Google DeepMind has discovered a way to create AI algorithms that run faster than those coded by humans, which could lead to more cost-effective software development and computing that is more efficient and sustainable, according to the Alphabet company. The breakthrough, detailed in the journal Nature, is called AlphaDev. It uses a form of machine learning called reinforcement that allows computers to build on their successes, honing strategies independent of human programmers. In this case, faster algorithms were developed for computer-science functions like sorting and hashing. Continue reading Google DeepMind’s AlphaDev Can Create Faster Algorithms

In a move it sees as a force multiplier, Alphabet is consolidating DeepMind and the Brain team from Google Research into a unit called Google DeepMind, uniting the teams responsible for Google Brain with DeepMind, the UK-based artificial intelligence research lab acquired in 2014. Collective accomplishments include AlphaGo, Transformers, WaveNet and AlphaFold, as well as software frameworks like TensorFlow and JAX for expressing, training and deploying large scale ML models. “Combining all this talent into one focused team, backed by the computational resources of Google, will significantly accelerate our progress in AI,” the company announced. Continue reading Google Restructures AI Research Units into Google DeepMind

Samsung is using Synopsys’ DSO.ai tool to design some of its next-gen Exynos mobile processors for 5G and AI, which will be used in smartphones including its own and other devices. Synopsys chair and co-chief executive Aart de Geus said this is the first example of a “real commercial processor design with AI.” Google, IBM and Nvidia are among the other companies that have discussed designing chips with AI. Synopsys, which works with dozens of companies, also has years of expertise in creating advanced designs to train an AI algorithm. Continue reading Samsung First to Design Commercial Semiconductor with AI

“AI and Ethics” was the topic of ETC@USC’s March 30th Executive Coffee with… discussion, the third installment of the Spring 2021 series. WarnerMedia’s Renard Jenkins, vice president of content transmission and production, and Michael Zink, vice president of emerging and creative technologies, led the discussion with 12 graduate and undergraduate USC philosophy, cinema, engineering and innovation majors. They explored how diversity and bias impact AI development, and how AI is expected to impact entertainment experiences. Continue reading ETC Executive Coffee: Warner Executives Discuss AI, Ethics

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

If measured in press impressions, 2017 has most definitely been the “Year of AI,” But looking past the hype, a few things are clear: 1) progress in actual machine intelligence capability has been slow and fragmented; 2) applied AI is still the domain of less than 20 companies; and 3) still, machine learning (not AI) is being deployed across enterprise domains of numerous business sectors and creating big value. Similarly, and since it will take another year or two for current advances in machine learning to trickle down to the consumer sector, we’re not really expecting much breakthrough in AI or even machine learning at CES 2018. Continue reading Artificial Intelligence at CES 2018: Expect More of the Same

China’s State Council released a statement of intent to build a domestic industry in artificial intelligence worth $150 billion and become the world leader in AI by 2030. China is also planning a multi-billion dollar investment in startups and academic research related to AI, say two professors consulting with the Chinese government. At the same time, the U.S. is cutting back on investments in science, and budget proposals from the Trump administration aim to cut funds from agencies supporting AI research. Continue reading China Issues Plan to Become the World’s AI Leader by 2030

As predicted, artificial intelligence has been one of the most repeated phrases of CES 2017. It seems every other vendor here is slapping the “AI” label on its technology. So much so that it inspired us to take a (short) step back and look at what AI is in relation to machine learning. The reality is: there are still very few applications that can be legitimately labeled as artificial intelligence. Self-driving cars, DeepMind’s AlphaGo, Hanson Robotics’ Sophia robot, and to a lesser extent Alexa, Siri and the Google Assistant, are all AI applications. Most of the rest, and certainly most of what we’ve seen here at CES, are robust, well productized machine learning applications (usually built on neural network architectures), often marketed as AI. Continue reading CES 2017: Distinguishing Between Machine Learning and AI

Artificial Intelligence is finally here. After nearly 50 years in the doldrums of research, the science of designing “thinking machines” has jumped from academic literature to the lab, and even from the lab to the store. This is largely because its precursor, machine learning, has been enjoying a dramatic revival, thanks in part to the commoditization of sensors and large-scale compute architectures, the explosion of available data (necessary to train advanced machine learning architectures such as recurrent neural networks), and the always burning necessity for tech companies to find something new. We expect AI to have a significant presence at next month’s CES in Las Vegas. Continue reading CES: From Learning to Thinking Machines – the AI Explosion

OpenAI, the Elon Musk-supported artificial intelligence lab, just debuted Universe, a virtual world that is a software training ground for everything from games to Web browsers. Universe begins with approximately 1,000 software titles, with games from Valve and Microsoft. OpenAI is also in discussions with Microsoft to add the Project Malmo platform, based on the game “Minecraft,” and hopes to add Google AI lab’s DeepMind Lab environment, which was just made public. The goal is that Universe will help machines develop flexible brainpower. Continue reading OpenAI Rolls Out Virtual World, Google Opens DeepMind Lab

Google’s DeepMind division has improved the speed and performance of its machine learning system with technology whose attributes are similar to how animals are thought to dream. Dubbed “Unreal” (Unsupervised Reinforcement and Auxiliary Learning), the system learned to complete Labyrinth, a 3D maze, ten times faster than the best existing artificial intelligence software and can now play up to 87 percent of expert human players’ performance. DeepMind researchers will now be able to try out new ideas much more quickly. Continue reading Google DeepMind Speeds AI Learning with Computer Dreams

Although up until now, augmented reality has had an inauspicious debut — think Google Glass — it’s poised to transform how we interact with computers in the next two decades. AR now has technical limitations including a narrow field of view, less-than-ideal resolution and latency issues. Furthermore, the only way to interact with AR is via bulky glasses or helmets. But many experts believe that we are in the midst of a speedy evolution to the point where AR will enable us to project a virtual screen on every surface. Continue reading Augmented Reality and Artificial Intelligence Shaping the Future