Recently Elon Musk made internet headlines by claiming that the probability we live in "base reality" is one in billions. Instead, we are much more likely to be living in a historical ancestor simulation created by an advanced future civilization some 10,000 years from now. "The strongest argument for us being in a simulation, probably being in a simulation is the following: 40 years ago, we had pong, two rectangles and a dot," the SpaceX and Tesla founder said. "That is what games were. Now 40 years later we have photorealistic 3D simulations with millions of people playing simultaneously and it's getting better every year. And soon we'll have virtual reality, augmented reality, if you assume any rate of improvement at all, the games will become indistinguishable from reality." It is probably not Musk's most original idea.

I guess it depends on what kind of project you are doing, but for something like scientific computing the issue is development time, not compile time. Integrating and using code is one of the main bottlenecks. Headers make this really easy. If you do scientific computing and need to use C, then you are probably doing a process or memory demanding task where compile time is non-existent in comparison.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

BMW CEO Krueger has made the boldest promise of any automotive brand about selfSelf-Driving Cars and the Looming Privacy Apocalypse. When he inherited the post, the company had enjoyed 10 hugely successful years following the Strategy NumberCube26: Driving the next wave of innovation. Read more ... » One plan created by his predecessor, Norbert Reithofer. Krueger arrived right as BMW and the overall industry found itself at a crossroads. First, the tremendous growth in sales and profits in China has disappeared so much so that BMW was forced to provide financial relief for dealers who had never before experienced sluggish demand in new car sales.

I recently read an article in which the winner of a Kaggle Competition was not shy about sharing his technique for winning not one, but several of the analytical competitions. "I always use Gradient Boosting," he said. And then added, "but the key is Feature Engineering." A couple days later, a friend who read the same article called and asked, "What is this Feature Engineering that he's talking about?" It was a timely question, as I was in the process of developing a risk model for a client, and specifically, I was working through the stage of Feature Engineering.

It seems a long time ago (1997), that IBM's Deep Blue computer beat Garry Kasparov -- then world champion -- in chess, and we started talking about artificial intelligence seriously. However, we all thought AI would be limited to logical, rational, linear models of "thinking" that a machine can be programmed to do. Computers can be taught to play chess, but would never be able to beat a human at the game Go, said many futurists, even as recently as two years ago.

Trader Navinder Singh Sarao, who is resisting market manipulation charges, at Westminster Magistrates' Court In Robert Harris's 2011 novel The Fear Index a secretive hedge fund builds a computer capable of making its own trading decisions. Gobbling up information, the machine starts to confuse its human creators by building huge stakes and making a handsome profit from a market panic. As they assess the outcome, one of the protagonists notes: "The beauty of it is that it was but 0.4 per cent of total market volatility. No one will ever notice, except us." As markets increasingly rely on computer algorithms, reality is imitating fiction: artificial intelligence is becoming a bigger part of investing and it is also helping regulators ensure that traders do not get away with bad behaviour.

Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

A more technical application, is computer vision to analyze rapid diagnostic tests. A pregnancy test is the most popular example of a rapid diagnostic test. Pharma companies now make these tests for all types of targets like malaria and cancer to get quick "yes" or "no" results in the field for low cost, usually pennies on the dollar. Mobalysis tech can quantify, or apply a measured number, to these tests. Instead of telling you you have cholesterol in your blood for instance, Mobalysis can tell you "how much".