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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.


Understanding the differences between biological 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. In fact, it wasn't until the early 2010s that image classifiers and object detectors were flexible and reliable enough to be used in mainstream applications.


Control of a Robot Arm with Artificial and Biological Neural Networks

AAAI Conferences

To perform research on learning in cultures of mouse neurons, a hardware and software system for interfacing a biological neuronal culture to a robot arm has been constructed. The software architecture is modular, which permits simulated neurons to be used in place of biological neurons. In both cases, the activity of the culture over time is represented as an activation vector that captures recent spatiotemporal patterns of neuron firing. The activation vector is converted into control signals for the arm in a manner that can be generalized to multiple degrees of freedom. Preliminary results from the system with both simulated and biological cultures are presented.


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.


Machine Learning Predicts Behavior of Biological Circuits

#artificialintelligence

Biomedical engineers at Duke University have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict. Their algorithms are generalizable to many kinds of biological systems. In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model. To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers.