3 ms·
Current top post quotes the most negative observation of the paper. Here's the most positive, and perhaps the most useful to HN readers or investors who are exp
by tacos 11y ago
Current top post quotes the most negative observation of the paper. Here's the most positive, and perhaps the most useful to HN readers or investors who are exploring the space:
"Deep learning has produced amazing discriminative models, generative models and feature extractors, but common to all of these is the use of a very large training dataset. Its place in the world is as a powerful tool for general-purpose pattern recognition... Very possibly it is the best tool for working in this paradigm. This is a very good fit for one particular class of problems that the brain solves: finding good representations to describe the constant and enormous flood of sensory data it receives."
- boltzmannbrain 11y agoI would argue the brain solves every problem by first representing sensory data, and then learns temporal patterns of the encoded information.
- auvrw 11y agothe last sentence is also positive as well as insightful Gradient descent in neural networks may well play a big part in helping to build the components of thinking machines, but it is not, itself, the stuff of thought. statistical techniques like these are often used to interpret experimental data. "helping to build the components of thinking machines," (and i think this is the author's intent?) may not mean _being_ the components of thinking machines, just as the 4004 is not a component of the computer that i'm currently using, but it did help build it. idk, though. as cool as the engineering (making) is, i do worry sometimes that the science (understanding) could get overlooked in the process.