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  • AlphaGo: Mastering the ancient game of Go with Machine Learning
    Posted by David Silver and Demis Hassabis, Google DeepMind

    Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history - the first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952 as a PhD candidate’s project. Then fell checkers in 1994. Chess was tackled by Deep Blue in 1997. The success isn’t limited to board games, either - IBM's Watson won first place on Jeopardy in 2011, and in 2014 our own algorithms learned to play dozens of Atari games just from the raw pixel inputs.

    But one game has thwarted A.I. research thus far: the ancient game of Go. Invented in China over 2500 years ago, Go is played by more than 40 million people worldwide. The rules are simple: players take turns to place black or white stones on a board, trying to capture the opponent's stones or surround empty space to make points of territory. Confucius wrote about the game, and its aesthetic beauty elevated it to one of the four essential arts required of any true Chinese scholar. The game is played primarily through intuition and feel, and because of its subtlety and intellectual depth it has captured the human imagination for centuries.

    But as simple as the rules are, Go is a game of profound complexity. The search space in Go is vast -- more than a googol times larger than chess (a number greater than there are atoms in the universe!). As a result, traditional “brute force” AI methods -- which construct a search tree over all possible sequences of moves -- don’t have a chance in Go. To date, computers have played Go only as well as amateurs. Experts predicted it would be at least another 10 years until a computer could beat one of the world’s elite group of Go professionals.

    We saw this as an irresistible challenge! We started building a system, AlphaGo, described in a paper in Nature this week, that would overcome these barriers. The key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network”, predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network”, is then used to reduce the depth of the search tree -- estimating the winner in each position in place of searching all the way to the end of the game.

    AlphaGo’s search algorithm is much more human-like than previous approaches. For example, when Deep Blue played chess, it searched by brute force over thousands of times more positions than AlphaGo. Instead, AlphaGo looks ahead by playing out the remainder of the game in its imagination, many times over - a technique known as Monte-Carlo tree search. But unlike previous Monte-Carlo programs, AlphaGo uses deep neural networks to guide its search. During each simulated game, the policy network suggests intelligent moves to play, while the value network astutely evaluates the position that is reached. Finally, AlphaGo chooses the move that is most successful in simulation.

    We first trained the policy network on 30 million moves from games played by human experts, until it could predict the human move 57% of the time (the previous record before AlphaGo was 44%). But our goal is to beat the best human players, not just mimic them. To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and gradually improving them using a trial-and-error process known as reinforcement learning. This approach led to much better policy networks, so strong in fact that the raw neural network (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees.

    These policy networks were in turn used to train the value networks, again by reinforcement learning from games of self-play. These value networks can evaluate any Go position and estimate the eventual winner - a problem so hard it was believed to be impossible.

    Of course, all of this requires a huge amount of compute power, so we made extensive use of Google Cloud Platform, which enables researchers working on AI and Machine Learning to access elastic compute, storage and networking capacity on demand. In addition, new open source libraries for numerical computation using data flow graphs, such as TensorFlow, allow researchers to efficiently deploy the computation needed for deep learning algorithms across multiple CPUs or GPUs.

    So how strong is AlphaGo? To answer this question, we played a tournament between AlphaGo and the best of the rest - the top Go programs at the forefront of A.I. research. Using a single machine, AlphaGo won all but one of its 500 games against these programs. In fact, AlphaGo even beat those programs after giving them 4 free moves headstart at the beginning of each game. A high-performance version of AlphaGo, distributed across many machines, was even stronger.
    This figure from the Nature article shows the Elo rating and approximate rank of AlphaGo (both single machine and distributed versions), the European champion Fan Hui (a professional 2-dan), and the strongest other Go programs, evaluated over thousands of games. Pale pink bars show the performance of other programs when given a four move headstart.
    It seemed that AlphaGo was ready for a greater challenge. So we invited the reigning 3-time European Go champion Fan Hui — an elite professional player who has devoted his life to Go since the age of 12 — to our London office for a challenge match. The match was played behind closed doors between October 5-9 last year. AlphaGo won by 5 games to 0 -- the first time a computer program has ever beaten a professional Go player.
    AlphaGo’s next challenge will be to play the top Go player in the world over the last decade, Lee Sedol. The match will take place this March in Seoul, South Korea. Lee Sedol is excited to take on the challenge saying, "I am privileged to be the one to play, but I am confident that I can win." It should prove to be a fascinating contest!

