First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.

DESCRIPTION: The cognitive revolution has begun and business leaders are being inundated with techno-buzzwords--machine learning, AI, deep learning, whitebox, neural networks. It's hard to separate hype from reality and even harder to execute strategies that generate real business value from machine learning.

Everyone has heard of Kaggle, but have you heard of London-based Google DeepMind? Their researchers build deep learning algorithms to conquer everything from Pong and the ancient game of go to blindness caused by diabetic retinopathy. If the latter sounds particularly familiar, you may be recalling the Diabetic Retinopathy Detection competition which ran on Kaggle from February 2015 to July 2015. In this blog post, I interview Jeffrey De Fauw who came in 5th place in this competition using convolutional neural networks and is first author of Google DeepMind's study spearheading efforts to automate analysis of ophthalmic images using machine learning in order to help clinicians diagnose sight-threatening diseases. He explains how he got started on Kaggle, how it led him to his current role at DeepMind, and what he's learned along the way.

IN FROM three to eight years, we will have a machine with the general intelligence of an average human being. I mean a machine that will be able to read Shakespeare, grease a car, play office politics, tell a joke, have a fight. At that point the machine will begin to educate itself with fantastic speed. In a few months it will be at genius level, and a few months after that, its powers will be incalculable. Such rumours of superhuman artificial intelligence have been doing the rounds lately, but this prediction doesn't come from AI oracles du jour Nick Bostrom or Elon Musk (New Scientist, 25 June, p 18). It was made in 1970 by the man widely considered to be the "father of artificial intelligence" – Marvin Minsky.

In a recently accepted manuscript titled "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data", scientists from Insilico Medicine, Inc located at the Emerging Technology Centers at Johns Hopkins University in collaboration with Datalytic Solutions and Mind Research Network presented a novel approach applying deep neural networks (DNNs) to predict pharmacologic properties of many drugs. In this study, scientists trained deep neural networks to predict the therapeutic use of a large number of drugs using gene expression data obtained from high-throughput experiments on human cell lines. Authors used a sophisticated approach of measuring the differential signaling pathway activation score for a large number of pathways to reduce the dimensionality of the data while retaining biological relevance and used these scores to train the deep neural networks. "The world of artificial intelligence is rapidly evolving and affecting every aspect of our daily life. And soon this progress will be felt in the pharmaceutical industry. We set up the Pharma.AI division to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs, but in the process we came up with over 800 strong hypotheses in oncology, cardiovascular, metabolic and CNS space and started basic validation. We are cautious about making strong statements, but if this approach works, it will uberize the pharmaceutical industry and generate unprecedented number of QALY", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

DEEPMIND, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases – age-related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. Moorfields is providing scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.

In the previous post in our Machine Learning series, we dived into the inner workings of deep learning. Given deep learning's unparalleled power, it's not surprising that technology companies are competing with one another to collect deep learning experts and apply the techniques to their own operations. How are companies using deep learning to drive business goals? The tech giants have been using the technique for improved image and video recognition, audio recognition, and language understanding and are actively contributing to open-source research tools. Meanwhile, start-ups are serving boutique needs.

More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI. Companies covered in the Artificial Intelligence and Machine Learning Market report are: Amazon, AOL, Apple, Augury Systems, Baidu, C-B4, Comfy, Facebook, FocusMotion, Glassbeam, Google, H2O.ai, IBM, Imagimob, Inbenta, Intel, Maana, Microsoft, mnubo, MoBagel, Moov, Neura, NVIDIA, OpenAI, PointGrab, Salesforce, Sentenai, Sentrian, Skype, SparkCognition, Tachyus, Tellmeplus, Tesla, Twitter, Veros Systems, x.ai, and Yahoo. Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information.

With the general availability of the "Knights Landing" Xeon Phi many core processors from Intel last month, some of the largest supercomputing labs on the planet are getting their first taste of what the future style of high performance computing could look like for the rest of us. We are not suggesting that the Xeon Phi processor will be the only compute engine that will be deployed to run traditional simulation and modeling applications as well as data analytics, graph processing, and deep learning algorithms. But we are suggesting that this style of compute engine – it is more than a processor since it includes high bandwidth memory and fabric interconnect adapters on a single package – is what the future looks like. And that goes for Knights family processors and co-processors as well as the "Pascal" and "Volta" accelerators made by Nvidia, the Sparc64-XIfx and ARM chips that will be used in the used in the Post-K system in Japan made by Fujitsu, the Matrix2000 DSP accelerator being created by China for one of its pre-exascale systems, or the CPU-GPU hybrids based on its "Zen" Opterons that AMD is cooking up for supercomputing systems in the United States and, with licensing partners, in China. During the recent ISC16 supercomputing conference in Frankfurt, Germany, Intel gathered up the executives in charge of some of the largest supercomputing facilities on the planet who are also – not coincidentally, but absolutely intentionally – also early adopters of the Knights Landing Xeon Phi and, in some cases, the Omni-Path interconnect that is a kicker to Intel's True Scale InfiniBand networking.