If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
But while his peer scientists Yann LeCun and Geoffrey Hinton have signed on to Facebook and Google, respectively, Bengio, 53, has chosen to continue working from his small third-floor office on the hilltop campus of the University of Montreal. Shum, who is in charge of all of AI and research at Microsoft, has just finished a dress rehearsal for next week's Build developers conference, and he wants to show me demos. Shum has spent the past several years helping his boss, CEO Satya Nadella, make good on his promise to remake Microsoft around artificial intelligence. Bill Gates showed off a mapping technology in 1998, for example, but it never came to market; Google launched Maps in 2005.
SANTA CLARA, CA--(Marketwired - Apr 17, 2017) - NVIDIA ( NASDAQ: NVDA) today announced that its deep learning platform is now available as part of Baidu Cloud's deep learning service, giving enterprise customers instant access to the world's most adopted AI tools. The new Baidu Cloud offers the latest GPU computing technology, including Pascal architecture-based NVIDIA Tesla P40 GPUs and NVIDIA deep learning software. It provides both training and inference acceleration for open-source deep learning frameworks, such as TensorFlow and PaddlePaddle. "Baidu and NVIDIA are long-time partners in advancing the state of the art in AI," said Ian Buck, general manager of Accelerated Computing at NVIDIA. "Baidu understands that enterprises need GPU computing to process the massive volumes of data needed for deep learning.
Word leaked Monday via The Wall Street Journal that Tesla / SpaceX industrialist Elon Musk has been funding a company called Neuralink-- allegedly with some of his own money -- attempting to connect computers directly into human brains. This is the same Musk profiled in this month's Vanity Fair, where he tells journalist Maureen Dowd in all seriousness that humanity needs a Mars colony to which we can escape'if AI goes rogue and turns on humanity." In short, Musk is one of many big thinkers who believe a human-computer hybrid is essential to allowing humans to keep their own machines from marginalizing them. Neuralink's technology is said to be a neural lace, which Musk has spoken about for over a year. But for most people, the first question isn't whether artificial intelligence will usurp our planet.
AI and NLP are two acronyms many in the world of chatbots toss around glibly, sometimes without understanding themselves what these terms mean. There's a third acronym that's an essential component beneath these two: ML, which stands for machine learning. Machine learning is a lot easier to explain in one tweet than AI or NLP: It's the process by which an advanced software system trains itself from a massive set of examples, rather than being explicitly programmed with rigid algorithms devised by human coders. Over time, it gets better and better as it acquires more data to train on. An ML system is still programmed with standard one-and-zero logic, but it's programmed to modify its behavior to meet specified goals based on patterns it discovers in the sample data.
Alan Bundy's Viewpoint "Smart Machines Are Not a Threat to Humanity" (Feb. Reducing the entire field of AI to four "successful AI systems"--DeepBlue, Tartan Racing, Watson, and AlphaGo--does not give the full picture of the impact of AI on humanity. Recent advances in pattern recognition, due mainly to deep learning, for computer vision and speech recognition have achieved benchmarks comparable to human performance;2 consider AI technologies power surveillance systems, as well as Apple's Siri and Amazon's Echo personal assistants. Looking at such AI algorithms one can imagine AI general intelligence being possible throughout our communication networks, computer interfaces, and tens of millions of Internet of Things devices in the near future. Toward this end, Deepmind Technologies Ltd. (acquired by Google in 2014) created a game-playing program combining deep learning and reinforcement learning that sees the board, as well as moves the pieces on the board.1 Recent advances in ...
In fact, similar kinds of biological algorithms might exist in people and govern not only how we learn and act but also how our species evolved. That is the firm belief of Professor Leslie Valiant, whose ground-breaking research has been fundamental to the development of machine learning, artificial intelligence and the broader field of computer science. The PAC model has become one of the most important contributions to machine learning, and is the foundation of the modern field of computational learning theory, where scientists study the design and analysis of machine learning algorithms. Cracking the codes of such ecorithms could also help scientists to develop more advanced robots that can better learn from their environment, allowing the machines to evolve in a manner similar to people and become more useful.
Last week, my University of Georgia colleague Professor John Knox taught his Data Assimilation (techniques for integrating data observations into computer models) students about a "hidden figure" of the field of meteorology. He gets credit for sharing the story of Klara Dan von Neumann with me. In April of 1950, a group of meteorologists at New Jersey's Institute for Advanced Study successfully produced the first weather forecast using the ENIAC and numerical prediction techniques. One of those meteorologists was John von Neumann, a mathematician at Princeton's Institute for Advanced Study.
Furthermore, the existing category leaders driving billions of dollars of compute heavy workload revenue in the legacy on-premise high performance computing (HPC) market are facing the innovator's dilemma needing to reinvent their entire business to provide effective Big Compute solutions in the space – providing a unique opportunity for the most innovative companies to become category leaders. Just like Big Data removed constraints on data and transformed major enterprise software categories, Big Compute eliminates constraints on compute hardware and provides the ability to scale computational workloads seamlessly on workload-optimized infrastructure configurations without sacrificing performance. A comprehensive Big Compute stack now enables frictionless scaling, application-centric compute hardware specialization, and performance-optimized workloads in a seamless way for both software developers and end-users. Specifically, Big Compute transforms a broad set of full-stack software services on top of specialty hardware into a software-defined layer, which enables programmatic high performance computing capabilities at your fingertips, or more likely, as back-end function evaluations part of software you touch every day.
Technology professionals are changing the rules of doing business – providing key data insights for informed decision-making, reimagining customer interaction, securing customer data, and enhancing operational scalability. Third-party services such as OpenStack help increase the computing power and building reactive microservices in order to compartmentalize and develop a scalable, resilient environment to continuously deploy software architecture using cloud computing tools. Technology professionals are changing the rules of doing business – providing key data insights for informed decision-making, reimagining customer interaction, securing customer data, and enhancing operational scalability. Third-party services such as OpenStack help increase the computing power and building reactive microservices in order to compartmentalize and develop a scalable, resilient environment to continuously deploy software architecture using cloud computing tools.
"When you are born, you know nothing." This is the kind of statement you expect to hear from a philosophy professor, not a Silicon Valley executive with a new company to pitch and money to make. A tall, rangy man who is almost implausibly cheerful, Hawkins created the Palm and Treo handhelds and cofounded Palm Computing and Handspring. His is the consummate high tech success story, the brilliant, driven engineer who beat the critics to make it big. Now he's about to unveil his entrepreneurial third act: a company called Numenta.