    We are thrilled to have mastered Go and thus achieved one of the grand challenges of AI. However, the most significant aspect of all this for us is that AlphaGo isn’t just an ‘expert’ system built with hand-crafted rules, but instead uses general machine learning techniques to allow it to improve itself, just by watching and playing games. While games are the perfect platform for developing and testing AI algorithms quickly and efficiently, ultimately we want to apply these techniques to important real-world problems. Because the methods we have used are general purpose, our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis.

  • Teach Yourself Deep Learning with TensorFlow and Udacity
    Posted by Vincent Vanhoucke, Principal Research Scientist

    Deep learning has become one of the hottest topics in machine learning in recent years. With TensorFlow, the deep learning platform that we recently released as an open-source project, our goal was to bring the capabilities of deep learning to everyone. So far, we are extremely excited by the uptake: more than 4000 users have forked it on GitHub in just a few weeks, and the project has been starred more than 16000 times by enthusiasts around the globe.

    To help make deep learning even more accessible to engineers and data scientists at large, we are launching a new Deep Learning Course developed in collaboration with Udacity. This short, intensive course provides you with all the basic tools and vocabulary to get started with deep learning, and walks you through how to use it to address some of the most common machine learning problems. It is also accompanied by interactive TensorFlow notebooks that directly mirror and implement the concepts introduced in the lectures.
    The course consists of four lectures which provide a tour of the main building blocks that are used to solve problems ranging from image recognition to text analysis. The first lecture focuses on the basics that will be familiar to those already versed in machine learning: setting up your data and experimental protocol, and training simple classification models. The second lecture builds on these fundamentals to explore how these simple models can be made deeper, and more powerful, and explores all the scalability problems that come with that, in particular regularization and hyperparameter tuning. The third lecture is all about convolutional networks and image recognition. The fourth and final lecture explore models for text and sequences in general, with embeddings and recurrent neural networks. By the end of the course, you will have implemented and trained this variety of models on your own machine and will be ready to transfer that knowledge to solve your own problems!

    Our overall goal in designing this course was to provide the machine learning enthusiast a rapid and direct path to solving real and interesting problems with deep learning techniques, and we're now very excited to share what we've built! It has been a lot of fun putting together with the fantastic team of experts in online course design and production at Udacity. For more details, see the Udacity blog post, and register for the course. We hope you enjoy it!

  • Why attend USENIX Enigma?
    Parisa Tabriz, Security Princess & Enigma Program Co-Chair

    Last August, we announced USENIX Enigma, a new conference intended to shine a light on great, thought-provoking research in security, privacy, and electronic crime. With Enigma beginning in just a few short weeks, I wanted to share a couple of the reasons I’m personally excited about this new conference.

    Enigma aims to bridge the divide that exists between experts working in academia, industry, and public service, explicitly bringing researchers from different sectors together to share their work. Our speakers include those spearheading the defense of digital rights (Electronic Frontier Foundation, Access Now), practitioners at a number of well known industry leaders (Akamai, Blackberry, Facebook, LinkedIn, Netflix, Twitter), and researchers from multiple universities in the U.S. and abroad. With the diverse session topics and organizations represented, I expect interesting—and perhaps spirited—coffee break and lunchtime discussions among the equally diverse list of conference attendees.

    Of course, I’m very proud to have some of my Google colleagues speaking at Enigma:

