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Understanding the differences between biological and computer vision

#artificialintelligence

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Since the early years of artificial intelligence, scientists have dreamed of creating computers that can "see" the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence. But like many other goals in AI, computer vision has proven to be easier said than done. In 1966, scientists at MIT launched "The Summer Vision Project," a two-month effort to create a computer system that could identify objects and background areas in images.


What's the difference between human eyes and computer vision?

#artificialintelligence

Since the early years of artificial intelligence, scientists have dreamed of creating computers that can "see" the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence. But like many other goals in AI, computer vision has proven to be easier said than done. In 1966, scientists at MIT launched "The Summer Vision Project," a two-month effort to create a computer system that could identify objects and background areas in images. But it took much more than a summer break to achieve those goals.


Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach

arXiv.org Artificial Intelligence

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about "What is information?" In particular, what is "visual information" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of "information", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.


The relationship between Biological and Artificial Intelligence

#artificialintelligence

Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI. Symbolic AI was the prevailing approach to AI until the early 90's. It is reliant on human programmers coding complex rules to enable machines to complete complex tasks. Continuing failure of this approach to solve many tasks crucial to intelligence provides a good contrast with Machine Learning -- an alternative approach to AI which is essential to the current advent of artificially intelligent machines. In 1994 the reigning chess champion Garry Kasparov was beaten by Deep Blue.


The relationship between Biological and Artificial Intelligence

arXiv.org Artificial Intelligence

Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI. Acknowledgements I thank Kate Wilkinson for extensive editing and proofreading of this article. Dr Kristjan Kalm's critical review of this article was essential for its scientific integrity; however, not all views expressed in this article reflect Kristjan's own views. Finally, I would like to thank illumr Ltd, especially its founder and CEO Jason Lee, for sponsoring this work. Symbolic AI was the prevailing approach to AI until the early 90's.