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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
Keep going…
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.