Recent comments in /f/MachineLearning

trajo123 t1_je7dgjz wrote

Reply to [D] Improvements/alternatives to U-net for medical images segmentation? by viertys

100 images??? Folks, neural nets are data hungry, if you don't have reams of data, don't fiddle with architectures, definitely not at first. The first thing to do when data is limited is to use pre-trained models. Then do data augmentation and only then look at other things like architectures and losses if you really have nothing better to do with your time.

SMP offers a wide variety of segmentation models with the option to use pre-trained weights.

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lostmsu t1_je78jfg wrote

Reply to comment by WindForce02 in [D] Prediction time! Lets update those Bayesian priors! How long until human-level AGI? by LanchestersLaw

You are missing the idea entirely. I am sticking to the idea of the original Turing test to determine if AI is human-level already or not yet.

The original Turing test is dead simple and can be applied to ChatGPT easily.

The only other thing in my comment is that "human-level" is vague, as intelligence differs from human to human, which allows for goalpost moving like in your comment. IQ is the best measure of intelligence we have. So it is reasonable to turn the idea of Turing test into a plethora of different tests Turing(I) which is like any regular Turing test, but the IQ of the humans participating in the tests (both machine's opponent, and the person who needs to guess which one is the machine) is <= I.

My claim is that I believe ChatGPT or ChatGPT + some trivial form of memory enhancements (like feeding previous failures back into prompts) quite possibly can already pass Turing(70).

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WokeAssBaller t1_je783go wrote

Reply to comment by lambertb in [D] GPT4 and coding problems by enryu42

Fair enough then give them problems to solve and measure their output. This feels like “90% of dentists claim crest improves your dental health”

I’ll take an independent study into consideration but today I find it more of a novelty

1

machineko t1_je70llx wrote

Reply to comment by LetGoAndBeReal in [D] The best way to train an LLM on company data by jaxolingo

Why would you say that fine-tuning is not viable? There are many production use cases of fine-tuning a model using in-house proprietary data.
If fact, if you have the resources you can do both fine-tuning of an existing model (whether is just supervised or unsupervised) and also use that for retrieval augmented generation.

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modernzen t1_je6xujz wrote

Reply to comment by rshah4 in [D] FOMO on the rapid pace of LLMs by 00001746

Totally agree with this. Something like ChatGPT is overkill for most use cases and comes at a cost of both money (using the API) and latency. Clever prompting and fine-tuning can let you build free, fast models that are tailored towards your specific problem at hand.

1

Cantareus t1_je6vn79 wrote

Reply to comment by Im_Unlucky in [D] The best way to train an LLM on company data by jaxolingo

>shove data into vector db
>
>embed incoming query
>
>return topk matches from vector db

Can these steps be done through the openai API?

You only reply using JSON. Write 5 queries that will return useful data to aid you in answering the users questions "What was Apple’s return compared to it’s sector last month" return in JSON array with no explanations. Each query must run independently. Use the return format [{"Reason":"Why the query is useful","query":"The sql query"}] The schema is:

(Imagine a valid schema is here)

GPT-4 Response:

[

{

"Reason": "Retrieve Apple's stock return last month",

"query": "SELECT stock_return FROM stocks WHERE company = 'Apple' AND date >= DATEADD(month, -1, GETDATE()) AND date < GETDATE();"

},

{

"Reason": "Retrieve sector for Apple",

"query": "SELECT sector FROM companies WHERE company_name = 'Apple';"

},

{

"Reason": "Retrieve companies in Apple's sector",

"query": "SELECT company_name FROM companies WHERE sector = (SELECT sector FROM companies WHERE company_name = 'Apple');"

},

{

"Reason": "Calculate average return for Apple's sector last month",

"query": "SELECT AVG(stock_return) as sector_return FROM stocks WHERE company IN (SELECT company_name FROM companies WHERE sector = (SELECT sector FROM companies WHERE company_name = 'Apple')) AND date >= DATEADD(month, -1, GETDATE()) AND date < GETDATE();"

},

{

"Reason": "Compare Apple's return to the average return of its sector last month",

"query": "SELECT (SELECT stock_return FROM stocks WHERE company = 'Apple' AND date >= DATEADD(month, -1, GETDATE()) AND date < GETDATE()) - (SELECT AVG(stock_return) as sector_return FROM stocks WHERE company IN (SELECT company_name FROM companies WHERE sector = (SELECT sector FROM companies WHERE company_name = 'Apple')) AND date >= DATEADD(month, -1, GETDATE()) AND date < GETDATE()) as comparison;"

}

]

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MadDanWithABox t1_je6vh9x wrote

Reply to [D] The best way to train an LLM on company data by jaxolingo

Just a caveat. Any training (fine-tuning really) that you do on an LLM is NOT guaranteed to be able to give you correct answers. The answers to your questions will seem plausible, and could be correct, but you'll need some system to verify if it's something you want to take action on.

5

kromem t1_je6uv46 wrote

Reply to comment by visarga in [D] The best way to train an LLM on company data by jaxolingo

"Moar layers" doesn't only need to apply to the NN.

CoT prompting works by breaking analysis down into smaller steps that each generate their own additional context.

Doing something similar with DB analysis is absolutely possible, such as preemptively summarizing schema and using that summary as part of the retrieval to contextualize the specific fragments.

Additionally, having static analysis examples on hand for related tables that's fed in to go from zero shot to few shot would go a long way at reducing some of the issues you highlight.

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