Breakthrough In Automatic Handwritten Character Recognition Sans Deep Learning (technologyreview.com)

subh_arya writes: Researchers from NYU, UToronto and MIT have come up with a technique that captures human learning abilities for a large class of simple visual concepts to recognize handwritten characters from World's Alphabet. Their computational model (abstract) represents concepts as simple programs that best explain observed examples under a Bayesian criterion. Unlike recent deep learning approaches that require thousands of examples to train an efficient model, their model can achieve human-level performance with only one example. Additionally, the authors present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

Comments

Great

[sexconker]

I'll never solve the new captchas.

Well that's cleverer

[Zero__Kelvin]

Seriously? Cleverer? Oodles ? What editor left those in the paper? Slashdot editors must be working for these guys on the side. I know somebody will say cleverer is technically correct, and while that may be true, it is a disaster aesthetically.

"“The real inflection point in AI is going to come when machines can actually understand language,” he says. “Not just doing mediocre translations, but really understanding what you mean.”"

Until we understand what it means to understand, how can we possibly know if we have taught these systems to understand? Even if it responds intelligibly, and what it says makes complete sense, is that the same as understanding? I suppose as Billy C. once said: "It depends on what the meaning of the word 'is' is".