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Lessons from YC AI Startups

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Re: Lessons from YC AI Startups

#51

Post by SkyMarshal »

epolanski wrote: post #43
Hermitian909 wrote: post #27

Earlier quoted context omitted.

Previously worked on the intersection of ML and Material Science (specifically batteries). I think the link to osium-ai[0] tells the story pretty well. Material science is plagued by long, expensive, exploration phases - even in places you wouldn't expect them. ML ends up being really good at cutting the expense of these phases by >50% by making you just a little smarter in your exploration. [0] https://www.ycombinat…

But this isn't new, isn't it? I remember, e.g, Italy and Brazil had projects more than ten years ago where they used some sort of machine learning to find hidden historical buildings under terrain or jungle by looking at patterns in satellite/aerial image and it was successful in finding archeological sites in both countries.

By “exploration” he’s referring to Materials Science stuff, like figuring out molecular combinations that produce materials with desired properties. Not Archeological/GIS exploration.

Re: Lessons from YC AI Startups

#52

Post by SkyMarshal »

malux85 wrote: post #32
baq wrote: post #28

Earlier quoted context omitted.

Kind of mind blowing. A light in the tunnel for the reproduction crisis? What specifically have you trained your coding LLM on? Is it lora or something more advanced? Have you created a corpus by hand specifically for training?

Yes, created by hand, lots of techniques are required to get a well running system, GoT (graph of thought) RAG (retrieval augmented generation), Ko detection, dynamic problem decomposition, and a few more techniques I have invented but dont really have names for. Its also quite a bit more complicated than the simplistic answer I gave before because you have to do things like experiments in abstraction laddering to ge…

That’s too bad, did YC give you a reason?

Re: Lessons from YC AI Startups

#53

Post by dcl »

epolanski wrote: post #42
noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

It really sounds like AI is a bubble if people ask how they can cram AI into their workflow rather than coming up with real problems they want to automate. I suspect that a very large number of applications that could've been "normal" programs are going to be AI'd with no clear pros but many cons.

It's been that way for years, but with data-science and machine learning.

Re: Lessons from YC AI Startups

#54

Post by Gerardo1 »

epolanski wrote: post #42
noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

It really sounds like AI is a bubble if people ask how they can cram AI into their workflow rather than coming up with real problems they want to automate. I suspect that a very large number of applications that could've been "normal" programs are going to be AI'd with no clear pros but many cons.

I keep bringing this up, but this launch HN post about Roundtable: https://news.ycombinator.com/item?id=36865625

still shocks me. It's a company that uses AI to produce survey results. I'll let you read their pitch/description and decide for yourself, but I think it's very fair to say that this is a service to fabricate survey results to validate whatever idea it is you had beforehand. But even side-stepping that, they claim to have overcome bias in their datasets and refused to elaborate on 1) how they did that and 2) how they could prove that they did that.

As long as this community, which is far more technically sophisticated than the general public, isn't laughing companies like that out of the room, we're in serious trouble.

There was also this thread https://news.ycombinator.com/item?id=37259753 which was an individual's project to provide an AI therapist and while people here and there did mention the cons of having a program provide medical treatment, the overall sentiment wasn't at all negative.

I'm not even some AI luddite: I use and greatly benefit from some AI tools. But just like crypto, AI isn't the be-all-end-all technology. The difference is that where crypto is primarily a financial risk to people duped into using dubious-at-best-scams-at-worse products, AI will cause real, concrete harm, i.e., https://www.euronews.com/next/2023/03/31/man-ends-his-life-a...

Re: Lessons from YC AI Startups

#55

Post by KirillPanov »

noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

> My considered determination is that the penalty from a single fuckup by an LLM in any important scenario would dramatically outweigh all other potential savings.

Attention all driverless car companies: hire this guy and make him your CEO.

Re: Lessons from YC AI Startups

#56

Post by sharemywin »

epolanski wrote: post #42
noduerme wrote: post #34

Most of my dev work is in logistics for a very staid industry which doesn't even make this list, that would have a difficult time finding a use for LLMs, because most of the day-to-day jobs involve manual labor. That said, I've been asked "how can AI help us" quite a bit (the unsaid ending to that sentence being, "...help us lay off workers"). Just as a thought experiment, I've considered the pros and cons of automat…

It really sounds like AI is a bubble if people ask how they can cram AI into their workflow rather than coming up with real problems they want to automate. I suspect that a very large number of applications that could've been "normal" programs are going to be AI'd with no clear pros but many cons.

First humans make errors too. Second, there's a lot of "generic" work flows that AI could enhance.

routing emails.

voice mail routing handling.

commenting code. easy enough to add something that strips comments and verifies the code is the same.

taking meeting notes.

going to google and pulling down notes from 10 websites.

writing a user guide for a module.

any kind of corporate-ese emails, memos etc.

generating test data.

I wouldn't allow this stuff to work with out human supervision for the most part, but this list of table types stuff seems to work pretty good and be worth time to ask it.

Re: Lessons from YC AI Startups

#57

Post by rjsw »

SkyMarshal wrote: post #51
epolanski wrote: post #43

Earlier quoted context omitted.

But this isn't new, isn't it? I remember, e.g, Italy and Brazil had projects more than ten years ago where they used some sort of machine learning to find hidden historical buildings under terrain or jungle by looking at patterns in satellite/aerial image and it was successful in finding archeological sites in both countries.

By “exploration” he’s referring to Materials Science stuff, like figuring out molecular combinations that produce materials with desired properties. Not Archeological/GIS exploration.

The examples of Materials Science and Archeology are both looking for patterns.

Re: Lessons from YC AI Startups

#58

Post by epolanski »

sharemywin wrote: post #56
epolanski wrote: post #42

Earlier quoted context omitted.

It really sounds like AI is a bubble if people ask how they can cram AI into their workflow rather than coming up with real problems they want to automate. I suspect that a very large number of applications that could've been "normal" programs are going to be AI'd with no clear pros but many cons.

First humans make errors too. Second, there's a lot of "generic" work flows that AI could enhance. routing emails. voice mail routing handling. commenting code. easy enough to add something that strips comments and verifies the code is the same. taking meeting notes. going to google and pulling down notes from 10 websites. writing a user guide for a module. any kind of corporate-ese emails, memos etc. generating test…

I'm not saying that AI hasn't some utility, I'm stating that the hype is gonna push for building AI'd applications that didn't need AI.

Few of the examples you make don't need ML at all (such as routing emails, voice mail, generating test data or pulling notes from 10 websites).

Re: Lessons from YC AI Startups

#59

Post by LeanderK »

epolanski wrote: post #58
sharemywin wrote: post #56

Earlier quoted context omitted.

First humans make errors too. Second, there's a lot of "generic" work flows that AI could enhance. routing emails. voice mail routing handling. commenting code. easy enough to add something that strips comments and verifies the code is the same. taking meeting notes. going to google and pulling down notes from 10 websites. writing a user guide for a module. any kind of corporate-ese emails, memos etc. generating test…

I'm not saying that AI hasn't some utility, I'm stating that the hype is gonna push for building AI'd applications that didn't need AI. Few of the examples you make don't need ML at all (such as routing emails, voice mail, generating test data or pulling notes from 10 websites).

Have fun pulling notes from websites without ML. NLP just plain didn't work before ML. The thing is that with ML you are able to work with unstructured data, e.g. routing email without having a complicated system the person mailing you has to understand or hundreds of brittle, hand written rules that in the end won't fire anyway because somebody spelled a word wrong.

I think ML/AI is just not ready for a lot of applications because it's not good enough, but our models are still improving every month. Impossible to know what it's like in 5 years.

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