Some considerations on how the human brain must be arranged in order to make its replication in a thinking machine possible Artificial Intelligence

For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.

The Thinking Machine

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"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. But what Hawkins, 49, really wants to talk about -- in fact, what he has really wanted to talk about for the past 30 years -- isn't gadgets or source codes or market niches.

Machine Learning Vs. Artificial Intelligence: Unpacking Their Histories AdExchanger


"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. There is a lot of excitement and some confusion across the ad industry around machine learning, and for good reason.

When AI becomes conscious: Talking with Bina48, an African-American robot


An executive guide to the technology and market drivers behind the $135 billion robotics market. Artist Stephanie Dinkins tells a fascinating story about her work with an AI robot made to look like an African-American woman and at times sensing some type of consciousness in the machine. She was speaking at the de Young Museum's Thinking Machines conversation series, along with anthropologist Tobias Rees, Director of Transformation with the Humans Program at the American Institute, Dinkins is Associate Professor of Art at Stony Brook University and her work includes teaching communities about AI and algorithms, and trying to answer questions such as: Can a community trust AI systems they did not create? She has worked with pre-college students in poor neighborhoods in Brooklyn and taught them how to create AI chat bots. They made a chat bot that told "Yo Mamma" jokes - which she said was a success because it showed how AI can be made to reflect local traditions.