It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated a world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

DeepMind's partnership with the NHS proves technology can improve the state of the health and care system The app can immediately alert a clinician as soon as it detects signs of kidney failure in patients as nearly 30 doctors and nurses at the Royal Free Hospital have now started using it on a daily basis. "The app is delivering cultural change to the way technology is being used to improve care. The technology is no longer passive, but is actively helping us to provide better and timelier care to patients. "For example on one day this week, the app alerted us to 11 patients, ranging from a young cancer patient to an elderly patient suffering life-threatening dehydration, who were at risk of developing AKI (Acute Kidney Injury). "These patients had a range of different conditions and without the app it would have taken our staff much longer to realise they were developing kidney problems. The app enabled us to monitor our patients' kidney function, detect kidney failure early and intervene rapidly to manage complications and accelerate their recovery," said Chris Laing, a Renal Consultant involved in the development of the Streams app.

Expanding on their Jetson TX1 and TK1 products for embedded computing, NVIDIA announced last week their Jetson TX2 platform--a hardware and software platform the size of a credit card designed to deliver AI computing at the edge. NVIDIA touts Jetson TX2 as delivering "unprecedented deep learning capabilities," and based on the form factor, they may be right as it paves the way for a number of cutting-edge uses--from highly intelligent factory robots and commercial drones, to cameras with AI for smart cities. NVIDIA has been running on all cylinders lately with datacenter machine learning, and I think this release, if it performs as promised, will solidify their place at the top of the machine learning class in certain classes of devices. NVIDIA announced the TX2 at an event I attended last week in San Francisco with many tier 1 vendors and startups with some interesting use cases. Jetson, by design, isn't targeted at every embedded device, it's for those non-mobile devices who need strong deep neural network performance at a given power draw. The TX2 is a significant step up from its predecessor.

Earlier, we came up with a list of some of the best Machine Learning books you should consider going through. In this article, we have come up with yet another list of the recommended books for Data Science. Written by Hopcroft and Kannan, this book is a great blend of lectures in the modern theoretical course in data science. This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning. The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.

Right now it's easiest to think about an artificial intelligence algorithm as a specific tool, like a hammer. A hammer is really good at hitting things, but when you need a saw to cut something in half, it's back to the toolbox. Train an facial recognition algorithm, but don't ask it to recognize cows. Alphabet's AI research arm, DeepMind, is trying to change that idea with a new algorithm that can learn more than one skill. Having algorithms that can learn multiple skills could make it far easier to add new languages to translators, remove bias from image recognition systems, or even have algorithms use existing knowledge to solve new complex problems.

Computer programs that learn to perform tasks also typically forget them very quickly,

Join leaders from MESA International, Bennit.AI, and Avid Solutions for a discussion about how the latest developments in artificial intelligence (AI) are changing the way we manufacture products and paving the way for factories of the future. In this panel-style Webinar, the team discussion focuses on machine learning, deep learning, neural networks, chatbots, and natural language processing and explores practical uses, real-world challenges, and creative solutions for incorporating these technologies into your manufacturing floor. Download the webinar now to learn about the productivity gains many companies are already seeing and how your operations can benefit from these new areas of research and development.

An automated test vehicle equipped with Bosch technology. On the heels of Intel's $15.3 billion plan to buy computer vision powerhouse Mobileye to lock in a big piece of the automated car tech market, Nvidia and Bosch are teaming up to make an AI-enabled computer that can be mass-produced to serve as the brains for driverless vehicles. The Silicon Valley maker of graphics processors and its new German partner, which ranks among the world's largest auto parts makers, will develop a computer that utilizes Nvidia's deep learning software and Drive PX processor, Bosch CEO Volkmar Denner announced at Bosch's ConnectedWorld conference in Berlin. Financial details for the project weren't included in their statement, and Nvidia and Bosch both declined to elaborate. "Bosch will build automotive-grade systems for the mass production of autonomous cars," Nvidia CEO and founder Jen-Hsun Huang said in the statement.

Through massive investments in artificial intelligence (AI) startups, the world's leading tech firms are racing to create markets to transform the economic landscape. DeepMind's technology is accessible to firms that run on Google's cloud. Intel's announcement to acquire Nervana came after Apple disclosed its deal to purchase Seattle-based machine learning and artificial intelligence startup Turi.

Editor's Note: This article first appeared in Spektrum der Wissenschaft, Scientific American's sister publication, as "Digitale Demokratie statt Datendiktatur." "Enlightenment is man's emergence from his self-imposed immaturity. Immaturity is the inability to use one's understanding without guidance from another." The digital revolution is in full swing. How will it change our world? The amount of data we produce doubles every year. In other words: in 2016 we produced as much data as in the entire history of humankind through 2015. Every minute we produce hundreds of thousands of Google searches and Facebook posts. These contain information that reveals how we think and feel. Soon, the things around us, possibly even our clothing, also will be connected with the Internet. It is estimated that in 10 years' time there will be 150 billion networked measuring sensors, 20 times more than people on Earth. Then, the amount of data will double every 12 hours. Many companies are already trying to turn this Big Data into Big Money. Everything will become intelligent; soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet? The field of artificial intelligence is, indeed, making breathtaking advances. In particular, it is contributing to the automation of data analysis. Artificial intelligence is no longer programmed line by line, but is now capable of learning, thereby continuously developing itself. Recently, Google's DeepMind algorithm taught itself how to win 49 Atari games. Algorithms can now recognize handwritten language and patterns almost as well as humans and even complete some tasks better than them. They are able to describe the contents of photos and videos. Today 70% of all financial transactions are performed by algorithms. News content is, in part, automatically generated. This all has radical economic consequences: in the coming 10 to 20 years around half of today's jobs will be threatened by algorithms. It can be expected that supercomputers will soon surpass human capabilities in almost all areas--somewhere between 2020 and 2060. Experts are starting to ring alarm bells.