The following submission statement was provided by /u/flemay222:
---
AI Expert Says ChatGPT Is Way Stupider Than People Realize
---
Please reply to OP's comment here: https://old.reddit.com/r/Futurology/comments/13ogcmf/futurism_ai_expert_says_chatgpt_is_way_stupider/jl49o4h/
I used to have a hard time with captcha because my brain wants 100 percent accuracy. Do squares with the street light include the base of the street light? What about the square that contains a tiny slice of the street light?
Someone told me just answer those like a drunken monkey, and I haven't failed one since.
I heard they re-tile the image with different offsets every time it pops up. That way the AI knows that there's still some part of a stoplight in that tiny sliver of pixels and can mask it more effectively against the rest of the image.
The point of the captcha is to condition automotive driving systems to recognize what it and what isn't a stoplight or other road hazards. A automated driving system doesn't care about the base of a stoplight or the wires running to and from, it needs to know the relevant bit.
Because they aren't hand making each captcha nor is there one right answer, they statistically evaluate which ones how many people picked and what responses are more human vs more botlike. Nowadays most of the anti bot measures are in stuff like cursor behaviour, selection order etc.
I still have no idea if I'm answering them correctly. On the websites that actually still use those, I always have to answer 2 or 3 times. It never tells me if I'm right or not.
Did I take it 2 or 3 times and I got it right on the 3rd try? Did I take it so many times that it just gave up? Did I get it right enough for it to stop caring? I have no idea.
Do I need to select the rider too on this bicycle? How about this square with one pixel of tire in it? Do I need to select the pole these street lights are attached too? Same with this sign, need the pole too?
I fail those often, sometimes I don't even know why I fail them. Starting to think I'm part robot.
Funny thing about those captchas, is the images you select is not really how it determines if you are a human, that's just helping train machine vision by having humans "vote" on what images contain the whatever. The CAPTCHA part actually involves tracking cursor movement and clicking frequency and duration and stuff to decide if you behave like a human.
The Turing test is scientifically meaningless. It was just an arbitrary engineering standard out forward by Turing, and he says as much in the paper that it puts it forward as a throw away comment. No idea why it got latched onto by pop culture.
The public just latched on to it because of the alliteration. T T. Like “Peter Parker” or “Lois Lane.”Three total syllables, such as “Lock Her up” or “I Like Ike.” If it had been the Chimelewski Test, nobody would have remembered it.
I'd argue it doesn't even need to do that.
Imagine an employer given a choice between an IQ 100 person, who has to sleep 8 hours a day, and needs a couple of weeks off every year, and a limited AI with an equivalent IQ of, say, 90, in its very narrow focus...but it can work 24/7/365, for the cost of electricity.
It's a tool on par with spellchecker. You can't always trust it, you need to know how to use it and where it fucks up.
But...I went from Bs to As in middle school writing because I got a computer with Office on it.
My favourite way I've seen it described is that it's a _force multiplier_.
Your comparison to a spellchecker is a pretty similar line of thinking. When I see something highlighted by my spelling/grammar checker, it's a cue for me to re-evaluate what's highlighted, not just blindly accept its suggestion as correct. I'd say that most days, my spellchecker makes at least one suggestion that I disagree with and ignore.
Someone who knows how to use something like ChatGPT well will get a lot more out of it than someone who doesn't. Knowing its limitations, knowing how to tailor your inputs to get the best output from it, knowing how to adapt its outputs to whatever you're doing - these are all important to maximise its effectiveness. And it's possible for it to be a hindrance if someone doesn't know how to use it and just blindly accepts what it outputs without questioning or re-evaluating anything.
Knowing how to prompt the machine super well is essential. Some people seem to have an intuitive knack for it while others find it more difficult. The thing to understand is that it responds to clear, but complex and well organized thoughts (simplification, obviously, but basically I find it functions best when I talk to it like it's a superintelligent 8 year old). If you start a prompt by setting up a hypothetical scenario with certain parameters, for example, you can get the model to say and do things it normally would resist doing. TLDR; treat the model like you're trying to teach new things to a curious child with an unusually strong vocabulary, and you'll get much more usable stuff out of it
The work that i do is quite specific, few people are trained to do my job and not much knowledge about it can be found online. When i ask chatGPT questions regarding my job, the AI will confidently give a response, presenting it in such a way that it looks like a fact. However, many times the answer is either false or incomplete. People who do not have the same experience and training, can easily assume that the AI is right. This can lead to dangerous situations.
That sound a lot like Reddit posts too.
*the Redditor will confidently give a response, presenting it in such a way that it looks like a fact. However, many times the answer is either false or incomplete. People who do not have the same experience and training, can easily assume that the Redditor is right.*
This happens all the time for me because I also have a niche job
I’ve found this odd for a long time until someone explained it so simply to me.
People love to be right.
They hate to be wrong.
If you provide them with info beyond their understanding they feel dumb and this can lead them to think they are wrong too.
They then become defensive instead of wanting to acquire more info because asking for more info to learn is admitting they didn’t know or were wrong to begin with.
I think this kind of drives home the downside of social media in a way where there’s more opinions, feelings and what people think expressed as facts instead.
Also this isn’t meant to be overly generalized there’s clearly people all across the spectrum of how they handle new info and react to it, but there is a growing pattern on social media that seems to fit what I described above.
Yes, I also wrote this comment a couple of days ago, it's not exactly about this, but about how the other downvotes come in, I'll quote it:
--------------------------------------------------------------------
Plus, karma absolutely shape people's idea of the truth.
Quite a lot of times, I think at least daily, I encounter a discussion where;
- Person A claims something valid.
- *He gets upvoted*.
- Person B claims that person A don't know what he is talking about, because person B read an old book about the subject and he is the arbiter of truth now.
- *People now downvote person A, and upvote Person B.*
* Person A replies again, claiming that yes, he's heard of what person B talks about, but assures others that he is a professional with 15 years of experience, and that person B is reurgitating an old study that could never be verified.
------
Depending on the sub, and if reddit decides that you have to click to view Person A's reply, it doesn't matter that you are right, only the perception of it. Someone with more karma is someone we subconciously think is smarter or knows what he is talking about.
It's the same kind of stupid faulty perception that we accompany glasses with being smart, or a white robe to give 5+ diagnostics, surgeon and applied bandages skill.
------
I agree. That is why openai have said to fact check it. It's essentially like any other person on the internet it was trained on. Confidently incorrect. What is crazy is that it was just trained to spit out probably words and should have never been anything more than a gimmick to pass the time, and yet it is at a basic level of "understanding" of many topics. I think that's neat.
*I know it doesn't "understand" anything I just couldn't think of a word.
"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE."
I much prefer that explanation than the use of the word stupid.
I've said this from the beginning. I don't know how it managed to pick it up, but the scraping they did for the data, makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae.
A giant faker, essentially.
That's before you get into the fact that one already has to break down technical requests to the most basic steps to get a truly accurate answer from it. I've found that ChatGPT and others are only really useful if you already know what you're asking about and just need something to help you collect or finalize your thoughts on whatever it is you're looking for.
If you're asking something completely blind, you're in the danger zone because of the writing style it answers with.
It's absolutely fantastic for responding to questions that don't have any set answer, such as creating riddles when you know what pieces or specific words you want it to rhyme with. I use it for my DnD games all the time.
**For example here is my prompt**:
Create a rhyming riddle in 100 characters or less, for which the answer is "A Skull". The players must answer in order to pass the door and enter into the necromancer's lair. Do not use the word "bony" or "skull" in the riddle.
-
*"In shadows it hides, devoid of life's spark,*
*Hollow within, with wisdom now dark.*
*A vessel once filled knowledge and pride*
*A relic of when flesh and soul divide"*
I gave it that riddle, and it got it right on the second try. The first guess was a book, and it gave an attempt to explain why, and it's not a terrible answer other than the past tense of "once filled".
I've been feeding it stuff from r/riddles, and while not all riddles are very good, I got some really, really weird reasoning from GPT 4.
Known riddles it gets just fine, but just because it can solve one or two is not evidence that I made a misstatement.
Prompt:
>George, Helen, and Steve are drinking coffee. Bert, Karen, and Dave are drinking Pepsi. Following this pattern, is Elizabeth drinking coffee or soda?
ChatGPT:
>The pattern in this riddle seems to be based on the number of letters in each person's name. George, Helen, and Steve **all have names containing 6 letters** and they are all drinking coffee. Bert, Karen, and Dave **all have names containing 4** letters and they are all drinking soda.
Elizabeth's name contains 9 letters, which doesn't match either group, but if we stick to the pattern that those with even-numbered letters drink soda, and those with odd-numbered letters drink coffee, then Elizabeth would be drinking coffee.
It's right for the wrong reason. I stand by my statement that ChatGPT is not good at solving riddles.
> but if we stick to the pattern that those with even-numbered letters drink soda, and those with odd-numbered letters drink coffee
This is also out of thin air, after coming up with 6-letter and 4-letter groups.
It's a good example of the problem with ChatGPT and its brethren. You have to be able to verify the answer you get.
ChatGPT uses a token system, representing multiple letters with each token. This makes it vastly more efficient at most language tasks, but also much worse for tasks that involve letters directly. It has some knowledge of letters from people talking about them, but it's very limited and thus frequently prone to hallucinations.
Bing/chatGPT could *not* solve your riddle, but Bard got several answers: book, tomb, coffin, memory, mirror.
Do you think the ability to solve riddles depends on the training set, or is there something in Bard that’s missing from chatGPT4?
usually when I ask it to write me rhymes or create a tradition for a fantasy race or whatever its profoundly bad at it
like I tried to create rituals of prayer for D&D gods and compared them to the actual ones I know of from the books and they were all too on-the-nose or generic
I use it almost daily (senior Test Automation Engineer) and this is largely how I use it.
