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It's interesting to see this article in juxtaposition to the one shared recently[1], where AI skeptics were labeled as "nuts", and hallucinations were "(more or
by imiric 1y ago
It's interesting to see this article in juxtaposition to the one shared recently[1], where AI skeptics were labeled as "nuts", and hallucinations were "(more or less) a solved problem".
This seems to be exactly the kind of results we would expect from a system that hallucinates, has no semantic understanding of the content, and is little more than a probabilistic text generator. This doesn't mean that it can't be useful when placed in the right hands, but it's also unsurprising that human non-experts would use it to cut corners in search of money, power, and glory, or worse—actively delude, scam, and harm others. Considering that the latter group is much larger, it's concerning how little thought and resources are put into implementing _actual_ safety measures, and not just ones that look good in PR statements.
[1]: https://news.ycombinator.com/item?id=44163063 https://news.ycombinator.com/item?id=44163063
- BlueTemplar 1y agoHeh, reminds me of cryptocurrencies... Or even of the Internet in general. I guess it's a common pitfall with information or communication technologies ? (Heck, or with technologies in general, but non-information or communication ones rarely scale as explosively...)
- imiric 1y agoIt's a common symptom of the Gartner hype cycle. This doesn't mean that there aren't very valid use cases for these technologies that can benefit humanity in many ways (and I mean this for both digital currencies and machine learning), but unfortunately those get drowned out by the opportunity seekers and charlatans that give the others the same bad reputation. As usual, it's best to be highly critical of opinions on both extreme sides of the spectrum until (and if) we start climbing the Slope of Enlightenment.
- JackC 1y agoThe difference in fields is key here: AI models are going to have a very different impact in fields where ground truth is available instantly (does the generated code have the expected output?) or takes years of manual verification. (Not a binary -- ground truth is available enough for AI to be useful to lots of programmers.)
- weatherlite 1y ago> does the generated code have the expected output? That's many times not easy to verify at all ...
- dathinab 1y agoyou can easily verify a lot like: - correct syntax - passes lints - type checking passes - fast test suite passes - full test suite passes and every time it doesn't you feed it back into the LLM, automatically, in a loop, without your involvement. The results are often -- sadly -- too good to not slowly start using AI. I say sadly because IMHO the IT industry has gone somewhere very wrong due to growing too fast, moving too fast and getting so much money so that the companies spear heading them could just throw more people at it instead of fixing underlying issues. There is also a huge diverge between sience about development, programming, application composition etc. (not to be confused with since about idk. data-structures and fundamental algorithms) and what the industry uses, how it advances etc. Now I think normally the industry would auto correct at some point, but I fear with LLMs we might get even further away from any fundamental improvements, as we find even more ways to still go along and continue the mess we have. Worse performance of LLM coding is highly dependent on how much very similar languages are represented in it's dataset, so new languages with any breakthrough/huge improvements or similar will work less good with LLMs. If that trend continues that would lock us in with very mid solutions long term.