The Turing test has always been an approximate benchmark for good AI. In the test, a human is supposed to converse with a machine over text for five minutes; if the human doesn't realize that they are talking to a machine, then the computer passes as AI "indistinguishable" from human intelligence. DON'T MISS: To make the iPhone exciting again, Apple has to launch... an Android phone? Earlier this year, Georgia Tech professor Ashok Goel noticed he was spread thin for teaching assistants for his computer science course. So Goel programmed IBM's Watson system to work as an online chatbot, answering some of the 10,000 online questions submitted by students during the course.

With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – and chatbot powered by IBM's Watson technology– to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Our biggest ever edition of TNW Conference is fast approaching!

With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – and chatbot powered by IBM's Watson technology– to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

Artificial intelligence: students were surprised to learn they had been dealing with a bot all semester. To help with his class this year, a Georgia Tech professor hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor's overworked teaching staff. But, in fact, Jill Watson was an artificial intelligence bot. Ashok Goel, a computer science professor, did not reveal Watson's true identity to students until after they'd turned in their final exams.

Now that high school seniors have made the fateful choice of a college to attend in the fall, their parents are free to pull out tufts of hair as they figure out how to pay for it. Scholarships are life-saving options, but they can be hard to ferret out, says San Francisco-based startup Schoold, which offers a free college-planning mobile app. The company, which uses artificial intelligence techniques to personalize help for individual students, today announced a new "Scholarship" function on its app. It will automatically surface details on study grants and awards that could work for each particular user. The app takes into account the student's intended college, major, interests, and other elements of the profile they create on the app.

To a class of over 300 students at the Georgia Institute of Technology, there didn't seem to be anything unusual about the new teaching assistant, Jill Watson. They never met Ms. Watson, but she always responded to emails quickly and casually. Like any good TA, Ms. Watson's involvement was low-key but helpful. "She was the person –well, the teaching assistant– who would remind us of due dates and post questions in the middle of the week to spark conversations," student Jennifer Gavin told the Journal. Some students envisioned their TA as a young PhD hopeful.

The following is an excerpt from a current work of mine. I thought I'd share it here, as some people have told me they enjoyed it. As I'll stress repeatedly, a matrix represents a linear map between two vector spaces. Writing it in the form of an matrix is merely a very convenient way to see the map concretely. But it obfuscates the fact that this map is, well, a map, not an array of numbers.

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?

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.