Neural networks, an important tool for processing data in a variety of industries, grew from an academic research area to a cornerstone of industry over the last few years. Convolutional Neural Networks (CNNs) have been particularly useful for extracting information …
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In the past few years, advances in artificial intelligence have captured our imaginations and led to the widespread use of voice services on our phones and in our homes.Read More
In late 2017 we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi (Japanese chess), and Go, beating a world-champion program in each case. We were excited by the preliminary results and thrilled to see the response from members of the chess community, who saw in AlphaZeros games a ground-breaking, highly dynamic and unconventional style of play that differed from any chess playing engine that came before it.Today, we are delighted to introduce the full evaluation of AlphaZero, published in the journal Science(Open Access version here), that confirms and updates those preliminary results. It describes how AlphaZero quickly learns each game to become the strongest player in history for each, despite starting its training from random play, with no in-built domain knowledge but the basic rules of the game.Read More
Conversational-AI systems have traditionally fallen into two categories: goal-oriented systems, which help users fulfill requests, and chatbots, which carry on informative or entertaining conversations.Read More
Amazon scientists have shown that our latest text-to-speech (TTS) system, which uses a generative neural network, can learn to employ a newscaster style from just a few hours of training data.Read More
Were excited to announce that the team behind Streams our mobile app that supports doctors and nurses to deliver faster, better care to patientswill be joining Google.Its been a phenomenal journey to see Streams go from initial idea to live deployment, and to hear how its helped change the lives of patients and the nurses and doctors who treat them. The arrival of world-leading health expert Dr. David Feinberg at Google will accelerate these efforts, helping to make a difference to the lives of millions of patients around the world.This is a major milestone for DeepMind! One of the reasons for joining forces with Google in 2014 was the opportunity to use Googles scale and experience in building billion-user products to bring our breakthroughs more rapidly to the wider world. Its been amazing to put this into practice in data centre efficiency, Android battery life, text-to-speech applications, and now the work of our Streams team.Over the past three years weve built a team of experts in what it takes to deploy clinical tools in practice – engineers, clinicians, translational researchers and more.Read More
In August, we announced the first stage of our joint research partnership with Moorfields Eye Hospital, which showed how AI could match world-leading doctors at recommending the correct course of treatment for over 50 eye diseases, and also explain how it arrives at its recommendations.Now were excited to start working on the next research challenge whether we can help clinicians predict eye diseases before symptoms set in.There are two types of age-related macular degeneration (AMD), one of the most common blinding eye diseases, with 170 million sufferers worldwide. The dry form is relatively common among those over 65, and often only causes mild sight loss. However, about 15% of patients with dry AMD go on to develop the more serious form of the disease wet AMD which can cause permanent, blinding sight loss.Currently, ophthalmologists diagnose wet AMD by analysing highly detailed 3D scans of the back of the eye, called OCT scans.Read More
This year, we’ve started to explore ways to make it easier for customers to find and engage with Alexa skills.Read More
At the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), Amazon researchers and their colleagues at the University of Sheffield and Imperial College London will host the first Workshop on Fact Extraction and Verification, which will explore how computer systems can learn to recognize false assertions online.Read More
Today we are open sourcing a new library of useful building blocks for writing reinforcement learning (RL) agents in TensorFlow. Named TRFL (pronounced truffle), it represents a collection of key algorithmic components that we have used internally for a large number of our most successful agents such as DQN, DDPG and the Importance Weighted Actor Learner Architecture.A typical deep reinforcement learning agent consists of a large number of interacting components: at the very least, these include the environment and some deep network representing values or policies, but they often also include components such as a learned model of the environment, pseudo-reward functions or a replay system.These parts tend to interact in subtle ways (often not well-documented in papers, as highlighted by Henderson and colleagues), thus making it difficult to identify bugs in such large computational graphs. A recent blog post by OpenAI highlighted this issue by analysing some of the most popular open-source implementations of reinforcement learning agents and finding that six out of 10 had subtle bugs found by a community member and confirmed by the author.One approach to addressing this issue, and helping those in the research community attempting to reproduce results from papers, is through open-sourcing complete agent implementations.Read More
