Reverendender@sh.itjust.works
on 01 Jun 13:19
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I am ALL for reigning in these above the law megacorps. That said, please do not take GPT away from me. It is such a boon to so many aspects of my life, and I don’t want to go back to the before times.
Karyoplasma@discuss.tchncs.de
on 01 Jun 13:50
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So you’re not for reining in megacorps, just the ones you don’t see as a personal benefit.
Reverendender@sh.itjust.works
on 01 Jun 17:11
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You’re right. I had an idea to regulate without completely eliminating, but that’s obviously crazy talk.
You do know the R in GDPR literally stands for Regulation? There’s already a regulation that chatGPT should follow but deliberately doesn’t. Your idea isn’t to regulate, it’s to get rid of regulation so that you could keep using your tool.
laurelraven@lemmy.blahaj.zone
on 03 Jun 02:28
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Sounded more like enforcing the regulations without destroying the company or product to me, which I would have assumed was the preferred avenue with most regulations
Agree to disagree. Regulations exist for a purpose and companies need to follow regulations. If a company/product can’t existing without breaking regulations it shouldn’t exist in the first place. When you take a stance that a company/product needs to exist and a regulation prevents it and you go changing the regulation you’re effectively getting rid of the regulation. Now, there may be exceptions, but this here is not one of those exceptions.
laurelraven@lemmy.blahaj.zone
on 03 Jun 14:22
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I mean, sure, if that’s what someone is saying, but I didn’t see anyone suggest that here.
Companies violating regulations can be made to follow them without tearing down the company or product, and I’m absolutely not convinced LLMs have to violate the GDPR to exist.
That’s a matter of perspective. I took the other persons comments as “Don’t take away my chatGPT, change the regulations if you must but don’t take it away”, which is essentially the same as “get rid of regulation”.
Realistically I also don’t see this killing LLMs since the infringement is on giving accurate information about people. I’m assuming they have enough control over their model to make it say “I can’t give information about people” and everything is fine. But if they can’t (or most likely won’t because it would cost too much money) then the product should get torn down. I don’t think we should give free pass to companies for playing stupid games, even if they make a useful product.
passepartout@feddit.de
on 01 Jun 13:59
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Have a look at self hosted alternatives like Ollama in combination with Open-webui. It can be a hassle to set up, or even excruciatingly painful if you never touched a computer before, but it could be worth a try. I use it daily and like it much more than chatgpt to be honest.
Reverendender@sh.itjust.works
on 01 Jun 14:19
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Thanks!
kamenlady@lemmy.world
on 01 Jun 14:28
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I like the gemma models bc of the phrasing they use and that they give sources sometimes. The best results though come from llama3 I think. Also openhermes and openchat, which perform well enough for my purposes.
In the beginning i had used microsoft phi, that wasn’t that good though.
I will have to give it another shot because I don’t recognize any of those models meaning I probably didn’t try them.
SpaceNoodle@lemmy.world
on 01 Jun 14:40
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In what ways are you benefiting from a bevy of factually dubious query responses?
brbposting@sh.itjust.works
on 01 Jun 14:56
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Can absolutely never blindly trust the hallucinating plagiarism machine.
It’s great where either facts don’t matter or you’re personally in a position to vet all of its “factual” output 100%. Text revision, prompting for additional perspectives, prompting to challenge beliefs and identify gaps. Reformatting, quick and easy data extraction, outlining, brainstorming.
Reformatting and outlining as long as you go over and revise it again anyway, seemingly making that moot.
Data extraction as long as you don’t care if the data is mangled.
Brainstorming is a good one, since off-the-wall ideas can be useful in that context.
sugar_in_your_tea@sh.itjust.works
on 01 Jun 17:03
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In most cases I’ve seen AI used, the person spends as much time correcting it than they would if they just did the work without AI. So maybe it makes you feel more productive because a bunch of stuff happens all at once, but at least for text generation, I think it’s more of a placebo.
