AI/machine learning…but how much?" With several hundred delegates packed into Room D5 at the Bella Center, speakers were tasked by the session moderators with defending a range of "optimum scenarios" to align the know-how of medical physicists versus emerging AI/machine-learning opportunities in the radiotherapy clinic.

Join the audience for a live webinar at 6 p.m. BST/1 p.m. EST on 12 August 2020 on the discovery of a novel battery electrolyte that was guided by machine-learning software without human intervention Want to take part in this webinar? Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly – a Bayesian machine-learning software package – to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows.

One day last summer, Microsoft's director of artificial intelligence research, Eric Horvitz, activated the Autopilot function of his Tesla sedan. The car steered itself down a curving road near Microsoft's campus in Redmond, Washington, freeing his mind to better focus on a call with a nonprofit he had cofounded around the ethics and governance of AI. Then, he says, Tesla's algorithms let him down. "The car didn't center itself exactly right," Horvitz recalls. Both tires on the driver's side of the vehicle nicked a raised yellow curb marking the center line, and shredded.

One day last summer, Microsoft's director of artificial intelligence research, Eric Horvitz, activated the Autopilot function of his Tesla sedan. The car steered itself down a curving road near Microsoft's campus in Redmond, Washington, freeing his mind to better focus on a call with a nonprofit he had cofounded around the ethics and governance of AI. Then, he says, Tesla's algorithms let him down. "The car didn't center itself exactly right," Horvitz recalls. Both tires on the driver's side of the vehicle nicked a raised yellow curb marking the center line, and shredded.

Google's heavy investment in artificial intelligence has helped the company's software write music and beat humans at complex board games. What unlikely feats could be next? The company's new head of AI says he'd like to see Google move deeper into areas such as healthcare. He also warns that the company will face some tricky ethical questions over appropriate uses for AI as it expands its use of the technology. The new AI boss at Google is Jeff Dean.

Since entrepreneur Chris Mansi cofounded Viz.ai in 2016, the best-funded wizards of artificial intelligence have taken on board games, and created emoji that mirror your facial expressions. Meanwhile, Mansi has been developing algorithms to save the brain cells of stroke patients. This month, the Food and Drug Administration cleared Viz.ai to market its algorithms to doctors and hospitals. It was a small breakthrough toward using AI to make healthcare more efficient and powerful. Someone in the US suffers a stroke every 40 seconds, according to the Centers for Disease Control.

Since entrepreneur Chris Mansi cofounded Viz.ai in 2016, the best-funded wizards of artificial intelligence have taken on board games, and created emoji that mirror your facial expressions. Meanwhile, Mansi has been developing algorithms to save the brain cells of stroke patients. This month, the Food and Drug Administration cleared Viz.ai to market its algorithms to doctors and hospitals. It was a small breakthrough toward using AI to make healthcare more efficient and powerful. Someone in the US suffers a stroke every 40 seconds, according to the Centers for Disease Control.

Executives at ascendant tech titans like Amazon and Google tend to look down on their predecessor IBM. The fading giant of Armonk, New York, once sustained itself inventing and selling cutting-edge technology, but now leans heavily on consulting. Renting out people to help other companies with tech projects is a messier and less scalable business than selling computing power on a distant cloud server, and leaving the customer to do the grunt work. Yet as Amazon and Google seek greater riches by infusing the world with artificial intelligence, they've started their own consulting operations, lending out some of their prized AI talent to customers. The reason: Those other businesses lack the expertise to take advantage of techniques such as machine learning.