Large Language Models (such as GPT) and AI image generators.
I follow certain AI related post tags on Tumblr and sometimes I see people expressing pure hatred towards these tools, as they only see the AIs as content thieves.
I don’t mind the tool itself if you use it as such. I do mind when people use its output as the final product. See: the lawyer who used chatgpt for a legal brief
The lawyer fuck up is what happens when someone doesn’t know or understand the limitations of a LLM.
If you want a GPT model tailored and specialized for a specific task, you have to train it with custom data, fine tune it and tweak the model’s parameters. You cannot do that from the ChatGPT web/app, you need a custom implementation coded in Python or some other language.
There are some uis that allow for fine tuning (assuming you have an extremely high end rig designed for ml). For example ChatGPT alternative and DALLE alternative.
Thanks. I have a quite powerful rig, but at the moment I work with OpenAI’s API using GPT 3.5 Turbo using a custom (but shitty) Python script with a simple Gradio web interface. However, I mostly stopped improving or updating it months ago. As long as I don’t use LlamaIndex, the cost is quite low.
I’m glad you understand my point.
Chatgpt is not Google. It’s a language model that will give you something that looks like the thing you asked for it to provide. It can and will pull facts out of its recycle bin if it fits the cadence of what it expects the answer to look like.
ChatGPT is not Google, but sometimes it can work as a glorified search engine or even compete with asking in forums.
I’ve lost count of how many times ChatGPT has produced Bash or Python code for what I needed. Yes, sometimes the code is wrong and/or requires tweaking and sometimes I resorted to look into the documentation, but no one will answer faster and anytime of the day like ChatGPT does, at least not for free.
It’s a tool to aid in creating a product, not a tool that magics out a finished product. That’s my point.
Too many people use it as the latter instead of the former.
Letting a language model do the work of thinking is like building a house and using a circular saw to put nails in.
It will do it but you should not trust the results.
It is not Google. It can, will, and has made up facts as long as it fits the format expected
Not at the very least proof reading and fact checking the output is beyond lazy and a terrible use of a tool. Using it to create the end product instead of as a tool to use in creation of an end product are two very different things.
The person you first replied to asked you to see the legal brief as an example of why they mind using the output as the finished product. You then asked for an explanation. To which I asked you, hey, have you actually looked at that example? You have not.
What exactly do you want here, other than be argumentative for combative reasons?
As an artist I think it’s a more complicated issue than a lot of people are making it out to be, and all the fearmongering some popular artists are promoting really doesn’t help.
I think it’s a more complicated issue than a lot of people are making it out to be
Agree.
Also. People are pissed that what they have taken years to master others can now get close to replicate with little effort and time.
I’ve just realized that although they call the AIs “content thieves”, what they really feel is that as AIs are able to replicate their skills quickly, it makes them feel their own merit diminished.
If an artist creates artwork inspired on some other artist eveyone’s cool; if an AI does the same, then it’s stolen work even if the generated image is a unique new one.
There was this uber hype around it, then we started using it … and it just makes so many errors, it’s literally just generating more work. Scrapped it after less than a week. It’s modern snakeoil.
Bard is the same, I asked it questions about two of my favourite bands whom I know a lot about. It omitted facts and invented things that were not true!
We used it for code generation. But we ended up spending more time fixen and debugging the generated code than it would have taken us to just write it.
Also it introduces the most annoying type of bugs. Like once it misspelled a property name, but only at one point in the code, got it right everywhere else.
That’s why, in the case of a GPT model you would feed it custom training data using something like LlamaIndex. I don’t know if there’s an API available for Bard, tho.
You’re wrong assuming that the free models that we have at our disposal are the only possible and best implementations of these LLMs.
What did you use it for? I helps me a lot with coding, scripting, translations, terminologies… Sometimes it makes mistakes, but other times it produces working code that accomplishes what I asked for.
In any case, ChatGPT is just a demo that uses the GPT-3.5 Turbo model. Many people is being misled assuming that the ChatGPT research preview is all that the model has to offer. You can also try the improved model GPT-4, but it’s not free.
If you really want to get its full potential you need a custom implementation in Python that works against the API and do things like fine tune the model, embeddings, feed it custom data or give it access to tools with LangChain.
Of course that’s not something easy to do, but don’t think that the ChatGPT web/app is GPT models’ full potential.
What! I have the opposite experience.
Im a tabletop roleplaying gamemaster and it has helped me immensely with translations, formatting of text, compiling and keeping track of my players character backgrounds and even coming up with plots and scenes that are suited for each player.
I have a feeling this one’s mostly operator error.
Once we found the issues, it was actually quite easy to tell the AI to fix them. But at this point you’re debugging generated code to imrpove your input for the code generator … and it just was faster to write the code by hand.
And yes, there was a vast overestimation of what it can do, especially by some managers that used to be coders and thought this would compensate for their lack of recent practical expirence. It didn’t … I had to fix it.
Interesting, I’m working as a network engineer and my current job is overhauling an old TV broadcast facility. There are a lot of random solutions like using off brand switches and lack of documentation, etc.