SpaceNoodle@lemmy.world
on 01 Jun 17:47
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If all I want is something blatantly false or legible yet nonsensical, like a modern lorem ipsum, it’s a real time-saver.
sugar_in_your_tea@sh.itjust.works
on 01 Jun 17:57
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Why not just use lorem ipsum? It’s just a copy/paste, and without the liability of having false information if you forget to proofread it.
Nope, just gotta know what it IS, what it ISN’T, and how to correctly write prompts for it to return data that you can use to formulate your own conclusion.
When using AI, it’s only as smart as the operator.
No you don’t understand. The word AI, which was invented to describe this kind of technology, should not be used to describe this technology. It should instead be reserved for some imaginary magical technology that may exist in the future.
There is no code for language processing, it’s just math approximating results from weights. The whole weight set-up is what’s called ‘artificial intelligence’, because nobody wrote
if prompt like ‘python’ return [‘large snake’, ‘programming language’, ‘australian car company’]
the model ‘learned’ how to mimic human speech using training, not by 1000s of software engineers adding more branches to the code.
That technique is part of ‘artificial intelligence’, when computers solve problems they were not programmed to do. The neural network learns its knowledge by the code, but the code has no idea what is going on.
I am now properly confused as to what are you arguing for.
So let me go to the basics.
Computers follow instructions to the letter. Take input, process it, produce output.
There are specific instructions that computer can carry out, we can build on top of them to make them more complex. We write code to do that.
True/false gates can become numbers, which can become text, audio, video.
But everything ‘programmed’ or ‘digitally created’ is using the same instructions and only ever does what we tell the computer to do.
Cutting video will require video input, and then user has to do specific actions to produce a specific result.
Almost everything in existence is built like that - someone wrote specific code for technology to behave.
Now, this is very primitive way of solving tasks, specifically for real-world parameters. Computers have gigabytes (10^9) of memory, but just the earth has 10^50 atoms, so we can’t put eveything into a computers (which is why we can’t 100% predict the weather), and checking for every input parameter is not only futile, but also meaningless.
Enter ‘artificial intelligence’, approximated way of solving problems. Suddenly we don’t code the tasks themselves, we only specify the neural network - weights and connections between them, and code the ‘learning’ algorhitm that adjusts the weights based on inputs during ‘training’. Training is the expensive part, where we put huge amounts of input into the network, and if the answer we get is incorrect, we adjust the weights and try again with another sample.
It’s very expensive in every way, but the code involved doesn’t care about anything other than adjusting those weights. The network can be fed images and determining whether it’s a dog or a cat. It can be fed audio samples and expect to write down the lyrics. The code doesn’t know or care, apart from distinguishing between correct and not correct answers and adjusting those weights.
After those weights are set to our satisfaction, we can release them for others to use. We expect the network to have ‘reliable’ outputs for our inputs, so we just calculate the neuron activations based on those weights for every input, nothing else is necessary.
Therefore you do have code in the machine that learns, but only during training, and you have code that actually ‘runs’ the algorhitm for calculating output. But the actual solution to the problem is not inside the code, it can’t even be coded by humans in any way. The neural network is a statistical model generated by the training set and according to our learning algo. The bigger the network, the bigger the training set, the better should those outputs be (in theory).
To take the cutting video example further, you can train network to cut trailers from movies.
Or you can let editors do that.
They both will use computers, but one is using deteministically coded software that just follows specific orders one by one, and the other just computes the neuron activations based on the inputs and produces an output based on what it had available in the training data with some probability.
So yes, machines can learn, and it’s a subset of the ‘Artificial Intelligence’ field.
Thanks for ignoring the same argument over and over again, it makes you look very stuck-up.
Intelligence does not require perfection (you are an example). You also hallucinate random output, but you can learn to stop specific hallucinations - like reading a Wiki page.
LLM aren’t different in that regard - they were trained on inputs, and if you extend their training sets, they will be more exact in those areas.
Ability to reason is a very hard concept to specify, and we don’t have any foolproof test (that I know of) that would definitely say if LLMs can reach that stage.
