Developers from Amazon, Meta, and more discuss how AI adoption is changing the industry and which skills developers will need to stay competitive.
OpenScholar, an innovative AI system by Allen Institute for AI and University of Washington, revolutionizes scientific research by processing 45 million papers instantly, offering researchers citation-backed answers and challenging proprietary AI systems.
An MRI scan shows a Chiari type-1 malformation, in which the cerebellum extends beyond the gap in the skull where it connects to the spinal cord.
Artificial intelligence identified 3 subtypes of Chiari type-1 malformations, could improve medical decision making.
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How can scientific discoveries based on large volumes of experimental data be accelerated by artificial intelligence (AI)? This can be achieved in heterogeneous catalysis, according to a recent study led by Prof. Weixue Li from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, published in Science.
The researchers developed a comprehensive theory of metal-support interaction (MSI), a key aspect of catalysis, by combining interpretable AI with domain knowledge, experimental data, and first-principles simulations.
Supported metal catalysts are widely used in industrial chemical production, petrochemical refining, and environmental control systems like exhaust catalysts. MSI influences interfacial activities, such as charge transfer, chemical composition, perimeter sites, particle shape, and suboxide encapsulation, in addition to stabilizing dispersed catalysts. As a result, modifying MSI is one of the few ways to enhance catalyst performance.
But according to recent research into patient attitudes on AI, providers should be thinking carefully about how they deploy those tools if they want to preserve patient trust.
Earlier this fall, Mark Polyak, president of analytics at IPSOS, and Dr. Lukasz Kowalczyk, a physician at Peak Gastroenterology Associates, spoke on a panel discussion at the HIMSS AI in Healthcare Forum that explored patients’ perspectives and attitudes about healthcare AI. Above all, they’re seeking healthcare interactions and experiences that are transparent and personalized, experts on the panel said.
Good news for anyone with a hankerin’ for going back in time to kill their grandfather before he had kids: a physicist named Germain Tobar from the University of Queensland in Australia says go for it since time travel paradoxes aren’t real. So feel free to kill your grandpappy without fear of deleting your own existence.
He didn’t explicitly frame it that way, but he does think that time travel paradoxes are bullshit. Tobar’s work uses Einstein’s theory of general relativity as a foundation and then builds from there. He says that, according to his calculations, events can exist both in the past and in the future simultaneously, independent of one another. Space-time will adjust itself to avoid paradoxes, thus allowing you to cause whatever mayhem you want throughout time without creating contradictions.
If true, famous time travel stories like The Terminator and Back to the Future wouldn’t be possible. A Terminator sent to the past to kill John Connor would not be killing John Connor in the future, theoretically. It would only kill John Connor in the past and space-time would find some way to adjust to ensure that John Connor is still alive in the future to continue to be a pain in every robot’s shiny metal ass.
In today’s AI news, Nvidia has three anonymous customers, referred to as Customers A, B, and C, who have each spent over $10 Billion on the company’s AI chips so far this year.
However, Hassabis’ true breakthrough came just a month ago, when he and two colleagues from DeepMind won the Nobel Prize in Chemistry for their development of AlphaFold, an AI tool capable of predicting the structure of the 200 million known proteins. This achievement would have been nearly impossible without AI, and solidifies Hassabis’ belief that AI is set to become one of the main drivers of scientific progress in the coming years.
Hassabis — the son of a Greek-Cypriot father and a Singaporean mother — reflects on the early days of DeepMind, which he founded in 2010, when “nobody was working on AI.” Over time, machine learning techniques such as deep learning and reinforcement learning began to take shape, providing AI with a significant boost. In 2017, Google scientists introduced a new algorithmic architecture that enabled the development of AGI. “It took several years to figure out how to utilize that type of algorithm and then integrate it in hybrid systems like AlphaFold, which includes other components,” he explains.
“During our first years, we were working in a theoretical space. We focused on games and video games, which were never an end in themselves. It gave us a controlled environment in which to operate and ask questions. But my passion has always been to use AI to accelerate scientific understanding. We managed to scale up to solving a real-world problem, such as protein folding,” recalls the engineer and neuroscientist.
Imagine sitting down with an AI model for a spoken two-hour interview. A friendly voice guides you through a conversation that ranges from your childhood, your formative memories, and your career to your thoughts on immigration policy. Not long after, a virtual replica of you is able to embody your values and preferences with stunning accuracy.
That’s now possible, according to a new paper from a team including researchers from Stanford and Google DeepMind, which has been published on arXiv and has not yet been peer-reviewed.