    • Adrienne Porter Felt will talk about blending research and engineering to solve usable security problems. You’ll hear how Chrome’s usable security team runs user studies and experiments to motivate engineering and design decisions. Adrienne will share the challenges they’ve faced when trying to adapt existing usable security research to practice, and give insight into how they’ve achieved successes.
    • Ben Hawkes will be speaking about Project Zero, a security research team dedicated to the mission of, “making 0day hard.” Ben will talk about why Project Zero exists, and some of the recent trends and technologies that make vulnerability discovery and exploitation fundamentally harder.
    • Kostya Serebryany will be presenting a 3-pronged approach to securing C++ code based on his many years of experiencing wrangling complex, buggy software. Kostya will survey multiple dynamic sanitizing tools him and his team have made publicly available, review control-flow and data-flow guided fuzzing, and explain a method to harden your code in the presence of any bugs that remain.
    • Elie Bursztein will go through key lessons the Gmail team learned over the past 11 years while protecting users from spam, phishing, malware, and web attacks. Illustrated with concrete numbers and examples from one of the largest email systems on the planet, attendees will gain insight into specific techniques and approaches useful in fighting abuse and securing their online services.

    In addition to raw content, my Program Co-Chair, David Brumley, and I have prioritized talk quality. Researchers dedicate months or years of their time to thinking about a problem and conducting the technical work of research, but a common criticism of technical conferences is that the actual presentation of that research seems like an afterthought. Rather than be a regurgitation of a research paper in slide format, a presentation is an opportunity for a researcher to explain the context and impact of their work in their own voice; a chance to inspire the audience to want to learn more or dig deeper. Taking inspiration from the TED conference, Enigma will have shorter presentations, and the program committee has worked with each speaker to help them craft the best version of their talk.

    Hope to see some of you at USENIX Enigma later this month!

  • Four years of - Recent Progress and Looking Forward
    Posted by Ramanathan Guha, Google Fellow

    In 2011, we announced, a new initiative from Google, Bing and Yahoo! to create and support a common vocabulary for structured data markup on web pages. Since that time, has been a resource for webmasters looking to add markup to their pages so that search engines can use that data to index content better and surface it in new experiences like rich snippets, GMail, and the Google App., which provides a growing vocabulary for describing various kinds of entity in terms of properties and relationships, has become increasingly important as the Web transitions to a multi-device, mobile-oriented world. We are now seeing being used on many millions of Web sites, defining data types and properties common across applications, platforms and products, in order to enhance the user experience by delivering the most relevant information they need, when they need it. in Google Rich Snippets in Google Knowledge Graph panels in Recipe carousels
    In Evolution of Structured Data on the Web, an overview article published this week on ACM, we report some key adoption metrics from a sample of 10 billion pages from a combination of the Google index and Web Data Commons. In this sample, 31.3% of pages have markup, up from 22% one year ago. Structured data markup is now a core part of the modern web.

    The group at W3C is now amongst the largest active W3C communities, serving as a hub for diverse groups exploring schemas covering diverse topics such as sports, healthcare, e-commerce, food packaging, bibliography and digital archive management. Other companies, also make use of the same data to build different applications, and as new use cases arise further schemas are integrated via community discussion at W3C. Each of these topics in turn have subtle inter-relationships - for example schemas for food packaging, for flight reservations, for recipes and for restaurant menus, each have different approaches to describing food restrictions and allergies. Rather than try to force a common unified approach across these domains,'s evolution is pragmatic, driven by the combination of available Web data, and the likelihood of mainstream consuming applications. is also finding new kinds of uses. One exciting line of work is the use of marked up pages as training corpus for machine learning. John Foley, Michael Bendersky and Vanja Josifovski used data to build a system that can learn to recognize events that may be geographically local to a particular user. Other researchers are looking at using pages with similar markup, but in different languages, to automatically create parallel corpora for machine translation.

    Four years after its launch, is entering its next phase, with more of the vocabulary development taking place in a more distributed fashion, as extensions. As adoption has grown, a number groups with more specialized vocabularies have expressed interest in extending with their terms. Examples of this include real estate, product, finance, medical and bibliographic information. A number of extensions, for topics ranging from automobiles to product details, are already underway. In such a model, itself is just the core, providing a unifying vocabulary and congregation forum as necessary.

  • Text-to-Speech for low resource languages (episode 2): Building a parametric voice
    Posted by Alexander Gutkin, Google Speech Team

    This is the second episode in the series of posts reporting on the work we are doing to build text-to-speech (TTS) systems for low resource languages. In the previous episode, we described the crowdsourced data collection effort for Project Unison. In this episode, we describe our work to construct a parametric voice based on that data.