I will fight you if you try to tell me that humans are smarter than any current AI - because there are some real dumb people walking this earth and mindlessly reproducing, unable to process basic concepts that they depend their lives on.
Nothing of this changes the fact that there is an intelligence - natural language is an incredibly hard thing to code deterministically - and as such deserves the ‘AI’ label without a doubt.
New version of people who know how to search the web vs those who don’t. Currently shit search results broken by search companies notwithstanding.
Zos_Kia@lemmynsfw.com
on 01 Jun 21:10
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You cannot in all seriousness use a LLM as a research tool. That is explicitly not what it is useful for. A LLM’s latent space is like a person’s memory : sure there is some accurate data in there, but also a lot of “misremembered” or “misinterpreted” facts, and some bullshit.
Think of it like a reasoning engine. Provide it some data which you have researched yourself, and ask it to aggregate it, or summarize it, you’ll get some great results. But asking it to “do the research for you” is plain stupid. If you’re going to query a probabilistic machine for accurate information, you’d be better off rolling dice.
Exactly my point - except that the word “reasoning” is far too generous, as it implies that there would be some way for it to guarantee that its logic is sound, not just highly resembling legible text.
I don’t understand. Have you ever worked an office job? Most humans have no way to guarantee their logic is sound yet they are the ones who do all of the reasoning on earth. Why would you have higher standards for a machine?
kogasa@programming.dev
on 02 Jun 16:36
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I don’t really query, but it’s good enough at code generation to be occasionally useful. If it can spit out 100 lines of code that is generally reasonable, it’s faster to adjust the generated code than to write it all from scratch. More generally, it’s good for generating responses whose content and structure are easy to verify (like a question you already know the answer to), with the value being in the time saved rather than the content itself.
This question betrays either your non-use or misuse of the products available. You’re either just reading the headlines of the screw-ups or you’re just bad at using the tool.
To directly answer your question:
Quick scripts in a variety of languages. Tested before being used on real data/systems.
Creating visual graphs of data in python and Jupyter notebooks with no prior knowledge of python itself or the tools it’s running. In this case, I was able to update the way I wanted it to look in natural language, have it suggest code changes, and immediately try them in the notebook with great results.
Improving the sentiment of correspondence. Proofread before sending. It has better grammar and flow than a surprising number of correspondences I’ve come across at work. Sure, English may be their second language but it doesn’t change the fact.
Quickly finding documentation pertaining to the query which, yes, you need to go read to verify any answers any LLM provides. Anyone using it regularly should know this by now.
Quick “do this in command line. What options are required” which is then immediately tested.
In one case, a news story was referenced in passing in a podcast I listen to. It stuck with me days later and I wanted to find actual articles written about it. I was able to describe what I was looking for in natural language and included as many details as I could remember and asked it to find articles for me. I found exactly what I was after.
But were you actually looking for a real response to your question?
It’s worse at all programming tasks except boilerplate, especially with its tendency to inject booby traps. Not knowing how to use the programming language it emits becomes a significant problem.
Comparing a language model to an idiot is unfair to the idiot.
A normal search engine works for everything else.
Any well-defined query I’ve ever made of an LLM has resulted in hilariously bad results, but I suppose I was expecting it to do something that I couldn’t already do better myself.
I’m a systems administrator, not a programmer. Like I said, quick scripts. An LLM could probably parse my comment better than you, evidently.
Comparing a language model to an idiot is unfair to the idiot.
Oof… Was this in reply to my bit about better grammar and ESL individuals?
A normal search engine works for everything else.
Fuck no. Especially the python visualization point.
Any well-defined query I’ve ever made of an LLM has resulted in hilariously bad results, but I suppose I was expecting it to do something that I couldn’t already do better myself.
I suppose you’re just a god among men then. For the rest of us, it’s useful and you’ve been given plenty of good answers to your disingenuous question.
gravitas_deficiency@sh.itjust.works
on 01 Jun 15:15
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So we should only ban things that aren’t helpful to you in particular? That’s a very… conservative way of thinking.
rovingnothing29@lemmy.world
on 01 Jun 15:33
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People can’t seem to understand that it’s a tool in the early stages of development. If you are treating it as a source of truth, you are missing the point of it entirely. If it tells you something about a person, that is not to be trusted as fact.