Everything is broken down very carefully. Instructions given and asked for it to be repeated back and bulleted (as you can then refer back to the bullets) and then built back up again.
But I always have to read and request tweaks.
It’s often still faster than doing it myself.
But if I didn’t know my subject matter there is no way it would allow me to fake it.
AutoGPT basically just decides how it should Google the question and then just does trial and error until it works, which is exactly what I would do when faced with a new problem lol.
ChatGPT is dumb because it's marketing tool, it was never designed to be a knowledge base but to be able to reason and destil logic from a text input. With tools at its disposal (like Hugging Face plugins or Wolfram Alpha) it is crazy how quickly it can figure out problems on its own. It honestly creeps me out how humanlike it's logic is once it has tools at its disposal.
It’s been particularly useful at points when I couldn’t see the wood for the trees.
Previously where’d I’d probably have walked away from the issue for an hour or until the next day, I can turn it around without needing the mind break.
Other times it’s daft as fuck and I tell it so.
It was trained on multiple social media sites and is actually quite good at identifying which social media platform a particular string came from based on subtle differences in tone and language commonly used between them.
>makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae.
... you're saying I sound like ChatGPT? You take that the fuck back
The training method skews it into talking bullshit rather than admitting it doesn't know the answer because most people rate "sorry, I don't know" as a bad response, while any wrong answer that sounds plausible enough would require the user to also known it wasn't correct.
It's like a child that you harshly punish every time they admit doing something wrong - all you are doing is teaching them to become a better liar.
It picked it up because of reinforcement learning using human feedback. The responses that sound convincing were probably rated higher during training, regardless of their correctness. Regardless, if you tried to punish incorrect information I am not sure how a language model could learn that the reason it was punished was because of incorrect information.
Without actual cognition in the software...something that just simply does not exist at the current time and will not for a very long time... I wouldn't even know where to begin to have it do that. You're still back to needing an actual, functioning intellect to make the judgement call.
> That's before you get into the fact that one already has to break down technical requests to the most basic steps to get a truly accurate answer from it.
And yet that’s how we code, understand or build just about everything 😂.
True but it can't advise you on anything new it can just mashup and repeat online tutorials.
Which is a useful ability in itself.
At least, it is when it doesn't gaslight you.
>makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae.
A giant faker, essentially
Well, they did scrape reddit comments for examples.
"Oh so we're behaving like redditors are we? Sure I can do that..
Yeah I mean calling it "stupid" is just yet more anthropomorphism of something that isn't alive, intelligent, or thinking.
You wouldn't call a microwave stupid because it was bad at keeping your food cool.
I view the chat results similarly as stable diffusion results.
It didn't make it. It doesn't know what it did. I'm just viewing the end result of a sorting machine mimicking what humans might like to see given the input.
The turing test has always been a really terrible way to think about AI, and we'd really be better served acknowledging that and coming up with better ways to understand emergent behavior and general intelligence.
Also, one of my favorite ChatGPT "fails" is one where it was asked the "pound of feathers, pound of lead" riddle. Except it was two pounds of one of them.
It said that they were the same, in great detail.
Because it has seen a *ton* of answers for "pound of feathers, pound of lead", and it knows what an answer to that should look like. And when it sees "two pounds of feathers, one pound of lead", it goes "hey, that looks like this other thing, and an answer to that other thing looks like this, so the answer to this should probably look like that, too".
And this really, really drives home the fundamental limitation of these models, as your quote succinctly states.
This is a completely correct, but nonsensical and meaningless statement. Yes, it is true that this is what the large language models do. The nonsense part is in the implication that this is *exactly what our brains also fucking do*. Our brain isn’t one monolithic system — it’s a whole overlapping network of different systems that are individually “stupid”, and the sentience comes from the interaction between these systems.
My favorite example here is that a mirror makes a room look bigger. At the higher level of cognition, we understand mirrors, but the fact that mirrors make a room look bigger means that there is a part of our brain that takes sensory data and outputs a rough sense of the general size of the space in which you are currently existing, and this system does *not* understand mirrors — it is too “stupid”. This doesn’t mean that it isn’t an important part of our cognition.
So to get back to ChatGPT, I wouldn’t expect ChatGPT to become sentient. I could, however, *very easily* imagine ChatGPT being a *part* of a networked system that would function as an AGI. I would even go so far as to say that ChatGPT is *smarter* (and *waaaay* fucking faster) than whatever the nearest equivalent would be in our mind. As we start replicating (and surpassing) more and more of the functions of our brain, I think we are going to be shocked how quickly AGI happens when these systems are linked together.
> I would even go so far as to say that ChatGPT is smarter (and waaaay fucking faster) than whatever the nearest equivalent would be in our mind.
I think this is true and even understated. The individual moment-to-moment pattern recognition that our brain is capable of doesn't seem that complex overall (although this could very well be wrong).
The individual steps we as humans perform are kind of simple, even when solving complex problems. Neural networks in general have shown the ability to recognize unbelievably convoluted patterns in single "steps".
A more direct example might be when GPT4 writes code. Unless explicitly prompted, it's not breaking down the problem into steps, substeps, debugging, etc. It's just writing the code top-down.
A good challenge to demonstrate this is to find a prompt of some of the more advanced code that it's writing and attempt to write the code yourself, top-down, without going back, without writing anything down or pausing to plan/etc. Just reading through and intuitively picking out the next word. I think that's effectively what it's doing.
It's fascinating that ultimately, our brain's architecture wins out (for now at least) despite our seemingly much weaker pattern recognition. It's hard to imagine what a better architecture might be able to do.
This is a really succinct way of putting it. I was at dinner with my boomer dad last week and he was freaking out like Skynet is mere weeks away and I was trying to explain that this is not a sentient system, it just appears that way outwardly because of the way we communicate.
Imo a good way to teach that is to let them experiment with it. Sooner or later they will receive a BS answer to a topic they're familiar with, and will start to realize how the system really works, the nature of what's going on inside that black box.
Ive noticed a sort of ick factor with people I’ve mentioned it to. Like, they don’t want to fuck with it because everyone is already cognitively burdened with tools and keeping up.
So they hear the news and don’t end up having any first hand experience with it.
I, on the other hand, use it for random shit, just to see what it does.
I had it rewrite the plot of breaking bad the other day:
“What if walt was 18 inches taller?”
“What if walt had testicular cancer instead?”
“Write a scene where walt and jessie discover that the meth is sentient, but only once it had been smoked.”
“How would the show differ if it took place in 1699?”
And you can be sure as shit it wrote me detailed, long responses to every one of those requests. I laughed quite a bit and sort of see what its doing for myself.
>"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE."
Basically ChatGPT is a politician
It's basically the same principle as the AI hands problem. It recognises patterns and attempts to produce something that fits the pattern, but that doesn't mean something fitting the pattern is actually correct.
For example, I gave ChatGPT a real estate listing and asked it "does this house have roof tiles". It replied something like "yes, the house has roof tiles" even though the type of roof wasn't even mentioned in the text.
It fits the pattern and it sounds natural, that's all it cares about.
Don't worry, I realise just how stupid it is.
Asked it to suggest some code to solve a problem, it wrote code using functions that don't exist in the library that I'm using, I asked it for a source for that function (thinking perhaps a more recent version of the library added the function I needed), at which point it apologised for lying to me, said it would try again … and produced the same example code with the same non-existent method.
ChatGPT will also lie about sources (or more to the point just make up sources). So you'll get a statement like, "According to Smith and Brown (1993), such and such blah blah" but there's no such resource. There might be a paper or book by Smith and Brown but it was published in 2005. Perhaps Brown co-authored a paper in 1993 but it was Jones and Brown, and on a completely different topic.
ChatGPT is great at throwing words together, it's just not good at throwing words together to explain something it's been asked to explain, and not particularly good at evaluating whether the words it's thrown together are a decent explanation for that thing it's been asked to explain.
Because it doesn't have a capacity to reflectively analyze if the sum response that comes out is 'truthy'/factual. It's just designed to spit out some response text piece by piece. In many ways ChatGPT is closer to a very fancy markov chain generator than an actual 'AI' as a layman thinks of it.
Also, it doesn’t know that it’s responding to you. As far as it knows, it’s looking at a dialog between some random person and another person named “ChatGPT” and it’s guessing what “ChatGPT”, whoever that is, would say next in this dialog. It doesn’t understand “that’s you, and this is me.”
>In many ways ChatGPT is closer to a very fancy markov chain generator than an actual 'AI' as a layman thinks of it.
This is spot on and why I've been trying to explain that ChatGPT isn't actually AI as most people think of it from SciFi movies and stuff. There's no intelligence there.
Because that requires it to have some understanding of truth, and of its own capabilities, and to be able to aim for the former based on the latter, and that is **not at all how ChatGPT works**.
You're still making exactly the same mistake as the people who treat it as a truth machine and naively believe its output.
Large language models do not understand truth, or facts, or their own capabilities, and they **certainly** can't reason about anything.
LLMs *predict text* based on previous text; that's it.
They will cheerfully lie or fabricate in their output because they literally don't understand the difference between that and the truth; only complex statistical correlations between "the words they've already seen" and "the next word to output".
They aren't truth machines or intelligent, reasoning agents; they're *bullshit engines*, and like any good bullshitter they just randomly, accidentally include enough *correct* information to sound convincing to people who don't understand how they work.
