Google’s DeepMind acquisition in reinforcement learning. I recently read a description of economists attributed to Robert Solow: “There are two kinds of economists: those who look for general results and those who look for illuminating examples.” Maybe this is the divide that separates Artificial Intelligence from cognitive science. AI seeks general results while the latter explains illuminating examples. Google’s recently reported $400 to $500 million acquisition of DeepMind, the University of Oxford’s Future of Humanity Institute affiliated company, brings it closer to achieving illuminating examples instead of general results.
DeepMind specializes in an advance form of Machine Learning called Reinforcement Learning. They have effectively developed algorithms to solve high-dimensional uncertain sequential decision-making problems. The more advanced reinforcement learning methods improve mechanisms for knowledge representation, search, and human-level reasoning. (A paper by the DeepMind founders on reinforcement learning can be found here). Inside DeepMind. A new paper from Google’s DeepMind team indicates that their technology is a “novel artificial agent” that combines two existing forms of brain-inspired machine intelligence: a deep neural network and a reinforcement-learning algorithm. Published this week in Nature, Human-level control through deep reinforcement learning, illustrates DeepMind’s neural agent learning to play dozens of computer games from only minimal information. In other words, the DeepMind algorithms help the game playing A.I. analyze its previous performance, decipher which actions led to better scores, and change its future behavior.
The co-founder of DeepMind Demis Hassabis recently said: “The artificial general intelligence we work on here automatically converts unstructured information into useful, actionable knowledge.” Effectively DeepMind has discovered a way of integrating memory into learning algorithms! If you liked this article, you may also be interested in: Talking Machines: History of machine learning, w. Geoffrey Hinton, Yoshua Bengio, Yann LeCun. Cognitive Computing to Transform Travel—All Over Again. Terry Jones, Executive Chairman, WayBlazer By Terry Jones My first job when I got out of college in 1971 was as a receptionist at a travel agency in Chicago. In those days, believe it or not, we used telegrams to make international reservations.
It’s amazing to think how far travel has come since then—and the role that information technology has played in those changes. Today, the travel industry is primed for yet another revolution. WayBlazer taps into the power of IBM’s Watson to help Web sites create travel experiences that fit the interests and budgets of individual consumers. Today’s travel sites are very good at helping people purchase flights, hotels, car rentals and travel packages. The online travel industry has been an amazing success. What the large travel sites are not good at is giving advice. IBM Watson is at the core of WayBlazer’s search engine. Our first customer is the Austin Convention & Visitor’s Bureau (ACVB). Say three friends are planning a “guys trip” to Austin. Draft-ietf-abfab-usecases-05. Multi-factor authentication. Multi-factor authentication (also MFA, two-factor authentication, two-step verification, TFA, T-FA or 2FA) is an approach to authentication which requires the presentation of two or more of the three authentication factors: a knowledge factor ("something only the user knows"), a possession factor ("something only the user has"), and an inherence factor ("something only the user is").
After presentation, each factor must be validated by the other party for authentication to occur. Background[edit] Two-factor authentication is commonly found in the electronic computer authentication, where basic authentication is the process of a requesting entity presenting some evidence of its identity to a second entity. Two-factor authentication seeks to decrease the probability that the requester is presenting false evidence of its identity. Two-factor authentication is often confused with other forms of authentication. Regulatory definition[edit] Limitations[edit] Password[edit] PIN[edit] Pattern[edit] Pubs/2009-IEEE-SP--hashing.pdf.
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