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(while I work at OAI, the opinion below is strictly my own) I feel like the current version is fairly hazardous to students and might leave them worse off. If
by nmca 2y ago
(while I work at OAI, the opinion below is strictly my own)
I feel like the current version is fairly hazardous to students and might leave them worse off.
If I offer help to nontechnical friends, I focus on:
- look at rate of change, not current point
- reliability substantially lags possibility, by maybe two years.
- adversarial settings remain largely unsolved if you get enough shots, trends there are unclear
- ignore the parrot people, they have an appalling track record prediction-wise
- autocorrect argument is typically (massively) overstated because RL exists
- doomers are probably wrong but those who belittle their claims typically understand less than the doomers do
- dimgl 2y agoWhat are “parrot people”? And what do you mean by “doomers are probably wrong?”
- moozilla 2y agoOP is likely referring to people who call LLMs "stochastic parrots" (https://en.wikipedia.org/wiki/Stochastic_parrot https://en.wikipedia.org/wiki/Stochastic_parrot), and by "doomers" (not boomers) they likely mean AI safetyists like Eliezer Yudkowsky or Pause AI (https://pauseai.info/ https://pauseai.info/).
- layoric 2y agoHow does this help the students with their use of these tools in the now, to not be left worse off? Most of the points you list seem like defending against criticism rather than helping address the harm.
- habinero 2y agoAgree. It's also a virtue to point out the emperor has no clothes and the tailor peddling them is a bullshit artist. This is no different than the crypto people who insisted the blockchain would soon be revolutionary and used for everything, when in reality the only real use case for a blockchain is cryptocoins, and the only real use case for cryptocoins is crime. The only really good use case for LLMs is spam, because it's the only use case for generating a lot of human-like speech without meaning.
- radlad 2y ago> The only really good use case for LLMs is spam, because it's the only use case for generating a lot of human-like speech without meaning. As someone who's been writing code for nearly 20 years now, and who spent a few weeks rewriting a Flutter app in Jetpack Compose with some help from Claude (https://play.google.com/store/apps/details?id=me.johnmaguire.eureka https://play.google.com/store/apps/details?id=me.johnmaguire...), I have to say I don't agree with this at all.
- habinero 2y agoOk? I too have been coding for over a decade and use Copilot as fancy autocomplete. I like it. It's not amazing.
- radlad 2y agoClaude isn't Copilot, and I wasn't using it as autocomplete. I was using it to do things such as: - Creating a migration from the old DB to the new DB, no modifications of the generated code necessary - Refactoring state in a component out into a ViewModel, again no modifications necessary - Creating all the classes necessary for interacting with a Room database (i.e. the data class, dao, and DI module) given a schema - Creating the first iteration of a download worker, which I modified Check out plugins like ClaudeMind for JetBrains! They can even intelligently (automatically) feed information from your current tab or other unopened but relevant-sounding files to the AI. It was an eye-opening experience.
- habinero 2y agoI would consider all of that fancy autocomplete. If it's just helping you type boring boilerplate things you would have typed anyway, that's autocomplete. It's neat, but it's not the paradigm shift people seem to think it is. Copilot mostly just replaces StackOverflow searches for me.
- gravatron 2y ago
- jdlshore 2y agoI read the whole course. Lesson 16, “The Next-Step Fallacy,” specifically addresses your argument here.
- nmca 2y agoThe discourse around synthetic data is like the discourse around trading strategies — almost anyone who really understands the current state of the art is massively incentivised not to explain it to you. This makes for piss-poor public epistemics.
- llm_trw 2y agoI'm happy to explain my strategies about synthetic data - it's just that you'll need to hear about the onions I wore in my day: https://www.youtube.com/watch?v=yujF8AumiQo https://www.youtube.com/watch?v=yujF8AumiQo
- habinero 2y agoNah, you don't need to know the details to evaluate something. You need the output and the null hypothesis. If a trading firm claims they have a wildly successful new strategy, for example, then first I want to see evidence they're not lying - they are actually making money when other people are not. Then I want to see evidence they're not frauds - it's easy to make money if you're insider trading. Then I want to see evidence that it's not just luck - can they repeat it on command? Then I might start believing they have something. With LLMs, we have a bit of real technology, a lot of hype, a bunch of mediocre products, and people who insist if you just knew more of the secret details they can't explain, you'd see why it's about to be great. Call it Habiñero's Razor, but for hype the most cynical explanation is most likely correct -- it's bullshit. If you get offended and DARVO when people call your product a "stochastic parrot", then I'm going to assume the description is accurate.
- llm_trw 2y agoI don't get offended when people call my work a stochastic parrot. I just put them in the same bucket of intelligence as an 8b model and weight their inputs accordingly.
- bo1024 2y agoThis seems like trying to offer help predicting the future or investing in companies, which is a different kind of help from how to coexist with these models, how to use them to do useful things, what their pitfalls are, etc.
- deleted 2y ago[deleted]
- owl_vision 2y agomy english teacher reminded us the same. +1