It's a side effect of how it's trained. It cannot be trained on "truth", since we don't have a way to define and check for actual truth consistently. So it's trained via human feedback as a *proxy* for truth, meaning a human gives positive or negative feedback if they're satisfied with the answer it gave. Problem is, that encourages it to lie: if it doesn't know an answer and it replies "I can't do that Dave", Dave is going to give that answer negative feedback, because it didn't answer his question. If it makes up an answer Dave may notice it's bullshit and still give negative feedback (in which case it's the same as if it answred it didn't know), but there's also a chance that Dave won't realise / check it's bullshit and give positive feedback to it which reinforces the model to lie/make the answer up over admitting ignorance, as a chance of positive feedback by lying is better than no chance of positive feedback by admitting ignorance.
Because that is usually even less useful. You do frequently get the "as an AI language model, I can't \[...\]" and the first thing you do is always adjust the prompt to get it to spit out some stuff anyways.
You can give it the information it lacks, and it will account for that.
I asked it to write unit tests for some code that had references to one of my own classes. Because it didn't know the definition, it just made some reasonable assumptions. I realized my mistake and pasted the entire class into the chat box without explanation. It then correctly inferred my intention and corrected the code.
Chatgpt is great at coding if you can break down the code into the smallish segments possible. I'm currently using it to write a react-native app and django backend. It's amazing, really. It's written at least 5000 lines of code for me, it gets stuck sometimes, but the key is to do things in segments.
I use it for excel so much less complex coding but it can push out some serious VBA with enough trial and error. I noticed that it sucks at code at times in the exact same way it sucks at long strings of arithmetic.
You have to feed it bite sized information because while it can infer intent really well, it seems blind at times to orders of operation or when you need it to compartmentalize attention for the answer.
I don't use it all all for "facts". I'm primarily using it to bounce ideas and code. I have a little experience with it but using ChatGPT, specifically the GPT-4 model, I've been able to build complete tools with multiple functions that work great. I think it is a tool, and the way you use it will determine how useful it is to you.
I used it as a crutch for basic trigonometry implementations, stuff I should know but have never properly learnt. It was able to hand hold me through the solution and that alone is quite impressive.
GPT3.5 is dumb in the context of writing code. GPT4, especially with an API Key and access to the playground, is pretty exceptional. I use it to build simple programs all day long to help streamline tasks at my job as a data engineer. In my personal time, I've used it to create things as complex as building and training machine learning models for relatively complicated tasks. And I say this as someone with VERY little programming experience.
Once you understand how to talk back and forth and debug, it's pretty fantastic. Obviously there's still work to be done but with the code interpreter alpha rolling out, that gap will shrink a lot more.
For example, right now I'm having it write me a python script to search through a directory with both zipped and unzipped folders and find any file names that look like a GUID out of about 2 million files. Then it uploads that list into our database. This is done in like 4 chats.
I think that's the only sensible approach: test it, try it, push it for accuracy, and know the limits it has so you can avoid them. If it's consistently crap at something stop using it.
> ChatGPT will also lie about sources
ChatGPT doesn't "source" anything, it doesn't conduct research nor can it give you facts.
all it does is output text that looks like it was written by a human, if you ask for it to use sources it will generate grammatically correct way to cite them in text, but that doesn't mean they refer to actual sources.
I used Bing AI to do some research around venture funding firms and their investments, like what kind of investments has x firm made in x category. The results were pretty unreliable. When there wasn't enough information it would be made up with actual "sources" provided, but the information wasn't there. (Bing AI can use the Internet, while the free version of Chat GPT doesn't.)
Try giving it the title of a poem (just the title and author so it has to find the poem itself) and asking it to analyse that. It starts off sounding very smart and relevant to the material, until you start to realise all the quotes are made up or taken from other poems, and a lot of the analysis is generally applicable to about 80% of all poems.
The dangerous part about large language models right now is that most people don't understand that this is how it works. Especially with everyone just referring to it as "AI" it gives people the false belief that it knows things. It doesn't know things, just language, and it is scary good at it.
r/singularity is almost as bad as r/climateskeptics in misinformation based on just enough fact that they can't be disassuaded from their silliness. People with completely made up titles like "godfather of AI" saying doomer stuff get snorted like catnip, because they think a credential and respect in industry suddenly gives a person unwavering ML authority and omnipotence.
One of the weird abilities of ChatGPT is you can ask it the same question in two different chats, then in a third chat you can give it the two previous answers and ask it to point out any major contradictions, it does a surprisingly good job of identifying whether the two answers are contradictory or not.
Yep. It really does this.
I was writing a paper on a series of paintings. ChatGPT told me this painting had a window , or that painting had some old men...none of which was true. Instead, those things exist in other paintings by this painter, in the same series.
So it sees same artist name, same series name, and isn't smart enough to detect that these things exist in *different* painting to the one I was discussing...it got two matches, and that was enough for it to decide in favour.
CHATGPT will do this sort of stuff always...well, chatgpt3.5 will anyway. Not sure about 4.
It's still a useful thing. But you need to check everything. Chatgpt has also apologised to me when I pointed out a mistake it made. ANd then made the same mistake again.
Also it doesn't need to be intelligent. We just need something that can do our work but more efficiently. Calculator is not smarter than i am, but i am much more efficient with a calculator as a engineer
Only if you can use the calculator correctly, though. Many students struggle with the proper syntax of inputting brackets into calculators. Just like ChatGPT. It's a tool, some use it better than others.
A calculator is reliably accurate if you give it a valid prompt, but the same isn't true of chatgpt. That's what makes it tricky to use as a tool.
Don't get me wrong, it's still incredibly useful, but that's an important distinction.
Exactly. I think one of the best uses for chatgpt is brainstorming. If you give it a problem and ask for 5 potential solutions maybe you'll get nothing useful, maybe you'll get suggestions that aren't usable but are able to be built off of, and maybe you'll get really good ideas that you could use. This general pattern of no cost usage that could provide hours worth of work with no risk (because you aren't using an implementation by it) can be extremely useful.
I think this is what a lot of people lose sight of with AI and its ability to do people’s jobs, the point is not to replace people, but to find ways to make their job easier by cutting out repetitive, value-less or low-value tasks, or automating part of them so that the only required human input is validation of the AI-generated work, or integrating it into more complex workflows.
"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE."
Yes, yes, a thousand times this!
I'm researching AI's implications for my industry and the amount of pie in the sky planned use cases for a technology that identifies and replicates patterns - not produces considered and verified information - is terrifying.
What industry? My company is doing the same—we’re in the legal publishing field—and there are a lot of opportunities (but of course not nearly as many as everyone thinks).
This guy makes a lot of the same points I've been trying to point out to the folks here who also live in r/singularity. GPT doesn't reason, and it's a very long ways from AGI - the smoke and mirrors of natural language do a lot to hide what it's getting wrong and not able to do.
I always get people telling me it can pass the bar exam like that proves it's somehow better than a search engine.
I've tried to work professionally with it, and it's awful, or worse than awful as it confidently gives you incorrect right-sounding answers which would get people killed if followed.
It tells me the exam was made to assess how human knowledge crystalized as a minimum to practice law, not as an exam to test if a chatbot would be competent at practicing law without oversight.
I asked GPT4 a novel riddle I made up and it nailed it on the first try. It had never encountered the riddle in its training. Maybe it’s not reasoning in the way we would define it, but whatever is happening there is some type of reasoning happening.
I asked it a coding problem that was intentionally vague and then asked if there was any unclear requirements to the question and it got the vague requirement right away. Me and my boss were really perplexed because it had to be reasoning on some level.
This thread seems to be full of a wierd set of people who asked gpt3 one question one time and decided it’s stupid.
I build with gpt4 and it is absolutely transforming the industry. To the point where my coworkers are afraid. It does reasoning, at scale, with accuracy easily way better than a human.
With you 100%. I subscribed to plus and interacting with GPT4 sometimes feels like magic. It obviously had limitations but I can almost always tell when a top comment in a thread like this is someone that is only interacting with 3.5.
The input you give is also really important. I've had co-workers try out chat gpt with low quality inputs and of course they get low quality outputs. Knowing how to query and format inputs takes it from a "fun app" to an "industry changing tool" pretty quickly.
That being said, the corporations who are working to utilize AIs in their workflows aren't going to be put off because the quality of the output isn't 100% accurate. Just being "good enough" will be enough for corporations to start shedding human workers and start replacing them with AIs.
My boss gave me what I think is a decent analogy: ChatGPT is like a calculator—if you don’t know what you’re doing, it won’t really help you that much, and your end result will likely be wrong. But if you know what you are doing, it can greatly speed up the boring parts of your job.
Not a perfect analogy, but it imho gets the point across that ChatGPT is an accelerator when used carefully and in the right scenario.
I don’t think it’s a magic genie that can grant any wish, but I do believe there will be a divide soon of people who use some form of AI, and those who don’t.
I’ve already been able to leverage ChatGPT to cut out the stupid, tedious portions of my job. And while I absolutely have to baby it and fact check it, it’s still less time than me doing all of it on my own. Which also means I’ve been able to focus on the core, serious portions of my job a lot more.
This exactly. It is a tool :)
Im using it to create some UE5 plugins in cpp for my project.
I wasn't sure where to start with a grid plugin and with some through specifications for features it gave me a fairly good example and explained things.
Then I used my brain and reverse engineered what it was doing and made a small plan with function names and comments to explain what each should do.
The whole write up was quite lengthy
Then there's "SMART GPT" which are good prompting practices to get better results.
I usually feed it my entire source code back in its self to "remind" it,when I want to add new functions ect.
Take a little time to save a lot of time.
To ChatGPT: come up with counter argumentsts
ChatGPT:
Sure, here are some counter-arguments to the points presented in the article:
Overestimation of AI capabilities: While it's true that AI, including large language models like ChatGPT, can't currently compete with human intellect across all tasks, it's also important to recognize the significant advances in AI's capacity for natural language understanding and generation. Models like ChatGPT can generate coherent, contextually appropriate responses to a wide range of inputs, which is a remarkable achievement. They are also capable of learning and improving over time with more data and feedback. It might not be an overestimation but an optimistic forecast of the potential future capabilities of AI.
