Machine learning will give cars the ability to analyze and learn from hundreds of thousands, even millions, of driving situations to learn better than any human being can, Audi execs told the press at CES 2016 Wednesday. "In one year a 16-year-old driver might encounter 1,000 situations with a four-way stop," said Peter Steiner, managing director of Audi Electronics Venture. "But our systems will learn from hundreds of thousands, even millions, of such situations that can be stored, analyzed and improved from, so these cars can learn even better than a human being can." Audi is continuing to work toward fully autonomous vehicles -- it sent a self-piloted A7 sedan from San Francisco to Las Vegas in time for last year's CES -- even as it continues to introduce increasingly sophisticated advanced driver assistance features that have seen a strong uptake among Audi buyers. Audi continues to work closely with NVIDIA, incorporating our Tegra processors into its zFAS driver assistance control unit and MIB infotainment system.

Softbank Robotics, the creator of Pepper has announced its plan to hit Android this week. The company was in the limelight last year after the overwhelming response it received for a robot it launched in Japan. Aldebaran, a subsidiary of SoftBank, programmed that robot on its own customized OS, the NAOqi. Last night, at the Google I/O conference, the creators of that robot announced their plans to integrate it with Android. They will soon open development to the entire Android developers' community to facilitate greater variety, and customization in its functions.

Google is planning to put the art in artificial intelligence. The announcement was made at a session in Moogfest, a four-day music and technology festival in Durham, North Carolina. The company's artificial researcher Douglas Eck had developed a new group that would focus on figuring out what computers can truly create. The group is named Magenta, and they will launch more publicly at the start of June. However, the Moogfest attendees got an introduction on what it will be working on.

Employees of Google stand in front of the company's logo at Frankfurt book fair on October 8, 2006 in Frankfurt, Germany. Google is one of the leading companies who venture into artificial intelligence or AI. In fact, the company is very eager in their AI journey. According to new reports, Google is working on a useful AI. During the Google I/O panel last Friday, John Giannandrea, Google's head of machine learning shared the use cases, interest and research in artificial intelligence.

At the top of the corporate world, Apple and Google are in a back-and-forth battle to be No. 1. It is not clear which of the two Silicon Valley giants will emerge on top in a contest that highlights the contrast of very different business models. Apple then regained, lost and recovered the leader position in May in a battle that appears set to continue for some time. As of the end of Friday, Apple was worth some 522 billion, to 496 billion for Alphabet. The two companies have both been hugely profitable in recent years, for different reasons.

Artificial intelligence has become a larger part of our everyday lives in recent years. It's being used in medical devices, smart-home systems, and video games--to say nothing of robots and autonomous cars. And AI has even started to do what many people have feared: outsmart humans. In its June special report, The Institute dives into today's AI applications as well as its latest development: deep learning, in which machines teach themselves, then make decisions on their own. Here to answer questions about the future of AI are three experts in the field.

If you enjoy Google Play Music's recommendations based on what you listen to, you can thank researcher Douglas Eck. The former University of Montreal computer science professor used machine learning principles on that project, and is now experimenting with it to see if he can teach computers to make art and music on their own. Eck, along with a handful of Google Brain team members, is gearing up to launch Magenta on June 1. The project will involve the use of Google's open-source AI platform TensorFlow to create algorithms that can generate music. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Our May meetup will be a robotics themed one with two speakers from KCL and Oxford. Inspired by the antagonistic human musculoskeletal system, the current trend in mechatronic design is to include physically compliant elements into the embodiment of robotic devices. The promise of such'soft robotic' systems, includes safety and agility. However, these offerings are tempered by the increased complexity of the system dynamics leading to difficulty in control. Learning (by demonstration or reinforcement) is often advocated as a means of dealing with this complexity, and can allow us to exploit the principles of human sensorimotor control to improve our robotic systems.

Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We'll go all the way from data preparation to the integration of predictive models in applications and their deployment in production. "Just like development where you don't need to know a thing about computability or big-O notation to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra." The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML & Cortana Intelligence Suite, Amazon ML, BigML) for developers getting started in Machine Learning.

Last week, Google released Parsey McParseface, a funny name for a state-of-the-art tool aimed at one of the most difficult problems in artificial intelligence. For all that computers have accomplished in the past five years, from winning on "Jeopardy!" to defeating a Go grandmaster, they are still terrible at figuring out what people are saying. Language is one of the most complex tasks that humans perform. That's why there has been such a hullaballo over McParseface, which is pretty much a glorified sentence diagrammer. McParseface does what most students learn to do in elementary school.