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.

Google has changed the way developers build applications that understand human language -- and in the finest tradition of the Internet, has named the result after Boaty McBoatface. The company announced a new SyntaxNet open-source neural network framework that developers can use to build applications that understand human language. As part of that release, Google also introduced Parsey McParseface, a new English language parser that was trained using SyntaxNet. The launch is a move to democratize the tools for building applications powered by machine learning. Google claims that Parsey is the most accurate model in the world for parsing English.

First, it is a brain dump: too long, epsilon-baked, and unpolished. Second, it is not obviously relevant to the topic of this site. Third, parts are more technical than most readers would want. However, a quick, bad post may be better than none. This post was prompted by discussions about Bayesianism and the LessWrong rationalist community, with Scott Alexander, Catharine G. Evans, muflax, and St. Rev. (among others). They are each brilliant, quirky, articulate, and fascinating; consider following them online! They might disagree with much of this post, though, and are not implicated in its defects.] This site concerns ways of thinking about some particularly important things: purpose, self, ethics, authority, and meaning, for instance. My aim is to point out common mistakes in thinking about those things, and how to do better. I enjoy thinking about thinking. That's one reason I spent a dozen years in artificial intelligence research. To make a computer think, you'd need to understand how you think. So AI research is a way of thinking about thinking that forces you to be specific. It calls your bluff if you think you understand thinking, but don't. I thought a lot about how to do AI. 1 In 1988, I put together "How to do research at the MIT AI Lab," a guide for graduate students. Although I edited it, it was a collaboration of many people. There are now many similar guides, some of them better, but this was the first.

The Internet of Things (IoT) marketplace is expected to run into the trillions of dollars during the next 5 years. Gartner estimates 6.4 billion devices will be part of the Internet of Things this year. And they project 3 trillion of endpoint spending by the year 2020. In our 2016 predictions blog, we said "If you missed the boat on cloud, you can't miss it on IoT too", and "IoT is where Big Data Analytics, Cognitive Computing, and Machine Learning come together for entirely new ways of managing business processes." IoT represents the ultimate convergence theme in the marketplace today.