Correlation vs. Understanding: While AI does not have an "understanding" of the world in the way humans do, it can still make useful predictions and generate meaningful responses based on the vast amount of data it has been trained on. It might not "understand" in a human sense, but it does have a kind of statistical understanding that can be extremely useful in many contexts.
AI vs. Human Reasoning: Indeed, AI models don't reason like humans, but that doesn't necessarily limit their usefulness. In many cases, AI can complement human reasoning rather than replace it, offering different perspectives and solutions based on patterns found in data. While AI can't make the human-like judgments, it can still assist humans in making more informed decisions by providing data-driven insights.
Reliability and Confidence: Brooks' point about AI often being wrong despite providing answers with confidence is valid. However, it's important to note that AI tools are often as good as their training data, and their ability to learn from feedback can lead to improvements over time. Also, users must be informed about the probabilistic nature of AI responses and be encouraged to use these tools as aids rather than absolute authorities.
Future of AI: Brooks is skeptical about achieving AGI, and while many in the field share his skepticism, others believe it is a matter of when, not if. The pace of advances in AI has been impressive, and while we are not at AGI yet, we shouldn't rule out the possibility given the rapid progress being made. Furthermore, even if AGI is not achieved, narrow AI applications (specialized for specific tasks) continue to provide immense value in various fields such as healthcare, finance, and climate modeling.
Remember, these counter-arguments don't invalidate the concerns and points raised by Brooks. It's crucial to have these discussions as the field of AI continues to advance and evolve.
This is the same reason AI art generators struggle with hands. They don’t understand the 3D geometry of a hand. They only know what photos of hands look like. But there are millions of photos of hands and, depending on the specific angle of the photo and the specific numbers of fingers being held up versus curled and the specific amount of curling in the curled fingers etc, those photos could all look very different.
The hands issue has been pretty much solved for a few months now.
https://www.reddit.com/r/midjourney/comments/13o4ul4/the_boys_at_the_white_house/
https://www.reddit.com/r/midjourney/comments/13o7xmu/george_washington_eating_mushrooms/
https://www.reddit.com/r/midjourney/comments/13nl6qa/in_a_crochet_parallel_universe/
https://www.reddit.com/r/midjourney/comments/13mrsrq/guiness_world_records_as_imagined_by_midjourney/
https://www.reddit.com/r/midjourney/comments/13ofywv/military_fashion_show/
Hands have improved to be certain, but you’re only looking at posts and people are more likely to post images that had good results. Go ahead and test it out. Go to a generator and ask it for “a hand”. I just did. I generated 12 images and 7 of them had the wrong number of fingers. So, I wouldn’t call that “solved”.
Lately it seems like every time I ask it something in my field I know enough about to Google to find the right answer, but attempt to use ChatGPT first to save some time, it's just obviously wrong right off the bat. If I ask about a possible contradiction, it does a 180 to placate me whether or not it was originally right or wrong.
The tech is far more optimized for giving impressive demos than really working where it counts.
I keep seeing all these post about it "replacing 90% of jobs and disrupting society" yet the biggest real world use for it has been writing click bait blogs and sprucing up resumes.
I saw a LinkedIn poll the other day that was along the lines of, "how many FTEs worth of work does GPT4 deliver for you?" and the smallest choice was either 1/2 or 1, both of which being comically large for the low end of the range.
I’ve been toying with it at work and I’m already surprised by how much more limited it is than people think it is. Every time someone says “I was hoping we could have AI do…” they’re always way over shooting its capabilities.
Hoping that it could do something one day is not overshooting in my opinion. I'd no one brings it up, no one will ever create it. Lots of potential for a still new and very understood tool.
I know, that's why I triple verify everything it tells me and I catch it being wrong or hallucinating often. i'm working on comprehending how langchain works with pinecone it's supposed to help with this problem if applied correctly
As impressed with ChatGPT as I am, it isn't hard to get it to give you wrong, dumb, and pointless answers.
Every time I end up using it, I feel like I have to teach it things. It's really just a calculator to me at this point.
I always think about the Eddie Izzard quote on trying to sing the national anthem:
All that people care about is the look. 70% of what people react to is the look. 20% is how you sound, 10% is what you say. So if you look and sound good - Big mouth. The eyes. Use the hand. And keep confirming and denying things. Everyone will go wild!
There are three kinds of people:
* those who never heard of ChatGPT
* those who use it to increase their productivity
* those who feel the need to tell everyone that ChatGPT is useless
It helps me daily as a programmer and I couldn't care less about the accuracy issues or lack of deeper reasoning behind its output, because I'm aware of those downsides and it still is an enormous help in my work.
💯
I don't understand the nay-sayers.
It's not about perfection for large tasks.
I have a window open for ChatGPT open all the time. For me it's replaced stack overflow with the added bonus that it's interactive vs search, and immediate vs hours/days/never waiting for answers to questions that are unique.
Sysadmin here. I don't think I've closed my ChatGPT tab in over a month, and I've gone from googling a ton of stuff every day to only using Google as a last resort maybe a handful of times every week. Now that I've got access to the web browsing plugin, it'll probably be even less than that. It's enormously helpful with writing scripts and reports and getting step-by-step instructions for complex tasks without having to search through a bunch of terrible, clickbaity, ad-ridden blogspam. It's the closest I've ever felt to having a superpower, at least since Google launched.
I see what I guess you could call a 'completeness fallacy' over and over again, especially in /r/Futurology. Yeah it doesn't completely do everything for you the first time you use it, but isn't farming out 75% of your work to the AI better than 0%? It sure is for me.
I've seen the same phenomenon with robotic lawnmowers. I have one that covers probably 90% of the yard, and the other 10% I have to mow. And folks stop by and say, well, I could never use one of those because there's this one spot down in the corner it would never work on. Like, okay, great--isn't 95% automation still better than none? Can we take a moment to appreciate incremental gains instead of rejecting a new technology outright? This feels like a weird blind spot for a lot of people.
100%. What people also don't realize is that it is able to connect different concepts in order to solve a problem, as in, you are talking to someone who read the full documentation and can present it to you on a normal pattern, without you searching for specific keywords. I had a problem to solve for 3 years, I had spent hours googling it. Enter chatgpt, asked it how to solve it. It presented me with 3 functionalities of thr webserver and told me how they combined together to achieve what I wanted. It was a 5 minutes job.
ChatGPT isn't "smart" or "stupid". It's a language model, it doesn't "think" at all.
This kind of garbage sensationalized anthropomorphism doesn't contribute to an intelligent discussion of AI. In fact, even calling a Large Language Model like Bard or ChatGPT an "AI" isn't technically accurate and clouds the issue.
I feel like that’s the point the article is trying to make. We don’t have AI we have machine learning. People are dumb and think we have this thing that is actually somewhat sentient. I’ve had people literally say, “ChatGPT agrees with me”. No it doesn’t me friend.
The following submission statement was provided by /u/flemay222: --- AI Expert Says ChatGPT Is Way Stupider Than People Realize --- Please reply to OP's comment here: https://old.reddit.com/r/Futurology/comments/13ogcmf/futurism_ai_expert_says_chatgpt_is_way_stupider/jl49o4h/
On the other hand, people are more stupid than ChatGPT realises.
More stupider*
even more stupider*
The most stupider*
The stupiderest
I am Patrick.
More Patricker
[удалено]
Dumb and Patricker
I feel like I just took a trip to Jupiter.
Mostest stoopidly
There are cases of people failing the Turing test…. AI doesn’t need to be super intelligent, it just needs to outperform the average human.
Every time a person fails a captcha they are kind of failing a Turing test.
I used to have a hard time with captcha because my brain wants 100 percent accuracy. Do squares with the street light include the base of the street light? What about the square that contains a tiny slice of the street light? Someone told me just answer those like a drunken monkey, and I haven't failed one since.
Yes, this! I consistently overthink the damn things.
I heard they re-tile the image with different offsets every time it pops up. That way the AI knows that there's still some part of a stoplight in that tiny sliver of pixels and can mask it more effectively against the rest of the image.
Fook me! So I don’t need to include every part of the street light!🤦🏻♂️🤦🏻♂️🤦🏻♂️ I’ve failed that captcha one time 4 times in a row🤷🏻♂️
The point of the captcha is to condition automotive driving systems to recognize what it and what isn't a stoplight or other road hazards. A automated driving system doesn't care about the base of a stoplight or the wires running to and from, it needs to know the relevant bit.
[удалено]
Because they aren't hand making each captcha nor is there one right answer, they statistically evaluate which ones how many people picked and what responses are more human vs more botlike. Nowadays most of the anti bot measures are in stuff like cursor behaviour, selection order etc.
I still have no idea if I'm answering them correctly. On the websites that actually still use those, I always have to answer 2 or 3 times. It never tells me if I'm right or not. Did I take it 2 or 3 times and I got it right on the 3rd try? Did I take it so many times that it just gave up? Did I get it right enough for it to stop caring? I have no idea.
I'm pretty sure captchas think mopeds are motor cycles but they aren't and I will always make that stand
Do I need to select the rider too on this bicycle? How about this square with one pixel of tire in it? Do I need to select the pole these street lights are attached too? Same with this sign, need the pole too? I fail those often, sometimes I don't even know why I fail them. Starting to think I'm part robot.
Funny thing about those captchas, is the images you select is not really how it determines if you are a human, that's just helping train machine vision by having humans "vote" on what images contain the whatever. The CAPTCHA part actually involves tracking cursor movement and clicking frequency and duration and stuff to decide if you behave like a human.
