Google is a giant in artificial intelligence. Every day, their exploits in AI make the news. As a result, AI startups can feel overshadowed by this mega-competitor, and their vision can be cloudy. Fortunately, to navigate through those murky waters, they can rely on Dr Fei-Fei Li, Director of Stanford's AI Lab (SAIL). She is also known as the teacher of an online course on neural networks for computer vision.

In a move that could shift the course of multiple technology markets, Google will soon launch a cloud computing service that provides exclusive access to a new kind of artificial-intelligence chip designed by its own engineers. CEO Sundar Pichai revealed the new chip and service this morning in Silicon Valley during his keynote at Google I/O, the company's annual developer conference. This new processor is a unique creation designed to both train and execute deep neural networks--machine learning systems behind the rapid evolution of everything from image and speech recognition to automated translation to robotics. Google says it will not sell the chip directly to others. Instead, through its new cloud service, set to arrive sometime before the end of the year, any business or developer can build and operate software via the internet that taps into hundreds and perhaps thousands of these processors, all packed into Google data centers.

The Future of Humanity Institute (FHI) will be joining the Partnership on AI, a non-profit organisation founded by Amazon, Apple, Google/DeepMind, Facebook, IBM, and Microsoft, with the goal of formulating best practices for socially beneficial AI development. We will be joining the Partnership alongside technology firms like Sony as well as third sector groups like Human Rights Watch, UNICEF, and our partners in Cambridge, the Leverhulme Centre for the Future of Intelligence. The Partnership on AI is organised around a set of thematic pillars, including Fair, transparent, and accountable AI, and AI and social good; FHI is will focus its work on the first of these pillars: Safety-critical AI. Where AI tools are used to supplement or replace human decision-making, we must be sure that they are safe, trustworthy, and aligned with the ethics and preferences of people who are influenced by their actions. Professor Nick Bostrom, director of FHI, said in response to the news, "We're delighted to be joining the Partnership on AI, and to be expanding our industry and nonprofit collaborations on AI safety."

OpenAI, the artificial intelligence research company set up by Elon Musk, has come up with a new method for teaching robots -- giving them a demo in virtual reality. The non-profit, which is funded to the tune of $1 billion, trained a self-learning algorithm to complete a task after a human demonstrated it once in virtual reality. In this case, the task was stacking coloured blocks. The team got a programmed robot to reproduce the behaviour shown during the demonstration in the virtual environment. "We've developed and deployed a new algorithm, one-shot imitation learning, allowing a human to communicate how to do a new task by performing it in VR," OpenAI wrote in a blog post on Tuesday.

It's going to take time for AMD to build any kind of market share in server, where it's basically starting from scratch, but the company seemed bullish on the prospects for its CPU architecture and where Zen will take it in the data center space. But the company appears determined to retake share in a market where its 1S and 2S platforms are going to compare quite well against what Intel is fielding. If Naples is as strong in server workloads as Ryzen has been in workstation and desktop work, Intel could have a serious fight on its hands. AMD's new platform could also win fans from companies building deep learning and AI platforms that want to field the maximum number of cards per socket without paying a premium for multi-socket solutions.

This is the AMD Radeon team here to say hi to the ML community. We have recently launched our Radeon Vega Frontier Edition graphics card, designed for deep learning and advanced visualization. We would like to invite you to an AMA this Thursday at 2PM PST on r/AMD with AMD's Raja Koduri to discuss what the Radeon Vega Frontier Edition means for the future of scientists, data analysts and professionals. Hope to see you there!

For most people, artificial intelligence is Siri, the lovable but modest digital assistant on iPhones. She can't really do much beyond answering simple questions. Advanced artificial intelligence is far from that. Algorithms are constantly learning, often in unusual ways, and at an exponential rate. Co-founder of Google DeepMind Mustafa Suleyman attends a Q&A with Special Projects Editor forTechCrunch, Jordan Crook during day 1 of TechCrunch Disrupt London at the Copper Box on December 5, 2016 in London, England.

We are at an inflection point in the evolution of artificial intelligence. For decades, AI has been incubating in research labs and at the same time capturing popular imagination with science fiction portrayals of AI. The reality is that thanks to a convergence of increasing compute power, big data and algorithmic advances, AI is becoming mainstream and finding practical applications in nearly every facet of our personal lives. Facebook identifies which friends to tag in photos, algorithms are improving medical diagnosis and saving lives, and GPS-based apps are predicting traffic patterns to optimize driving routes. The AI revolution is also taking hold in our business lives.

The first step to create an algorithm is providing a program with lots and lots of data which has been organized by humans, like tagged photos. This is how Facebook suggests friends to tag in photos and Google Photos searches by people. The takeaway here is the machine learning allows companies to build better applications that interact with things people create: pictures, speech, text, and other messy things. Like their other products, both Google Search and Facebook Photos demonstrate how RDAs generate significant network effects.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.