As Matt Asay observed last week, AI appears to be reaching "peak ludicrous mode," with almost every software vendor laying claim to today's most hyped technology. Hang on -- see what I did there? I used "AI" and "machine learning" interchangeably, which should get me busted by the artificial thought police. The first thing you need to know about AI (and machine learning) is that it's full of confusing, overlapping terminology, not to mention algorithms with functions that are opaque to all but a select few. This combination of hype and nearly impenetrable nomenclature can get pretty irritating.

Imaging and understanding the brain is getting so good we are on the cusp of truly enhancing it. This article originally appeared on SOSV.) As I start to look at the emerging trends of 2017 from the vantage of IndieBio, where we see hundreds of biotech startup applications and technologies per year, a few key themes are already emerging. Even as political landscapes change, science and technology continue to push forward. Most of us have seen science fiction shows that show future doctors regrowing and replacing entire organs.

IBM's Watson may be most famous for winning at the game show "Jeopardy!" In a room at IBM offices, software developers and business customers can query the famous computer and see a demonstration of its work as a research partner in fields ranging from land use to medicine. The room itself has a display wall on one side and a touch screen in the center and near the window. In a recent demonstration of how the machine approaches search queries, Rachel Liddell, a "Watson Experience Leader," used the central touch screen to search through a series of TED talks. As she touched the screen to look up lectures on human psychology, Watson created a set of associated topics, such as "education," and touching one of those words generated more specific topics that appeared in the talk.

Standardized testing is also expensive and time-consuming. On the other hand, we should expect some sort of accountability in education, right? Schools are expensive, and, as new industries demand more educated workers, the stakes are higher than ever when it comes to the global economy and class mobility. Developed economies no longer have the safety net of middle-class manufacturing jobs. Whatever Trump says, that's permanent.

You'd have to wear an EEG cap for the technique to work, since CSAIL's system needs to be able to read and record your brain activity. The machine-learning algorithms it created then classifies brain waves within 10 to 30 milliseconds, focusing on detecting "error-related potentials" or ErrPs. These are signals your brain generates when you spot a mistake. If you disagree with a robot's decision to, say, place a can of paint in a basket marked "wire," the system picks up on the ErrPs in your thoughts to correct the machine's course of action. "As you watch the robot, all you have to do is mentally agree or disagree with what it is doing. You don't have to train yourself to think in a certain way -- the machine adapts to you, and not the other way around."

A groundbreaking project led by a Florida State University researcher makes an exponential advance in suicide prediction, potentially giving clinicians the ability to predict who will attempt suicide up to two years in advance with 80 percent accuracy. FSU Psychology researcher Jessica Ribeiro feels an urgency to confront this relentless problem. Shadowing her research is the ever-present awareness that 120 Americans take their lives every day, nearly 45,000 a year. Ribeiro's paper, titled "Predicting Risk of Suicide Attempts over Time through Machine Learning," will be published by the journal Clinical Psychological Science. The study offers a fascinating finding: machine learning -- a future frontier for artificial intelligence -- can predict with 80-90 percent accuracy whether someone will attempt suicide as far off as two years into the future.

Radiologists bring home $395,000 each year, on average. In the near future, however, those numbers promise to drop to $0. Don't blame Obamacare, however, or even Trumpcare (whatever that turns out to be), but rather blame the rise of machine learning and its applicability to these two areas of medicine that are heavily focused on pattern matching, a job better done by a machine than a human. This is the argument put forward by Dr. Ziad Obermeyer of Harvard Medical School and Brigham and Women's Hospital and Ezekiel Emanuel, PhD, of the University of Pennsylvania, in an article for the New England Journal of Medicine, one of the medical profession's most prestigious journals. Machine learning will produce big winners and losers in healthcare, according to the authors, with radiologists and pathologists among the biggest losers.

While participating in Jeremy Howard's excellent deep learning course I realized I was a little rusty on the prerequisites and my fuzziness was impacting my ability to understand concepts like backpropagation. I decided to put together a few wiki pages on these topics to improve my understanding. Here is a prettier version of my linear algebra page. In the context of deep learning, linear algebra is a mathematical toolbox that offers helpful techniques for manipulating groups of numbers simultaneously. It provides structures like vectors and matrices (spreadsheets) to hold these numbers and new rules for how to add, subtract, multiply, or divide them.

This is a single day course from 9:00am to 2:00pm. You will need to bring your laptop and have python, TensoFflow 1.0 and pandas installed before the class. You can find the instructions here. If you have any difficulties let us know before the day of the training and we will provide you with support. We will be running two parallel sessions, one for new users who have minimal or no experience with TensorFlow and another one for advanced users.

When you think of sports analysis, you probably think of raw stats like time in the opposing half or shots on goal. However, that doesn't really tell teams how they should have played beyond vague suggestions. Researchers at Disney, Caltech and STATS believe they can do better: they've developed a system that uses deep learning to analyze athletes' decision-making processes. After enough training based on players' past actions, the system's neural networks can predict future moves and create a "ghost" of a player's typical performance. If a team flubbed a play, it could compare the real action against the predictive ghosts of more effective teams to see how players should have acted.