In March, Microsoft revealed that it was using the open-world game Minecraft to train AI agents to learn how to do things like climbing a hill. The company also promised to make it available to the public so they could work on their own artificial intelligence projects and research, and it's finally available today. Project Malmo (formerly known as Project AIX) is a Minecraft mod that works on Windows, Mac and Linux, and supports just about any programming language you might want to use. So yes, that means you will need to know how to code – but Microsoft says that even novice programmers can get in on the action. You can learn more about Project Malmo here and grab the mod from this GitHub repository to try it for yourself.

The Home Depot Product Search Relevance competition challenged Kagglers to predict the relevance of product search results. Over 2000 teams with 2553 players flexed their natural language processing skills in attempts to feature engineer a path to the top of the leaderboard. In this interview, the second place winners, Thomas (Justfor), Sean (sjv), Qingchen, and Nima, describe their approach and how diversity in features brought incremental improvements to their solution. Thomas is a pharmacist, with his PhD in Informatics and Pharmaceutical Analytics and works in Quality in the pharmaceutical industry. At Kaggle he joined earlier competitions and got the Script of the Week award.

The idea of having sex with a robot might seem more like something out of a science fiction film, but one in five of us are now open to the idea, according to new research. A recent survey found 21 per cent of British people would have sex with a droid, and one in three would go on a date. It comes as a leading expert on future technology claims human-on-robot sex will be more common than human-on-human sex by 2050. The survey was done VoucherCodesPro who asked 2,816 sexually active Brits aged 18 to describe which activities they would then carry out with a cyborg. Researchers asked those participants who said they would have sex with a robot why they would do it.

Drone technology is developing so quickly--and morphing into commercial uses never before contemplated--that aviation regulators are having trouble keeping pace. Air-safety authorities on both sides of the Atlantic have acknowledged that traditional rule making is too slow and rigid to cope with the rapidly expanding applications of the flying machines, from bridge inspections to land surveys to news photography. And the pressure to spell out exactly what's allowed and what isn't is growing as the industry booms. Millions of hobbyists already operate drones, and over the next few years businesses are projected to begin flying millions more in the U.S. alone. Now regulators are scrambling to draft new, more-nimble rules and procedures.

Many datasets can be viewed as a noisy sampling of an underlying space, and tools from topological data analysis can characterize this structure for the purpose of knowledge discovery. One such tool is persistent homology, which provides a multiscale description of the homological features within a dataset. A useful representation of this homological information is a persistence diagram (PD). Efforts have been made to map PDs into spaces with additional structure valuable to machine learning tasks. We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs. The discriminatory power of PIs is compared against existing methods, showing significant performance gains. We explore the use of PIs with vector-based machine learning tools, such as linear sparse support vector machines, which identify features containing discriminating topological information. Finally, high accuracy inference of parameter values from the dynamic output of a discrete dynamical system (the linked twist map) and a partial differential equation (the anisotropic Kuramoto-Sivashinsky equation) provide a novel application of the discriminatory power of PIs.

The minimum description length (MDL) principle in supervised learning is studied. One of the most important theories for the MDL principle is Barron and Cover's theory (BC theory), which gives a mathematical justification of the MDL principle. The original BC theory, however, can be applied to supervised learning only approximately and limitedly. Though Barron et al. recently succeeded in removing a similar approximation in case of unsupervised learning, their idea cannot be essentially applied to supervised learning in general. To overcome this issue, an extension of BC theory to supervised learning is proposed. The derived risk bound has several advantages inherited from the original BC theory. First, the risk bound holds for finite sample size. Second, it requires remarkably few assumptions. Third, the risk bound has a form of redundancy of the two-stage code for the MDL procedure. Hence, the proposed extension gives a mathematical justification of the MDL principle to supervised learning like the original BC theory. As an important example of application, new risk and (probabilistic) regret bounds of lasso with random design are derived. The derived risk bound holds for any finite sample size $n$ and feature number $p$ even if $n\ll p$ without boundedness of features in contrast to the past work. Behavior of the regret bound is investigated by numerical simulations. We believe that this is the first extension of BC theory to general supervised learning with random design without approximation.

Artificial intelligence (AI) is intelligence created by machines or software. The artificial intelligence and robotics market was worth US 10.7 billion in 2014 and is expected to be worth US 153 billion by 2020, and to have a disruptive impact of between US 14 to US 33 trillion. The component for artificial intelligence alone is worth US 70 billion. There are perils with artificial intelligence. Don't let anyone tell you there aren't.

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.

"With artificial intelligence, we are summoning the demon," chief executive of Tesla and Space X Elon Musk eerily warned listeners at the MIT Aeronautics and Astronautics department's Centennial Symposium in October of 2014. "In all those stories where there's the guy with the pentagram and the holy water, it's like, yeah he's sure he can control the demon. Some people believe artificial intelligence is evil and will end the human race while others believe they will only enhance our well-being. The thought of an evil robot stampeding through a mound of human skulls are deeply ingrained by way of modern pop culture and movies like James Cameron's iconic Terminator. These outrageous, though plausible, thoughts make the idea of artificial intelligence less attractive when giving a helping hand to everything in your everyday life. Someone that truly understood the beginnings of A.I. was the brilliant yet tragic Alan Turing. He was an English scientist who broke the Nazi Enigma Machine's code and helped bring WWII to an end. Turning made many predictions about artificial intelligence, his lesser known yet most significant warns about AI's future threat. In 1951 he wrote, "At some stage… we should have to expect the machines to take control.

The headline isn't mine – it was from Bloomberg (I think'robots' sell). The article is really talking about online dispute resolution tools powered by artificial intelligence. Most people are familiar with this kind of tool primarily because of eBay. Its site has an automated dispute-resolution tool which settles 60 million claims every year. Now, some countries are deploying similar technology to let people negotiate divorces, landlord-tenant disputes, and other legal conflicts.