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.

SAN FRANCISCO -- Google CEO Sundar Pichai says the next big evolution for technology is artificial intelligence. "Looking to the future, the next big step will be for the very concept of the'device' to fade away," Pichai wrote in the technology giant's annual founders' letter to shareholders. His vision: Over time, computers, whatever shape they take, a mobile device in your hand or a mini computer on your wrist, "will be an intelligent assistant helping you through your day." Google is far from alone. Artificial intelligence in which Google was a pioneer, is an increasingly crowded field.

Metz goes on, "The last-minute offers--some made at the conference itself--were large enough to force Musk and Altman to delay the announcement of the new startup. 'The amount of money was borderline crazy,' says Wojciech Zaremba, a researcher who was joining OpenAI after internships at both Google and Facebook and was among those who received big offers at the eleventh hour. How many dollars is'borderline crazy'? Two years ago, as the market for the latest machine learning technology really started to heat up, Microsoft Research vice president Peter Lee said that the cost of a top AI researcher had eclipsed the cost of a top quarterback prospect in the National Football League--and he meant under regular circumstances, not when two of the most famous entrepreneurs in Silicon Valley were trying to poach your top talent. Zaremba says that as OpenAI was coming together, he was offered two or three times his market value."

While it's not always straightforward to see an opportunity that others can't yet see, this sort of foresight and vision is essential to true innovation. Coupled with persistence and hard-work, such a long-term vision can realize great rewards for any organization. I recently had the pleasure of addressing the annual Sabre Airline Solutions Global Conference, where I shared some of the key opportunities defining the future of the airline business. Presenting to hundreds of decision makers from across the airline business, the emphasis was on the customer-centric approach that makes the most impact in travel. "The farther a business can get ahead of customer needs, the quicker the customer can realize the value of the service -- and the faster the business can recognize the revenue from customer purchases and adoption."