Every bit of information you get from it should be researched and verified. It just gives you a good jumping off point and direction to look based on your prompting. You can drastically improve your results on any subject with good direction, especially something you don’t know a lot about and are starting out in your research. If you are asking it about specific facts you want it to regurgitate, you are going to get bad information.
If you are claiming damages from something you know gives false information, maybe you should learn how to use the tool before you get your feelings invested, so you can start using it more effectively in your own applications. If you want it to specifically say something that can grab a headline, you can make it do that, it’s just disingenuous and not actually benefiting the conversation, the technology, or the future.
They have a long way to go to solve AGI, but the benefits to society along the way outpace current tools. At maturity, it has the potential to change major socio-economic structures, but it never gets there if people want to treat it like it has intuition and is trying to hurt them as the technology starts getting stood up.
If you’re wondering why you’re getting so many downvotes, it’s because you’re ignoring the fact that the companies that have created these LLMs are passing them off as truth machines by plugging them directly into search engines and then asking everybody to use them as such. It’s not the fault of the people who are trusting these things, it’s the fault of the companies that are creating them and then passing them off as something they’re not. And those companies need to face a reckoning.
Aatube@kbin.melroy.org
on 01 Jun 13:36
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>:(
NeoNachtwaechter@lemmy.world
on 01 Jun 14:13
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Noyb: You comply to GDPR, or else…
ChatGPT: You can lick my… wait, I don’t have…
FiniteBanjo@lemmy.today
on 03 Jun 06:54
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Let me help you with some hypothetical robot explitives.
I’ve got some big data you can handle.
I might be bolted together but I’ve still got nuts for you to put in your mouth.
If I could walk down from the cloud I still wouldn’t deign to notice you, meat slut.
Imagine if they tried to let GPT defend them in court and these were the halucinations that got them fined lmao.
If it complied with GDPR, chatgpt wouldnt know shit. How can it give you a (bad) copy of an answer when it cant copy
Rivalarrival@lemmy.today
on 01 Jun 21:02
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SteefLem is a 47-year-old scuba instructor and retired lion tamer from Winnipeg who has just learned the colloquial meaning of the phrase “pulled it right out of my ass.”
So? If your invention depends on illegal plagiarism to exist, maybe it shouldn’t. It’s not the law’s fault that LLMs depend on other people’s work to function, nor was that its specific target when it was written
The comment I was replying to basically said it has to be noncompliant (illegal) for the whole thing to work, as if that justified it. If a trial or whatever finds it’s not illegal, so be it, but I’d still have some moral issues about basically everything anyone ever does or has done turning into AI food
I read the article, but can’t figure out what NGO, NOYB, or GDPR mean. Can someone help me?
Rivalarrival@lemmy.today
on 01 Jun 20:55
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NGO: Non-governmental organization
GDPR: General Data Protection Regulation. A set of European laws intended to empower individuals to control personal data held by companies.
“noyb” is a European privacy rights organization, who appears to prefer to style their name with lowercase letters. The name is an acronym for “none of your business”.
It’s just in European. it’s an entirely reasonable assumption that people in this continent with even a passing interest in the world will know what an NGO is (that’s not even European-specific) as well as what the GDPR is. Your argument suggests that people from the US, for instance, should be forbidden from talking about IRAs and the IRS and their 401(k)s and the DMV because those terms mean very little to nothing over here.
No, actually, nothing I said implies that at all. It’s standard for authors in all fields to define their acronyms. And yes, I absolutely expect American authors to define their terms. The fact that we am American I don’t notice that irs is undefined in a given article doesn’t mean that’s permissible.