AI has been absolutely critical, it doesn’t do the work for me, but like any good tool it amplifies my ability to do work by cutting out the middle man of sifting through pages of spice works and stack overflow articles trying to figure out what command a ten year old Avaya needs to accomplish whatever task I require of it.
Is it always correct? No. That’s why the engineer behind the screen exists. It does usually get me a workable answer more quickly than just having to look it up myself, though. Between my knowledge of terminal CLI commands and the AI, I’ve been able to get a lot done.
Hell I had it walk me through the process of setting up automated backups, it even suggested the tftp server I used to do it. Shits been working great.
Even our service desk has been able to use it to help with more advanced problems by telling it the issue and describing what has already been done.
Idk why no one else sees the value, I’m over here like Captain Picard solving problems by talking to the LCARS system.
I do see the potential value and I’m happy it worked out for you. But don’t end up like the lawyers that used chatGPT like a search engine and it just made up fictiional cases they cited in an actual court.
LLM is way overhyped. So if your boss bought into that hype you’re gonna have a certain amount of animosity towards it. I’m a developer and it can be helpful at times, but managers seem to think it can write software on its own.
It’s basically an iterative improvement over a search engine, but unlike a search engine it cuts off the people creating the content it’s scraping from any kind of revenue stream.
And yeah there’s some real problems with it stealing content. Which isn’t being addressed at all. And bringing up these issues tend to get treated like Luddites by those that have bought into the hype.
I wouldn’t say “hate”, to me it’s more… so what? They’re really bad at what they do, only impressive at first glance. Not bad for some brainstorming, but then you end up with a facsimile of what the actual result would be, and now have to use that as a guideline to create the result.
IMO they’re not bad, but they require a lot of tweaking and trial and error.
I’ve learnt some Python thanks to ChatGPT’s help. When I say “some” I mean that I was able to create a custom implementation that uses a web interface and custom tools. The more lI learnt, the less I needed ChatGPT, but I always require some more coding help.
However, these LLMs are not sentient super smart AIs.
Large Language Models (such as GPT) and AI image generators.
I follow certain AI related post tags on Tumblr and sometimes I see people expressing pure hatred towards these tools, as they only see the AIs as content thieves.
I don’t mind the tool itself if you use it as such. I do mind when people use its output as the final product. See: the lawyer who used chatgpt for a legal brief
The lawyer fuck up is what happens when someone doesn’t know or understand the limitations of a LLM.
If you want a GPT model tailored and specialized for a specific task, you have to train it with custom data, fine tune it and tweak the model’s parameters. You cannot do that from the ChatGPT web/app, you need a custom implementation coded in Python or some other language.
There are some uis that allow for fine tuning (assuming you have an extremely high end rig designed for ml). For example ChatGPT alternative and DALLE alternative.
Thanks. I have a quite powerful rig, but at the moment I work with OpenAI’s API using GPT 3.5 Turbo using a custom (but shitty) Python script with a simple Gradio web interface. However, I mostly stopped improving or updating it months ago. As long as I don’t use LlamaIndex, the cost is quite low.
I already use Stable Diffusion WebUI, tho.
Also the “fine tuning” I was talking about is this https://platform.openai.com/docs/guides/fine-tuning
I am aware what fine tuning is. It is available from the train tab while the base checkpoint is loaded in both cases.
I’m glad you understand my point. Chatgpt is not Google. It’s a language model that will give you something that looks like the thing you asked for it to provide. It can and will pull facts out of its recycle bin if it fits the cadence of what it expects the answer to look like.
ChatGPT is not Google, but sometimes it can work as a glorified search engine or even compete with asking in forums.
I’ve lost count of how many times ChatGPT has produced Bash or Python code for what I needed. Yes, sometimes the code is wrong and/or requires tweaking and sometimes I resorted to look into the documentation, but no one will answer faster and anytime of the day like ChatGPT does, at least not for free.
It’s a tool to aid in creating a product, not a tool that magics out a finished product. That’s my point. Too many people use it as the latter instead of the former.
100% agree.
Maybe, with lots of training, weaking and testing the latter could be achieved, but that’s it.
Why do you mind that?
Letting a language model do the work of thinking is like building a house and using a circular saw to put nails in. It will do it but you should not trust the results.
It is not Google. It can, will, and has made up facts as long as it fits the format expected
Not at the very least proof reading and fact checking the output is beyond lazy and a terrible use of a tool. Using it to create the end product instead of as a tool to use in creation of an end product are two very different things.
Have you seen that legal brief?
No. Communicate please and we can have a real conversation.
The person you first replied to asked you to see the legal brief as an example of why they mind using the output as the finished product. You then asked for an explanation. To which I asked you, hey, have you actually looked at that example? You have not.
What exactly do you want here, other than be argumentative for combative reasons?
As an artist I think it’s a more complicated issue than a lot of people are making it out to be, and all the fearmongering some popular artists are promoting really doesn’t help.
Agree.
Also. People are pissed that what they have taken years to master others can now get close to replicate with little effort and time.
