Training nets to model aspects of human intelligence is a fine art.
Success with backpropagation and other connectionist learning methods may depend on quite subtle adjustment of the algorithm and the training set. Training typically involves hundreds of thousands of rounds of weight adjustment. Given the limitations of computers presently available to connectionist researchers, training a net to perform an interesting task may take days or even weeks. Some of the difficulty may be resolved when parallel circuits specifically designed to run neural network models are widely available. But even here, some limitations to connectionist theories of learning will remain to be faced. Humans (and many less intelligent animals) display an ability to learn from single events; for example an animal that eats a food that later causes gastric distress will never try that food again.
Connectionist learning techniques such as backpropagation are far from explaining this kind of ‘one shot’ learning.
Success with backpropagation and other connectionist learning methods may depend on quite subtle adjustment of the algorithm and the training set. Training typically involves hundreds of thousands of rounds of weight adjustment. Given the limitations of computers presently available to connectionist researchers, training a net to perform an interesting task may take days or even weeks. Some of the difficulty may be resolved when parallel circuits specifically designed to run neural network models are widely available. But even here, some limitations to connectionist theories of learning will remain to be faced. Humans (and many less intelligent animals) display an ability to learn from single events; for example an animal that eats a food that later causes gastric distress will never try that food again.
Connectionist learning techniques such as backpropagation are far from explaining this kind of ‘one shot’ learning.
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See that post with different algorithms in metabole
See the journal French Metablog with today different posts
