Elon Musk's Boring Company pitched that its Vegas Loop, underground tunnels built below Las Vegas, would reduce gridlock in some of the busiest parts of the city, offering a new transport solution that isn't a monorail. People are transported by ordinary Tesla vehicles in tunnels and terminals that are often difficult to get to. It hasn't been the transport game changer the company promised, though. A report from Fortune elaborated on what's actually happening in those tunnels, saying there have been at least 67 trespassing reports since 2022 and 22 instances of other vehicles following Teslas into the tunnels and stations. Boring's monthly reports to the Las Vegas Convention and Visitors Authority also showed several instances of "property damage, theft, technical issues or injuries, near-misses and trespassing or intrusions."

The 2024 Nobel prize in chemistry has been awarded to David Baker, Demis Hassabis and John Jumper for their work on understanding the structure of proteins, which play vital roles in all living organisms. Hassabis and Jumper, of Google DeepMind, developed an artificial intelligence that predicts the structure of proteins. Baker, at the University of Washington in Seattle, has been recognised for his work on designing new proteins. Proteins are the molecules that make life happen. All of the key machinery of life is made of proteins, from the muscles that power us and the molecules that read and copy DNA to the antibodies that protect us from infections.

Three computer scientists, including an artificial intelligence researcher at Google DeepMind, won the Nobel Prize in chemistry on Wednesday for their work in protein science, including cracking the code for proteins' structures. The Royal Swedish Academy of Sciences gave half of the prize to David Baker, a professor at the University of Washington in Seattle, for computational protein design, and the other half to Demis Hassabis of University College London and John Jumper, CEO of London-based Google DeepMind, for work on predicting protein structure. "One of the discoveries being recognized this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities," said Heiner Linke, chair of the Nobel Committee for Chemistry.

Two scientists at Google DeepMind and an American biochemist have been awarded the 2024 Nobel prize in chemistry for breakthroughs in predicting and designing the structure of proteins. Demis Hassabis, DeepMind's British founder, and John Jumper, who led the development of the company's AlphaFold protein prediction software, share half of the prize. The other half of the prize was awarded to Prof David Baker, of the University of Washington, whose computational research has led to the creation of entirely new kinds of proteins. Announced by the Royal Swedish Academy of Sciences in Stockholm, the winners share the 11m Swedish kronor ( 810,000) prize for "Computational protein design and protein structure prediction".

Scientists David Baker, John Jumper and Demis Hassabis have won the 2024 Nobel Prize in Chemistry for their work on predicting the structure of proteins using artificial intelligence. The Royal Swedish Academy of Sciences on Wednesday announced one half of the prize to Baker "for computational protein design" and the other half jointly to Hassabis and Jumper "for protein structure prediction". Baker works at the University of Washington in Seattle, in the United States, while Hassabis and Jumper both work at Google Deepmind in London. The laureates revealed proteins' secrets through computing and artificial intelligence, the academy said, noting that "chemists have long dreamed of fully understanding and mastering the chemical tools of life – proteins". While Hassabis and Jumper used artificial intelligence "to predict the structure of almost all known proteins", Baker "has learned how to master life's building blocks and create entirely new proteins".

Better understanding proteins has driven huge breakthroughs in medicine. Mr Hassabis and Prof Jumper used artificial intelligence to predict the structures of almost all known proteins and created a tool called AlphaFold2. The committee called it a "complete revolution" in chemistry, and the tool is now used for 200 million proteins worldwide. Professor Baker used amino acids to design a new protein, opening the door to the creation of new proteins used in pharmaceuticals, vaccines and other tools. Prof Baker told the committee shortly after the announcement that he was "very excited and very honoured".

Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, frequently failing to adhere to specific length constraints due to their token-level operations and insufficient training on data with strict length limitations. We identify this issue as stemming from a lack of positional awareness and propose novel approaches--PositionID Prompting and PositionID Fine-Tuning--to address it. These methods enhance the model's ability to continuously monitor and manage text length during generation. Additionally, we introduce PositionID CP Prompting to enable LLMs to perform copy and paste operations accurately. Furthermore, we develop two benchmarks for evaluating length control and copy-paste abilities. Our experiments demonstrate that our methods significantly improve the model's adherence to length constraints and copy-paste accuracy without compromising response quality.

Two pioneers of artificial intelligence – John Hopfield and Geoffrey Hinton – won the Nobel Prize in physics Oct. 8 for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats to humanity, one of the winners said. Mr. Hinton, who is known as the Godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Mr. Hopfield is an American working at Princeton. "This year's two Nobel Laureates in physics have used tools from physics to develop methods that are the foundation of today's powerful machine learning," the Nobel committee said in a press release. Ellen Moons, a member of the Nobel committee at the Royal Swedish Academy of Sciences, said the two laureates "used fundamental concepts from statistical physics to design artificial neural networks that function as associative memories and find patterns in large data sets." She said that such networks have been used to advance research in physics and "have also become part of our daily lives, for instance in facial recognition and language translation. Mr. Hinton predicted that AI will end up having a "huge influence" on civilization, bringing improvements in productivity and health care. "It would be comparable with the Industrial Revolution," he said in the open call with reporters and the officials from the Royal Swedish Academy of Sciences. "Instead of exceeding people in physical strength, it's going to exceed people in intellectual ability.

When the Swedish inventor Alfred Nobel wrote his will in 1895, he designated funds to reward those who "have conferred the greatest benefit to humankind." The resulting Nobel Prizes have since been awarded to the discoverers of penicillin, X-rays, and the structure of DNA--and, as of today, to two scientists who, decades ago, laid the foundations for modern artificial intelligence. Today, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for groundbreaking statistical methods that have advanced physics, chemistry, biology, and more. In the announcement, Ellen Moons, the chair of the Nobel Committee for Physics and a physicist at Karlstad University, celebrated the two laureates' work, which used "fundamental concepts from statistical physics to design artificial neural networks" that can "find patterns in large data sets." She mentioned applications of their research in astrophysics and medical diagnosis, as well as in daily technologies such as facial recognition and language translation.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children "a personalized and supportive" experience in education. Two pioneers of artificial intelligence -- John Hopfield and Geoffrey Hinton -- won the Nobel Prize in physics Tuesday for helping create the building blocks of machine learning that is revolutionizing the way we work and live but also creates new threats for humanity. Hinton, who is known as the godfather of artificial intelligence, is a citizen of Canada and Britain who works at the University of Toronto, and Hopfield is an American working at Princeton. "These two gentlemen were really the pioneers," said Nobel physics committee member Mark Pearce. "They ... did the fundamental work, based on physical understanding which has led to the revolution we see today in machine learning and artificial intelligence."