    In our previous episode, we described building TTS systems for low resource languages, and how one of the objectives of data collection for such systems was to quickly build a database representing multiple speakers. There are two main justifications for this approach. First, professional voice talents are often not available for under-resourced languages, so we need to record ordinary people who get tired reading tedious text rather quickly. Hence, the amount of text a person can record is rather limited and we need multiple speakers for a reasonably sized database that can be used by others as well. Second, we wanted to be able to create a voice that sounds human but is not identifiable as a real person. Various concatenative approaches to speech synthesis, such as unit selection, are not very suitable for this problem. This is because the selection algorithm may join acoustic units from different speakers generating a very unnatural sounding result.

    Adopting parametric speech synthesis techniques is an attractive approach to building multi-speaker corpora described above. This is because in parametric synthesis the training stage of the statistical component will take care of multiple-speakers by estimating an averaged out representation of various acoustic parameters representing each individual speaker. Depending on number of speakers in the corpus, their acoustic similarity and ratio of speaker genders, the resulting acoustic model can represent an average voice that is indistinguishable from human and yet cannot be traced back to any actual speakers recorded during the data collection.

    We decided to use two different approaches to acoustic modeling in our experiments. The first approach uses Hidden Markov Models (HMMs). This well-established technique was pioneered by Prof. Keiichi Tokuda at Nagoya Institute of Technology, Japan and has been widely adopted in academia and industry. It is also supported by a dedicated open-source HMM synthesis toolkit. The resulting models are small enough to fit on mobile devices.

    The second approach relies on Recurrent Neural Networks (RNNs) and vocoders that jointly mimic the human speech production system. Vocoders mimic the vocal apparatus to provide a parametric representation of speech audio that is amenable to statistical mapping. RNNs provide a statistical mapping from the text to the audio and have feedback loops in their topology, allowing them to model temporal dependencies between various phonemes in human speech. In 2015, Yannis Agiomyrgiannakis proposed Vocaine, a vocoder that outperforms the state-of-the-art technology in speed as well as quality. In 2013, Heiga Zen, Andrew Senior and Mike Schuster proposed a neural network-based model that mimics deep structure of human speech production for speech synthesis. The model has further been extended into a Long Short-Term Memory (LSTM) RNN. This allows long term memorization, which is good for speech applications. Earlier this year, Heiga Zen and Hasim Sak described the LSTM RNN architecture that has been specifically designed for fast speech synthesis. The LSTM RNNs are also used in our Automatic Speech Recognition (ASR) systems recently mentioned in our blog.

    Using the Hidden Markov Model (HMM) and LSTM RNN synthesizers described above, we experimented with a multi-speaker Bangla corpus totaling 1526 utterances (waveforms and corresponding transcriptions) from five different speakers. We also built a third system that utilizes LSTM RNN acoustic model, but this time we made it small and fast enough to run on a mobile phone.

    We synthesized the following Bangla sentence "এটি একটি বাংলা বাক্যের উদাহরণ" translated from “This is an example sentence in Bangla”. Though HMM synthesizer output can sound intelligible, it does exhibit some classic downsides with a voice that sounds buzzy and muffled. With the LSTM RNN configuration for mobile devices, the resulting audio sounds clearer and has improved intonation over the HMM version. We also tried a LSTM RNN configuration with more network nodes (and thus not suitable for low-end mobile devices) to generate this waveform - the quality is slightly better but is not a huge improvement over the more lightweight LSTM RNN version. We hypothesize that this is due to the fact that a neural network with many nodes has more parameters and thus requires more data to train.

    These early results are encouraging for several reasons. First, they confirm that natural-sounding speech synthesis based on multiple speakers is practically possible. It is also significant that the total number of recordings used was relatively small, yet were able to build intelligible parametric speech synthesis. This means that it is possible to collect training data for such a speech synthesizer by engaging the help of volunteers who are not professional voice artists, for a short period of time per person. Using multiple volunteers is an advantage: it results in more diverse data, and the resulting synthetic voice does not represent any specific individual. This approach may well be the foundation for bringing speech technology to many more traditionally under-served languages.

    NEXT UP: But can it say, “Google”? (Ep.3)

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