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

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

The new Microsoft Research AI lab is now open for business, targeting the creation of a single system of general artificial intelligence that can flexibly work on a range of problems. Based at company headquarters in Washington state, the lab will be home to more than 100 scientists whose AI research spans fields including perception, learning, reasoning and natural language processing. The lab’s goal of general AI differs from narrow AI, which performs one task very well, such as facial recognition. Continue reading Microsoft Takes a Bigger Stake in AI With New Lab, Projects

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

Amazon, Facebook, Google, IBM and Microsoft established the Partnership on AI to create ground rules for protecting people and their jobs in the face of rapidly expanding artificial intelligence. The organization is also intended to address the public’s concern about increasingly capable machines, and corporations’ worries about potential government regulation. One of the organization’s first efforts was to agree upon and then issue basic ethical standards for development and research in artificial intelligence. Continue reading Tech Behemoths Establish Partnership on Artificial Intelligence

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

Twitter announced that it has acquired an artificial intelligence startup known as Madbits. The social network is buying into Madbits’ technology that can search an image and understand its content. This new image search engine is based on deep learning, a type of AI that relies on convolutional neural nets, much like a human’s network of neurons in the brain. Twitter is just the latest in a line of tech companies to invest in this type of technology. Continue reading Twitter Acquires AI Startup Madbits, Explores Image Search

In another deal involving “deep learning,” Google is purchasing London-based DeepMind Technologies, a somewhat secretive artifical intelligence startup. The move is viewed as a talent acquisition to bring CEO Dennis Hassabis to Google. The games prodigy and neuroscientist was named “probably the best games player in history” by the Mind Sports Olympiad. While it is unclear what DeepMind does exactly, its website describes building algorithms for games, e-commerce and simulations. Continue reading Deep Learning: Google Plans to Acquire AI Startup DeepMind