When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, and Pandora magically creates your ideal playlist, these are examples of machine learning over a Big Data stream. With Big Data projected to drive enterprise IT spending to 242 billion according to Gartner, Big Data is here to stay, and as a result, more businesses of every size are getting into the game. To many enterprise organizations Big Data represents a strategic asset -- it reflects the aggregate experience of the organization. Each customer, partner, or supplier response or non-response, transaction, defection, credit default, and complaint provides the enterprise the experience from which to learn. From a consumer perspective, every action performed online, every sales process, product interaction, prescribed drug, and environmental anomaly, is being tracked by various sources.

TutorSpace, as it has been named by multimedia analytics scientists at Xerox Research Centre India, is intended to turn instructional videos into "next-generation" textbooks. As Om Deshmuk, a Xerox senior research scientist in multimedia analytics, explained in a video of the project, right now, the amount of instructional content available in video form online can be overwhelming to students. Through machine learning TutorSpace also makes it possible to find content tailored to a student's learning patterns. Now Xerox has licensed TutorSpace to education technology company Impartus for use in its e-learning products.

Deep Q-networks (DQNs) [1] have reignited interest in neural networks for reinforcement learning, proving their abilities on the challenging Arcade Learning Environment (ALE) benchmark [2]. The ALE is a reinforcement learning interface for over 50 video games for the Atari 2600; with a single architecture and choice of hyperparameters the DQN was able to achieve superhuman scores on over half of these games. The original work has now been superseded with several advancements, several of which can be found on GitHub. As training on the ALE can take over a week on a GPU, the code is also set up to learn how to play a simpler game of catch in a couple of hours on a CPU. Most recent deep learning research has focused around supervised learning, which involves finding a mapping from input data \(x\) to target data \(y\).

Given that the reality of AI may be fast approaching, it's of the utmost importance that we work out what might a future with artificial intelligence might look like. Last year, an open letter with signatories including Stephen Hawking and Nick Bostrom called for AI to be of demonstrable benefit to humanity, or risk something that exceeds our ability to control it. AI, as conceived of in popular culture, does not yet exist, even if autonomous and expert systems do. Smartphones might not be supercomputers, but they are called "smartphones" for good reason, in terms of how their operating systems function. Equally, we are happy to talk about a computer game's "AI", but gamers quickly learn to take advantage of its limitations and inability to "think" creatively.

My mother enrolled in a high school physics course in 1968. This wouldn't be especially notable except for the fact that it was the first time in her school's history that girls were permitted to take physics. In prior years, boys were allowed to study physics while girls were expected to enroll in home economics. While my mother acknowledges she was not destined for a career in physics, there were women of her generation that did aspire to enter the scientific field: Dr. France Córdova, Director of the National Science Foundation; Shirley Ann Jackson, President of Rensselaer Polytechnic Institute; and Persis Drell, former director of the SLAC National Accelerator Laboratory, are just a few of the women who not only studied physics, but excelled and built their careers in the field despite the barriers of their generation. Women have progressed significantly since 1968.

Artificial intelligence, or AI, has been touted as a potential panacea for many of our national health services' current ills. But we need to employ intelligence and care of our own before jumping in'gung ho', says one NHS England boss.

Artificial intelligence has been getting tons of attention lately, but for all its trendiness, it's far from a new concept. So why is this area of technology suddenly reinvigorated? In this edition of Talking Data, we address this question and try to assess what the future holds for the AI market. It turns out the sudden surge in interest in AI is closely linked to big data, a more recent tech trend that has breathed fresh life into AI development, a trend that has itself been around for decades. Now that businesses have huge troves of data, they are increasingly able to leverage it in smart applications. The podcast also looks at what businesses can expect from AI software in the future.

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. You can use it from Python code, and soon from other languages. To get started, you'll need to have Python 2.7 or Python 3.5. You can later run pip install -e .[all] to do a full install (this requires cmake and a recent pip version).

Google's artificial intelligence has already taken on the form of a human nerd, but now it's time for its next act. Can AI be an artist? Douglas Eck, a researcher working on Google Brain, recently revealed that the team will soon launch a new project called Magenta. Though it took some inspiration from DeepDream, another Google Brain scheme that yielded trippy-as-hell images (amongst other things), Magenta has one key difference: It will try to figure out if computers can actually create art, instead of reproducing or distorting it. Magenta is set to launch in a more official capacity in early June, but Eck provided some early insights during a recent discussion at Moogfest, a music, art and technology festival.

"Artificial intelligence is the art and science of making machines intelligent," Corrado explained. According to Corrado, the brain's billions of neurons all make tiny decisions based on small amounts of information, but working together they can perform advanced thinking tasks. Moving back to the image recognition example, Corrado explained that these artificial neurons will individually scan tiny patches of pixels in an image and make some judgment about them. Asked how machine learning works for things like booking a movie ticket -- a task Google's AI-powered Google Assistant performed during CEO Sundar Pichai's keynote -- Corrado explained that parts of that task were not done by AI.