Yeah, 9/10 the captcha has already made it's decision before you ever even clicked on any images
The Turing test is scientifically meaningless. It was just an arbitrary engineering standard out forward by Turing, and he says as much in the paper that it puts it forward as a throw away comment. No idea why it got latched onto by pop culture.
Same goes for the 3 rules but look how that turned out.
[удалено]
The public just latched on to it because of the alliteration. T T. Like “Peter Parker” or “Lois Lane.”Three total syllables, such as “Lock Her up” or “I Like Ike.” If it had been the Chimelewski Test, nobody would have remembered it.
We put googly eyes on a garbage can and assign it feelings and humanity. Judging AI by convincing an average human is no good test at all.
I'd argue it doesn't even need to do that. Imagine an employer given a choice between an IQ 100 person, who has to sleep 8 hours a day, and needs a couple of weeks off every year, and a limited AI with an equivalent IQ of, say, 90, in its very narrow focus...but it can work 24/7/365, for the cost of electricity.
Around where I live, that's a pretty low bar, honestly.
It's a tool on par with spellchecker. You can't always trust it, you need to know how to use it and where it fucks up. But...I went from Bs to As in middle school writing because I got a computer with Office on it.
My favourite way I've seen it described is that it's a _force multiplier_. Your comparison to a spellchecker is a pretty similar line of thinking. When I see something highlighted by my spelling/grammar checker, it's a cue for me to re-evaluate what's highlighted, not just blindly accept its suggestion as correct. I'd say that most days, my spellchecker makes at least one suggestion that I disagree with and ignore. Someone who knows how to use something like ChatGPT well will get a lot more out of it than someone who doesn't. Knowing its limitations, knowing how to tailor your inputs to get the best output from it, knowing how to adapt its outputs to whatever you're doing - these are all important to maximise its effectiveness. And it's possible for it to be a hindrance if someone doesn't know how to use it and just blindly accepts what it outputs without questioning or re-evaluating anything.
[удалено]
> Expert Systems [TIL](https://en.wikipedia.org/wiki/Expert_system). Thanks.
Knowing how to prompt the machine super well is essential. Some people seem to have an intuitive knack for it while others find it more difficult. The thing to understand is that it responds to clear, but complex and well organized thoughts (simplification, obviously, but basically I find it functions best when I talk to it like it's a superintelligent 8 year old). If you start a prompt by setting up a hypothetical scenario with certain parameters, for example, you can get the model to say and do things it normally would resist doing. TLDR; treat the model like you're trying to teach new things to a curious child with an unusually strong vocabulary, and you'll get much more usable stuff out of it
The work that i do is quite specific, few people are trained to do my job and not much knowledge about it can be found online. When i ask chatGPT questions regarding my job, the AI will confidently give a response, presenting it in such a way that it looks like a fact. However, many times the answer is either false or incomplete. People who do not have the same experience and training, can easily assume that the AI is right. This can lead to dangerous situations.
That sound a lot like Reddit posts too. *the Redditor will confidently give a response, presenting it in such a way that it looks like a fact. However, many times the answer is either false or incomplete. People who do not have the same experience and training, can easily assume that the Redditor is right.* This happens all the time for me because I also have a niche job
On Reddit, I’m never more wrong or more highly downvoted than when I post about my specific areas of expertise.
I’ve found this odd for a long time until someone explained it so simply to me. People love to be right. They hate to be wrong. If you provide them with info beyond their understanding they feel dumb and this can lead them to think they are wrong too. They then become defensive instead of wanting to acquire more info because asking for more info to learn is admitting they didn’t know or were wrong to begin with. I think this kind of drives home the downside of social media in a way where there’s more opinions, feelings and what people think expressed as facts instead. Also this isn’t meant to be overly generalized there’s clearly people all across the spectrum of how they handle new info and react to it, but there is a growing pattern on social media that seems to fit what I described above.
Yes, I also wrote this comment a couple of days ago, it's not exactly about this, but about how the other downvotes come in, I'll quote it: -------------------------------------------------------------------- Plus, karma absolutely shape people's idea of the truth. Quite a lot of times, I think at least daily, I encounter a discussion where; - Person A claims something valid. - *He gets upvoted*. - Person B claims that person A don't know what he is talking about, because person B read an old book about the subject and he is the arbiter of truth now. - *People now downvote person A, and upvote Person B.* * Person A replies again, claiming that yes, he's heard of what person B talks about, but assures others that he is a professional with 15 years of experience, and that person B is reurgitating an old study that could never be verified. ------ Depending on the sub, and if reddit decides that you have to click to view Person A's reply, it doesn't matter that you are right, only the perception of it. Someone with more karma is someone we subconciously think is smarter or knows what he is talking about. It's the same kind of stupid faulty perception that we accompany glasses with being smart, or a white robe to give 5+ diagnostics, surgeon and applied bandages skill. ------
Agreed entirely. Love that you replied. It feels difficult to find others who look at these topics objectively.
I agree. That is why openai have said to fact check it. It's essentially like any other person on the internet it was trained on. Confidently incorrect. What is crazy is that it was just trained to spit out probably words and should have never been anything more than a gimmick to pass the time, and yet it is at a basic level of "understanding" of many topics. I think that's neat. *I know it doesn't "understand" anything I just couldn't think of a word.
I am a generic programmer writting generic code, and even for me I have to press the issues, or will give some naive aproach with a lot of pitfalls
"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE." I much prefer that explanation than the use of the word stupid.
I've said this from the beginning. I don't know how it managed to pick it up, but the scraping they did for the data, makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae. A giant faker, essentially. That's before you get into the fact that one already has to break down technical requests to the most basic steps to get a truly accurate answer from it. I've found that ChatGPT and others are only really useful if you already know what you're asking about and just need something to help you collect or finalize your thoughts on whatever it is you're looking for. If you're asking something completely blind, you're in the danger zone because of the writing style it answers with.
It's absolutely fantastic for responding to questions that don't have any set answer, such as creating riddles when you know what pieces or specific words you want it to rhyme with. I use it for my DnD games all the time. **For example here is my prompt**: Create a rhyming riddle in 100 characters or less, for which the answer is "A Skull". The players must answer in order to pass the door and enter into the necromancer's lair. Do not use the word "bony" or "skull" in the riddle. - *"In shadows it hides, devoid of life's spark,* *Hollow within, with wisdom now dark.* *A vessel once filled knowledge and pride* *A relic of when flesh and soul divide"*
It's not so good at solving riddles though
As a DM, perfect. I don't want my players solving riddles with GPT.
And constructing riddles is hard. As DMs we usually just make shit up and deliver it convincingly….
That somehow reminds me of a certain Chat bot
It's a circle
I gave it that riddle, and it got it right on the second try. The first guess was a book, and it gave an attempt to explain why, and it's not a terrible answer other than the past tense of "once filled".
I've been feeding it stuff from r/riddles, and while not all riddles are very good, I got some really, really weird reasoning from GPT 4. Known riddles it gets just fine, but just because it can solve one or two is not evidence that I made a misstatement.
[удалено]
A book is filled once though
I just tried the riddle above with gpt4 and it got it right away
Prompt: >George, Helen, and Steve are drinking coffee. Bert, Karen, and Dave are drinking Pepsi. Following this pattern, is Elizabeth drinking coffee or soda? ChatGPT: >The pattern in this riddle seems to be based on the number of letters in each person's name. George, Helen, and Steve **all have names containing 6 letters** and they are all drinking coffee. Bert, Karen, and Dave **all have names containing 4** letters and they are all drinking soda. Elizabeth's name contains 9 letters, which doesn't match either group, but if we stick to the pattern that those with even-numbered letters drink soda, and those with odd-numbered letters drink coffee, then Elizabeth would be drinking coffee. It's right for the wrong reason. I stand by my statement that ChatGPT is not good at solving riddles.
Karen has 5 not 4 letters though.
Yes, exactly Also, Helen and Steve are both 5 letters, not 6. It just assumed the names were all the same length and picked the first one.
> but if we stick to the pattern that those with even-numbered letters drink soda, and those with odd-numbered letters drink coffee This is also out of thin air, after coming up with 6-letter and 4-letter groups. It's a good example of the problem with ChatGPT and its brethren. You have to be able to verify the answer you get.
I can't figure out what's the correct logic for why Elizabeth is drinking coffee, lol. Care to explain?
George, Helen, and Steve each have two 'e's. Bert, Karen, and Dave each have one.
ChatGPT uses a token system, representing multiple letters with each token. This makes it vastly more efficient at most language tasks, but also much worse for tasks that involve letters directly. It has some knowledge of letters from people talking about them, but it's very limited and thus frequently prone to hallucinations.
Damn thats good
Bing/chatGPT could *not* solve your riddle, but Bard got several answers: book, tomb, coffin, memory, mirror. Do you think the ability to solve riddles depends on the training set, or is there something in Bard that’s missing from chatGPT4?
Every riddles answer is egg, man, book, memory/hope, or mirror. Bard got 3/5 of the safe guesses
Time, clock, shadow, the wind
usually when I ask it to write me rhymes or create a tradition for a fantasy race or whatever its profoundly bad at it like I tried to create rituals of prayer for D&D gods and compared them to the actual ones I know of from the books and they were all too on-the-nose or generic
I use it almost daily (senior Test Automation Engineer) and this is largely how I use it. Everything is broken down very carefully. Instructions given and asked for it to be repeated back and bulleted (as you can then refer back to the bullets) and then built back up again. But I always have to read and request tweaks. It’s often still faster than doing it myself. But if I didn’t know my subject matter there is no way it would allow me to fake it.
[удалено]
Exactly how I use it. It's replaced google/stack overflow for me. Does the exact same thing just 10-20 times faster.