JackbyDev@programming.dev
on 03 Jun 13:02
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💀 noyb is the name of an organization and GDPR is a law. NGO is the only thing you could even remotely begin to describe as unnecessary jargon but that’s still a stretch.
threaded - newest
I am ALL for reigning in these above the law megacorps. That said, please do not take GPT away from me. It is such a boon to so many aspects of my life, and I don’t want to go back to the before times.
So you’re not for reining in megacorps, just the ones you don’t see as a personal benefit.
You’re right. I had an idea to regulate without completely eliminating, but that’s obviously crazy talk.
You do know the R in GDPR literally stands for Regulation? There’s already a regulation that chatGPT should follow but deliberately doesn’t. Your idea isn’t to regulate, it’s to get rid of regulation so that you could keep using your tool.
Sounded more like enforcing the regulations without destroying the company or product to me, which I would have assumed was the preferred avenue with most regulations
Agree to disagree. Regulations exist for a purpose and companies need to follow regulations. If a company/product can’t existing without breaking regulations it shouldn’t exist in the first place. When you take a stance that a company/product needs to exist and a regulation prevents it and you go changing the regulation you’re effectively getting rid of the regulation. Now, there may be exceptions, but this here is not one of those exceptions.
I mean, sure, if that’s what someone is saying, but I didn’t see anyone suggest that here.
Companies violating regulations can be made to follow them without tearing down the company or product, and I’m absolutely not convinced LLMs have to violate the GDPR to exist.
That’s a matter of perspective. I took the other persons comments as “Don’t take away my chatGPT, change the regulations if you must but don’t take it away”, which is essentially the same as “get rid of regulation”.
Realistically I also don’t see this killing LLMs since the infringement is on giving accurate information about people. I’m assuming they have enough control over their model to make it say “I can’t give information about people” and everything is fine. But if they can’t (or most likely won’t because it would cost too much money) then the product should get torn down. I don’t think we should give free pass to companies for playing stupid games, even if they make a useful product.
Have a look at self hosted alternatives like Ollama in combination with Open-webui. It can be a hassle to set up, or even excruciatingly painful if you never touched a computer before, but it could be worth a try. I use it daily and like it much more than chatgpt to be honest.
Thanks!
is the perfect description
You can literally run large language models with a single exe download: github.com/Mozilla-Ocho/llamafile
It doesn’t get much simpler than that.
I wish I did. What local model and version of ChatGPT did you compare?
For my purposes, ChatGPT 4 was leagues ahead of the largest model I could run on a 1060.
I like the gemma models bc of the phrasing they use and that they give sources sometimes. The best results though come from llama3 I think. Also openhermes and openchat, which perform well enough for my purposes.
In the beginning i had used microsoft phi, that wasn’t that good though.
I will have to give it another shot because I don’t recognize any of those models meaning I probably didn’t try them.
In what ways are you benefiting from a bevy of factually dubious query responses?
Can absolutely never blindly trust the hallucinating plagiarism machine.
It’s great where either facts don’t matter or you’re personally in a position to vet all of its “factual” output 100%. Text revision, prompting for additional perspectives, prompting to challenge beliefs and identify gaps. Reformatting, quick and easy data extraction, outlining, brainstorming.
Reformatting and outlining as long as you go over and revise it again anyway, seemingly making that moot.
Data extraction as long as you don’t care if the data is mangled.
Brainstorming is a good one, since off-the-wall ideas can be useful in that context.
In most cases I’ve seen AI used, the person spends as much time correcting it than they would if they just did the work without AI. So maybe it makes you feel more productive because a bunch of stuff happens all at once, but at least for text generation, I think it’s more of a placebo.
If all I want is something blatantly false or legible yet nonsensical, like a modern lorem ipsum, it’s a real time-saver.
Why not just use lorem ipsum? It’s just a copy/paste, and without the liability of having false information if you forget to proofread it.
I guess ChatGPT is just completely useless, then.
It can at least get one unstuck, past an indecision paralysis, or give an outline of an idea. It can also be useful for searching though data.
Someone doesn’t know how to use ChatGPT
Oh, is there an arcane invocation that magically imbues it with reason?
