Recent comments in /f/MachineLearning

disbeam t1_je920cv wrote

What some people have done is to use Azure Cognitive Search as a pre-cursor to the LLM.

You use Cognitive Search to extract information from your organisation's own documentation and ask the LLM to only provide the correct answer from the details found in the search, otherwise responding with saying it doesn't know. It then answers complete with references. Having seen it in action with one of our customer's, I've been quite impressed.

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TheAdvisorZabeth t1_je90pzt wrote

Hi!~ <3

umm... I am just an uneducated idiot, but I've been having a lot of ideas lately and I think some of them might be real Science too.

But I have no credentials or anyone to discuss ideas with or to help fact-check me about stuff I don't have nearly the Time to Learn.

You seem like a kind person, (and like you might have more Time than Puzzles with which to fruitfully spend that Time on.), do you think you might care to to chat about my ideas? Or possibly offer any sincere advice that is a bit more useful to an autistic puppy than: "That is not a real Theory."?

I never used Tumblr before, but Neil Gaiman made a point to explicitly state that he hangs out there a lot; and since there's no living Author who I have more respect for, I recently began posting my ideas there.

From my perspective I am writing 100% Non-Fiction.

From my perspective I am just a very strange Harmless-Holistic-Aberrant; who managed to dumb-luck their way into figuring out how to gain "Coherent-Root-Access-To-My-Own-Brain".

I am being fully sincere.

I would just ask that if you (or anyone else) thinks that I am just being a stupid Fool, that you please tell me gently, I am pretty sensitive.

Love ya either way!~

Keep on doin your awesome Science stuff no matter what! Cause it's just the coolest thing! (hehe, I wonder if they got that joke?)

hugs!~~~

bye!^!^(for, now...)

OH! I almost forgot to actually Hand You One End Of The Thread lol~

https://www.tumblr.com/baby-ghost-in-the-machine-lovers

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icedrift t1_je8zqd2 wrote

This is really cool! Maybe I'm lacking creativity, but why bother generating imaginary functions and introducing risk that they aren't deterministic when you could just hit OpenAI's API for the data? For example in your docs you present a feature for recommending column names for a given table. Why is the whole function generated? Wouldn't it be more reliable to write out the function and use OAI's API to get the recommended column names?

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SFDeltas t1_je8y9n7 wrote

I consulted, ahem, a Magic 8 Ball for some responses to your discussion topic.

"Ah, the weekly ChatGPT hype thread. It's become a ritual at this point. 🙄"

"Not another ChatGPT discussion! Can we please just focus on other ML advancements?"

"Honestly, it's not the technology that's the problem, it's people overhyping it. ChatGPT has its uses, but it's not going to replace every job out there."

"It's frustrating that people outside the field make such strong statements without understanding the limitations of current AI systems."

"You know it's bad when your mom starts asking you about ChatGPT and how it's going to change the world."

"Hype is just part of the game. Remember the craze around deep learning a few years back? This too shall pass."

"I can't wait for the next big thing in ML to come along so we can finally move on from ChatGPT."

"The hype is annoying, but you have to admit that ChatGPT is a major milestone in NLP. Let's not completely dismiss its achievements."

"ChatGPT has its fair share of fans and critics on this sub, but it's important to stay grounded and remember that it's just one tool among many."

"I'm just waiting for the day when the ChatGPT hype dies down and we can go back to our regular, insightful discussions on r/machine_learning."

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tysam_and_co t1_je8wno6 wrote

This will be one of the key things that I think will keep people up and going in their respective subfields. Perhaps not ironically at all, DougDoug, a popular YT creator has a great video on how to be a content creator that I find to be pretty exceptional and well-targeted to the current state of ML research, including some unique strategies on how to pick a unique fusion that only the person doing the researching is able to compete well in (while still contributing somewhat to the field), if one assumes that the research or software created is content that people might want

It's helped me as a researcher in producing research, I've recommended it to a number of people, and he just won a decently-sized award recognizing him doing in the way he's doing it. May not seem at all related but it has been one of the best guidebooks so far for me, and it has not failed me quite yet.

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9182763498761234 t1_je8wfs4 wrote

Work in a niche field instead! There are hundreds of smaller topics in ML that are yet unexplored and only a couple people working on it. I’m working on one of those and it is awesome. The field is slowly progressing but slowly enough that I can make a valuable contribution without getting scooped all the time.

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utopiah t1_je8vryb wrote

It's pretty cool and thanks for providing the playground. I wouldn't have bothered without it. I think it's very valuable but also is quite costly, both economically and computationally, while creating privacy risks (all your data going through OpenAI) so... again in some situation I can imagine it being quite powerful but in others and absolute no. That being said there are others models (I just posted on /r/selfhosted minutes ago about the HuggingFace/Docker announcement enabling us to run Spaces locally) e.g Alpaca or SantaCoder or BLOOM that might enable us to follow the same principle, arguably with different quality, without the privacy risks. Have you considering relying on another "runtime"?

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sparkpuppy t1_je8v49k wrote

Hello! Super-n00b question but I couldn't find an answer on google. When an image generation model has "48 M parameters", what does the term "parameter" mean in this sentence? Tags, concepts, image-word pairs? Does the meaning of "parameter" vary from model to model (in the context of image generation)?

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