We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.

The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a 5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

There is no dearth of articles around the web comparing and contrasting data science terminology. There are all sorts of articles written by all types of people relaying their opinions to anyone who will listen. So let me set the record straight, for those wondering if this is one of those types of posts. I think that, while there may be an awful lot of opinion pieces defining and comparing these related terms, the fact is that much of this terminology is fluid, is not entirely agreed-upon, and, frankly, being exposed to other peoples' views is one of the best ways to test and refine your own. So, while one may not agree entirely (or even minimally) with my opinion on much of this terminology, there may still be something one can get out of this.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

Artificial intelligence researchers at Google DeepMind are celebrating after reaching a major breakthrough that's been pursued for more than 20 years: The team taught a computer program the ancient game of Go, which has long been considered the most challenging game for an an artificial intelligence to learn. Not only can the team's program play Go, it's actually very good at it. The computer program AlphaGo was developed by Google DeepMind specifically with the task of beating professional human players in the ancient game. The group challenged the three-time European Go Champion Fan Hui to a series of matches, and for the first time ever, the software was able to beat a professional player in all five of the games played on a full-sized board. The team announced the breakthrough in a Nature article published today.

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool's errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree. Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world's most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report "Frontiers in Massive Data Analysis." San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014. IEEE Spectrum: I infer from your writing that you believe there's a lot of misinformation out there about deep learning, big data, computer vision, and the like. Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are.

Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks. Neural networks have been around for decades, just that no one used to call them deep networks back then. Now we have all sorts of different flavors of neural networks - deep belief networks (DBNs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and more.

For example, Dean's affinity for cats comes in handy with his line of work. In this context, cats are a mere vehicle for determining how much a computer can see, learn, communicate and understand. It also turns out that machines and humans are complementary in skills. While some computers are capable of beating a human opponent in a game such as Go, it's challenging for the same computers to perform more interpretive functions such as identifying and describing images. On the other hand, humans (and cats) are challenged by performing algorithmic functions on large sets of data, a task at that machines excel at.

For example, Dean's affinity for cats comes in handy with his line of work. In this context, cats are a mere vehicle for determining how much a computerMachine learning is next big thing in programming. Read more ... » can see, learn, communicate and understand. It also turns out that machinesAI research nerve centre launched in Cambridge. Read more ... » and humans are complementary in skills.

Ever since I read "Singularity is Near" I've been fascinated by Ray Kurzweil – his wirings, ideas, a predictions. He's not been afraid to go on the limb and make some brave and seemingly outlandish forecasts about the upcoming technological advances and their oversize impact on people and society. One of the main reasons why I always found his predictions credible is that they can, in a nutshell, be reduced to just a couple of seemingly simple observations: 1. Information-technological advances are happening exponentially, and 2. Information technology in particular is driving all the other technological and societal changes. The rest, to put it rather crudely, are the details. In "How to Create a Mind" Kurzweil zeroes in on just one scientific/technological project – creating a functioning replica of the human mind. He uses certain insights from information technology and neurology to propose his own idea of what human mind (and by extension human intelligence) are all about, and to propose how to go about emulating it "in silico."