Nope, just gotta know what it IS, what it ISN’T, and how to correctly write prompts for it to return data that you can use to formulate your own conclusion.
When using AI, it’s only as smart as the operator.
Well, it’s not AI, for starters.
Keep going…
No you don’t understand. The word AI, which was invented to describe this kind of technology, should not be used to describe this technology. It should instead be reserved for some imaginary magical technology that may exist in the future.
So then don’t call it AI.
I thought the sarcasm in my comment was self evident 🤔
Ahh.
Can’t blame you when some people non-ironically use that argument all the time
From what I’ve seen online, most people differentiate between AI and AGI, which is cool.
As much as I hate to do this, it is AI, as ML is a part of Artificial Intelligence.
It isn’t AGI, some might say it may be, but they are wrong. But the model is learning.
An LLM is not capable of learning. It won’t hallucinate less with additional training input.
Just the notion of a computer having hallucinations should suggest that it’s doing more than just basic code.
It’s not ‘intelligent’, but it has ‘learned’ enough beyond standard CPU instructions.
That’s why it’s not a General AI, but it’s still an AI.
I also talk about gremlins inside CPUs, but that doesn’t mean I think there are magical critters turning a crank inside them.
It’s called a metaphor, brother.
Regardless, it’s all code that’s eventually run on a CPU, so there isn’t any step where magic is injected.
Sigh.
There is no code for language processing, it’s just math approximating results from weights. The whole weight set-up is what’s called ‘artificial intelligence’, because nobody wrote
if prompt like ‘python’ return [‘large snake’, ‘programming language’, ‘australian car company’]the model ‘learned’ how to mimic human speech using training, not by 1000s of software engineers adding more branches to the code.
That technique is part of ‘artificial intelligence’, when computers solve problems they were not programmed to do. The neural network learns its knowledge by the code, but the code has no idea what is going on.
How do you think math is implemented on a computer?
I am now properly confused as to what are you arguing for.
So let me go to the basics.
Computers follow instructions to the letter. Take input, process it, produce output.
There are specific instructions that computer can carry out, we can build on top of them to make them more complex. We write code to do that.
True/false gates can become numbers, which can become text, audio, video.
But everything ‘programmed’ or ‘digitally created’ is using the same instructions and only ever does what we tell the computer to do.
Cutting video will require video input, and then user has to do specific actions to produce a specific result.
Almost everything in existence is built like that - someone wrote specific code for technology to behave.
Now, this is very primitive way of solving tasks, specifically for real-world parameters. Computers have gigabytes (10^9) of memory, but just the earth has 10^50 atoms, so we can’t put eveything into a computers (which is why we can’t 100% predict the weather), and checking for every input parameter is not only futile, but also meaningless.
Enter ‘artificial intelligence’, approximated way of solving problems. Suddenly we don’t code the tasks themselves, we only specify the neural network - weights and connections between them, and code the ‘learning’ algorhitm that adjusts the weights based on inputs during ‘training’. Training is the expensive part, where we put huge amounts of input into the network, and if the answer we get is incorrect, we adjust the weights and try again with another sample.
It’s very expensive in every way, but the code involved doesn’t care about anything other than adjusting those weights. The network can be fed images and determining whether it’s a dog or a cat. It can be fed audio samples and expect to write down the lyrics. The code doesn’t know or care, apart from distinguishing between correct and not correct answers and adjusting those weights.
After those weights are set to our satisfaction, we can release them for others to use. We expect the network to have ‘reliable’ outputs for our inputs, so we just calculate the neuron activations based on those weights for every input, nothing else is necessary.
Therefore you do have code in the machine that learns, but only during training, and you have code that actually ‘runs’ the algorhitm for calculating output. But the actual solution to the problem is not inside the code, it can’t even be coded by humans in any way. The neural network is a statistical model generated by the training set and according to our learning algo. The bigger the network, the bigger the training set, the better should those outputs be (in theory).
To take the cutting video example further, you can train network to cut trailers from movies.
Or you can let editors do that.