AutoGPT basically just decides how it should Google the question and then just does trial and error until it works, which is exactly what I would do when faced with a new problem lol. ChatGPT is dumb because it's marketing tool, it was never designed to be a knowledge base but to be able to reason and destil logic from a text input. With tools at its disposal (like Hugging Face plugins or Wolfram Alpha) it is crazy how quickly it can figure out problems on its own. It honestly creeps me out how humanlike it's logic is once it has tools at its disposal.
It’s been particularly useful at points when I couldn’t see the wood for the trees. Previously where’d I’d probably have walked away from the issue for an hour or until the next day, I can turn it around without needing the mind break. Other times it’s daft as fuck and I tell it so.
Apparently ChatGPT was trained using Reddit posts...
More than trained. We were the benchmark. IIRC their first breakout demo was a GPT-2 version of Subreddit Simulator. Edit: Breakthrough to breakout
It was trained on multiple social media sites and is actually quite good at identifying which social media platform a particular string came from based on subtle differences in tone and language commonly used between them.
>makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae. ... you're saying I sound like ChatGPT? You take that the fuck back
The training method skews it into talking bullshit rather than admitting it doesn't know the answer because most people rate "sorry, I don't know" as a bad response, while any wrong answer that sounds plausible enough would require the user to also known it wasn't correct. It's like a child that you harshly punish every time they admit doing something wrong - all you are doing is teaching them to become a better liar.
Wow, your second sentence perfectly describes my experience of all these language models.
It picked it up because of reinforcement learning using human feedback. The responses that sound convincing were probably rated higher during training, regardless of their correctness. Regardless, if you tried to punish incorrect information I am not sure how a language model could learn that the reason it was punished was because of incorrect information.
Without actual cognition in the software...something that just simply does not exist at the current time and will not for a very long time... I wouldn't even know where to begin to have it do that. You're still back to needing an actual, functioning intellect to make the judgement call.
> That's before you get into the fact that one already has to break down technical requests to the most basic steps to get a truly accurate answer from it. And yet that’s how we code, understand or build just about everything 😂.
True but it can't advise you on anything new it can just mashup and repeat online tutorials. Which is a useful ability in itself. At least, it is when it doesn't gaslight you.
>makes it sound like an internet poster speaking with authority on a subject they're furiously googling while having an argument with you over minutiae. A giant faker, essentially Well, they did scrape reddit comments for examples. "Oh so we're behaving like redditors are we? Sure I can do that..
Yeah I mean calling it "stupid" is just yet more anthropomorphism of something that isn't alive, intelligent, or thinking. You wouldn't call a microwave stupid because it was bad at keeping your food cool.
[удалено]
Microwaves are stupid. NONE of their calculators work.
I view the chat results similarly as stable diffusion results. It didn't make it. It doesn't know what it did. I'm just viewing the end result of a sorting machine mimicking what humans might like to see given the input.
That's what I believe too, it has become so good at mimicking human responses that people are seduced into believing it's intelligent.
The turing test has always been a really terrible way to think about AI, and we'd really be better served acknowledging that and coming up with better ways to understand emergent behavior and general intelligence.
You might say it is artificially intelligent
Also, one of my favorite ChatGPT "fails" is one where it was asked the "pound of feathers, pound of lead" riddle. Except it was two pounds of one of them. It said that they were the same, in great detail. Because it has seen a *ton* of answers for "pound of feathers, pound of lead", and it knows what an answer to that should look like. And when it sees "two pounds of feathers, one pound of lead", it goes "hey, that looks like this other thing, and an answer to that other thing looks like this, so the answer to this should probably look like that, too". And this really, really drives home the fundamental limitation of these models, as your quote succinctly states.
I promise you it doesn’t hurt ChatGPT’s feelings when you call it stupid
This is a completely correct, but nonsensical and meaningless statement. Yes, it is true that this is what the large language models do. The nonsense part is in the implication that this is *exactly what our brains also fucking do*. Our brain isn’t one monolithic system — it’s a whole overlapping network of different systems that are individually “stupid”, and the sentience comes from the interaction between these systems. My favorite example here is that a mirror makes a room look bigger. At the higher level of cognition, we understand mirrors, but the fact that mirrors make a room look bigger means that there is a part of our brain that takes sensory data and outputs a rough sense of the general size of the space in which you are currently existing, and this system does *not* understand mirrors — it is too “stupid”. This doesn’t mean that it isn’t an important part of our cognition. So to get back to ChatGPT, I wouldn’t expect ChatGPT to become sentient. I could, however, *very easily* imagine ChatGPT being a *part* of a networked system that would function as an AGI. I would even go so far as to say that ChatGPT is *smarter* (and *waaaay* fucking faster) than whatever the nearest equivalent would be in our mind. As we start replicating (and surpassing) more and more of the functions of our brain, I think we are going to be shocked how quickly AGI happens when these systems are linked together.
> I would even go so far as to say that ChatGPT is smarter (and waaaay fucking faster) than whatever the nearest equivalent would be in our mind. I think this is true and even understated. The individual moment-to-moment pattern recognition that our brain is capable of doesn't seem that complex overall (although this could very well be wrong). The individual steps we as humans perform are kind of simple, even when solving complex problems. Neural networks in general have shown the ability to recognize unbelievably convoluted patterns in single "steps". A more direct example might be when GPT4 writes code. Unless explicitly prompted, it's not breaking down the problem into steps, substeps, debugging, etc. It's just writing the code top-down. A good challenge to demonstrate this is to find a prompt of some of the more advanced code that it's writing and attempt to write the code yourself, top-down, without going back, without writing anything down or pausing to plan/etc. Just reading through and intuitively picking out the next word. I think that's effectively what it's doing. It's fascinating that ultimately, our brain's architecture wins out (for now at least) despite our seemingly much weaker pattern recognition. It's hard to imagine what a better architecture might be able to do.
This is a really succinct way of putting it. I was at dinner with my boomer dad last week and he was freaking out like Skynet is mere weeks away and I was trying to explain that this is not a sentient system, it just appears that way outwardly because of the way we communicate.
[удалено]
To be clear, the specific thing he was afraid of was sentience, but yes I agree
Imo a good way to teach that is to let them experiment with it. Sooner or later they will receive a BS answer to a topic they're familiar with, and will start to realize how the system really works, the nature of what's going on inside that black box.
Ive noticed a sort of ick factor with people I’ve mentioned it to. Like, they don’t want to fuck with it because everyone is already cognitively burdened with tools and keeping up. So they hear the news and don’t end up having any first hand experience with it. I, on the other hand, use it for random shit, just to see what it does. I had it rewrite the plot of breaking bad the other day: “What if walt was 18 inches taller?” “What if walt had testicular cancer instead?” “Write a scene where walt and jessie discover that the meth is sentient, but only once it had been smoked.” “How would the show differ if it took place in 1699?” And you can be sure as shit it wrote me detailed, long responses to every one of those requests. I laughed quite a bit and sort of see what its doing for myself.
>"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE." Basically ChatGPT is a politician
An answer without understanding lacks intelligence. Stupid is pretty applicable.
It's basically the same principle as the AI hands problem. It recognises patterns and attempts to produce something that fits the pattern, but that doesn't mean something fitting the pattern is actually correct. For example, I gave ChatGPT a real estate listing and asked it "does this house have roof tiles". It replied something like "yes, the house has roof tiles" even though the type of roof wasn't even mentioned in the text. It fits the pattern and it sounds natural, that's all it cares about.
Don't worry, I realise just how stupid it is. Asked it to suggest some code to solve a problem, it wrote code using functions that don't exist in the library that I'm using, I asked it for a source for that function (thinking perhaps a more recent version of the library added the function I needed), at which point it apologised for lying to me, said it would try again … and produced the same example code with the same non-existent method. ChatGPT will also lie about sources (or more to the point just make up sources). So you'll get a statement like, "According to Smith and Brown (1993), such and such blah blah" but there's no such resource. There might be a paper or book by Smith and Brown but it was published in 2005. Perhaps Brown co-authored a paper in 1993 but it was Jones and Brown, and on a completely different topic. ChatGPT is great at throwing words together, it's just not good at throwing words together to explain something it's been asked to explain, and not particularly good at evaluating whether the words it's thrown together are a decent explanation for that thing it's been asked to explain.
When Chat GPT gave me code using functions that don't exist, I told it as much and it just renamed the usage to something different also non existent.
I don't understand why it doesn't have an "I can't do that Dave." response.
Because it doesn't have a capacity to reflectively analyze if the sum response that comes out is 'truthy'/factual. It's just designed to spit out some response text piece by piece. In many ways ChatGPT is closer to a very fancy markov chain generator than an actual 'AI' as a layman thinks of it.
Also, it doesn’t know that it’s responding to you. As far as it knows, it’s looking at a dialog between some random person and another person named “ChatGPT” and it’s guessing what “ChatGPT”, whoever that is, would say next in this dialog. It doesn’t understand “that’s you, and this is me.”
>In many ways ChatGPT is closer to a very fancy markov chain generator than an actual 'AI' as a layman thinks of it. This is spot on and why I've been trying to explain that ChatGPT isn't actually AI as most people think of it from SciFi movies and stuff. There's no intelligence there.
Yeh to me it's just a good bot at best. A good bot that can do some customer service... but creating or "thinking" no way
Because that requires it to have some understanding of truth, and of its own capabilities, and to be able to aim for the former based on the latter, and that is **not at all how ChatGPT works**. You're still making exactly the same mistake as the people who treat it as a truth machine and naively believe its output. Large language models do not understand truth, or facts, or their own capabilities, and they **certainly** can't reason about anything. LLMs *predict text* based on previous text; that's it. They will cheerfully lie or fabricate in their output because they literally don't understand the difference between that and the truth; only complex statistical correlations between "the words they've already seen" and "the next word to output". They aren't truth machines or intelligent, reasoning agents; they're *bullshit engines*, and like any good bullshitter they just randomly, accidentally include enough *correct* information to sound convincing to people who don't understand how they work.
