A startup uses quantum computing to boost machine learning


A company in California just proved that an exotic and potentially game-changing kind of computer can be used to perform a common form of machine learning. The feat raises hopes that quantum computers, which exploit the logic-defying principles of quantum physics to perform certain types of calculations at ridiculous speeds, could have a big impact on the hottest area of the tech industry: artificial intelligence. Researchers at Rigetti Computing, a company based in Berkeley, California, used one of its prototype quantum chips--a superconducting device housed within an elaborate super-chilled setup--to run what's known as a clustering algorithm. Clustering is a machine-learning technique used to organize data into similar groups. Rigetti is also making the new quantum computer--which can handle 19 quantum bits, or qubits--available through its cloud computing platform, called Forest, today.

The Many Tribes of Artificial Intelligence – Intuition Machine – Medium


One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.

Fight Against Cancer with Artificial Intelligence and Big Data - OpenMind


From anywhere and with just a mobile phone, anyone can become an air traffic controller, or at least a virtual air traffic controller. One can follow the world traffic flow of airplanes live and find out where an aircraft is coming from and where it is headed. One just has to take advantage of the millions of pieces of data that fly across the Internet. This is the magic power of Big Data. Artificial intelligence then enters the picture to find patterns and give meaning to the massive and heterogeneous information stream.

How To Become a Neural Networks Master in 3 Simple Steps


Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

For The First Time, AI Can Teach Itself Any Language On Earth


To understand the potential of these new systems, it helps to know how current machine translation works. The current de facto standard is Google Translate, a system that covers 103 languages from Afrikaans to Zulu, including the top 10 languages in the world–in order, Mandarin, Spanish, English, Hindi, Bengali, Portuguese, Russian, Japanese, German, and Javanese. Google's system uses human-supervised neural networks that compare parallel texts–books and articles that have been previously translated by humans. By comparing extremely large amounts of these parallel texts, Google Translate learns the equivalences between any two given languages, thus acquiring the ability to quickly translate between them. Sometimes the translations are funny or don't really capture the original meaning but, in general, they are functional and, overtime, they're getting better and better.

5G to AR: Here are 7 technologies to watch in 2018


USA TODAY's Ed Baig looks at the top Tech trends to watch for in 2018. Visitors walk past a 5G logo during the Mobile World Congress in Barcelona, on March 1, 2017. Blistering fast wireless networks, digital assistants that are, well, everywhere, and a coming out bash for augmented reality. These and other technologies mentioned here, some of which are already familiar but really just getting started, are worth keeping an eye on in 2018. You can bet we'll also learn about innovations in the months to come that are for now, completely under the radar.

The Gardens of Learning

AI Magazine

"Can we actually know the universe? My God, it's hard enough finding your way around Chinatown." "Know then thyself, presume not God to scan; The proper study of mankind is man." The field of AI is directed at the fundamental problem of how the mind works; its approach, among other things, is to try to simulate its working--in bits and pieces. History shows us that mankind has been trying to do this for certainly hundreds of years, but the blooming of current computer technology has sparked an explosion in the research we can now do. The center of AI is the wonderful capacity we call learning, which the field is paying increasing attention to. Learning is difficult and easy, complicated and simple, and most research doesn't look at many aspects of its complexity. However, we in the AI field are starting. Let us now celebrate the efforts of our forebears and rejoice in our own efforts, so that our successors can thrive in their research. This article is the substance, edited and ...

For Artificial Intelligence, the Future Is Now


Watershed technologies like AlphaGo make it easy to forget that artificial intelligence (AI) isn't just a futuristic dream. It's already here -- and we interact with it every day. Sensing traffic lights, fraud detection, mobile bank deposits, and, of course, internet search -- each of these technologies involves AI of some kind. As we have grown used to AI in these instances, it has become part of the scenery -- we see it, but we no longer notice it. Expect that trend to continue: As AI grows increasingly ubiquitous, it'll become increasingly invisible.

Book Reviews

AI Magazine

Review of The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology If you are interested in writing a review, contact chandra@ cis.ohio-state.edu. AT question: Which one of the following doesn't belong with the rest? It is the only discipline in the list that is not under attack for being conceptually or methodologically confused. Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. However, all of them come down to one simple observation: Humans seem a lot smarter than computers--not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. All this is because of another truth: The computational paradigm is the best thing to come down the pike since the wheel. The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology, Jerry Fodor, Cambridge, Massachusetts, The MIT Press, 2000, 126 pages, $22.95. Jerry Fodor believes this latter claim. He says: [The computational theory of mind] is, in my view, by far the best theory of cognition that we've got; indeed, the only one we've got that's worth the bother of a serious discussion.… There is, in short, every reason to suppose that Computational Theory is part of the truth about cognition. It is a fascinating read. This dispute about quantity of truth is where the book gets its title. In 1997, Steven Pinker published an important book describing the current state of the art in cognitive science (see also Plotkin [1997]). Pinker's book is entitled How the Mind Works. In it, he describes how computationalism, psychological nativism (the idea that many of our concepts are innate), massive modularity (the idea that most mental processes occur within a domain-specific, encapsulated specialpurpose processor), and Darwinian adaptationism combine to form a robust (but nascent) theory of mind. Fodor, however, thinks that the mind doesn't work that way or, anyhow, not very much of the mind works that way. Fodor dubs the synthesis of computationalism, nativism, massive modularity, and adaptationism the new synthesis (p.


AI Magazine

Department of Coqmter Scieme, Carnegie-Melloll Ulziversity, Pittsburgh, Penmylvania 15213 One, is that most of these people make essentially no distinction between computers, broadly defined, and artificial intelligence-probably for very good reason. As far as they're concerned, there is no difference; they're just worried about the impact of very capable, smart computers. Enthusiasm and exaggerated expectations were very much in evidence. The computer seems to be a mythic emblem for a bright, high-tech future that is going to make our lives so much easier. But it was interesting to hear the subjects that people were interested in.