Customer experience personalization is all about data first. Get the data right and you can shape the overall customer experience by applying data science and machine learning. Recommendation engines are very powerful personalization tools because it's a great way to do "discovery" – showing people items they will like, but are unlikely to discover by themselves. They improve a visitor's experience by offering relevant items at the right time and on the right page. In the immortal words of Steve Jobs - "a lot of times, people don't know what they want until you show it to them."

While Google was building an artificial intelligence that could beat a grandmaster at the ancient game of Go, researchers at the University of Cincinnati took a different tack. They designed an AI that could take on a fighter pilot. Dubbed ALPHA, this system recently beat retired United States Air Force Colonel Gene Lee in multiple flight simulator trials, as the researchers explain in a paper recently published in the Journal of Defense Management. The idea isn't to replace human fighter pilots. According to Nicholas Ernest, a University of Cincinnati alum and the founder of Psibernetix, a company that aims to commercialize the technology behind ALPHA, this AI may ultimately act as a kind of digital assistant that provides real-time advice to pilots.

Editor's Note: Don't miss our upcoming Free Code Friday on July 1st. Carol will give an overview of machine learning with Apache Spark's MLlib, and you'll also learn how MLlib decision trees can be used to predict flight delays. Decision trees are widely used for the machine learning tasks of classification and regression. In this blog post, I'll help you get started using Apache Spark's MLlib machine learning decision trees for classification. In general, machine learning may be broken down into two classes of algorithms: supervised and unsupervised.

As a keen poker player I love the thrill and excitement of playing against my opponents and beating their hand. I recently spent 2 weeks in Las Vegas to play in the World Series of Poker (WSOP) but when I'm not in the casino I'm playing online. What I love about the casino is not just the environment but you know who you are playing, and you certainly know they are real, but when online pokerbots can come in to play, which asks the questions can they be beaten? Quite simply a pokerbot is a computer program designed by an individual to play against opponents online, meaning a game can be played without the person being present (and because of this they are massively frowned upon in the poker world). There are different levels of pokerbots, many bots in smaller online casinos have software issues and can be manipulated and beaten (as long as you spot it is a bot before it wins) however there are stories that emerge, mainly in America, of very strong bots and'bot-rings' that have beaten and "won" a lot of money from top professional poker players.

A serious problem in the Turing test for computer intelligence is exposed in a study published in the Journal of Experimental and Theoretical Artificial Intelligence. If a machine were to'take the Fifth Amendment' – that is, exercise the right to remain silent throughout the test – it could, potentially, pass the test and thus be regarded as a thinking entity, authors Kevin Warwick and Huma Shah of Coventry University argue. However, if this is the case, any silent entity could pass the test, even if it were clearly incapable of thought. The test, devised in 1950 by pioneering computer scientist Alan Turing, assesses a machine's ability to exhibit intelligent behaviour indistinguishable from that of a human. Also known as the'imitation game', it requires a human judge to converse with two hidden entities, a human and a machine, and then determine which is which.

Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But despite their recent popularity I've only found a limited number of resources that throughly explain how RNNs work, and how to implement them. That's what this tutorial is about. It's a multi-part series in which I'm planning to cover the following: As part of the tutorial we will implement a recurrent neural network based language model. The applications of language models are two-fold: First, it allows us to score arbitrary sentences based on how likely they are to occur in the real world.

Scientists have discovered a way to reconstruct the images in a person's mind. In a new study, researchers concluded that it's possible to visualize a person's memory using only an fMRI machine and some machine learning software. For the study, published in the Journal of Neuroscience, the researchers showed 23 participants hundreds of faces while hooked up to an fMRI machine. While they were looking at the faces, an AI program was learning to associate their perception of certain features with different brain activity. Then, once the machine has learned which activity corresponds to which feature, the participants are shown a picture of a brand-new face, and the machine reconstructs it.

That's the takeaway from a draft report on robotics produced by the European Parliament, which warns that artificial intelligence and increased automation present legal and ethical challenges that could have dire consequences. "Within the space of a few decades [artificial intelligence] could surpass human intellectual capacity in a manner which, if not prepared for, could pose a challenge to humanity's capacity to control its own creation and ... the survival of the species," the draft states. The report offers a series of recommendations to prepare Europe for this advanced breed of robot, which it says now "seem poised to unleash a new industrial revolution." The proposal suggests that robots should have to register with authorities, and says laws should be written to hold machines liable for damage they cause, such as loss of jobs. Contact between humans and robots should be regulated, with a special emphasis "given to human safety, privacy, integrity, dignity and autonomy." If advanced robots start replacing human workers in large numbers, the report recommends the European Commission force their owners to pay taxes or contribute to social security.

A matched formula is a CNF formula whose incidence graph admits a matching which matches a distinct variable to every clause. Such a formula is always satisfiable. Matched formulas are used, for example, in the area of parametrized complexity. We prove that the problem of counting the number of the models (satisfying assignments) of a matched formula is #P-complete. On the other hand, we define a class of formulas generalizing the matched formulas and prove that for a formula in this class one can choose in polynomial time a variable suitable for splitting the tree for the search of the models of the formula. As a consequence, the models of a formula from this class, in particular of any matched formula, can be generated sequentially with a delay polynomial in the size of the input. On the other hand, we prove that this task cannot be performed efficiently for linearly satisfiable formulas, which is a generalization of matched formulas containing the class considered above.

Recent work has shown how to improve delete relaxation heuristics by computing relaxed plans, i.e., the hFF heuristic, in a compiled planning task PiC which represents a given set C of fact conjunctions explicitly. While this compilation view of such partial delete relaxation is simple and elegant, its meaning with respect to the original planning task is opaque, and the size of PiC grows exponentially in |C|. We herein provide a direct characterization, without compilation, making explicit how the approach arises from a combination of the delete-relaxation with critical-path heuristics. Designing equations characterizing a novel view on h+ on the one hand, and a generalized version hC of hm on the other hand, we show that h+(PiC) can be characterized in terms of a combined hcplus equation. This naturally generalizes the standard delete-relaxation framework: understanding that framework as a relaxation over singleton facts as atomic subgoals, one can refine the relaxation by using the conjunctions C as atomic subgoals instead. Thanks to this explicit view, we identify the precise source of complexity in hFF(PiC), namely maximization of sets of supported atomic subgoals during relaxed plan extraction, which is easy for singleton-fact subgoals but is NP-complete in the general case. Approximating that problem greedily, we obtain a polynomial-time hCFF version of hFF(PiC), superseding the PiC compilation, and superseding the modified PiCce compilation which achieves the same complexity reduction but at an information loss. Experiments on IPC benchmarks show that these theoretical advantages can translate into empirical ones.