Hello, I'm a computer scientist student, I will finish CS this year so I already started my graduation thesis. I work on a Computer Vision - Robotics lab here on my university and my main field of interest and that I want to pursue as an academic field is machine learning / deep learning, so I thought about mixing robotics with machine learning which is something very common. My main idea is Outdoor Autonomous Navigation, I want my robot to know what a grass is, what a tree is, what people and cars are so he can avoid it or do the things I will set it to do, my approach to the problem so far and what I already did is: For every image frame I slice the image into subImages and for each subImage I calculate it's histogram and compare with a huge data base containing tons of histograms of grass/sky/trees (for example) and run a knn/svm to classify the subImage into one of the closest histograms, and if everything goes by the script I will have a full labeled system for the robot, but I'm facing some problems and I'm not a really expert on the field yet so I really wan't some guidance because I don't know what to do, my professor told me this will be kinda hard to do this way and for a graduation thesis, I have implemented a LBP descriptor to classificate some textures like grass and asphalt but I can't use LBP for everything, I don't even know if the LBP will be accurate for grass and asphalt (if my dataset is huge enough), anyways, sorry for the long text, I just don't know what path to seek now, I don't even know if my current approach is a good one or I'm doing something silly.

Graphics chipmaker Nvidia handily beat fourth quarter earnings targets Wednesday thanks to strong customer interest in its deep learning technology. The company reported a net income of 297 million, or 35 cents per share (statement). Non-GAAP earnings were 52 cents per share on a revenue of 1.4 billion, up 12 percent year-over-year. Wall Street was looking for earnings of 32 cents per share with 1.31 billion in revenue. For the year, Nvidia brought in 5.01 billion in revenue, a 7 percent increase from fiscal 2015, with earnings of 1.67 per share.

In Episode #111, Jesse speaks with futurist and author Calum Chase about his book Surviving AI: The promise and peril of artificial intelligence, and the increasing impacts of artificial intelligence on the world's business and society -- and the future of humanity itself. The prospect of creating a true "AGI" (Artificial General Intelligence) capable of matching human-level thinking is probably the most transformative tech possibility in the coming decades. But that's ignoring the more exciting/terrifying piece of the AI speculation game: If we can figure out how to manufacture human-level cognitive performance, there's no reason to suspect that at that point, the sky is not the limit. Buckle up for a conversation that is both sober and mind-bending -- covering topics from Technological Unemployment to how we can guarantee the morality of political systems when we don't know for sure which intelligences are actually conscious. And hang around -- if you dare -- for a Ruthless Listener Retention Gimmick about one of the most taboo subjects on the Internet: A rationalist, secular recipe for Hell (or something close).

Scientists agree that machines will begin to think for themselves in the near future. What's not clear is how they'll feel about us when they do. Advances in artificial intelligence (AI) have recently seen computers beat humans at games like chess and pass IQ tests set for humans . And plenty of experts have cautioned against the rising intelligence of machines. Physics professor Stephen Hawking and SpaceX CEO Elon Musk are some of the names that have spoken about the dangers of robots turning on humanity.

The supply chain needs to pay attention to the potential ways that augmented reality can revolutionize the industry, and artificial intelligence (AI) will be a driving force behind this change. AI is quickly taking over the tech world, cropping up in smart digital assistants such as Apple's Siri, Amazon's Echo and Microsoft's Cortana. You might already know that AI can help you get directions, play chess, or order a pizza, but are you aware of how it can transform supply chain management? Artificial intelligence was first developed more than 60 years ago, but it has really taken off over the past few years. Whether it's autonomous car technology or facial recognition software, AI taking over and the supply chain needs to get on board.

VC-backed artificial intelligence startups, which have seen a sevenfold increase in funding since 2010, have also seen corporations take a greater interest. While acquisitions of VC-backed AI startups by major corporations -- including Vocal IQ by Apple, Wit.ai by Facebook, and DeepMind by Google -- have been in the spotlight recently, corporate involvement in investing in these startups has also grown. Deals to AI startups involving corporates saw a 15x increase between 2010 and 2015. Just in the last quarter, companies including H2O.ai (Transamerica Ventures and Capital One Ventures), Gridspace (Wells Fargo Startup Accelerator) and Mobvoi (Google) saw backing from prominent corporates. Our artificial intelligence category covers startups primarily focused on developing AI, across areas including image processing, natural language processing, machine learning, deep learning, and predictive APIs, among other core applications.

Facebook is one of several businesses preparing for a future of chatbot communication. The social media company plans to make some key announcements around chatbots and live chat during next week's F8 conference in San Francisco. Chatbots are chat robots designed to simplify digital interaction. Through the power of artificial intelligence and human communication, chatbots are able to hold conversations and perform tasks for users. This presents a tremendous opportunity to businesses, which could use chatbots to replace 1-800 numbers as part of their customer service strategies.

Artificial Intelligence (AI) has four seasons: hype, disappointment, funding drought, and renewed interest. I've been involved in AI research for quite some time -- I became a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1993 -- and I've weathered several seasonal cycles. What I'm seeing now, however, is the most puzzling cycle yet; either I'm getting old and addled, or the current cycle is unique in its magnitude. In these Big Data days, the big talk about AI's potential reminds me of what happened at the peak of earlier cycles (see, for example, the recent Wall Street Journal article. Once again, the focus is on a single technical component -- deep learning -- and hopes seem to be building that it can solve many very hard problems easily and more or less magically.

Appendix: Is there a simple algorithm for intelligence? If you benefit from the book, please make a small donation. I suggest 3, but you can choose the amount. Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Neural Networks and Deep Learning is a free online book.

If someone asked you what professional problem you're addressing at the current time, you'd be able to offer a short overview of the project and why you've taken it on. That explanation, and the reasons behind it, are sure to be sound and strong. Ask yourself instead a different question: Assuming anything was possible technologically, what problems would you be working on today? Chances are, that's what you should be worrying about. Machine-learning technology turns that exploratory intellectual exercise into an actionable reality.