Today's weather forecasts rely on simulations that require a lot of computing power Google DeepMind claims its latest weather forecasting AI can make predictions faster and more accurately than existing physics-based simulations. GenCast is the latest in DeepMind's ongoing research project to use artificial intelligence to improve weather forecasting. The model was trained on four decades of historical data from the European Centre for Medium-Range Weather Forecasts's (ECMWF) ERA5 archive, which includes regular measurements of temperature, wind speed and pressure at various altitudes around the globe. Data up to 2018 was used to train the model and then data from 2019 was used to test its predictions against known weather. The company found that it beat ECMWF's industry-standard ENS forecast 97.4 per cent of the time in total, and 99.8 per cent of the time when looking ahead more than 36 hours.

An artificial intelligence system can now determine not only how proteins fold but also how they interact with other proteins, drug molecules or DNA. Biochemists and pharmaceutical researchers say the tool has the potential to vastly speed up their work, such as helping to discover new drugs. Proteins, which play many important roles in living things, are made up of chains of amino acids, but their complex 3D shapes are difficult to predict. How this moment for AI will change society forever (and how it won't) The AI company DeepMind first announced in 2020 that its AlphaFold AI could accurately predict protein structure from amino acid sequences, solving one of the biggest challenges in biology. By the middle of 2021, the company said that it had mapped 98.5 per cent of the proteins in the human body.

Google DeepMind claims to have made the first ever scientific discovery with an AI chatbot by building a fact-checker to filter out useless outputs, leaving only reliable solutions to mathematical or computing problems. Previous DeepMind achievements, such as using AI to predict the weather or protein shapes, have relied on models created specifically for the task at hand, trained on accurate and specific data. Large language models (LLMs), such as GPT-4 and Google's Gemini, are instead trained on vast amounts of varied data to create a breadth of abilities. But that approach also makes them susceptible to "hallucination", a term researchers use for producing false outputs. Gemini – which was released earlier this month – has already demonstrated a propensity for hallucination, getting even simple facts such as the winners of this year's Oscars wrong.

A crystal structure predicted by the GNoME AI. It contains barium (blue), niobium (white) and oxygen (green). An artificial intelligence created by Google DeepMind may help revolutionise materials science, providing new ways to make better batteries, solar panels, computer chips and many more vital technologies. "Anytime somebody wants to improve their technology, it inevitably includes improving the materials," says Ekin Dogus Cubuk at DeepMind. "We just wanted them to have more options."

Can AI tell you if you will need an umbrella? AI can predict the weather 10 days ahead more accurately than current state-of-the-art simulations, says AI firm Google DeepMind – but meteorologists have warned against abandoning weather models based in real physical principles and just relying on patterns in data, while pointing out shortcomings in the AI approach. Existing weather forecasts are based on mathematical models, which use physics and powerful supercomputers to deterministically predict what will happen in the future. These models have slowly become more accurate by adding finer detail, which in turn requires more computation and therefore ever more powerful computers and higher energy demands. Rémi Lam at Google DeepMind and his colleagues have taken a different approach.

Google DeepMind's AlphaMissense AI can predict whether mutations will affect how proteins such as haemoglobin subunit beta (left) or cystic fibrosis transmembrane conductance regulator (right) will function Artificial intelligence firm Google DeepMind has adapted its AlphaFold system for predicting protein structure to assess whether a huge number of simple mutations are harmful. The adapted system, called AlphaMissense, has done this for 71 million possible mutations of a kind called missense mutations in the 20,000 human proteins, and the results made freely available. "We think this is very helpful for clinicians and human geneticists," says Jun Cheng at Google DeepMind. "Hopefully, this can help them to pinpoint the cause of genetic disease." Almost everyone is born with between about 50 and 100 mutations not found in their parents, resulting in a huge amount of genetic variation between individuals.

An algorithm used trillions of times a day around the world could run up to 70 per cent faster, thanks to an artificial intelligence created by UK-based firm DeepMind. It has found an improved way for computers to sort data that has been overlooked by human programmers for decades. "We honestly didn't expect to achieve anything better: it's a very short program, these types of programs have been studied for decades," says Daniel Mankowitz at DeepMind. Known as sorting algorithms, they are one of the workhorses of computation, used to organise data by alphabetising words or ranking numbers from smallest to largest. Many different sorting algorithms exist, but innovations are limited as they have been highly optimised over the decades.

DeepMind has developed an artificial intelligence that can solve tasks it has never seen before as fast and as accurately as humans – a possible step towards generally intelligent AI that could master an array of jobs in the real world. The AI, called Adaptive Agent or AdA, works in a 3D virtual world where it is asked to solve tasks that involve navigating, planning and manipulating objects. Humans are excellent at solving new problems in very different environments, including ones they haven't seen before.

A pair of researchers have found a more efficient way to multiply grids of numbers, beating a record set just a week ago by the artificial intelligence firm DeepMind. The company revealed on 5 October that its AI software had beaten a record that had stood for more than 50 years for the matrix multiplication problem – a common operation in all sorts of software where grids of numbers are multiplied by each other. DeepMind's paper revealed a new method for multiplying two …

Today, companies everywhere are generating unprecedented amounts of data. While data has always grown naturally as a byproduct of economic and business activity, these days, as more and more of our personal and work lives take place online, humans are creating an abundance of data daily. In fact, 90% of all the world's internet data has been created since 2016. For more than a decade, only the so-called FAANG companies (Facebook, Apple, Amazon, Netflix, and Google) were in the position to take advantage of collecting vast amounts of data at scale. For these companies, data is the prime product and inherent to their value proposition, so they invested early in AI teams, servers, network infrastructure, and more.