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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.


Getting Artificial Neural Networks Closer to Animal Brains

#artificialintelligence

Lately, I've been thinking and reading a lot about consciousness and how the human mind works. A question that emerges all the time is whether machines can emulate human thought. An even more interesting one is whether consciousness (a subjective experience) can arise from a machine, but I'll leave that discussion for a future post (I'll need 20 more years to think about that before I can write about it). So, how far are we from _behaviorally _imitating a human? Truth is, we achieved a lot in the past 5 years (see AlphaGo, OpenGPT-2, OpenAI Jukebox, Tesla Autopilot, Alphastar, OpenAI Dota2 Team, OpenAI API), but we're still quite not there.


Deep Convolutional Neural Networks as Models of the Visual System: Q&A

#artificialintelligence

Yes. First, artificial neural networks as whole were inspired--as their name suggests--by the emerging biology of neurons being developed in the mid-20th century. Artificial neurons were designed to mimic the basic characteristics of how neurons take in and transform information. Second, the main features and computations done by convolutional networks were directly inspired by some of the early findings about the visual system. In 1962 Hubel and Wiesel discovered that neurons in primary visual cortex respond to specific, simple features in the visual environment (particularly, oriented edges). Furthermore, they noticed two different kinds of cells: simple cells--which responded most strongly to their preferred orientation only at a very particular spatial location--and complex cells--which had more spatial invariance in their response.


Questions to Guide the Future of Artificial Intelligence Research

arXiv.org Artificial Intelligence

The field of machine learning has focused, primarily, on discretized sub-problems (i.e. vision, speech, natural language) of intelligence. While neuroscience tends to be observation heavy, providing few guiding theories. It is unlikely that artificial intelligence will emerge through only one of these disciplines. Instead, it is likely to be some amalgamation of their algorithmic and observational findings. As a result, there are a number of problems that should be addressed in order to select the beneficial aspects of both fields. In this article, we propose leading questions to guide the future of artificial intelligence research. There are clear computational principles on which the brain operates. The problem is finding these computational needles in a haystack of biological complexity. Biology has clear constraints but by not using it as a guide we are constraining ourselves.