artificial intelligence News for September 09 2017

Sam Harris on Artificial Intelligence

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Artificial Intelligence | LinkedIn

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C# Artificial Intelligence (AI) Programming: A Basic …

A Neural Network is an Artificial Intelligence (AI) methodology that attempts to mimic the behavior of the neurons in our brains. Neural networks really shine when it …
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Robots and Artificial Intelligence, or How Robots Make us More Human

Obviously, when robots and Artificial Intelligence are combined together, you expect to get super clever robots. How can it help you to make a robot? Well if robots and Artificial Intelligence can simulate emotions, they may be able to single out the important information they need. Marvin Minsky shows how it could be used to build better robots and artificial intelligence in his books “Society of Agents” and “The Emotion Machine”. The more I study robots and human intelligence, the more I am impressed by what we can do every day without even thinking about it. The point is that robots and artificial intelligence will extend their possibilities by thinking like us.
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IBM and MIT to pursue joint research in artificial intelligence, establish new MIT-IBM Watson AI Lab

IBM and MIT today announced that IBM plans to make a 10-year, $240 million investment to create the MIT-IBM Watson AI Lab in partnership with MIT. The lab will carry out fundamental artificial intelligence research and seek to propel scientific breakthroughs that unlock the potential of AI. The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society. IBM’s $240 million investment in the lab will support research by IBM and MIT scientists. The new lab will be one of the largest long-term university-industry AI collaborations to date, mobilizing the talent of more than 100 AI scientists, professors, and students to pursue joint research at IBM’s Research Lab in Cambridge, Massachusetts – co-located with the IBM Watson Health and IBM Security headquarters in Kendall Square – and on the neighboring MIT campus. Application of AI to industries: Given its location in IBM Watson Health and IBM Security headquarters in Kendall Square, a global hub of biomedical innovation, the lab will develop new applications of AI for professional use, including fields such as health care and cybersecurity. In addition to IBM’s plan to produce innovations that advance the frontiers of AI, a distinct objective of the new lab is to encourage MIT faculty and students to launch companies that will focus on commercializing AI inventions and technologies that are developed at the lab. The lab’s scientists also will publish their work, contribute to the release of open source material, and foster an adherence to the ethical application of AI. “The field of artificial intelligence has experienced incredible growth and progress over the past decade. Yet today’s AI systems, as remarkable as they are, will require new innovations to tackle increasingly difficult real-world problems to improve our work and lives,” says John Kelly III, IBM senior vice president, Cognitive Solutions and Research. “The extremely broad and deep technical capabilities and talent at MIT and IBM are unmatched, and will lead the field of AI for at least the next decade.” “True breakthroughs are often the result of fresh thinking inspired by new kinds of research teams. The combined MIT and IBM talent dedicated to this new effort will bring formidable power to a field with staggering potential to advance knowledge and help solve important challenges.” Both MIT and IBM have been pioneers in artificial intelligence research, and the new AI lab builds on a decades-long research relationship between the two. In 2016, IBM Research announced a multiyear collaboration with MIT’s Department of Brain and Cognitive Sciences to advance the scientific field of machine vision, a core aspect of artificial intelligence. IBM and the Broad Institute of MIT and Harvard have established a five-year, $50 million research collaboration on AI and genomics. Currently, the Computer Science and Artificial Intelligence Laboratory, the Media Lab, the Department of Brain and Cognitive Sciences, the Center for Brains, Minds and Machines, and the MIT Institute for Data, Systems, and Society serve as connected hubs for AI and related research at MIT. For more than 20 years, IBM has explored the application of AI across many areas and industries. IBM researchers invented and built Watson, which is a cloud-based AI platform being used by businesses, developers, and universities to fight cancer, improve classroom learning, minimize pollution, enhance agriculture and oil and gas exploration, better manage financial investments, and much more. For information about employment opportunities with IBM at the new AI Lab, please visit MITIBMWatsonAILab.
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Artificial Intelligence And Big Data: Good For Innovation?

Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence together with customers’ prior sales histories, to predict potential purchases in the future, to name but a few examples. Machine learning requires lots of data to create, test and “Train” the AI. Thus, as AI is becoming more important to the economy, so too is data. The Economist highlighted the important role of data in a recent cover story in which it stated “The world’s most valuable resource is no longer oil, but data.” In this sense, both the ability to obtain data about customers, together with the ability to program AI to analyze the data, have become important tools businesses use to compete against each other, and against potential entrants. A potential entrant that lacks access to good data faces substantial hurdles, and this has led some regulators to question the extent to which control over data creates barriers to entry. In December 2015 FTC Commissioner Terrell McSweeney asked: “Can one company controlling vast amounts of data possess a kind of market power that creates a barrier to entry?” This is a worry, because if barriers to entry are too high, entrants will not enter, established firms will not feel competitive pressures, and innovation may suffer. Thus, in March 2017 CFPB Director Richard Cordray noted: “We recognize that data access makes it possible to realize the many benefits of competition and innovation.” A common technique that entrants currently use to overcome the lack of customer data is to train their AI on publicly available datasets. An established firm’s access to data may allow it to take advantage of a learning curve, which may exacerbate barriers to entry for other firms. An established firm’s access to data allows it to refine its AI over time, allowing it to get better at offering its AI-enabled product over time. Due to this learning-curve effect, there are increasing returns to scale in data control. As Daron Acemoglu and Simon Johnson put it, “The real power-for good and ill-is in software and increasing returns to data. If one self-driving car company does well initially, it will be able to collect more data-and then further improve its algorithm. Other companies will not be able to catch up.” The learning-curve effect is not just about learning how to use your data better, it is about how to better organize your business around the new technology. The two firms had very different business models: Blockbuster targeted impulse renters; Netflix targeted technologically sophisticated movie buffs via DVDs by mail. Blockbuster eventually realized the threat posed by Netflix and attempted to replicate Netflix’s DVD by mail business, but was unable to do so successfully.
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Using artificial intelligence to identify protestors wearing hats or scarves is entirely possible

Artificial intelligence is giving rise to unprecedented capabilities for surveillance, from facial recognition at bridge crossings to the ability to identify thousands of people at once. Now, new research suggests that AI could potentially be used to identify people who have taken steps to conceal their identities by wearing hats, sunglasses, or scarves over their faces. The paper, accepted to appear in a computer vision conference workshop next month and detailed in Jack Clark’s ImportAI newsletter, shows that identifying people covering their faces is possible, but there’s a long way to go before it’s accurate enough to be relied upon. Researchers used a deep-learning algorithm-a flavor of artificial intelligence that detects patterns within massive amounts of data-to find specific points on a person’s face and analyze the distance between those points. When asked to compare a face concealed by a hat or scarf against photos of five people, the algorithm was able to correctly identify the person 56% of the time. If the face was also wearing glasses, that number dropped to 43%. But those imperfect results don’t mean the paper should be ignored. The team, with members from the University of Cambridge, India’s National Institute of Technology, and the Indian Institute of Science, also released two datasets of disguised and undisguised faces for others to test and improve the technology. The algorithm for identifying disguised faces maps 14 points on a person’s face, and then uses the distance between those points to identify them again in other images. A team of Carnegie Mellon researchers printed glasses with a pattern custom-built to fool facial-recognition algorithms into misidentifying the wearer as someone else, such as a celebrity. Other attempts at beating identifying algorithms have included custom scarves with patterns that look, to machines, like human faces. Faces aren’t the only way to identify a person-other AI research indicates that the way a person walks is almost like a fingerprint. Researchers achieved more than 99% accuracy when using a deep-learning algorithm to identify one person’s gait from 154 others. The new research skips past generic fear-mongering about artificial intelligence to get more directly at the realistic implications of AI systems being developed and used by unchecked or authoritarian government powers. Facial-recognition technology that could bypass disguises, for example, would be immensely useful for identifying political dissidents or protestors.
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