In 2011, Artificial Intelligence (AI) came of age when IBM's Watson computer beat two human contestants to win Jeopardy. These were not any two contestants. Ken Jennings had won 74 times consecutively and Brad Rutter had pocketed the biggest pot in history -- 3.25 million. In the battle between men and machine, Watson's win was historic. Jennings was sanguine about losing: "I, for one, welcome our new computer overlords."

Facebook launched chatbots to much fanfare at its Developer Conference in April. Since then, the hype has been replaced by a deathly silence, but the social network's Artificial Intelligence (AI) chief, Yann LeCun, is working on changing that. LeCun, known for his innovative work with AI in the field of deep learning, plans to teach Facebook chatbots common sense. Speaking at the Wired Business Conference, LeCun claimed that his strategy is essential to his company's goal of creating a comprehensive digital assistant in the form of Facebook M. Recently Facebook decided to take a new approach and open-source its AI hardware, a transparency effort that is meant to accelerate growth, said LeCun.

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started.

The 2016 Embedded Vision Summit recently took place in the heart of Silicon Valley. The summit started with a bang when Jeff Dean announced some impressive results using reduced precision deep learning models for inference. For embedded and edge applications of deep learning models, reduced precision inference is a big deal. A brief primer is that model size is reduced by four times since normally single precision uses 32 bits per value. The power draw is significantly reduced as 16 bit arithmetic is nearly two times as fast and memory transfers can account for the majority of the power budget.

Google announced Thursday that it is opening a dedicated machine learning (ML) center in Europe, which will be based in Zurich, Switzerland. It is extending its biggest non-U.S. The search giant revealed the new artificial intelligence research push on Thursday in a blog post. Through its latest effort, Google aims to build a centralized venue for machine language researchers and software engineers to join forces and develop ideas that further enhance the available technology today. The Zurich lab will focus on the development of research and products in the areas of Machine Intelligence.

Google also informed that why it took initiative to have a research center in Europe because world's top technical universities reside in Europe. This isn't Google first research center in Europe either. Google Germany, the research center for artificial intelligence in a nonprofit and base home to 450 scientists, academics and other researchers who work mostly on language technology, embedded intelligence, augmented reality, knowledge management and multimedia analysis, and data mining. Google later invested Oxford Universities AI research team in the startup project.

Google also informed that why it took initiative to have a research center in Europe because world's top technical universities reside in Europe. Google is taking this opportunity to build up their own team for the betterment of future AI creations. Google Germany, the research center for artificial intelligence in a nonprofit and base home to 450 scientists, academics and other researchers who work mostly on language technology, embedded intelligence, augmented reality, knowledge management and multimedia analysis, and data mining. Google recently bought a UK based startup named DeepMind with 500 million USD. Google later invested Oxford Universities AI research team in the startup project.

A colleague recently asked me if deep learning lives up to the hype. Is it really revolutionary or just the same old thing dressed up with a new name? As with most fads, the truth is somewhere in between. Let's start by dismissing the hype. I've heard deep learning referred to as the breakthrough that will lead to "Strong AI" (universal, general purpose AI) and seems to have gotten some rich (Elon Musk), smart (Stephen Hawking) guys (Steve Wozniak) all riled up about the end of humanity.

We are starting a series of articles explaining some of the more popular deep learning models and their architects. We'll start off with one called Neural Storyteller by Jamie Ryan Kiros. If you haven't seen it already, Neural Storyteller is a model that when given an image, can generate a romance story about the image. I had a train over the bridge and at the same time, it hurt so much . But it did n't take long for me to understand what he was saying .

Tech Insider recently published an article about the first artificially intelligent lawyer. ROSS – the name of the artificial intelligence (AI) – can search court rulings from 13 years ago, along with opinions about the case at hand and its significance to past court verdicts. ROSS also sifts through all of this information and provides the user with only highly relevant data. ROSS even operates in plain language, which is a huge step in artificial intelligence, as current systems search and process data in list form, which is not how court rulings and cases are documented. ROSS was recently'hired' at a bankruptcy firm, and several other firms have requested the AI lawyer as well.