Over the past year, Google's DeepMind AlphaGo AI has taken on (and defeated) worldwide Go masters in a series of high-profile matches. But in a sly move similar to a game-playing Turing test, DeepMind recently unleashed AlphaGo on some unsuspecting online Go players, thoroughly trouncing them in the process. The mysterious player, simply called "Master" or "Magister," started showing up on Tygem and FoxGo servers over the past few days and went on to play dozens of matches against some of the top Go players in the world. When Master won more than 50 straight, Go players on Reddit started to catch on, Business Insider reports. So a mysterious AI, 'Master', is trouncing top Go players online 50-0, likely superhuman, and no one knows who created it.

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read}, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.

Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel Nervana has built a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. Nervana's platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.

Google DeepMind's Go-playing AI has done it again. After beating top player Lee Sedol at the ancient Chinese game in 2016, the AlphaGo AI has been secretly taking on more of the world's best players – and beating them. For the last few days, an unknown player called "Master" has been thrashing players on an online Go platform called Tygem. Master beat the world number one player Ke Jie twice, and won 50 out of 51 games that it played, drawing the one it didn't win outright due to an internet connection time out. There was a lot of speculation about who could be behind the Master account, with many people suspecting that artificial intelligence was making the moves but unsure who could have developed it. On Thursday, DeepMind founder Demis Hassabis revealed in a tweet that Master was in fact a new version of AlphaGo.

As a species, we've always been used to having choices. Whether it's browsing the shelves of a bookshop or scanning the menu of a restaurant, we're naturally accustomed to making decisions based on a finite number of options in front of us. But within the online world, those finite choices have started evolving towards the infinite, and we're left with more choice than we've ever been used to. We shop on websites that sell everything we could possibly dream of buying, and we listen to music on streaming services that gives us access to tens of millions of records -- far more than we could ever listen to in our lifetime. This newfound wealth of opportunities should be liberating us, but instead it's overwhelming us and making it more difficult for us to actually make choices.

Humans use language to tap into the knowledge of others and learn skills faster. This helps us hone our intuition and go through our daily activities more efficiently. Inspired by this, Google Research, DeepMind (its UK artificial intelligence lab), and Google X have decided to allow their robots share their experiences. Sharing the learning process among multiple robots, the research team has considerably expedited general-purpose skill acquisition of robots. Using an artificial neural network, we can teach a robot to achieve a goal by analyzing the result of its previous experiences.

Google researchers got an eye-scanning algorithm to figure out on its own how to detect a common form of blindness, showing the potential for artificial intelligence to transform medicine remarkably soon. The algorithm can look at retinal images and detect diabetic retinopathy--which affects almost a third of diabetes patients--as well as a highly trained ophthalmologist can. It makes use of the same machine-learning technique that Google uses to label millions of Web images. Diabetic retinopathy is caused by damage to blood vessels in the eye and results in a gradual deterioration of vision. If caught early it can be treated, but a sufferer may experience no symptoms early on, making screening vital.

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Imagine walking in to see your doctor with an ache or pain. After listening to your symptoms, she inputs them into her computer, which pulls up the latest research she might need to know about how to diagnose and treat your problem. You have an MRI or an xray and a computer helps the radiologist detect any problems that could be too small for a human to see.