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Did you read the article or are you responding to what you imagine it says? > a whole toolchain of specialized models, … all of these specialized models are co
by wokwokwok 3y ago
Did you read the article or are you responding to what you imagine it says?
> a whole toolchain of specialized models, … all of these specialized models are combined with tons of just normal code and logic that creates the end result
They are not referring to a toolchain as “write a compiler”.
They are referring to it as “fine tune models with specific purposes and glue them together with normal code”.
It’s a no-brainer that any startup that doesnt do this is a thin wrapper around the openAI api, has zero moat, and is therefore:
A) deeply vulnerable to having any meaningful product copied by others (including openAI)
B) lazy AF now that fine tuning is so simple to do.
C) will be technically out competed by their competitors because fine tuned models are better.
D) therefore, probably doomed.
> The most important thing is to not use AI at first.
> Explore the problem space using normal programming practices to determine what areas need a specialized model in the first place.
> Remember, making “supermodels” is generally not the right approach.
This is good advice.
> The real secret is to already have a viable business that AI can subsequently improve
You realise that what you said, is the equivalent of what they said, which is: use AI to solve problems, rather than slapping it on meaninglessly.
- johnsonjo 3y agoI don't really have much beef with your comment as it has pretty substantive points, but I just wanted to remind and let everybody know about a Hacker News guideline outlined on their guidelines under the comments section. Sorry, I just recently re-read the guidelines, so I thought I might point others to it too. I honestly believe there are a lot more people breaking all these guidelines on this site, so the whole thing is a good read for anyone uninformed, and yes there are definitely more egregious breakages of guidelines elsewhere. > Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that". [1] I just mainly brought this one up, because I see it come up often, and because I didn't even notice it was really a violation until I reread the guidelines the other day. [1]: https://news.ycombinator.com/newsguidelines.html#comments https://news.ycombinator.com/newsguidelines.html#comments
- meiraleal 3y ago> Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken. If you're worried about abuse, email hn@ycombinator.com and we'll look at the data. Please don't break the guidelines while?
- dang 3y agojohnsonjo's post was fine. It's perfectly ok for users to remind each other of the HN guidelines, as long as they do so respectfully. The parent comment didn't make any insinuations about astroturfing etc., so it didn't break the guideline you quoted. The phrase "if you're worried about abuse" is scoped to that form of abuse—we don't want users accusing each other of being shills and whatnot, but if people are worried that actual astroturfing or shillage is going on, they're welcome to email and we'll be happy to take a look. I hope that is clear!
- johnsonjo 3y agoI’m genuinely curious on whether this is an insinuation on me breaking that rule you posted in my previous comment or towards the general public. If anyone can clarify what is meant by the above comment then I will gladly fix whatever I’m doing wrong. I’m just unsure (a) whether I’ve done something wrong and (b) what part of that statement I did wrong.
- bob1029 3y ago> They are referring to it as “fine tune models with specific purposes and glue them together with normal code”. > will be technically out competed by their competitors because fine tuned models are better. I disagree that fine tuning is the way to go. We spent a large amount of effort on that path and found it to be untenable for our business cases - not from an academic standpoint, but from a practical data management/discipline standpoint. For better or worse, we don't have super clean, structured data about our business. We also aren't big enough to run a full-time data science team. Picking targeted feature verticals and applying few-shot learning w/ narrowly-scoped, dynamic prompts seems to give us a lot more value per $$$ and unit time. For us, things like the function calling API are fine-tuning, because we can now insist that we get a certain shape of response. I have a hard time squaring an implied, simultaneous agreement with "supermodels are generally not the right approach" and "fine tuned models are better". These ideas seem (to me) to be generally at odds with one another. Few-shot learning is still the real magic trick in my book.
- lhnz 3y agoI think this is exactly the kind of issue that the startup Klu [0] is trying to solve. Not everybody should be building their own custom toolchains for finetuning/prompting AI models and there is really quite a lot of work involved in data management/evaluation. There's definitely a lot of value in adding some AI features into your applications, but if it's not your core business you shouldn't be spending a lot of your time building a toolchain to do so. [0] https://klu.ai https://klu.ai
- meiraleal 3y ago> and there is really quite a lot of work involved in data management/evaluation This is boring tech already, we have been doing it for the past 2 decades in the web with CRUD. It doesn't make sense to be an openAI + VC-backed tools wrapper
- lhnz 3y agoI don't agree. You should be collecting data, running experiments and A/B tests and finetuning your own models, but you shouldn't be building CRUD apps to automate this yourself, even if you have two decades of experience doing so. You shouldn't be building out this stuff yourself, unless you are a massive business that can afford to, or it's the core of your business.