On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to us about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. Is it true that you and Deep Blue joined IBM at the same time?

On May 11, 1997, a computer showed that it could outclass a human in that most human of pursuits: playing a game. The human was World Chess Champion Garry Kasparov, and the computer was IBM's Deep Blue, which had begun life at Carnegie Mellon University as a system called ChipTest. One of Deep Blue's creators, Murray Campbell, talked to us about the other things computers have learned to do as well as, or better than, humans, and what that means for our future. What follows is an edited version of that conversation. Is it true that you and Deep Blue joined IBM at the same time? A group of us, including myself, joined IBM from Carnegie-Mellon University in Pittsburgh in 1989, but we didn't come up with the name Deep Blue until about a year later.

We can see that on average this model configuration achieved a test RMSE of about 92 monthly shampoo sales with a standard deviation of 5. A box and whisker plot is also created from the distribution of test RMSE results and saved to a file. The plot provides a clear depiction of the spread of the results, highlighting the middle 50% of values (the box) and the median (green line).

About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.

Much discussion and debate surround the topic of physicians and the use of artificial intelligence. The notion that AI could ever fully replace a doctor is not a completely absurd one -- there are many jobs, including white-collar professions, that eventually will be replaced by automation and various levels of machine-learning technology. Certainly, from a pragmatic perspective, it is interesting to consider the possibility of a physician who never needs to eat, never tires, can read thousands of pages of new research every day, can record and remember every experience and can even communicate in multiple languages. In a recent Harvard Business Review article, authors Richard Susskind, chairman of the advisory board of the Oxford Internet Institute, and his son Daniel, an economics fellow at the University of Oxford's Balliol College, say that AI will not only support physicians in their work, but also ultimately replace them. The argument that technology cannot be empathic is moot, they argue, and many aspects of professional work do not require compassion.

This is a very nice introduction into Neural Networks. I have been recommending this to my friends and family. Even if you are afraid of the mathematics involved, the appendix in the book covers what you need to know in order to make sense of the math (most of it is simple algebra) with just a bit of derivatives that involve the chain rule. This is one of the few books that not only goes over the theory but also the step by step implementation (training your network to recognize handwritten numbers in Python) as well as testing the code and making minor tweaks to show how that will affect the overall accuracy of the network. For an added bonus, the author includes a chapter describing how you can train the network to recognize your own handwriting and things you can do to further increase the accuracy.

As big data continues to grow, extracting value from it calls for new tools. Increasingly, businesses that rely on data to drive decisions are applying AI to surface actionable insight quickly and accurately. Finding innovative solutions to the problems raised in data analytics, particularly with respect to adapting machine learning to credit scores, is what they've been working on for the past six years at Experian's DataLabs. The EVP and Global Head of the labs, Eric Haller, spoke to All Analytic about the new direction for predictive modeling. There's a difference between how modeling was done in the past and the possibilities of current approaches.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this! Calculus is not even required.

AI is one of the buzzwords of the moment in the world of tech, with startups coming at the concept from all angles -- computer vision, machine learning, unstructured data inference and natural language processing being just a handful -- in a wider effort to create more intelligent machines. Now comes a new organization that hopes to find and foster the next wave of AI businesses and products, co-founded by the ex-CEO of Evernote, Phil Libin (pictured above), who has left his role as a managing director at General Catalyst to build it (but he tells me he'll stay on as an advisor). All Turtles, as the new company is called, is not your traditional startup incubator. In an interview with TechCrunch earlier, Libin (whose other co-founders are Jessica Collier (Product Design) and Jon Cifuentes (Research and Operations) described it as "startup studio", more akin to Netflix's push to develop original content than to 500 Startups. It will start out with locations in San Francisco, Tokyo and Paris.