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.

Commercially available motorized microscopes can yield data at a throughput of 105 images per day, raising a strong need for automated data analysis (Conrad and Gerlich, 2010; Lock and Strömblad, 2010). Computational data analysis not only reduces the workload for the experimentalist, but also ensures objectivity and consistency in the annotation of large data sets (Danuser, 2011). The complexity and diversity in microscopic image data, however, poses challenges for developing suitable data analysis workflows. Bioimage informatics methods offer powerful solutions for specific image analysis tasks, such as object detection, motion analysis or measurements of morphometric features (Danuser, 2011; Murphy, 2011; Eliceiri et al., 2012; Myers, 2012). Most image analysis algorithms, however, have been developed for specific biological assays.

Imagine if your teaching assistant was actually a robot--and looked like Alicia Vikander. A picture of the "Ex Machina" lead actress graces a Wall Street Journal article that explores students' surprise when they discovered one of their teaching assistants was, in fact, a bot. Her responses--which fooled even former IBM employees--were based on looking through nearly 40,000 questions and answers on a discussion forum. Ashok Goel, the professor who teaches the online Georgia Tech computer science course, believes that bots like Jill can answer 40 percent of all students' questions--many of the mundane variety--within a year. Best quote of the story goes to a student: "We're taking an artificial intelligence class. There should be some artificial intelligence here."

Movidius has announced the Fathom Neural Compute Stick, which is claimed to be the first acceleration module for deep learning in the world, alongside the Fathom deep learning software framework. The Fathom Neural Compute Stick utilizes Movidius' ultra-low power, high performance processor, the Myriad 2, allowing the device to operate neutral networks using power below 1 watt. The device not only looks like a USB stick, but it also functions as one, connecting to a USB port across a wide range of devices. In combination with the Fathom deep learning software framework, the Fathom Neural Compute Stick will allow neural networks to be taken down from the cloud and then deployed directly into end-user devices. Embedded in the Fathom Neural Compute Stick is Google's machine learning software named TensorFlow, which is primarily used for vision processing.

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.

The first job after law school can be horrendous--not simply because of the intense workload and long hours, but also the drudgery. A huge amount of legal work given to those on the lowest rung of the ladder consists of reading through hundreds of pages of notes, articles, and case precedents, to provide senior lawyers with legal details that can help build their case. Fortunately, artificial intelligence is up to the task. So much so that century-old law firm BakerHostetler has formally hired its first "digital attorney," ROSS, as an artificially intelligent legal researcher. ROSS is working with BakerHostetler's bankruptcy team as part of a partnership first announced last month, at Vanderbilt Law School's "Watson, Esq." conference on law and artificial intelligence.

Hi there, my name is Harrison and I frequently do Python programming tutorials on PythonProgramming.net and YouTube.com/sentdex. I do my best to produce tutorials for beginner-intermediate programmers, mainly by making sure nothing is left to abstraction and hand waving. The most recent series is an in-depth machine learning course, aimed at breaking down the complex ML concepts that are typically just "done for you" in a hand-wavy fashion with packages and modules. The machine learning series is aimed at just about anyone with a basic understanding of Python programming and the willingness to learn. If you're confused about something we're doing, I can either help, or point you towards a tutorial that I've done already (I have about 1,000) to help.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

I am a sixth-year graduate student in Computer Science at UC Berkeley, advised by Dan Klein. I work on a range of topics in statistical natural language processing, including coreference resolution, entity linking, and syntactic parsing. My work combines two broad thrusts: first, designing joint models that combine information across different tasks (TACL 2014) or across different views of a problem (ACL 2015), and second, building systems that strike a balance between being linguistically motivated and data driven (EMNLP 2013, ACL 2014). When I'm not doing NLP, I like to play the clarinet. I currently play in the UC Berkeley Symphony Orchestra.