Recently I outlined a short series of posts I'll be writing about how I landed a job in autonomous vehicles. My background is that I have a pretty solid foundation in software engineering, including an undergraduate degree in computer science. But most recently my programming has been on the web, not so much in the machine learning and embedded systems areas that dominate vehicle software. Artificial Intelligence for Robotics (Udacity): This is a terrific and super-fun introduction into self-driving cars by Sebastian Thrun. Thrun is both the founder of Udacity and also the founder of Google's self-driving car project and also a former professor at Stanford. Taking the class is like being in the presence of greatness.

The second "Computers Gone Wild: Impact and Implications of Developments in Artificial Intelligence on Society" workshop took place on February 19, 2016 at Harvard Law School. Marin Solja?i?, Max Tegmark, Bruce Schneier, and Jonathan Zittrain convened this informal workshop to discuss recent advancements in artificial intelligence research. Participants represented a wide range of expertise and perspectives and discussed four main topics during the day-long event: the impact of artificial intelligence on labor and economics, algorithmic decision-making, particularly in law, autonomous weapons, and the risks of emergent human-level artificial intelligence. Each session opened with a brief overview of the existing literature related to the topic from a designated participant, followed by remarks from two or three provocateurs. The session leader then moderated a discussion with the larger group.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Boštjan Kaluža, PhD, is a researcher in artificial intelligence and machine learning. Boštjan is the chief data scientist at Evolven, a leading IT operations analytics company, focusing on configuration and change management. He works with machine learning, predictive analytics, pattern mining, and anomaly detection to turn data into understandable relevant information and actionable insight. Prior to Evolven, Boštjan served as a senior researcher in the department of intelligent systems at the Jozef Stefan Institute, a leading Slovenian scientific research institution, and led research projects involving pattern and anomaly detection, ubiquitous computing, and multi-agent systems. Boštjan was also a visiting researcher at the University of Southern California, where he studied suspicious and anomalous agent behavior in the context of security applications.

The edtech trend on the tip of everyone's tongue at this year's EdtechXEurope event is artificial intelligence. By harnessing the power of AI and deep learning, educators can glean insights from the vast quantities of data hoovered up from their students. AI could also help lecturers make better decisions and could improve student retention rates, according to experts. "AI is a tool to make better sense of data," says Satya Nitta, director of education and cognitive sciences at IBM. The world's top online learning platforms are working with business schools such as Yale SOM and Duke Fuqua, and are offering advanced analytical tools to help them refine and enhance student learning.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

The new AI learning software would be able to pinpoint with which concepts a student struggles, so he or she may receive tutoring specific to that subject, instead of having to repeat all concepts taught in high school. The program would also reform how traditional lessons are taught in the classroom, as teachers would be able to keep tabs on how each student performs across endless factors and variables. If the program finds most students in a classroom reasonably understand gerunds, for example, the English lesson can go on.

Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning. That knowledge, he says, was already out there, because it's something universities routinely do. Over time, tutors build an understanding of how each student learns, yet that data is trapped in a system which simply isn't scalable. Claned set out to solve this by combining these tried-and-tested academic evaluation metrics with machine learning algorithms and Artificial Intelligence.

Dato, a Seattle machine learning startup, will put its services in front of a large audience of potential customers through an integration with the next product release from its neighbor, Tableau Software. Tableau (NYSE: DATA), the data visualization and analytics company headquartered in Seattle's Fremont neighborhood, is currently beta-testing Tableau 10, which is due out later this summer and will include the Dato Predictive Services integration along with a host of new features. Dato product manager Roman Schindlauer says the integration will allow Tableau users to create predictive datasets within Tableau using the Python programming language, along with its hundreds of machine learning libraries and tools. That will enable "more complex scenarios" within Tableau--things like sentiment analysis, churn prediction, lead scoring and other predictive analytics that help companies put the reams of data they gather to good use, he says. "It's really the ability to make predictions about the potential future behavior of your users as a company," he says.

In the standard setting of approachability there are two players and a target set. The players play repeatedly a known vector-valued game where the first player wants to have the average vector-valued payoff converge to the target set which the other player tries to exclude it from this set. We revisit this setting in the spirit of online learning and do not assume that the first player knows the game structure: she receives an arbitrary vector-valued reward vector at every round. She wishes to approach the smallest ("best") possible set given the observed average payoffs in hindsight. This extension of the standard setting has implications even when the original target set is not approachable and when it is not obvious which expansion of it should be approached instead. We show that it is impossible, in general, to approach the best target set in hindsight and propose achievable though ambitious alternative goals. We further propose a concrete strategy to approach these goals. Our method does not require projection onto a target set and amounts to switching between scalar regret minimization algorithms that are performed in episodes. Applications to global cost minimization and to approachability under sample path constraints are considered.