Unconstrained by evolution, AI has the potential to churn through vast amounts of data to surpass our puny, fatty central processors. Yet even as a single algorithm beats humans in a specific problem—chess, Go, Dota, medical diagnosis for breast cancer—humans kick their butts every time when it comes to a brand new task. Somehow, the innate structure of our brains, when combined with a little worldly experience, lets us easily generalize one solution to the next. State-of-the-art deep learning networks can’t.
In a new paper published in Neuron, [Dr. Andreas] Tolias and colleagues in Germany argue that more data or more layers in artificial neural networks isn’t the answer. Rather, the key is to introduce inductive biases—somewhat analogous to an evolutionary drive—that nudge algorithms towards the ability to generalize across drastic changes.
“The next generation of intelligent algorithms will not be achieved by following the current strategy of making networks larger or deeper. Perhaps counter-intuitively, it might be the exact opposite…we need to add more bias to the class of [AI] models,” the authors said.
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What better source of inspiration than our own brains?
Read full, original post: Deep Learning Networks Can’t Generalize—But They’re Learning From the Brain
