Chris Maddison was just an intern when he started working on the Go-playing AI that would eventually become AlphaGo. In March 2016, Google DeepMind's artificial intelligence system AlphaGo shocked the world. In a stunning five-match series of Go, the ancient Chinese board game, the AI beat the world's best player, Lee Sedol - a moment that was televised in front of millions and hailed by many as a historic moment in the development of artificial intelligence. Chris Maddison, now a professor of artificial intelligence at the University of Toronto, was then a master's student and helped get the project off the ground. Alex Wilkins: How did the idea for AlphaGo first come about?

Has the technology lived up to its potential? The first time that AlphaGo revealed its full power, it prompted a visceral reaction . Lee Sedol, the world's greatest player of the ancient Chinese board game Go, had grown visibly agitated at the artificial intelligence's prowess. The hushed crowd in downtown Seoul, South Korea, could barely contain its gasps. It was quickly dawning on Lee, and the tens of millions watching at home, that this AI was different to those that had come before. It wasn't just beating Lee, but it was doing so with an almost human-like aptitude.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

DeepMind, a British company owned by Google, may be on the verge of achieving human-level artificial intelligence (AI). Nando de Freitas, a research scientist at DeepMind and machine learning professor at Oxford University, has said'the game is over' in regards to solving the hardest challenges in the race to achieve artificial general intelligence (AGI). AGI refers to a machine or program that has the ability to understand or learn any intellectual task that a human being can, and do so without training. According to De Freitas, the quest for scientists is now scaling up AI programs, such as with more data and computing power, to create an AGI. Earlier this week, DeepMind unveiled a new AI'agent' called Gato that can complete 604 different tasks'across a wide range of environments'. Gato uses a single neural network – a computing system with interconnected nodes that works like nerve cells in the human brain.

At one time, the AI that beat humans at chess calculated strategies by studying the outcomes of human moves. In October 2017, the DeepMind team published details of a new Go-playing system, AlphaGo Zero, that studied no human games at all. Instead, it started with the game's rules and played against itself. The first moves it made were completely random. After each game, it folded in new knowledge of what led to a win and what didn't.

DeepMind, the British artificial intelligence (AI) company owned by Google, has solved a 50-year-old problem in biology. DeepMind's AI system, AlphaFold, cracked the so-called'protein folding problem' – figuring out how a protein's amino acid sequence dictates its 3D atomic structure. A protein's structure is closely linked with its function, and the ability to predict its structure unlocks a greater understanding of what it does and how it works. AlphaFold's neural network was trained with 170,000 known protein sequences and their different structures. The system registered an average accuracy score of 92.4 out of 100 for predicting protein structure, and a score of 87 in the category for most challenging proteins. Because almost all diseases, including cancer and Covid-19, are related to a protein's 3D structure, the AI could pave the way for faster development of treatments and drug discoveries by determining the structure of previously-unknown proteins.

A country's AI prowess has major implications for how its citizens live and work -- and its economic and military strength moving into the future. With so much at stake, the narrative of an AI "arms race" between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US' focus on private-sector development. But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It's an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China. Generation China is a CNET series that looks at the areas of technology where the country is looking to take a leadership position.

Kai-Fu Lee has mapped out the challenging artificial intelligence race between America and China in his book AI Superpowers. He has a diversified background working in the field for 35 years. He has interesting viewpoints from holding prominent positions in the US at Google and Apple. Lee is currently CEO/Chairman at Sinovation Ventures in China and held the position of President at Google China. AlphaGo is a deep mind AI computer(super powered machine that runs on power, data and algorithms) acquired by Google in 2014.

Over the past few years, we've seen computer programs winning games which we believe humans were unbeatable. This belief held considering this games had so many possible moves for a given position that would be impossible to computer programs calculate all of then and choose the best ones. However, in 1997 the world witnessed what otherwise was considered impossible: the IBM Deep Blue supercomputer won a six game chess match against Gary Kasparov, the world champion of that time, by 3.5 – 2.5. Such victory would only be achieved again when DeepMind's AlphaGo won a five game Go match against Lee Sedol, 18 times world champion, by a 4-1 score. The IBM Deep Blue team relied mostly in brute force and computation power as their strategy to win the matches.

Deep learning technology is making great progress in solving the challenging problems of artificial intelligence, hence machine learning based on artificial neural networks is in the spotlight again. In some areas, artificial intelligence based on deep learning is beyond human capabilities. It seemed extremely difficult for a machine to beat a human in a Go game, but AlphaGo has shown to beat a professional player in the game. By looking at the statistical distribution of the distance in which the Go stones are laid in succession, we find a clear trace that Alphago has surpassed human abilities. The AlphaGo than professional players and professional players than ordinary players shows the laying of stones in the distance becomes more frequent. In addition, AlphaGo shows a much more pronounced difference than that of ordinary players and professional players.