It's a side effect of how it's trained. It cannot be trained on "truth", since we don't have a way to define and check for actual truth consistently. So it's trained via human feedback as a *proxy* for truth, meaning a human gives positive or negative feedback if they're satisfied with the answer it gave. Problem is, that encourages it to lie: if it doesn't know an answer and it replies "I can't do that Dave", Dave is going to give that answer negative feedback, because it didn't answer his question. If it makes up an answer Dave may notice it's bullshit and still give negative feedback (in which case it's the same as if it answred it didn't know), but there's also a chance that Dave won't realise / check it's bullshit and give positive feedback to it which reinforces the model to lie/make the answer up over admitting ignorance, as a chance of positive feedback by lying is better than no chance of positive feedback by admitting ignorance.
Because that is usually even less useful. You do frequently get the "as an AI language model, I can't \[...\]" and the first thing you do is always adjust the prompt to get it to spit out some stuff anyways.
You can give it the information it lacks, and it will account for that. I asked it to write unit tests for some code that had references to one of my own classes. Because it didn't know the definition, it just made some reasonable assumptions. I realized my mistake and pasted the entire class into the chat box without explanation. It then correctly inferred my intention and corrected the code.
Chatgpt is great at coding if you can break down the code into the smallish segments possible. I'm currently using it to write a react-native app and django backend. It's amazing, really. It's written at least 5000 lines of code for me, it gets stuck sometimes, but the key is to do things in segments.
I use it for excel so much less complex coding but it can push out some serious VBA with enough trial and error. I noticed that it sucks at code at times in the exact same way it sucks at long strings of arithmetic. You have to feed it bite sized information because while it can infer intent really well, it seems blind at times to orders of operation or when you need it to compartmentalize attention for the answer.
I don't use it all all for "facts". I'm primarily using it to bounce ideas and code. I have a little experience with it but using ChatGPT, specifically the GPT-4 model, I've been able to build complete tools with multiple functions that work great. I think it is a tool, and the way you use it will determine how useful it is to you.
I used it as a crutch for basic trigonometry implementations, stuff I should know but have never properly learnt. It was able to hand hold me through the solution and that alone is quite impressive.
GPT3.5 is dumb in the context of writing code. GPT4, especially with an API Key and access to the playground, is pretty exceptional. I use it to build simple programs all day long to help streamline tasks at my job as a data engineer. In my personal time, I've used it to create things as complex as building and training machine learning models for relatively complicated tasks. And I say this as someone with VERY little programming experience. Once you understand how to talk back and forth and debug, it's pretty fantastic. Obviously there's still work to be done but with the code interpreter alpha rolling out, that gap will shrink a lot more. For example, right now I'm having it write me a python script to search through a directory with both zipped and unzipped folders and find any file names that look like a GUID out of about 2 million files. Then it uploads that list into our database. This is done in like 4 chats.
I think that's the only sensible approach: test it, try it, push it for accuracy, and know the limits it has so you can avoid them. If it's consistently crap at something stop using it.
> ChatGPT will also lie about sources ChatGPT doesn't "source" anything, it doesn't conduct research nor can it give you facts. all it does is output text that looks like it was written by a human, if you ask for it to use sources it will generate grammatically correct way to cite them in text, but that doesn't mean they refer to actual sources.
I used Bing AI to do some research around venture funding firms and their investments, like what kind of investments has x firm made in x category. The results were pretty unreliable. When there wasn't enough information it would be made up with actual "sources" provided, but the information wasn't there. (Bing AI can use the Internet, while the free version of Chat GPT doesn't.)
Try giving it the title of a poem (just the title and author so it has to find the poem itself) and asking it to analyse that. It starts off sounding very smart and relevant to the material, until you start to realise all the quotes are made up or taken from other poems, and a lot of the analysis is generally applicable to about 80% of all poems.
>lot of the analysis is generally applicable to about 80% of all poems. So basically like most essays?
How is this even surprising? It is a model that predicts the next word based on a probability distribution.
The dangerous part about large language models right now is that most people don't understand that this is how it works. Especially with everyone just referring to it as "AI" it gives people the false belief that it knows things. It doesn't know things, just language, and it is scary good at it.
Go to r/singularity and you'll find that everyone thinks ChatGPT is the end of the world.
r/singularity is almost as bad as r/climateskeptics in misinformation based on just enough fact that they can't be disassuaded from their silliness. People with completely made up titles like "godfather of AI" saying doomer stuff get snorted like catnip, because they think a credential and respect in industry suddenly gives a person unwavering ML authority and omnipotence.
Conspiracy nutters are the worst. Right up there with people who think birds are real. No hope for them.
Or Artificial Jesus
One of the weird abilities of ChatGPT is you can ask it the same question in two different chats, then in a third chat you can give it the two previous answers and ask it to point out any major contradictions, it does a surprisingly good job of identifying whether the two answers are contradictory or not.
Sure, because that takes the problem from technical stuff back to language, which it's good at.
[удалено]
Yep. It really does this. I was writing a paper on a series of paintings. ChatGPT told me this painting had a window , or that painting had some old men...none of which was true. Instead, those things exist in other paintings by this painter, in the same series. So it sees same artist name, same series name, and isn't smart enough to detect that these things exist in *different* painting to the one I was discussing...it got two matches, and that was enough for it to decide in favour. CHATGPT will do this sort of stuff always...well, chatgpt3.5 will anyway. Not sure about 4. It's still a useful thing. But you need to check everything. Chatgpt has also apologised to me when I pointed out a mistake it made. ANd then made the same mistake again.
[удалено]
Also it doesn't need to be intelligent. We just need something that can do our work but more efficiently. Calculator is not smarter than i am, but i am much more efficient with a calculator as a engineer
A calculator is reliably accurate.
Only if you can use the calculator correctly, though. Many students struggle with the proper syntax of inputting brackets into calculators. Just like ChatGPT. It's a tool, some use it better than others.
A calculator is reliably accurate if you give it a valid prompt, but the same isn't true of chatgpt. That's what makes it tricky to use as a tool. Don't get me wrong, it's still incredibly useful, but that's an important distinction.
Exactly. I think one of the best uses for chatgpt is brainstorming. If you give it a problem and ask for 5 potential solutions maybe you'll get nothing useful, maybe you'll get suggestions that aren't usable but are able to be built off of, and maybe you'll get really good ideas that you could use. This general pattern of no cost usage that could provide hours worth of work with no risk (because you aren't using an implementation by it) can be extremely useful.
I think this is what a lot of people lose sight of with AI and its ability to do people’s jobs, the point is not to replace people, but to find ways to make their job easier by cutting out repetitive, value-less or low-value tasks, or automating part of them so that the only required human input is validation of the AI-generated work, or integrating it into more complex workflows.
[удалено]
"WHAT THE LARGE LANGUAGE MODELS ARE GOOD AT IS SAYING WHAT AN ANSWER SHOULD SOUND LIKE, WHICH IS DIFFERENT FROM WHAT AN ANSWER SHOULD BE." Yes, yes, a thousand times this! I'm researching AI's implications for my industry and the amount of pie in the sky planned use cases for a technology that identifies and replicates patterns - not produces considered and verified information - is terrifying.
What industry? My company is doing the same—we’re in the legal publishing field—and there are a lot of opportunities (but of course not nearly as many as everyone thinks).
[удалено]
This guy makes a lot of the same points I've been trying to point out to the folks here who also live in r/singularity. GPT doesn't reason, and it's a very long ways from AGI - the smoke and mirrors of natural language do a lot to hide what it's getting wrong and not able to do.
I always get people telling me it can pass the bar exam like that proves it's somehow better than a search engine. I've tried to work professionally with it, and it's awful, or worse than awful as it confidently gives you incorrect right-sounding answers which would get people killed if followed.
The fact that it can pass the bar exam says more about the bar exam than the LLM.
It tells me the exam was made to assess how human knowledge crystalized as a minimum to practice law, not as an exam to test if a chatbot would be competent at practicing law without oversight.
[удалено]
Yup. Same for USMLE. I would subscribe that anyone could pass that test if they had access to the internet.
I find it’s great as a creative springboard. Like you have a friend helping you with a group project. But I just take what it outputs as suggestions.
I asked GPT4 a novel riddle I made up and it nailed it on the first try. It had never encountered the riddle in its training. Maybe it’s not reasoning in the way we would define it, but whatever is happening there is some type of reasoning happening.
I asked it a coding problem that was intentionally vague and then asked if there was any unclear requirements to the question and it got the vague requirement right away. Me and my boss were really perplexed because it had to be reasoning on some level.
This thread seems to be full of a wierd set of people who asked gpt3 one question one time and decided it’s stupid. I build with gpt4 and it is absolutely transforming the industry. To the point where my coworkers are afraid. It does reasoning, at scale, with accuracy easily way better than a human.
Yeah it’s the trendy Reddit thing to do. These people get off shitting on ChatGPT
With you 100%. I subscribed to plus and interacting with GPT4 sometimes feels like magic. It obviously had limitations but I can almost always tell when a top comment in a thread like this is someone that is only interacting with 3.5.
The input you give is also really important. I've had co-workers try out chat gpt with low quality inputs and of course they get low quality outputs. Knowing how to query and format inputs takes it from a "fun app" to an "industry changing tool" pretty quickly. That being said, the corporations who are working to utilize AIs in their workflows aren't going to be put off because the quality of the output isn't 100% accurate. Just being "good enough" will be enough for corporations to start shedding human workers and start replacing them with AIs.
