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Sep 8, 2015

Finding Artificial Intelligence Through Storytelling — An Interview with Dr. Roger Schank

Posted by in categories: machine learning, robotics/AI

The media is all-abuzz with tales of Artificial Intelligence (AI). The provocative two-letter symbol conjures up images of invading autonomous robot drones and Terminator-like machines wreaking havoc on mankind. Then there’s the pervading presence of deep learning and big data, also referred to as artificial intelligence. This might leave some of us wondering, is artificial intelligence one or all of these things?

In that sense, AI leaves a bit of an ambiguous trail – there does not seem to be a clear definition, even amongst scientists and researchers in the field. There are certainly many different branches of AI. I asked Dr. Roger Schank, Professor Emeritus at Northwestern University, for a more clear definition; he told me that artificial intelligence is not big data and deep learning algorithms, at least not in the pure sense of the definition.

Roger emphasizes that intelligence has everything to do with the intersection of learning and interaction and memory. “I will tell you the number one thing people do, it’s pretty obvious – they talk to each other. Guess how hard that is? That is phenomenally hard, that is the subsection of AI called natural language processing, the part that I worked on my whole life, and I understand how far away we are from that.”

Take a “simple” AI concept, such as how to create a computer that plays chess, to better understand the challenge. There are, more or less, two approaches to creating an intelligent machine that can play chess like a champion. The first approach requires programming the computer to predict thousands of moves ahead of time, while the second approach involves building a computer system that tries to imitate a grand master. In the historical pursuit of how to create an artificially intelligent entity, a vast majority of scientists chose the first option of programming based on prediction.

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Aug 30, 2015

AI Dangerous for Economics? The Other Threat Flying Under Radars

Posted by in categories: economics, machine learning, security

Dr. Nils J. Nilsson spent almost a lifetime in the field of Artificial Intelligence (AI) before writing and publishing his book, The Quest for Artificial Intelligence (2009). I recently had the opportunity to speak with the former Stanford computer science professor, now retired at the age of 82, and reflect on the earlier accomplishments that have led to some of the current trends in AI, as well as the serious economic and security considerations that need to be made about AI as society moves ahead in the coming decades.

The Early AI that Powers Today’s Trends

One key contribution of early AI developments included rules-based expert systems, such as MYCIN, which was developed in the mid-1970s by Ted Shortliffe and colleagues at Stanford University. The information built into the diagnostic system was gleaned from medical diagnosticians, and the system would then ask questions based on that information. A person could then type in answers about a patient’s tests, symptoms, etc., and the program would then attempt to diagnose diseases and prescribe therapy.

“Bringing us more up to the future was the occurrence of huge databases (in the 1990s) — sometimes called big data — and the ability of computers to mine that data and find information and make inferences,” remarks Nils. This made possible the new work on face recognition, speech recognition, and language translation. “AI really had what might be called a take off at this time.” Both of these technologies also feed into the launch of IBM’s Watson Healthcare, which combines advanced rules-based systems with big data capabilities and promises to give healthcare providers access to powerful tools in a cloud-based data sharing hub.

Work in neural networks, another catalyst, went through two phases, an earlier phase in the 1950s and 1960s and a latter phase in the 1980s and 1990s. “The second phase (of neural networks) allowed…people to make changes in the connected strength in those networks and multiple layers, and this allowed neural networks that can steer and drive automobiles.” More primitive networks led to the cutting-edge work being done by today’s scientists in the self-driven automobile industry via companies like Tesla and Google.

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Jul 23, 2015

2015 IEEE International Geoscience and Remote Sensing Symposium | July 26–31, 2015 | Milan, Italy

Posted by in categories: big data, complex systems, computing, food, information science, machine learning, mapping, space, surveillance, sustainability

MilanPhotoCollage_md


Hosted by the IEEE Geoscience and Remote Sensing Society, the International Geoscience and Remote Sensing Symposium 2015 (IGARSS 2015) will be held from Sunday July 26th through Friday July 31th, 2015 at the Convention Center in Milan, Italy. This is the same town of the EXPO 2015 exhibition, whose topic is “Feeding the planet: energy for life”.

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Jun 16, 2015

Deep Learning Machine Beats Humans in IQ Test and performs between bachelor and masters degree level — Brian Wang Next Big Future

Posted by in categories: machine learning, robotics/AI

http://www.top10films.co.uk/img/data_star-trek_human-robots.jpg

They took each word and looked for other words that often appear nearby in a large corpus of text. They then use an algorithm to see how these words are clustered. The final step is to look up the different meanings of a word in a dictionary and then to match the clusters to each meaning.

This can be done automatically because the dictionary definition includes sample sentences in which the word is used in each different way. So by calculating the vector representation of these sentences and comparing them to the vector representation in each cluster, it is possible to match them. Read more

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