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I used to teach the calculus in a private high school where I was headmaster and would occasionally play a game called "WFF'N Proof" with my students. They had to roll a set of six dice that had sides marked with the following: p, q, r, C, A, K, E or N. After a throw they had to see if they could construct a WFF, a well-formed-formula, by arranging the outcomes in various combinations. A WFF was defined as any of the following: p, q or r. (Addendum 1, 2/7/16: Note that there is no indication of p, q, or r having common features.) Also WFF's were combinations of the letters C, A, K or E followed by two WFF's. Or N followed by a single WFF. So beginning with p, q, or r, one could possibly construct the new WFF's, Np or Nq or Nr. Continuing in this vein, one might construct Apq, or ANpp, or CAqKqrr, … (Addendum 2, 2/7/16: Note also that no two complex WFF's need have common features, e.g.

After being away from the research on AI over the last few years, I took a deep dive over the last week into the current state of affairs. I downloaded and worked through Tutorials on Theano, Tensorflow and Torch. I read a number of scientific papers from arXiv and a book on a roadmap for machine learning, which you've probably come across. I tweeted a bunch of notes and thoughts while I was training my own Deep Learning model with new data (aka my human brain). And so what was I looking for as a result of all this research about learning?

Despite predictions of a tech slowdown, big-name businesses like Facebook, Apple, and IBM are pouring resources into artificial intelligence (AI), changing the field and gaining the interest of venture capitalists everywhere. As investors actively seek innovative players in the AI space, more entrepreneurs are gravitating toward the technology as they build and grow their own companies. Businesses such as Sentient, Context Relevant, and Scaled Inference all continue to work on their own multifunction AI platforms for computation and analysis, but this is only a small segment of the AI market. Multiple businesses now invest time and effort into purpose-built AI products. Here are a few of the businesses that are capturing attention in their respective industries for their AI work. With total capital of 58 million, AI specialist Kensho is already showing just how serious investors are about the technology.

Giant manta rays have been filmed checking out their reflections in a way that suggests they are self-aware. Only a small number of animals, mostly primates, have passed the mirror test, widely used as a tentative test of self-awareness. "This new discovery is incredibly important," says Marc Bekoff, of the University of Colorado in Boulder. "It shows that we really need to expand the range of animals we study." But not everyone is convinced that the new study proves conclusively that manta rays, which have the largest brains of any fish, can do this – or indeed, that the mirror test itself is an appropriate measure of self-awareness. Csilla Ari, of the University of South Florida in Tampa, filmed two giant manta rays in a tank, with and without a mirror inside.The fish changed their behaviour in a way that suggested that they recognised the reflections as themselves as opposed to another manta ray.

In this post I consider three learning approaches and argue that it could be a bad idea to avoid the mathematics and theory when starting out with machine learning. There are three approaches to starting out in machine learning that I have seen practiced. One is a bottom-up approach, in which the student starts with the mathematics and theory and then puts it into practice in either a high-level programming language -- such as Matlab, Python, R or Octave -- or by coding from scratch in a 3GL like Java, C# or C . The second is the top-down approach, in which machine learning tools and/or libraries are used to shelter the student from the coding, mathematics and theory. S/he is instructed to worry about how it all works later and to instead practice working with datasets.

In Japan, humanoid robots are seen as an important part of the solution to the looming double problem of a shortage of labor and an aging society. The first challenge is for robots to be seen as a normal part of society by helping out with everyday tasks, which is why Aldebaran Robotics' diminutive NAO robot recently undertook a two week internship at the main Mitsubishi UFJ bank in central Tokyo. Gizmag called in to see how NAO was doing. NAO was created in 2006 and most recently came to our attention when helping kids learn to write in Switzerland. During the two week internship in Tokyo, NAO helped customers with questions or queries they had, in Chinese and English as well as Japanese.

Most of our readers are likely very familiar with the concept of startup competitions. It almost seems impossible to keep up with the awards and prizes handed out at such events being held week in and week out. However, we are very proud to share that we were able to pull of a truly unique startup competition yesterday at PAPIs Connect in Valencia, one where human judges stepped aside and let Artificial Intelligence figure out the winner. The brains behind this program was designed by PreSeries, BigML's joint venture with Telefónica Open Future_. As such, this organization marked the first public demo of PreSeries, which also began collecting beta user applications on its web site simultaneously.

The past couple of decades have seen technology forge ahead relentlessly, enabling us to communicate in unprecedented ways, work more smartly and from any location, carry our phones in our pocket and much more. And artificial intelligence has evolved somewhat too, alongside all of the'usual' tech that we use daily, bringing with it a more urgent perceived need to consider the threat that it might pose. Bill Gates has come out to state that he's firmly in the'AI is a threat camp', so too has Stephen Hawking, one of the greatest thinkers of our time. But not everyone is in agreement with them. At the Dublin Web Summit, I attended a talk – the name now escapes me unfortunately when it comes to who it was by – and when the speaker, a high profile tech name (that much I do know) was asked about the threat posed by AI, he said something that I found interesting.

Unexpected convergent consequences…this is what happens when eight different exponential technologies all explode onto the scene at once. This post (sixth in a series of seven) is a look at robotics. Be sure to read the first five posts if you haven't already: When the World Is Wired: The Magic of the Internet of Everything Where Artificial Intelligence Is Now and What's Just Around the Corner The Near Future of VR and AR: What You Need to Know Drones Have Reached at Tipping Point--Here's What Happens Next How 3D Printing Is Transforming the Way We Make Things An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., 3D Printing), but try to predict the future when AI, robotics, VR, drones, and computation are all doubling, morphing and recombining…You have a very exciting (read: unpredictable) future. This post is the result of an interview with Rodney Brooks on the top five recent robotics breakthroughs (2012-2015) and the top five anticipated robotics breakthroughs (2016-2018). Rodney is the Panasonic Professor of Robotics at MIT.