Microsoft will begin providing customers of its Azure cloud platform with chips made by U.K. startup Graphcore, with the goal of speeding up the computations for artificial intelligence projects. Graphcore, founded in Bristol in 2016, has attracted several hundred million dollars in investment and the attention of many AI researchers. Microsoft invested in Graphcore last December, with the hope of making its cloud services more compelling. Graphcore’s chips have not previously been available publicly. Continue reading Microsoft Pairs Azure Cloud Platform, Graphcore AI Chips

Researchers recently introduced a series of rigorous benchmark tasks that measure the performance of sophisticated language-understanding AI. Facebook AI Research with Google’s DeepMind, University of Washington and New York University introduced SuperGLUE last week, based on the idea that deep learning models for today’s conversational AI require greater challenges. SuperGLUE, which uses Google’s BERT representational model as a performance baseline, follows the 2018 introduction of GLUE (General Language Understanding Evaluation), and encourages the creation of models that can understand more nuanced, complex language. Continue reading SuperGLUE Is Benchmark For Language-Understanding AI

With Sam Altman as chief executive, OpenAI, the nonprofit artificial intelligence lab he founded with Elon Musk, has become a for-profit company pursuing investments. In fact, Altman, who stepped down as head of Y Combinator, just inked an impressive $1 billion contract with Microsoft. With Microsoft as a marquee investor, OpenAI will now pursue its lofty goal of creating artificial general intelligence (AGI), a system that can mimic the human brain. Alphabet’s DeepMind lab is also pursuing the creation of AGI. Continue reading Microsoft Invests in OpenAI to Pursue Challenging AI Goal

At DeepMind, Alphabet’s AI labs, researchers built virtual video-game players that master the game by playing other bots. Most of the time, the bots played a capture-the-flag video game better than human game testers who are professional. DeepMind researcher Max Jaderberg said that the work, first described in the company blog last year, is moving towards “developing the fundamental algorithms” that could in the future lead to a “more human intelligence.” Not every lab, however, can afford the compute power required. Continue reading Bots Take On Gamers to Help Advance Artificial Intelligence

Quantum computing is coming and it’s safe to say that only a handful of people know what it is. At NAB 2019, USC Viterbi School of Engineering Ph.D. candidate Bibek Pokharel did an excellent job of breaking down the basics. First, according to quantum computer scientists, all the computers we have used thus far are “classical computers.” Although IBM, Intel, Google, Microsoft, Rigetti and D-Wave have built quantum computers, the task is so incredibly complex that you won’t be able to purchase one at Best Buy. Continue reading Quantum Computing Era Approaches as Moore’s Law Ends

Google unveiled the Deep Planning Network (PlaNet) agent, created in collaboration with DeepMind, to provide reinforcement learning via images. Reinforcement learning uses rewards to improve AI agents’ decision-making. Whereas model-free techniques work by getting agents to predict actions from observations, agents created with model-based reinforcement learning come up with a general model of the environment leveraged for decision-making. In unfamiliar surroundings, however, agents must create rules from experience. Continue reading Google Debuts Deep Planning Network Agent with DeepMind

Alphabet’s London-based DeepMind loosed AlphaZero, its AI-powered system that can master games without human intervention, on Stockfish, the highest rated chess game engine, and crushed it. DeepMind developed the self-training method, dubbed deep reinforcement learning, specifically to attack strategy board game “Go,” and an earlier iteration of the system beat one of the world’s best “Go” players, although it needed human guidance. AlphaZero trained itself in chess in three days, rejecting red-marked moves after a mere 1,000 simulations. Continue reading DeepMind’s AlphaZero Defeats Leading Chess Game Engine

DeepMind recently released the full evaluation of AlphaZero, a single system capable of playing “Go,” chess, and shogi (Japanese chess). This new project builds on AlphaGo, a program that beat one of the best players in the world at the board game “Go” in 2016, and AlphaGo Zero, software capable of mastering the game from first principles. AlphaZero represents a dramatic step forward in AI research as it is one of the first intelligent systems capable of generalizing solutions to new problems with little to no human input. Continue reading DeepMind’s Learning Algorithm Could Prove a Game-Changer

Amazon is training Alexa to speak like a newscaster, a feature that will roll out in a few weeks. The new speaking style is based on Amazon’s neural text-to-speech (NTTS) developments. The new voice style doesn’t sound human, but does stress words as a TV or radio announcer would. Before creating this voice, Amazon did a survey that showed that users prefer this newscaster style when listening to articles. The new voice is also an example of “the next generation of speech synthesis,” based on machine learning. Continue reading New Alexa Speaking Style Created by Neural Text-to-Speech

A research team at Google’s AI unit DeepMind, led by Ali Eslami and Danilo Rezende, has created software via a generative query network (GQN) to create a new perspective of a scene that the neural network has never seen. The U.K.-based unit developed the deep neural network-based software that applies the network to a handful of shots of a virtual scene to create a “compact mathematical representation” of the scene — and then uses that representation to render an image with a new perspective unfamiliar to the network. Continue reading DeepMind Intros Intriguing Deep Neural Network Algorithm

OpenAI, an artificial intelligence research group backed by Elon Musk, stated that its software can beat “teams of five skilled human players” in Valve’s video game “Dota 2.” If verified, the achievement would be a milestone in computer science and a leap beyond other AI researchers working on mastering complex games. IBM’s software mastered chess in the late 1990s, and Alphabet’s DeepMind created software that dominated “Go” in 2016. “Dota 2” is a multiplayer sci-fi fantasy game where teams advance through exploration. Continue reading OpenAI Beats Human-Player Team at Complex Video Game

According to members of Google’s Brain and Machine Perception teams, researchers at the tech giant have developed “ways to make machine-generated speech sound more natural to humans,” even providing examples of the more expressive speech in a company blog post, reports VentureBeat. Google also announced the release of its Cloud Text-to-Speech services, which could “be used to bring more natural speech to devices, apps or digital services that utilize voice control or voice computing,” the article explains.

Continue reading Google’s Machine-Generated Speech Will Sound More Human

In May, research project Google Brain debuted its AutoML artificial intelligence system that can generate its own AIs. Now, Google has unveiled an AutoML project to automate the design of machine learning models using so-called reinforcement learning. In this system, AutoML is a controller neural network that develops a “child” AI network for a specific task. The near-term goal is that AutoML would be able to create a child that outperforms human versions. Down the line, AutoML could improve vision for autonomous vehicles and AI robots. Continue reading Google Intends to Advance Machine Learning With its AutoML

Google Senior Fellow Jeff Dean, who works on the Google Brain team, recently highlighted AutoML (for machine learning), a project aimed at using AI-empowered machines to build other AI machines, removing humans from the equation. The need for AI algorithms grows as its capabilities are becoming important to a wide range of industries. But only an estimated 10,000 people worldwide have the education, expertise and ability to construct those algorithms, and Facebook, Google and Microsoft pay millions of dollars for them. Continue reading Google Project Aims to Use AI to Develop More AI Algorithms

China wants to become the most dominant nation in artificial intelligence, and it’s got three advantages that might help that become a reality. In addition to strong government support, which includes a willingness to share data about its citizens, China also has an immense number of engineers to write software and 751 million Internet users who can test out the work they do. As China seeks to gain market share, President Xi Jinping seeks to strengthen intellectual property laws to give its startups an advantage. Continue reading China Set to Toughen IP Laws in Pursuit of Tech Dominance