Last year DeepMind presented AlphaFold v2, which predicts 3D structures of proteins down to atomic accuracy. Today they share the methods in their latest paper at Nature along with open source codes. It is inspiring to see the research this enables. This new model, AlphaFold v2.0 has been published in Nature and entered into the CASP14 competition. Deepmind has pushed the boundaries of computing.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. Apple is uncharacteristically opening up its artificial intelligence, but how smart is it? Apple is uncharacteristically opening up its artificial intelligence, but how smart is it?

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. Holding AI to account: will algorithms ever be free from bias if they're created by humans? Holding AI to account: will algorithms ever be free from bias if they're created by humans?

Artificial intelligence developed by the likes of Google's DeepMind and Elon Musk's OpenAI is taught within the confines of game worlds – including navigating around mazes, dodging deadly cliffs, playing laser tag and flying through space. In a mission to build a general AI capable of solving any problem put in front of it, DeepMind is open-sourcing its game code to everyone. The software and 14 levels from DeepMind Labs will be put on GitHub later this week. And, not to be outdone, Elon Musk's own OpenAI is also releasing its own'computer training ground' called Universe. Universe is open-source software that supports Gym; OpenAI's toolkit for testing its algorithms which help software play games, for example, using a reward scheme.

Thanks to the likes of Google, Amazon, and Facebook, the terms artificial intelligence (AI) and machine learning have become much more widespread than ever before. They are often used interchangeably and promise all sorts from smarter home appliances to robots taking our jobs. But while AI and machine learning are very much related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behaviour, while Stanford University defines machine learning as "the science of getting computers to act without being explicitly programmed". You need AI researchers to build the smart machines, but you need machine learning experts to make them truly intelligent.