In a recent article on 5 Computer Vision and Image Understanding Companies, we talked about how artificial intelligence is enabling computers to see as well as humans when recognizing images and in even some cases better. A company we wrote about before called Enlitic has developed a deep learning algorithm that can increase the accuracy of a radiologist's interpretation by 50-70% and at a speed 50,000 times faster. Not only that, but Affectiva's technology can also evaluate your emotions in real-time through your webcam. They have an online demo you can try and see for yourself how it works. The ability for a computer to detect human emotions falls into a field of study called "affective computing".

Mobile apps, video games, spreadsheets, and accurate weather forecasts: that's just a sampling of the life-changing things made possible by the reliable, exponential growth in the power of computer chips over the past five decades. But in a few years technology companies may have to work harder to bring us advanced new use cases for computers. The continual cramming of more silicon transistors onto chips, known as Moore's Law, has been the feedstock of exuberant innovation in computing. But it looks to be slowing to a halt. "We have to ask, is this going to be a problem for areas like mobile devices, data centers, and self-driving cars?" says Thomas Wenisch, an assistant professor at the University of Michigan.

This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one's skill set. The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data?

Earlier this year, we used DataRobot, a machine learning platform, to test a large number of preprocessing, imputation and classifier combinations to predict out-of-sample performance. In this blog post, I'll take some time to first explain the results from a unique data set assembled from strategies run on Quantopian. From these results, it became clear that while the Sharpe ratio of a backtest was a very weak predictor of the future performance of a trading strategy, we could instead use DataRobot to train a classifier on a variety of features to predict out-of-sample performance with much higher accuracy. Backtesting is ubiquitous in algorithmic trading. Quants run backtests to assess the merit of a strategy, academics publish papers showing phenomenal backtest results, and asset allocators at hedge funds take backtests into account when deciding where to deploy capital and who to hire.

The competition between the biggest tech companies in the world is no doubt an intense one. Just take the heated battles over cloud computing dominance as an example. Companies like Amazon, Microsoft, and Google want to be at the top of the mountain and are trying to do so with cutting edge technological advances and better deals for consumers. The same can be said of the mobile device race. We've already seen how Apple, Google, and others are trying to one-up each other with better devices showcasing revolutionary new features.

The new work, which the researchers are presenting this week at the International Conference on Robotics and Automation, builds on a long sequence of papers on origami robots from the research group of Daniela Rus, the Andrew and Erna Viterbi Professor in MIT's Department of Electrical Engineering and Computer Science. "It's really exciting to see our small origami robots doing something with potential important applications to health care," says Rus, who also directs MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether." Joining Rus on the paper are first author Shuhei Miyashita, who was a postdoc at CSAIL when the work was done and is now a lecturer in electronics at the University of York, in England; Steven Guitron, a graduate student in mechanical engineering; Shuguang Li, a CSAIL postdoc; Kazuhiro Yoshida of Tokyo Institute of Technology, who was visiting MIT on sabbatical when the work was done; and Dana Damian of the University of Sheffield, in England.

A pill that unfolds into a little robot could one day give parents everywhere a little more peace of mind. Once swallowed, it can open up inside a person's stomach, crawling across the stomach wall to retrieve a single-cell button battery, and even patch wounds. This is no small thing. In the US every year, over 3,500 incidents of swallowed button batteries are reported in the US, and most cases of battery swallowing involve toddlers. Although most of these batteries are safely digested, sometimes they can leak and cause tissue burns, bleeding, and death.

Futurist, inventor and Google Inc. director of engineering Ray Kurzweil has some high praise -- and a friendly dig -- for the Waterloo region. Kurzweil said he visits many communities and gives many speeches like the one he delivered Thursday at the Tech Leadership Conference, hosted by the innovation hub Communitech. Wherever he goes, people tell him he's visiting the region's equivalent of Silicon Valley: "Our community is the Silicon Valley of the Left Bank of Paris, our community is the Silicon Valley of Tel Aviv." "Kitchener-Waterloo and the Toronto area really are a Silicon Valley, second only maybe to the actual Silicon Valley. A place that celebrates the idea that failure is something to be, if not encouraged, at least accepted," he said. "We have a word for failure. The only way to make these innovations in the world is to accept these frustrations and setbacks."

This new design is a follow up to an older origami robot also developed by a team headed by MIT CSAIL director Daniela Rus. It has a completely different design and propels itself by using its corners that can stick to the stomach's surface. The team decided to focus on battery retrieval, because people swallow 3,500 button batteries in the US alone. While they can be digested normally, they sometimes burn people's stomach and esophagus linings. This robot can easily fish them out of one's organs before that happens. Besides origami surgeons, Rus-led teams created a plethora of other cool stuff in the past, including robots that can assemble themselves in the oven.

An innovation in artificial intelligence that was described in a 2001 paper by a UCLA computer science professor has found a somewhat unexpected application: helping car buyers customize their vehicles online. The software that powers the sites, called a "product configurator," is based on a logical form of artificial intelligence that was devised by Professor Adnan Darwiche. The websites use artificial intelligence to perform sophisticated, real-time reasoning to ensure that if a consumer wants a specific vehicle -- for example, a red Camry with a tan interior and a performance package -- that exact combination of options could be manufactured by the company or is available in its inventory. The websites can also reason about features that are co-dependent, such as removing a minimum number of features when a combination is not feasible or determining which features must be bought together. "I was very pleased to see this appreciation for the practical significance of my work to the point of adopting it for this massive commercial application," Darwiche said.