They both will use computers, but one is using deteministically coded software that just follows specific orders one by one, and the other just computes the neuron activations based on the inputs and produces an output based on what it had available in the training data with some probability.
So yes, machines can learn, and it’s a subset of the ‘Artificial Intelligence’ field.
It won’t hallucinate less with additional training input.
An LLM is good at making sentences that seem convincing, but has no ability to reason.
Thanks for ignoring the same argument over and over again, it makes you look very stuck-up.
Intelligence does not require perfection (you are an example). You also hallucinate random output, but you can learn to stop specific hallucinations - like reading a Wiki page.
LLM aren’t different in that regard - they were trained on inputs, and if you extend their training sets, they will be more exact in those areas.
Ability to reason is a very hard concept to specify, and we don’t have any foolproof test (that I know of) that would definitely say if LLMs can reach that stage.
I will fight you if you try to tell me that humans are smarter than any current AI - because there are some real dumb people walking this earth and mindlessly reproducing, unable to process basic concepts that they depend their lives on.
Nothing of this changes the fact that there is an intelligence - natural language is an incredibly hard thing to code deterministically - and as such deserves the ‘AI’ label without a doubt.
There is a complete lack of intelligence, just a passable facade that crumbles under scrutiny.
New version of people who know how to search the web vs those who don’t. Currently shit search results broken by search companies notwithstanding.
You cannot in all seriousness use a LLM as a research tool. That is explicitly not what it is useful for. A LLM’s latent space is like a person’s memory : sure there is some accurate data in there, but also a lot of “misremembered” or “misinterpreted” facts, and some bullshit.
Think of it like a reasoning engine. Provide it some data which you have researched yourself, and ask it to aggregate it, or summarize it, you’ll get some great results. But asking it to “do the research for you” is plain stupid. If you’re going to query a probabilistic machine for accurate information, you’d be better off rolling dice.
Exactly my point - except that the word “reasoning” is far too generous, as it implies that there would be some way for it to guarantee that its logic is sound, not just highly resembling legible text.
I don’t understand. Have you ever worked an office job? Most humans have no way to guarantee their logic is sound yet they are the ones who do all of the reasoning on earth. Why would you have higher standards for a machine?
I have higher expectations for machines than humans, yes.
Sounds like a recipe for disappointment tbh. But on the other hand, sounds like you trust techno marketing a bit too much.
No, I just know how to spot the lies in a datasheet.
I"m not sure what lie and what datasheet you’re referring to ?
Just in general.
I don’t really query, but it’s good enough at code generation to be occasionally useful. If it can spit out 100 lines of code that is generally reasonable, it’s faster to adjust the generated code than to write it all from scratch. More generally, it’s good for generating responses whose content and structure are easy to verify (like a question you already know the answer to), with the value being in the time saved rather than the content itself.
It’s good at regurgitating boilerplate, from what I’ve gathered.
This question betrays either your non-use or misuse of the products available. You’re either just reading the headlines of the screw-ups or you’re just bad at using the tool.
To directly answer your question:
But were you actually looking for a real response to your question?
It’s worse at all programming tasks except boilerplate, especially with its tendency to inject booby traps. Not knowing how to use the programming language it emits becomes a significant problem.
Comparing a language model to an idiot is unfair to the idiot.
A normal search engine works for everything else.
Any well-defined query I’ve ever made of an LLM has resulted in hilariously bad results, but I suppose I was expecting it to do something that I couldn’t already do better myself.
I’m a systems administrator, not a programmer. Like I said, quick scripts. An LLM could probably parse my comment better than you, evidently.
Oof… Was this in reply to my bit about better grammar and ESL individuals?
Fuck no. Especially the python visualization point.
I suppose you’re just a god among men then. For the rest of us, it’s useful and you’ve been given plenty of good answers to your disingenuous question.
So we should only ban things that aren’t helpful to you in particular? That’s a very… conservative way of thinking.
Don’t you realize everyone exists to serve me?
No, they think everyone exists to serve them.