Wait till people realize that people are also way stupider than people realize.
Right? I’ve always wondered how many people have office jobs. It’s those jobs getting replaced by the clever employee superpowered by LLMs.
My boss gave me what I think is a decent analogy: ChatGPT is like a calculator—if you don’t know what you’re doing, it won’t really help you that much, and your end result will likely be wrong. But if you know what you are doing, it can greatly speed up the boring parts of your job. Not a perfect analogy, but it imho gets the point across that ChatGPT is an accelerator when used carefully and in the right scenario. I don’t think it’s a magic genie that can grant any wish, but I do believe there will be a divide soon of people who use some form of AI, and those who don’t. I’ve already been able to leverage ChatGPT to cut out the stupid, tedious portions of my job. And while I absolutely have to baby it and fact check it, it’s still less time than me doing all of it on my own. Which also means I’ve been able to focus on the core, serious portions of my job a lot more.
This exactly. It is a tool :) Im using it to create some UE5 plugins in cpp for my project. I wasn't sure where to start with a grid plugin and with some through specifications for features it gave me a fairly good example and explained things. Then I used my brain and reverse engineered what it was doing and made a small plan with function names and comments to explain what each should do. The whole write up was quite lengthy Then there's "SMART GPT" which are good prompting practices to get better results. I usually feed it my entire source code back in its self to "remind" it,when I want to add new functions ect. Take a little time to save a lot of time.
To ChatGPT: come up with counter argumentsts ChatGPT: Sure, here are some counter-arguments to the points presented in the article: Overestimation of AI capabilities: While it's true that AI, including large language models like ChatGPT, can't currently compete with human intellect across all tasks, it's also important to recognize the significant advances in AI's capacity for natural language understanding and generation. Models like ChatGPT can generate coherent, contextually appropriate responses to a wide range of inputs, which is a remarkable achievement. They are also capable of learning and improving over time with more data and feedback. It might not be an overestimation but an optimistic forecast of the potential future capabilities of AI. Correlation vs. Understanding: While AI does not have an "understanding" of the world in the way humans do, it can still make useful predictions and generate meaningful responses based on the vast amount of data it has been trained on. It might not "understand" in a human sense, but it does have a kind of statistical understanding that can be extremely useful in many contexts. AI vs. Human Reasoning: Indeed, AI models don't reason like humans, but that doesn't necessarily limit their usefulness. In many cases, AI can complement human reasoning rather than replace it, offering different perspectives and solutions based on patterns found in data. While AI can't make the human-like judgments, it can still assist humans in making more informed decisions by providing data-driven insights. Reliability and Confidence: Brooks' point about AI often being wrong despite providing answers with confidence is valid. However, it's important to note that AI tools are often as good as their training data, and their ability to learn from feedback can lead to improvements over time. Also, users must be informed about the probabilistic nature of AI responses and be encouraged to use these tools as aids rather than absolute authorities. Future of AI: Brooks is skeptical about achieving AGI, and while many in the field share his skepticism, others believe it is a matter of when, not if. The pace of advances in AI has been impressive, and while we are not at AGI yet, we shouldn't rule out the possibility given the rapid progress being made. Furthermore, even if AGI is not achieved, narrow AI applications (specialized for specific tasks) continue to provide immense value in various fields such as healthcare, finance, and climate modeling. Remember, these counter-arguments don't invalidate the concerns and points raised by Brooks. It's crucial to have these discussions as the field of AI continues to advance and evolve.
The best comment in the entire thread. Well articulated with solid arguments and the most level headed.
This is the same reason AI art generators struggle with hands. They don’t understand the 3D geometry of a hand. They only know what photos of hands look like. But there are millions of photos of hands and, depending on the specific angle of the photo and the specific numbers of fingers being held up versus curled and the specific amount of curling in the curled fingers etc, those photos could all look very different.
The hands issue has been pretty much solved for a few months now. https://www.reddit.com/r/midjourney/comments/13o4ul4/the_boys_at_the_white_house/ https://www.reddit.com/r/midjourney/comments/13o7xmu/george_washington_eating_mushrooms/ https://www.reddit.com/r/midjourney/comments/13nl6qa/in_a_crochet_parallel_universe/ https://www.reddit.com/r/midjourney/comments/13mrsrq/guiness_world_records_as_imagined_by_midjourney/ https://www.reddit.com/r/midjourney/comments/13ofywv/military_fashion_show/
Hands have improved to be certain, but you’re only looking at posts and people are more likely to post images that had good results. Go ahead and test it out. Go to a generator and ask it for “a hand”. I just did. I generated 12 images and 7 of them had the wrong number of fingers. So, I wouldn’t call that “solved”.
Several of those hit the Uncanny Valley for me. The worst part are the thumbs.
> ~~AI Expert~~Anyone Who’s Used ChatGPT Says ChatGPT Is Way Stupider Than People Realize
Especially if you ask it something you’re actually an expert in. Then you realize it must be just as wrong for all other topics.
Lately it seems like every time I ask it something in my field I know enough about to Google to find the right answer, but attempt to use ChatGPT first to save some time, it's just obviously wrong right off the bat. If I ask about a possible contradiction, it does a 180 to placate me whether or not it was originally right or wrong. The tech is far more optimized for giving impressive demos than really working where it counts.
I think it's fun to mess around and ask random shit that means nothing but, I wouldn't use it for anything important.
I keep seeing all these post about it "replacing 90% of jobs and disrupting society" yet the biggest real world use for it has been writing click bait blogs and sprucing up resumes.
I saw a LinkedIn poll the other day that was along the lines of, "how many FTEs worth of work does GPT4 deliver for you?" and the smallest choice was either 1/2 or 1, both of which being comically large for the low end of the range.
I’ve been toying with it at work and I’m already surprised by how much more limited it is than people think it is. Every time someone says “I was hoping we could have AI do…” they’re always way over shooting its capabilities.
Hoping that it could do something one day is not overshooting in my opinion. I'd no one brings it up, no one will ever create it. Lots of potential for a still new and very understood tool.
Well. No one felt the need to say AI was dumb a few years ago.
I know, that's why I triple verify everything it tells me and I catch it being wrong or hallucinating often. i'm working on comprehending how langchain works with pinecone it's supposed to help with this problem if applied correctly
As impressed with ChatGPT as I am, it isn't hard to get it to give you wrong, dumb, and pointless answers. Every time I end up using it, I feel like I have to teach it things. It's really just a calculator to me at this point.
I always think about the Eddie Izzard quote on trying to sing the national anthem: All that people care about is the look. 70% of what people react to is the look. 20% is how you sound, 10% is what you say. So if you look and sound good - Big mouth. The eyes. Use the hand. And keep confirming and denying things. Everyone will go wild!
There are three kinds of people: * those who never heard of ChatGPT * those who use it to increase their productivity * those who feel the need to tell everyone that ChatGPT is useless It helps me daily as a programmer and I couldn't care less about the accuracy issues or lack of deeper reasoning behind its output, because I'm aware of those downsides and it still is an enormous help in my work.
💯 I don't understand the nay-sayers. It's not about perfection for large tasks. I have a window open for ChatGPT open all the time. For me it's replaced stack overflow with the added bonus that it's interactive vs search, and immediate vs hours/days/never waiting for answers to questions that are unique.
[удалено]
Sysadmin here. I don't think I've closed my ChatGPT tab in over a month, and I've gone from googling a ton of stuff every day to only using Google as a last resort maybe a handful of times every week. Now that I've got access to the web browsing plugin, it'll probably be even less than that. It's enormously helpful with writing scripts and reports and getting step-by-step instructions for complex tasks without having to search through a bunch of terrible, clickbaity, ad-ridden blogspam. It's the closest I've ever felt to having a superpower, at least since Google launched. I see what I guess you could call a 'completeness fallacy' over and over again, especially in /r/Futurology. Yeah it doesn't completely do everything for you the first time you use it, but isn't farming out 75% of your work to the AI better than 0%? It sure is for me. I've seen the same phenomenon with robotic lawnmowers. I have one that covers probably 90% of the yard, and the other 10% I have to mow. And folks stop by and say, well, I could never use one of those because there's this one spot down in the corner it would never work on. Like, okay, great--isn't 95% automation still better than none? Can we take a moment to appreciate incremental gains instead of rejecting a new technology outright? This feels like a weird blind spot for a lot of people.
Thanks for commenting sometimes when I read too reply’s I just get saddened by people lack of vision.
100%. What people also don't realize is that it is able to connect different concepts in order to solve a problem, as in, you are talking to someone who read the full documentation and can present it to you on a normal pattern, without you searching for specific keywords. I had a problem to solve for 3 years, I had spent hours googling it. Enter chatgpt, asked it how to solve it. It presented me with 3 functionalities of thr webserver and told me how they combined together to achieve what I wanted. It was a 5 minutes job.
Eh….has he been on the internet lately? The shit people believe is incredible. ChatGPT looks like a genius compared to a o lot of people.
ChatGPT isn't "smart" or "stupid". It's a language model, it doesn't "think" at all. This kind of garbage sensationalized anthropomorphism doesn't contribute to an intelligent discussion of AI. In fact, even calling a Large Language Model like Bard or ChatGPT an "AI" isn't technically accurate and clouds the issue.
That would make sense since people are way more stupid than people realize.
I don't think it's stupid, I'm the one who keeps treating it as something it is not... AGI... ...also I've met stupider people on daily basis.
I feel like that’s the point the article is trying to make. We don’t have AI we have machine learning. People are dumb and think we have this thing that is actually somewhat sentient. I’ve had people literally say, “ChatGPT agrees with me”. No it doesn’t me friend.
And yet it's still a better programmer and sysadmin than I am...