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This thesis seems very credible, but it misses one downside of his "software 2.0" definition: you need significant amounts of data to train the neural networks.

by ricw 4y ago

This thesis seems very credible, but it misses one downside of his "software 2.0" definition: you need significant amounts of data to train the neural networks. Most problems do not have this amount of data. Not even close. So yes, this will revolutionize and enable unseen performance in the few areas where there is significant data. For all the rest it'll be business as usual.

hn_throwaway_99 4y ago
> you need significant amounts of data to train the neural networks. Most problems do not have this amount of data. Not even close. Going to push back against this one. I think we have a lot more training data than most people realize. I wrote this comment yesterday, https://news.ycombinator.com/item?id=34862450 https://news.ycombinator.com/item?id=34862450, about how a large government contractor is using ChatGPT to generate first drafts of responses to government RFPs. Now, most of these RFPs are in very specific areas, technologically speaking (e.g. specific technologies around cloud network security, for example). These folks were actually blown away by how technically accurate ChatGPT was on many different areas, even very specific niche areas, and even considering ChatGPT's view of the world hasn't been updated since late 2021. Again, the first draft needed to be edited, but there are is a huge amount of data out there that ChatGPT is able to use coherently on even niche, esoteric topics.