MIT & Harvard On Brain-Inspired A.I. Vision

An anonymous reader writes with this excerpt from TGDaily: "Researchers from Harvard and MIT have demonstrated a way to build better artificial visual systems with the help of low-cost, high-performance gaming hardware. [A video describing their research is available.] 'Reverse engineering a biological visual system — a system with hundreds of millions of processing units — and building an artificial system that works the same way is a daunting task,' says David Cox, Principal Investigator of the Visual Neuroscience Group at the Rowland Institute at Harvard. 'It is not enough to simply assemble together a huge amount of computing power. We have to figure out how to put all the parts together so that they can do what our brains can do.' The team drew inspiration from screening techniques in molecular biology, where a multitude of candidate organisms or compounds are screened in parallel to find those that have a particular property of interest. Rather than building a single model and seeing how well it could recognize visual objects, the team constructed thousands of candidate models, and screened for those that performed best on an object recognition task. The resulting models outperformed a crop of state-of-the-art computer vision systems across a range of test sets, more accurately identifying a range of objects on random natural backgrounds with variation in position, scale, and rotation. Using ordinary CPUs, the effort would have required either years or millions of dollars of computing hardware. Instead, by harnessing modern graphics hardware, the analysis was done in just one week, and at a small fraction of the cost."

Low hardware

[gmuslera]

Eyes, brain raw power, could be considered somewhat "low" technology, But you need to be smart to implement a pattern recognition engine (and integration with existing data) as the brain have. Think that you can have "vision" with something far less precise than eyes (with i.e. this [seeingwithsound.com] and similar low res devices).

How much power requires that pattern recognition? By standards approachs probably a lot, but the approach they seem to use there (like in compare how much fits what they have with thousands of candidate models) could require less, and far better if you use for that hardware that are more adequated for that task.