A day after recognizing former Google vice president and engineering fellow Geoffrey Hinton for his contributions to the field of physics, the Royal Swedish Academy of Sciences has honored a pair of current Google employees. On Wednesday, DeepMind CEO Demis Hassabis and senior research scientist John Jumper won half of the 2024 Nobel Prize in Chemistry, with the other half going to David Baker, a professor at the University of Washington. Baker, Hassabis and Jumper all advanced our understanding of those essential building blocks of life that are responsible for functions both inside and outside the human body. The Nobel Committee cited Baker's seminal work in computational protein design. Since 2003, Baker and his research team have been using amino acids and computers to design entirely new proteins.

Researchers from the University of Oxford and Google's artificial intelligence division DeepMind have claimed that there is a high probability of advanced forms of AI becoming "existentially dangerous to life on Earth". In a recent article in the peer-reviewed journal AI Magazine, the researchers warned that there would be "catastrophic consequences" if the development of certain AI agents continues. Leading philosphers like Oxford University's Nick Bostrom have previously spoken of the threat posed by advanced forms of artificial intelligence, though one of authors of the new paper claimed such warnings did not go far enough.

Could artifical intelligence make better funding decisions than senators? A "democratic" AI system has learned how to develop the most popular policy for redistributing public money among people playing an online game. "Many of the problems that humans face are not merely technological, but require us to coordinate in society and in our economies for the greater good," says Raphael Koster at UK-based AI company DeepMind. "For AI to be able to help, it needs to learn directly about human values." The DeepMind team trained its artificial intelligence to learn from more than 4000 people as well as from computer simulations in an online, four-player economic game.

Are giant AI language models like GPT-3 or PaLM under-trained? A Deepmind study shows that we can expect further leaps in performance. Big language models like OpenAI's GPT-3, Deepmind's Gopher, or most recently Google's powerful PaLM rely on lots of data and gigantic neural networks with hundreds of billions of parameters. PaLM, with 540 billion parameters, is the largest language model to date. The trend toward more and more parameters stems from the previous finding that the capabilities of large AI models scale with their size.

Programming has been for a long time a high-status, high-demand skill. Companies and businesses across industries depend at a very foundational level on the ability of human developers: People who write and understand the language of computers. Recently, with the advent of large language models, AI companies have begun to explore the possibilities of systems that can learn to code. OpenAI's Codex -- embedded into GitHub Copilot -- was the first notable example. Codex can read simple natural language commands and instructions and write code that matches the intention of the user. Yet, writing small programs and solving easy tasks is "far from the full complexity of real-world programming." AI models like Codex lack the problem-solving skills that most programmers rely on in their day-to-day jobs. That's the gap DeepMind wanted to fill with AlphaCode, an AI system that has been trained to "understand" natural language, design algorithms to solve problems, and then implement them into code. AlphaCode displays a unique skillset of natural language understanding and problem-solving ability, combined with the statistical power characteristic of large language models. The system was tested against human programmers on the popular competitive programming platform Codeforces. AlphaCode averaged a ranking of 54.3% across 10 contests, which makes it the first AI to reach the level of human programmers in competitive programming contests. I've studied the AlphaCode paper to understand what AlphaCode is and isn't, what these impressive results mean, what are the implications, and what the future holds for AI and human developers. I've also researched what AI experts and competitive programmers are saying about AlphaCode, so you have different independent perspectives to form your own. This article is a thorough review divided into 6 sections (and their respective subsections). I will include comments throughout the article to explore some questions, ideas, and results in more depth.

DeepMind, a Google-owned artificial intelligence (AI) company based in the United Kingdom, made scientific history when it announced last November that it had a solution to a 50-year-old grand challenge in biology--protein folding. This AI machine learning breakthrough may help accelerate the discovery of new medications and novel treatments for diseases. On July 15, 2021 DeepMind revealed details on how its AI works in a new peer-reviewed paper published in Nature, and made its revolutionary AlphaFold version 2.0 model available as open-source on GitHub. The three-dimensional (3D) shape and function of proteins are determined by the sequence of its amino acids. AlphaFold predicts three-dimensional (3D) models of protein structures.

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.

Google users contribute more than 20 million pieces of information on Maps every day – that's more than 200 contributions every second. The uncertainty of traffic can crash the algorithms predicting the best ETA. There is also a chance of new roads and buildings being built all the time. Though Google Maps gets its ETA right most of the time, there is still room for improvement. Researchers at Alphabet-owned DeepMind have partnered with the Google Maps team to improve the accuracy of the real-time ETAs by up to 50% in places like Berlin, Jakarta, São Paulo, Sydney, Tokyo, and Washington D.C.

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic. What gets used in hospitals and clinicians' offices may be much simpler, and a lot less like AI than you would think. In the latest issue of Nature magazine, DeepMind researchers published the results of a deep learning project that can predict kidney failure of patients in the hospital up to 48 hours before the onset of symptoms, with far greater accuracy than existing computer programs for such predictive uses. Also this week, the DeepMind team published the results of a third-party survey of the use of a computer program called "Streams," which uses no artificial intelligence but which can be useful to physicians for things such as being alerted to warning signs about a patient. The first project, the deep learning one, has some ways to go to be put into practice, while the Streams software is already in use by doctors and hospital staff.

DeepMind is furthering its cancer research efforts with a newly announced partnership. Today, the London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018. It'll use that data to refine its artificially intelligent (AI) breast cancer detection algorithms. Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.'s Optimam (an image database of over 80,000 scans extracted from the NHS' National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue. The collaboration builds on DeepMind's work with the Cancer Research UK Imperial Center at Imperial College London, where it has already analyzed roughly 7,500 mammograms.