Tech billionaire Elon Musk is leaving the board of OpenAI, the nonprofit research group he co-founded with Y Combinator president Sam Altman to study the ethics and safety of artificial intelligence. The move was announced in a short blog post, explaining that Musk is leaving in order to avoid a conflict of interest between OpenAI's work and the machine learning research done by Telsa to develop autonomous driving. "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon," says the post. Musk will stay on as a donator to OpenAI and will continue to advise the group. The blog post also announced a number of new donors, including video game developer Gabe Newell, Skype founder Jaan Tallinn, and the former US and Canadian Olympians Ashton Eaton and Brianne Theisen-Eaton.

I don't know who produced this image, but I've found it posted on the Deep Learning group on Facebook. For other data science concepts explained in one picture, click here.

Every decade seems to have its technological buzzwords: we had personal computers in 1980s; Internet and worldwide web in 1990s; smart phones and social media in 2000s; and Artificial Intelligence (AI) and Machine Learning in this decade. As mentioned in a previous article [56], the 1950-82 era saw a new field of Artificial Intelligence (AI) being born, lot of pioneering research being done, massive hype being created but eventually fizzling out. The 1983-2004 era saw research and development in AI gradually picking up and leading to a few key accomplishments (e.g., Deep Blue beating Kasparov in Chess) and commercialized solutions (e.g., Cyberknife), but its pace really picked up during 2005 and 2010 [57]. Since 2011, AI research and development has been witnessing hypergrowth, and researchers have created several AI solutions that are almost as good as – or better than – humans in several domains; these include playing games, healthcare, computer vision and object recognition, speech to text conversion, speaker recognition, and improved robots and chat-bots for solving specific problems. The table in the Appendix lists key AI solutions that are rivaling humans in various domains and six of these solutions are described below.

At times, progress occurs so quickly that it's difficult to separate science fiction from real life. Just five decades ago, computers were massive, unwieldy machines running on punch cards and primitive circuits. Today, a single smartphone has more processing power than the computer used on the Apollo missions. AI has benefited greatly from this explosion in computing power and capability. Today, highly complex deep learning algorithms, patterned on the structure of the human brain, can master Go, trade stocks, and even write Harry Potter novels (though admittedly not very good ones). Given this versatility, some fear that deep learning AIs will reshape our economy by force, rendering hundreds, if not thousands, of occupations obsolete.

Check out the AI Conference in New York City, April 29 to May 2, 2018. Hurry--early price ends March 16. Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development.

If you weren't already worried about AI, the ominously named The Malicious Use of Artificial Intelligence report has just been released to fuel your nightmares. Written by 26 authors from 14 academic and industrial institutions and think tanks including nonprofit research OpenAI, the Electronic Frontier Foundation, and the national security think tank, Center for a New … Continue reading "AI is coming for you, warns ominous new study" ...

Frameworks are only an intermediary step to the wider adoption of machine learning in applications. What's needed are more visual products and those are still a couple of years away. The current machine learning (ML) focus on frameworks is a middle step in the needed evolution of the productization of ML and its inclusion through the application environment. In order to truly succeed, the ML vendors need to think more like a business user and less like a programmer. One way to start is to learn the lesson the business intelligence (BI) sector provides.

Until very recently, the machines that could trounce champions were at least respectful enough to start by learning from human experience. To beat Garry Kasparov at chess in 1997, IBM engineers distilled centuries of chess wisdom into a formula that was hard-wired into their Deep Blue computer. In 2016, Google DeepMind's AlphaGo thrashed champion Lee Sedol at the ancient board game Go after poring over millions of positions from tens of thousands of human games. But now artificial intelligence researchers are rethinking the way their bots incorporate the totality of human knowledge. The current trend is: Don't bother. Last October, the DeepMind team published details of a new Go-playing system, AlphaGo Zero, that studied no human games at all. Instead, it started with the game's rules and played against itself.

This Machine Learning vs Deep Learning vs Artificial Intelligence video will help you understand the differences between ML, DL and AI, and how they are related to each other. The tutorial video will also cover what Machine Learning, Deep Learning and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different. A glimpse into the future ( 25:46) Subscribe to our channel for more Machine Learning & AI Tutorials: https://www.youtube.com/user/Simplile... Machine Learning Articles: https://www.simplilearn.com/what-is-a... To gain in-depth knowledge of Machine Learning, Deep learning and Artificial Intelligence, Check out our Artificial Intelligence Engineer Program: https://www.simplilearn.com/artificia... #SimplilearnMachineLearning #SimplilearnAI #SimplilearnDeepLearning #Artificialintelligence #MachineLearningTutorial - - - - - - - - About Simplilearn Artificial Intelligence Engineer course: What are the learning objectives of this Artificial Intelligence Course? By the end of this Artificial Intelligence Course, you will be able to accomplish the following: 1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making 2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline 3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning 4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before 5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning 6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces - - - - - - What skills will you learn with our Masters in Artificial Intelligence Program? 1. Learn about major applications of Artificial Intelligence across various use cases in various fields like customer service, financial services, healthcare, etc 2. Implement classical Artificial Intelligence techniques such as search algorithms, neural networks, tracking 3. Ability to apply Artificial Intelligence techniques for problem-solving and explain the limitations of current Artificial Intelligence techniques 4. Formalise a given problem in the language/framework of different AI methods such as a search problem, as a constraint satisfaction problem, as a planning problem, etc - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn Get the Android app: http://bit.ly/1WlVo4u