Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations, some use it to better understand the risk characteristics of large portfolios, and others compute it before executing trades to help make informed and immediate decisions. For reasons that we will delve into later, reaching an accurate estimate of VaR can be a computationally expensive process.

All of these are concepts aim to make human types of problems computable, whether it be a self-driving car, a health care-providing robot, or a walking and talking assistant robot for the home or office. R&D teams around the world are working to create a whole new world of machines more intelligent than humans. Designing systems as complex as the human brain -- which is still largely a mystery -- is no small task. For example, tomorrow's bleeding edge cars will be the ultimate in efficient system-level design sophistication given the complexity, integration, interdependencies, safety, convenience and comfort required on so many levels. "It fundamentally changes the paradigm and even what we expect of processors to be doing," said Chris Rowen, a Cadence fellow and CTO of the IP Group.

You can try the notebook service, as well as all of the Azure ML Studio, using a free guest account. The Jupyter project is an open source project with many contributors. Thank you to the Jupyter team, as well as the IRKernel team who did the integration work between Jupyter and R.

Smart, quasi-autonomous robots and machines are replacing humans in workplaces all over the world. They learn fast, work hard, and certainly complain less. Intelligent technologies are increasingly delivering greater value for less money. But "better than human" comes with its own managerial challenges. What happens when these algorithmic ensembles underperform?

Generally when I think of artificial intelligence I use a bit of skepticism before arriving at the conclusion that the Terminator movies are an unlikely end to the human race. Not that the movies aren't great but Hollywood has missed the boat on AI in so many examples of real world AI application that it is doubtful that many people have grasped the reality of it all. Watson, an IBM supercomputer now quite well known for it's Jeaopardy performance some time back is an example of primarily safe AI development and as it adapts machine learning to master answering questions most of us can't conceive of a present danger that we can actually explain. What Watson does differently from most AI isn't dangerous, in fact it mostly weighs questions and seeks answers by checking the data it has related to the question. It doesn't manipulate anyone elses coffee maker anywhere, it doesn't close garage doors on puppies.

Microsoft wants people to have more intelligent conversations with their computers. The giant software company is promoting new tools for software developers to build intelligent "bots" or commercial programs that will work with Cortana, its voice-activated digital assistant, to perform tasks like booking a hotel room, ordering a meal or arranging a delivery. Microsoft recently shut down an experimental messaging bot after some Twitter users taught it to make offensive statements. CEO Satya Nadella said the episode showed the importance of designing technology to be "inclusive and respectful." Nadella touted the power of "conversational" computing at the company's annual Build conference for software developers in San Francisco, where Microsoft also announced some updates to its flagship Windows 10 software.

Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what's happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1--Part 2 is here. We are on the edge of change comparable to the rise of human life on Earth. It seems like a pretty intense place to be standing--but then you have to remember something about what it's like to stand on a time graph: you can't see what's to your right. So here's how it actually feels to stand there: Imagine taking a time machine back to 1750--a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery. This is all before you show him the internet or explain things like the International Space Station, the Large Hadron Collider, nuclear weapons, or general relativity.

Microsoft CEO Satya Nadella, a respected leader of one of the world's largest and most important technology companies, speaks as if humanity lives on the cusp of science-fiction. At today's Microsoft Build press conference, Nadella said of the not-so-distant-future, "It's not going to be about man versus machine, it's going to be about man with machines." The line addresses a sincere concern held by esteemed scientists like Stephen Hawking that artificial intelligence could one day eliminate human life. Nadella points to Microsoft's own artificially intelligent assistant Cortana as a positive example of our early coexistence with AI. The tool follows its users across Microsoft platforms, solving problems over the course of a day.

In addition to heavy investments from mammoths like Google and Facebook, smaller companies are also hopping on board. East-coast based ADP, for example, is using deep learning systems to help scout out necessary information, run big data analysis, and present prepared report to its CEO, a process that is improved upon each time the machine performs its operations. Saffron, a division of Intel, is using deep learning to match broad patterns of customer behavior to specific individuals, claiming that the technology predicts correct next moves - such as how the person will contact a company - 88 percent of the time. Companies such as Rare Mile Technologies are also offering up customized machine learning algorithms to a range of industries for various uses, including insurance fraud detection.

This is a follow-up to my earlier post on learning curves. A learning curve is a plot of predictive error for training and validation sets over a range of training set sizes. Here we're using simulated data to explore some fundamental relationships between training set size, model complexity, and prediction error. The input columns are named X1, X2, etc.; these are all categorical variables with single capital letters representing the different categories. Cardinality is the number of possible values in the column; our default cardinality of 10 means we sample from the capital letters A through J. Next we'll add an outcome variable (y); it has a base level of 100, but if the values in the first two X variables are equal, this is increased by 10.