Larger data sets and faster computers have enabled a recent flurry of progress--and investment--in artificial intelligence. David Cox of Harvard thinks the next big jump will depend on understanding what happens inside the head of a rat when it plays video games. Cox leads a 28 million project called Ariadne, funded by the U.S. Office of the Director of National Intelligence, that is looking for clues in mammalian brains to make software smarter. "This is a huge, moonshot-like effort to go into the brain and see what clues and tricks are hiding there for us to find," he said today at EmTech MIT 2016. Recent progress in tasks such as image recognition and translation sprang from putting more computing power behind a technique known as deep learning, which is loosely inspired by neuroscience.

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE. When it comes to new technologies and products, we tend to think of "digital" as synonymous with advanced, modern, and high-def, while "analog" is considered retrograde, outmoded, and low-resolution. But if you think analog is dead, you'd be wrong. Analog processing not only remains at the heart of many vital systems we depend on today, it is now going to make its way into a new breed of compute and intelligent systems that will power some of the most exciting technologies of the future: artificial intelligence and robotics.

We are happy to announce that we can finally answer the question on how to cite mlr properly in publications. Our paper on mlr has been published in the open-access Journal of Machine Learning Research (JMLR) and can be downloaded on the journal home page. The paper gives a brief overview of the features of mlr and also includes a comparison with similar toolkits. For an in-depth understanding we still recommend our excellent online mlr tutorial which is now also available as a PDF on arxiv.org Once mlr 2.10 hits CRAN you can retrieve the citation information from within R:

Today I'm announcing a 100M commitment to Kernel in an effort to enhance human intelligence and reimagine our future. Unlocking our brain is the most significant and consequential opportunity in history -- and it's time sensitive. We're starting to identify the mechanisms underlying neural code and make them programmable. Our biology and genetics have become increasingly programmable; our neural code is next in line. Programming our neural code will enable us to author ourselves and our existence in ways that were previously unimaginable. I started Kernel in 2016 (read more at the Washington Post) to build the world's first neural prosthetic for human intelligence enhancement.

Some executives may wring their hands over the impact of bot automation, machine learning and AI on job roles. But others see the potential of combining software with people-centric tasks to drive efficiency and enhance human work. At KLM Royal Dutch Airlines, customer service agents deal with a high volume of requests for flight information or help with trip cancellations, particularly during bad weather. In 2015, KLM began experimenting with a hybrid approach -- chatbots combined with human customer service -- to deal with high-volume customer interaction on social media platforms. Bot automation is just one of the many types of artificial intelligence (AI) that major software providers have touted during the past year as the next great thing in technology.

This post is the third in a three part series. In the first post 2 weeks ago, we gave some insight and context into why your CEO is asking this question, why now, and why you the CIO. In the second post last week, we gave you a foundational framework to think about AI so you could give your CEO a thoughtful response. This week we will discuss how you can engage the business on the topic of AI and consider important criteria when evaluating AI vendors. So now you have the AI TD ML HITL conceptual model to apply to your business, it's time to start engaging with your executive counterparts in Marketing, Product, Sales and Customer Support.

ANI has never been easy to build or test. The incumbent technologies are too simple and limited (like decision trees), and the newer technologies are non trivial to use, and deeply scientific (like machine learning). What the world has been missing is a new, more powerful rules-based technology that can be used by business people. A platform without the one-dimensional limitations of decision trees where you can build human models of knowledge that can solve multiple problems. A platform that can be connected to existing data in order to make nuanced judgements at scale, with an audit trail for each and every decision.

In this episode of Planet Lex, host Daniel Rodriguez speaks with Northwestern Pritzker School of Law George C. Dix Professor in Constitutional Law John McGinnis and Northwestern University's McCormick School of Engineering Professor of Electrical Engineering and Computer Science Larry Birnbaum about emergent technology and its effects on the law. Dan opens the interview by reminding everyone that it has been 10 years since the publication of Raymond Kurzweil's book, "The Singularity is Near: When Humans Transcend Biology," and poses the question of exactly how close we are to the day when computer intelligence surpasses human intelligence. Larry shares his belief that the singularity is coming, though the time table is unknown. John agrees and states that the victory of IBM's computer system Watson over its human competition on Jeopardy shows the accelerating technology and that software and connectivity are improving, in addition to hardware computation. Both guests analyze how this technology might significantly impact intermediary positions within the workforce and consider the implications on the practice of law.

Computing has evolved through a series of distinct architectural eras: mainframes, distributed computing, client-server, Internet, and cloud. Each offered a new way of organizing information and connecting people. However, the volume (and velocity and variety) of information is exploding. Today a PC generates only about 90 megabytes of network data per day, but in the not too distant future, a connected autonomous vehicle will produce many terabytes and a connected automated factory will generate over a petabyte. We need more than simply new ways to organize and connect to information, we need new ways to uncover the hidden insights within and to harness the full potential of machines.

LONDON Jaguar Land Rover, Ford and Tata Motors are testing connected cars which can communicate with each other using technology designed to speed up journeys and cut accidents, the first such trials in Britain.