Intel has pulled open the curtain on a secretly developed mega-chip called Knights Mill, a key component in its artificial-intelligence strategy. The chip -- which belongs to the family of high-performance Xeon Phi processors -- gives Intel a legitimate opportunity to tackle machine learning. It is targeted at servers and workstations, and will be available in 2017. Intel was caught off-guard with the emergence of artificial intelligence as a way to analyze and present data. Knights Mill, introduced on Wednesday at the ongoing Intel Developer Forum, will fill a big hole in company's chip lineup.

Artificial Lawyer caught up with Canadian legal consultant and futurist Jordan Furlong of Law Twenty One and asked him about his perspective on what opportunities and challenges AI faced in the legal sector. Do you see a strategic advantage for the law firms that embrace AI? If yes, how would that advantage manifest itself? We should probably begin by creating a working definition of'AI', which is a term applied so broadly in the legal sphere that, as Ryan McClead has pointed out, it might as well just be written as'magic'. Michael Mills of Neota Logic has suggested instead the term'cognitive technologies', which encompasses a wide range of tech applications including machine learning, natural language processing, and expert systems.

NEW YORK (Reuters) – IBM is in an unusual fix in telling big U.S. banks they can use its Watson software of Jeopardy-winning fame as a cost-saving solution: bankers say they like it, but cannot afford it. IBM is in good company. Banks are in the fifth year of their belt-tightening campaigns that began in 2011, chasing billions of dollars' worth of savings, and vendors that offer everything from technology to janitorial services are getting squeezed. With persistently low interest rates hurting revenue and businesses like bond trading hemmed in by new regulations, few on Wall Street expect the austerity to end any time soon. For IBM the irony lies in the fact that senior bank executives say they believe its artificial intelligence software could help them achieve cost-cutting goals in coming years, but are not ready to pay for Watson today.

For nearly a quarter of a century, this organization has championed a specific way of thinking about tomorrow. If it's true, as the writer William Gibson once had it, that the future is already here, just unevenly distributed, then our task has been to locate the places where various futures break through to our present and identify which one we hope for. Our founders--Louis Rossetto, Jane Metcalfe, and Kevin Kelly--all supported a strain of optimistic libertarianism native to Silicon Valley. The future they endorsed was the one they saw manifested in the early Internet: one where self-organizing networks would replace old hierarchies. To them, the US government was one of those kludgy, inefficient legacy systems that mainly just get in the way.

This is the fourth post in a series of posts on how to build a Data Science Portfolio. If you like this and want to know when the next post in the series is released, you can subscribe at the bottom of the page. In the past few posts in this series, we've talked about how to build a data science project that tells a story, how to build an end to end machine learning project, and how to setup a data science blog. In this post, we'll take a step back, and focus on your portfolio at a high level. We'll discuss what skills employers want to see a candidate demonstrate, and how to build a portfolio that demonstrates all of those skills effectively.

Prescriptive analysis is the best way to see how to make a sale or encourage a customer in the future. Recommendation systems are the another name for prescriptive analysis. Customer activity is used to recommend items and improve conversion in the digital store. The history of previous purchases and interests are used to recommend new products. To make the recommendation we employ both descriptive and predictive analysis several times.

If you passed high school math and can hack around in Python, I want to teach you Deep Learning. Well folks, I've decided to write a Deep Learning book in the same style as my blog, teaching Deep Learning from an intuitive perspective, all in Python, using only numpy. I wanted to make the lowest possible barrier to entry to learn Deep Learning. The Problem with most entry level Deep Learning resources these days is that they either assume advanced knowledge of Calculus, Linear Algebra, Differential Equations, and perhaps even Convex Optimization, or they just teach a "black box" framework like Torch, Keras, or TensorFlow (where you just hit "train" but you don't actually know what's going on under the hood). Both have their appropriate audience, but I don't believe that either are appropriate for your average python hacker looking for a 101 on the fundamentals.

Learn from OpenAI CTO Greg Brockman on how NVIDIA DGX-1 has the power to create impactful AI technologies and achieve unimaginable breakthroughs.

This A.I. Chatbot Is Scaring Lawyers and Fighting Homelessness

This A.I. Chatbot Is Scaring Lawyers and Fighting Homelessness