As seen at Google DeepMind's conference this week, machine learning with AI has seeped into a number of industries in recent years. Whereas in the past it was more a topic of discussion on theoretical applications, we now see machine learning being applied in smart cars, video games, digital marketing, virtual personal assistants, chatbots, and other areas of daily life. As AI moves to disrupt and improve more sectors, there are still barriers to overcome before we need to fear for our jobs. In a recent translation competition, human beings beat AI, but it's only a matter of time before machines become digital babel fish. It's worth recapping how machine learning and AI have already surpassed human abilities. In 1996, IBM's Deep Blue computer first challenged world-leading chess player Garry Kasparov.

In the first of several games being held in China this week, a revamped AlphaGo defeated Ke Jie, a Chinese grandmaster who currently ranks as the game's number one player. Besides demonstrating the software's mastery over the abstract and intuitive board game, the contest reveals how the machine-learning techniques that underpin AlphaGo are developing. DeepMind's Go Summit, held in the picturesque water town of Wuhzen, is also a significant event for its parent company, Alphabet. The company's dealings in China have been fraught ever since Google left the country, in 2010, in protest over censorship. And beyond this, it shows how strong the appetite for cutting-edge artificial intelligence is becoming in that country.

Over the last few years, data-intensive machine-learning techniques have made dramatic strides in speech recognition and image analysis. Now these methods are making significant advances on another long-standing challenge: translation of written text between languages. Until a couple of years ago, the steady progress in machine translation had always been dominated by Google, with its well-supported phrase-based statistical analysis, said Kyunghyun Cho, an assistant professor of computer science and data science at New York University (NYU). However, in 2015, Cho (then a post-doc in Yoshua Bengio's group at the University of Montreal) and others brought neural-network-based statistical approaches to the annual Workshop on Machine Translation (WMT 15), and for the first time, the "Google translation was not doing better than any of those academic systems." Since then, "Google has been really quick in adapting this (neural network) technology" for translation, Cho observed.

Training robots how to grasp various objects without dropping them usually requires a lot of practice. But a new robot, designed by researchers at UC Berkeley and Siemens and described in an upcoming paper, can learn how to grip new objects just by studying a database of 3D shapes. The robot is connected to a 3D sensor and a deep-learning neural network to which researchers fed images of objects. They included information about the objects' shapes, visual appearances and the physics of how to go about grabbing them. So, when a new object is placed in front of the robot, it just has to match it to a similar object in the database.

Sogaard said that these deep learning techniques have shown promise in finding disease patterns across large groups of people, but the ultimate goal is to eventually help individual patients. Sogaard believes a handful of cloud computing providers will have AI technologies that drug companies could eventually use for research and development. Federal regulations have not yet caught up to the rapid pace of innovation that could one day help predict and diagnose diseases using a combination of genomic, protein, and medical imaging data. But Sogaard is hopeful, and based on Pfizer's meetings with regulators, he believes the Federal Drug Administration is "open-minded" to AI-assisted medical treatment.

Artificial intelligence – or AI – is a true part of our world, as well as a substantial hub of interest for science and business. Companies are ferociously investing in, engaging in and including artificial intelligence in their operations. It is a fascinating technology that enables new options for companies, from detecting security intrusions to anticipating future consumer purchases. Some significant moves from tech giants to acquire AI competitiveness have been made in the past five years. This includes the creation of Microsoft Ventures' fund for AI startups and Uber's acquisition of AI startup Geometric Intelligence; both in December 2016, Apple acquiring Emotient in early 2016, Google buying Deepmind for $400 million in 2014, and IBM pairing up with Nvidia to enable faster responses for the cognitive system, Watson.

It controls what news we read, whose updates we get and what events we learn about. Its scope, power and influence extend beyond what we could imagine. So who -- or what -- decides what we see? It's literally impossible to sort the News Feed without superhuman brain power -- a problem that only AI can likely solve. At the ReWork summit in Boston on May 25, Facebook research engineer Andrew Tulloch explained how the social network is using emerging technology to prioritize what you see and better serve your needs. Facebook has been known to tweak how its system processes and ranks content in the News Feed, but it recently turned to "deep learning" -- an advanced form of artificial intelligence -- to help sift through information.

The research follows similar work published by a separate group at the University of Oxford earlier this month. Using related techniques, these scientists were able to create a lip-reading program called LipNet that achieved 93.4 percent accuracy in tests, compared to 52.3 percent human accuracy. However, LipNet was only tested on specially-recorded footage that used volunteers speaking formulaic sentences. By comparison, DeepMind's software -- known as "Watch, Listen, Attend, and Spell" -- was tested on far more challenging footage; transcribing natural, unscripted conversations from BBC politics shows. More than 5,000 hours of footage from TV shows including Newsnight, Question Time, and the World Today, was used to train DeepMind's "Watch, Listen, Attend, and Spell" program.

Next time you hear a voice generated by Baidu's Deep Voice 2, you might not be able to tell whether it's human. Baidu, the Beijing-based juggernaut that commands 80 percent of the Chinese internet search market, is investing heavily in artificial intelligence. In 2013, it opened the Institute of Deep Learning, an R&D center focused on machine learning. And in May, it took the wraps off the newest version of Deep Voice, its AI-powered text-to-speech engine. Deep Voice 2, which follows on the heels of Deep Voice's public debut earlier this year, can produce real-time speech that's nearly indistinguishable from a human voice.

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel HD Graphics Driver and Intel Iris graphics (also referred to as Intel Processor Graphics).