Isomorphic Labs president Max Jaderberg said at WIRED Health in London that the startup has built a "broad and exciting pipeline of new medicines." Google DeepMind's AlphaFold has already revolutionized scientists' understanding of proteins . Now, the ability of the platform to design safe and effective drugs is about to be put to the test. Isomorphic Labs, the UK-based biotech spinoff of Google DeepMind, will soon begin human trials of drugs designed by its Nobel Prize-winning AI technology. "We're gearing up to go into the clinic," Isomorphic Labs president Max Jaderberg said on April 16 at WIRED Health in London.

DeepMind's bots work in pairs to capture the opposing team's flag on indoor and outdoor maps in Quake III Arena. Human gamers know just how hard it is to win a new spin on the classic computer game Quake: In a mazelike arena, they must work with other players to capture floating flags--all while dodging deadly gunfire. Now, for the first time, artificial intelligence (AI) has mastered teamwork in a complex first-person video game, coordinating its actions with both human and computer teammates to consistently beat opponents. "The scale of the experiments is remarkable," says Michael Littman, an AI expert at Brown University. Getting AI agents to work together is incredibly tough, he says. Although AI can drive cars and easily defeat the world's greatest chess and Go players one on one, researchers have struggled to get it to master teamwork.

Few games are simpler in principle than capturing the flag (excepting perhaps tag or kick the can). Two teams each have a marker located at their respective bases, and the objective is to capture the other team's marker and return it safely back to their base. What's easily understood by humans is not quite so quickly grasped by machines, though. Where capture the flag is concerned in the video game domain, non-player characters have traditionally been programmed with heuristics and rules affording limited freedom in choice. But AI and machine learning promise to turn this paradigm on its head. In a paper published this week in the journal Science roughly a year following the preprint, researchers at DeepMind, the London-based subsidiary of Google parent company Alphabet, describe a system capable not only of learning how to play capture the flag in Id Software's Quake III Arena, but of devising entirely novel human-level team-based strategies.

WASHINGTON - It's official: the machines are going to destroy you (if, that is, you're a professional gamer). A team of programmers at a British artificial intelligence company has designed automated "agents" that taught themselves how to play the seminal first-person shooter "Quake III Arena," and became so good they consistently beat human opponents. The work of the researchers from DeepMind, which is owned by Google's parent company, Alphabet Inc., was described in a paper published in Science on Thursday and marks the first time the feat has ever been accomplished. To be sure, computers have been proving their dominance over humans in one-on-one turn-based games such as chess ever since IBM's Deep Blue beat Garry Kasparov in 1997. More recently, a Google AI agent beat the world's No. 1 go player in 2017.

Artificial intelligence can already beat humans at video games like StarCraft II and Dota 2, but now they've also mastered the art of working cooperatively. DeepMind has trained AIs to team up to play Quake III Arena, a first-person shooter video game. They can outperform human players and are also able to successfully work with human teammates. Up until now, AI has not been able to master the complexity of games that require teamwork and interaction between multiple players. A group of 30 AIs were collectively trained to play five-minute rounds of Capture the Flag, a game mode in which teams must retrieve flags from their opponents while retaining their own.

We develop a Deep-Text Recurrent Network (DTRN)that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered highlevel sequence from a whole word image, avoiding the difficult character segmentation problem. Then a deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently. Our model has a number of appealing properties in comparison to existing scene text recognition methods: (i) It can recognise highly ambiguous words by leveraging meaningful context information, allowing it to work reliably without either pre- or post-processing; (ii) the deep CNN feature is robust to various image distortions; (iii) it retains the explicit order information in word image, which is essential to discriminate word strings; (iv) the model does not depend on pre-defined dictionary, and it can process unknown words and arbitrary strings. It achieves impressive results on several benchmarks, advancing the-state-of-the-art substantially.