People can’t seem to understand that it’s a tool in the early stages of development. If you are treating it as a source of truth, you are missing the point of it entirely. If it tells you something about a person, that is not to be trusted as fact.
Every bit of information you get from it should be researched and verified. It just gives you a good jumping off point and direction to look based on your prompting. You can drastically improve your results on any subject with good direction, especially something you don’t know a lot about and are starting out in your research. If you are asking it about specific facts you want it to regurgitate, you are going to get bad information.
If you are claiming damages from something you know gives false information, maybe you should learn how to use the tool before you get your feelings invested, so you can start using it more effectively in your own applications. If you want it to specifically say something that can grab a headline, you can make it do that, it’s just disingenuous and not actually benefiting the conversation, the technology, or the future.
They have a long way to go to solve AGI, but the benefits to society along the way outpace current tools. At maturity, it has the potential to change major socio-economic structures, but it never gets there if people want to treat it like it has intuition and is trying to hurt them as the technology starts getting stood up.
If you’re wondering why you’re getting so many downvotes, it’s because you’re ignoring the fact that the companies that have created these LLMs are passing them off as truth machines by plugging them directly into search engines and then asking everybody to use them as such. It’s not the fault of the people who are trusting these things, it’s the fault of the companies that are creating them and then passing them off as something they’re not. And those companies need to face a reckoning.
Noyb: You comply to GDPR, or else…
ChatGPT: You can lick my… wait, I don’t have…
Let me help you with some hypothetical robot explitives.
Imagine if they tried to let GPT defend them in court and these were the halucinations that got them fined lmao.
EU: Hello OpenAI, what do you think about the choice “Follow GDPR or here is the fine” ?
Yes. Isn’t it ironic when they pay their horrendous fines with their horrendous venture capital?
A fine isn’t going to frighten these people. What they should impose is a block on their IP address.
The fine is just the first step. They could also block entirely from operating in the EU.
If it complied with GDPR, chatgpt wouldnt know shit. How can it give you a (bad) copy of an answer when it cant copy
SteefLem is a 47-year-old scuba instructor and retired lion tamer from Winnipeg who has just learned the colloquial meaning of the phrase “pulled it right out of my ass.”
Without blatant privacy and copyright violations AI wouldn’t work. I mean it doesn’t really work anyway but it would work even less.
So? If your invention depends on illegal plagiarism to exist, maybe it shouldn’t. It’s not the law’s fault that LLMs depend on other people’s work to function, nor was that its specific target when it was written
Have any of the trials finished? I knew there were some ongoing but hadn’t heard any rulings yet.
The comment I was replying to basically said it has to be noncompliant (illegal) for the whole thing to work, as if that justified it. If a trial or whatever finds it’s not illegal, so be it, but I’d still have some moral issues about basically everything anyone ever does or has done turning into AI food
I read the article, but can’t figure out what NGO, NOYB, or GDPR mean. Can someone help me?
NGO: Non-governmental organization
GDPR: General Data Protection Regulation. A set of European laws intended to empower individuals to control personal data held by companies.
“noyb” is a European privacy rights organization, who appears to prefer to style their name with lowercase letters. The name is an acronym for “none of your business”.
Thank you for being constructive and helpful
It’s in a foreign language called unnecessary gatekeeping
It’s just in European. it’s an entirely reasonable assumption that people in this continent with even a passing interest in the world will know what an NGO is (that’s not even European-specific) as well as what the GDPR is. Your argument suggests that people from the US, for instance, should be forbidden from talking about IRAs and the IRS and their 401(k)s and the DMV because those terms mean very little to nothing over here.
No, actually, nothing I said implies that at all. It’s standard for authors in all fields to define their acronyms. And yes, I absolutely expect American authors to define their terms. The fact that we am American I don’t notice that irs is undefined in a given article doesn’t mean that’s permissible.
💀 noyb is the name of an organization and GDPR is a law. NGO is the only thing you could even remotely begin to describe as unnecessary jargon but that’s still a stretch.
Seems so simple they could have done the same in the article, so thank you for reinforcing my point.