I’ve just realized that although they call the AIs “content thieves”, what they really feel is that as AIs are able to replicate their skills quickly, it makes them feel their own merit diminished.
If an artist creates artwork inspired on some other artist eveyone’s cool; if an AI does the same, then it’s stolen work even if the generated image is a unique new one.
It’s not that I hate it, but like, chatGPT sucks.
There was this uber hype around it, then we started using it … and it just makes so many errors, it’s literally just generating more work. Scrapped it after less than a week. It’s modern snakeoil.
Bard is the same, I asked it questions about two of my favourite bands whom I know a lot about. It omitted facts and invented things that were not true!
We used it for code generation. But we ended up spending more time fixen and debugging the generated code than it would have taken us to just write it. Also it introduces the most annoying type of bugs. Like once it misspelled a property name, but only at one point in the code, got it right everywhere else.
That’s why, in the case of a GPT model you would feed it custom training data using something like LlamaIndex. I don’t know if there’s an API available for Bard, tho.
You’re wrong assuming that the free models that we have at our disposal are the only possible and best implementations of these LLMs.
What did you use it for? I helps me a lot with coding, scripting, translations, terminologies… Sometimes it makes mistakes, but other times it produces working code that accomplishes what I asked for.
In any case, ChatGPT is just a demo that uses the GPT-3.5 Turbo model. Many people is being misled assuming that the ChatGPT research preview is all that the model has to offer. You can also try the improved model GPT-4, but it’s not free.
If you really want to get its full potential you need a custom implementation in Python that works against the API and do things like fine tune the model, embeddings, feed it custom data or give it access to tools with LangChain.
Of course that’s not something easy to do, but don’t think that the ChatGPT web/app is GPT models’ full potential.
What! I have the opposite experience.
Im a tabletop roleplaying gamemaster and it has helped me immensely with translations, formatting of text, compiling and keeping track of my players character backgrounds and even coming up with plots and scenes that are suited for each player.
I have a feeling this one’s mostly operator error.
Or you vastly overestimated what it could do.
Once we found the issues, it was actually quite easy to tell the AI to fix them. But at this point you’re debugging generated code to imrpove your input for the code generator … and it just was faster to write the code by hand.
And yes, there was a vast overestimation of what it can do, especially by some managers that used to be coders and thought this would compensate for their lack of recent practical expirence. It didn’t … I had to fix it.
My point is that it’s not just for coding, if you think that’s the only use case then sure I get why you’d think it was shitty.
I’ve used it a bit for general knowledge things and fun facts, and on more than a couple of occasions it just made shit up.
I’m sure it has some uses, I see a lot of AI generated porn in my “all” feed … just haven’t found one for myself or my work.
Interesting, I’m working as a network engineer and my current job is overhauling an old TV broadcast facility. There are a lot of random solutions like using off brand switches and lack of documentation, etc.
AI has been absolutely critical, it doesn’t do the work for me, but like any good tool it amplifies my ability to do work by cutting out the middle man of sifting through pages of spice works and stack overflow articles trying to figure out what command a ten year old Avaya needs to accomplish whatever task I require of it.
Is it always correct? No. That’s why the engineer behind the screen exists. It does usually get me a workable answer more quickly than just having to look it up myself, though. Between my knowledge of terminal CLI commands and the AI, I’ve been able to get a lot done.
Hell I had it walk me through the process of setting up automated backups, it even suggested the tftp server I used to do it. Shits been working great.
Even our service desk has been able to use it to help with more advanced problems by telling it the issue and describing what has already been done.
Idk why no one else sees the value, I’m over here like Captain Picard solving problems by talking to the LCARS system.
I do see the potential value and I’m happy it worked out for you. But don’t end up like the lawyers that used chatGPT like a search engine and it just made up fictiional cases they cited in an actual court.
Yeah, that happened.
You only end up like those morons by trusting AI to be perfect, I do not trust AI to even be “good” let alone perfect.
If you’re willing to just throw your job into an LLM and hope for the best you deserve to get fired.
LLM is way overhyped. So if your boss bought into that hype you’re gonna have a certain amount of animosity towards it. I’m a developer and it can be helpful at times, but managers seem to think it can write software on its own.
It’s basically an iterative improvement over a search engine, but unlike a search engine it cuts off the people creating the content it’s scraping from any kind of revenue stream.
And yeah there’s some real problems with it stealing content. Which isn’t being addressed at all. And bringing up these issues tend to get treated like Luddites by those that have bought into the hype.
I wouldn’t say “hate”, to me it’s more… so what? They’re really bad at what they do, only impressive at first glance. Not bad for some brainstorming, but then you end up with a facsimile of what the actual result would be, and now have to use that as a guideline to create the result.
IMO they’re not bad, but they require a lot of tweaking and trial and error.
I’ve learnt some Python thanks to ChatGPT’s help. When I say “some” I mean that I was able to create a custom implementation that uses a web interface and custom tools. The more lI learnt, the less I needed ChatGPT, but I always require some more coding help.
However, these LLMs are not sentient super smart AIs.