It's been a busy year for Google Deepmind. You might remember AlphaGo resoundingly beating Go grandmaster Lee Sedol by four games to one and secretly schooling some of the world's best Go players online, but the team has also found time to help Britain's national health service treat patients and arm its computer with new tricks to help it learn faster and "remember" previous knowledge. AlphaGo can now justifiably be considered one of the world's best Go players, but the Deepmind team can't make a bonafide claim until its AI has beaten the world number one: 19-year-old Korean Ke Jie. Deepmind co-founder and CEO Demis Hassabis has now confirmed that after months of speculation, the match is on. At the Future of Go Summit between May 23rd to May 27th, Google and the China Go Association (with help from the Chinese government) will bring together AlphaGo and some of the world's best Go players and AI experts to "explore the mysteries" of the ancient board game. There will be a variety of games on offer including Pair Go, where Chinese professionals will face off against each other but alternate moves with an AlphaGo teammate.

A year on from its victory over Go star Lee Sedol, Google DeepMind is preparing a "festival" of exhibition matches for its board game-playing AI, AlphaGo, to see how far it has evolved in the last 12 months. Headlining the event will be a one-on-one match against the current number one player of the ancient Asian game, 19-year-old Chinese professional Ke Jie. DeepMind has had its eye on this match since even before AlphaGo beat Lee. On the eve of his trip to Seoul in March 2016, the company's co-founder, Demis Hassabis, told the Guardian: "There's a young kid in China who's very, very strong, who might want to play us." As well as the one-on-one match with Jie, which will be played over the course of three games, AlphaGo will take part in two other games with slightly odder formats. One, "Pair Go", will see two human Go professionals play against each other, each partnered up with their own iteration of AlphaGo.

DeepMind's AlphaGo program will test its artificial intelligence capabilities in May against top Go player Ke Jie. The match of three games in Wuzhen, China between AlphaGo and the Chinese player comes about a year after the computer program beat by 4-1 a key player, the South Korean Lee Se-dol, in a game that is regarded as involving more complex strategy than even chess. During the game, players take turns to place black or white pieces, called "stones," on the 19-by-19 line grid. The aim is to capture the opponent's stones by surrounding them and encircling more empty space as territory. AlphaGo has been seen as a major contender because of its ability to learn from its experience, sometimes resulting in far-from-human but nevertheless successful moves.

It's just over a year since Google's DeepMind unit stunned the world when its AlphaGo AI beat Go legend Lee Se-dol 4-1 in a five-game match; the result demonstrated mastery of a feat that had eluded computer scientists for decades and sparked a flood of new interest in the field of artificial intelligence. But there was one possible "gotcha" that Go devotees could hold onto: Lee Se-dol was once, but is no longer, quite considered the greatest player on the planet. That distinction is now considered to belong to Ke Jie, a 19-year-old Chinese player ranked number 1 worldwide. A professional since the age of ten, Ke has beaten Lee several times in high-profile matches in recent years, including three finals victories in the three months leading up to Lee's AlphaGo match. And next month, Ke will get his own showdown with DeepMind's AI.

Earlier, we came up with a list of some of the best Machine Learning books that you should consider reading through. In this article, we have come up with yet another list of the recommended books for Data Science. Written by Blum, Hopcroft, and Kannan, Foundations of Data Science is a great blend of lectures in the modern theoretical course in data science. This tutorial on UFLDL aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning. The Python Data Science Handbook introduces the core libraries essential for working with data in Python -- particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.

Leaf is a open Machine Learning Framework for hackers to build classical, deep or hybrid machine learning applications. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf has one of the simplest APIs, is lean and tries to introduce minimal technical debt to your stack. See the Leaf - Machine Learning for Hackers book for more. Leaf is a few months old, but thanks to its architecture and Rust, it is already one of the fastest Machine Intelligence Frameworks available.

When it comes to artificial intelligence (AI) and Chinese tech companies, thoughts often begin and end with Baidu. But Tencent, Asia's second highest-valued tech company behind Alibaba, has reminded the world that it too is investing in the field. Search giant Baidu was one of the first to make a major commitment to deep learning. It spent over $2.9 billion on R&D over a 2.5 year period, according to Bloomberg, and currently has more than 1,300 specialists working on a variety of technologies that include AI and augmented reality. Baidu, however, suffered a blow when its chief scientist Andrew Ng, who heads up its U.S.-based research team, announced his departure last week.

Speed measures for the TPU (blue), GPU (red) and CPU (gold). In 2011 Google realized they had a problem. They were getting serious about deep learning networks with computational demands that strained their resources. Google calculated they would have to have twice as many data centers as they already had if people used their deep learning speech recognition models for voice search for just three minutes a day. They needed more powerful and efficient processing chips.

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.