Google's self-driving car just got a boost from the National Highway Traffic Safety Administration. SAN FRANCISCO -- Federal auto safety and standards regulators should set rules governing self-driving cars -- not state agencies that may not have the technological know-how to assess the rapidly evolving technology. That was the message delivered to federal administrators Wednesday by Chris Urmson, the chief architect of Google's seven-year-old autonomous car program. Urmson was one of a variety of auto experts speaking at a Stanford University forum organized by the National Highway Traffic Safety Administration, which is soliciting comments as it aims to establish guidelines later this summer for companies developing autonomous cars. The event took place the day after Google announced it was part of the Self-Driving Coalition for Safer Streets, a lobbying group of autonomous-car focused companies that also includes Ford, Lyft, Uber and Volvo.

Each year, Google founders Larry Page and Sergey Brin write a letter to share their thoughts on what the company stands for and where it's going. But this year's Founders' Letter is different: it wasn't written by a founder. Last year, Google become Alphabet, news that was itself shared in a letter. We joked at the time that Alphabet was so big it actually owns Google. Now, to let Google have its time in the spotlight again, Page and Brin have handed the Founders' Letter over to Google CEO Sundar Pichai.

Good features are informative, independent, and simple. In this episode, we'll introduce these concepts by using a histogram to visualize a feature from a toy dataset. I'd love to release these episodes faster, but I'm writing them as we go. That way, I can see what works and (more importantly) where I can improve. We've covered a lot of ground already, so next episode I'll review and reinforce concepts, introduce clearer syntax, spend more time on testing, and continue building intuition for supervised learning.

I was not sure to post here or in /robotics but I thought really nice how they use unsupervised training to solve this task. And I ask why there is not so many works in deep learning about 2d lidar data.

This repo contains a collection of IPython notebooks detailing various machine learning algorithims. In general, the mathematics follows that presented by Dr. Andrew Ng's Machine Learning course taught at Stanford University (materials available from ITunes U, Stanford Machine Learning), Dr. Tom Mitchell's course at Carnegie Mellon (materials avialable here), and Christopher M. Bishop's "Pattern Recognition And Machine Learning". Unless otherwise noted, the Python code is orginal and any errors or ommissions should be attribued to me and not the aforemention authors. Each ipynb provides a list of the pertinent reading material. It is suggested that the material be read in the order provided.

Today we have some very good AI/ML posts. First, Pete Warden tells us how to break into machine learning. It is some very practical advice on how to get started. David Lettier shows us how to develop K-Means from scratch. Lastly, on An Uncommon Lab we get to see how Kalman Filters work.

As with many companies over the last couple of years, InfoSys is seeing a major shift in away from "big data" to more of an emphasis on machine learning an AI research. But unlike their competitors, which are heavily investing in proprietary solutions such as Microsoft's Azure Machine Learning Studio, InfoSys decided a cooperative approach would be more efficient. The result of this decision is OpenAI, a non-profit artificial intelligence research company. Officially launched in December, this research group has a billion dollars in funding from InfoSys, Amazon Web Services, and several private donors. The reason we're talking about OpenAI today is they just released the public beta of OpenAI Gym.

Why do investors spend so much time focusing on'differentiation'? The job of an investor is to allocate money to its best use. Investors shouldn't allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem. This is why I emphasize "visible differentiation". Should be obvious in the first 5 minutes why you are different.

With respect to artificial intelligence, some people are squarely in the "optimist" camp, believing that we are "nearly there" as far as producing human-level intelligence. Microsoft co-founder's Paul Allen has been somewhat more prudent: While we have learned a great deal about how to build individual AI systems that do seemingly intelligent things, our systems have always remained brittle--their performance boundaries are rigidly set by their internal assumptions and defining algorithms, they cannot generalize, and they frequently give nonsensical answers outside of their specific focus areas. So Allen does not believe that we will see human-level artificial intelligence in this century. But he nevertheless generously created a foundation aiming to develop such human-level intelligence, the Allen Institute for Artificial Intelligence Science. The Institute is lead by Oren Etzioni who obviously shares some of Allen's "pessimistic" views.

Google CEO Sundar Pichai touts the company's AI efforts. It's a given that the most important thing to Google -- the company/verb/website that's synonymous with finding things using the Internet -- is its search business. Now, as the company lays out its master plan for the future, Google is making sure that business is injected with a healthy dose of artificial intelligence. At least that's the big takeaway from Google's annual letter, published Thursday, and penned for the first time by CEO Sundar Pichai rather than Google co-founders Larry Page and Sergey Brin. Instead of users having to type words into a search box on a computer or phone, Google wants to fetch info and do stuff for you without you having to ask.