Feel like we’ve got a lot of tech savvy people here seems like a good place to ask. Basically as a dumb guy that reads the news it seems like everyone that lost their mind (and savings) on crypto just pivoted to AI. In addition to that you’ve got all these people invested in AI companies running around with flashlights under their chins like “bro this is so scary how good we made this thing”. Seems like bullshit.
I’ve seen people generating bits of programming with it which seems useful but idk man. Coming from CNC I don’t think I’d just send it with some chatgpt code. Is it all hype? Is there something actually useful under there?
Yes, it is useful. I use ChatGPT heavily for:
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Brainstorming meal plans for the week given x, y, and z requirements
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Brainstorming solutions to abstract problems
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Helping me break down complex tasks into smaller, more achievable tasks.
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Helping me brainstorm programming solutions. This is a big one, I’m a junior dev and I sometimes encounter problems that aren’t easily google-able. For example, ChatGPT helped me find the python moto library for intercepting and testing the boto AWS calls in my code. It’s also been great for debugging hand-coded JSON and generating boilerplate. I’ve also used it to streamline unit test writing and documentation.
By far it’s best utility (imo) is quickly filling in broad strokes knowledge gaps as a kind of interactive textbook. I’m using it to accelerate my Rust learning, and it’s great. I have EMT co-workers going to paramedic school that use it to practice their paramedic curriculum. A close second in terms of usefulness is that it’s like the world’s smartest regex, and it’s capable of very quickly parsing large texts or documents and providing useful output.
This. ChatGPT strength is super specific answers of things or broad strokes. I use it for programming and I always use it for “how can I do XYZ” or “write me a function using X library to do Y with Z documentation”. It’s more useful for automating the busy work
The brainstorming is where its at. Telling ChatGPT to just do something is boring. Chatting with it about your problem and having a conversation about the issue you’re having? Hell yes.
I’m a dungeon master and I use it for help world building and its exceptional.
I actually think that ChatGPT could eventually become the way to play tabletop RPGs. It’s not quite there yet, though. It’s not the most creative writer, still often has internal consistency flaws, and of course it would have to be trained specifically on the rules of the RPG you’re playing. But once it has been, it could probably act as a DM for groups that lack one. Or as a very closely coupled assistant to less experienced DMs who may need hand holding. It could even likely replace players, which could be useful for solo players who can’t find a group (or, say, have incompatible scheduling).
Unlike a regular video game, the format of tabletop RPGs seems perfect for our current rudimentary AIs and the constraints are ones that they can probably handle with careful training alone. It’s also a useful niche since there’s no replacing the open endedness of tabletop RPGs with current technology. There’s also a lot of people out there that I’m sure would like to play tabletop RPGs but just lack a group. Anyone who’s played them before knows that scheduling is really hard and has killed a lot of groups. That’s something an AI could help with.
I’m a dungeon master and I use it for help world building and its exceptional.
Oh that sounds neat. Can you give some examples of your process and results?
Honestly, not really. It’s a communication thing with the bot. Just talk to it like a person. Say what you want to do and what ideas you have, then ask if ChatGPT has any suggestions. Keep talking. It’ll recommend ideas and you can tweak them or ignore them.
When talking about code though I’ve come to notice that it will happily follow the corrections you tell it whether they are right or wrong. That’s not all that helpful but it can still give you ideas about how to solve your problem with a bit of basic knowledge of the topic you’re dealing with.
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Nursing student here. Quizlet has an AI function that lets you paste text into it and it outputs a studyset.
Most of my classes provide a study guide of some kind - just a list of topics we need to be familiar with. I’ll take those and plug em into the AI thing: bam! Instantly generate like 200 flash cards to study for the next test.
It even auto-fills the actual subject matter. For example, the study guide will say sometime like “Summarize Louis Pasteur’s contributions to the field of microbiology” and turn that into a flash card that reads:
(front)
Louis Pasteur
(back)
Verified the germ theory of disease
Developed a method to prevent the spoilage of liquids through heating (pasteurization)
Developed early anthrax and rabies vaccines
So I take my list of AI generated cards, then sift through the powerpoints and lecture videos etc from class: instead of building the study set from scratch, all I have to do is verify that the information it spit out is accurate (so far it’s been like 98% on target, often explaining concepts better than the actual professor, lol), add images, and play with the formatting a bit so it reads a little easier on the eyes.
People always talk about AI in school in the context of cheating, but it is RIDICULOUSLY useful for students actually trying to learn.
Looking ahead, this tech has a ton of potential to be used as a kind of personal tutor for each student. There will be some growing pains for sure, but we definitely shouldn’t ignore its constructive potential.
I’ve been using it at my job to help me write code, and it’s a bit like having a soux chef. I can say “I need an if statement that checks these values” or “Give me loop that does x y and z” and it’ll almost always spit out the right answer. So coding, at least most of the time, changes from avoiding syntax errors and verifying the exact right format and turns into asking for and assembling parts.
But the neat thing is that if you have a little experience with a language you can suddenly start writing a lot of code in it. I had to figure out something with Ansible with zero experience. ChatGPT helped me get a fully functioning Ansible deployment in a couple days. Without it I’d have spent weeks in StackOverflow and documentation trying to piece together the exact syntax.
You should try out Codeium if you haven’t. It’s a VSCode toolkit completely free for personal use. I’ve had better results with it than ChatGPT
AI is nothing like cryptocurrency. Cryptocurrencies didn’t solve any problems. We already use digital currencies and they’re very convenient.
AI has solved many problems we couldn’t solve before and it’s still new. I don’t doubt that AI will change the world. I believe 20 years from now, our society will be as dependent on AI as it is on the internet.
I have personally used it to automate some Excel stuff I do at work. I just described my sheet and what I wanted done and it gave me a block of code that did it. I had spent time previously looking stuff up on forums with no luck. My issue was too specific to my work that nobody seemed to have run into it before. One query to ChatGTP solved my issue perfectly in seconds, and that’s just a new online tool in its infancy.
Cryptocurrencies didn’t solve any problems
Well XMR solved one problem, but yeah the rest are just gambling with extra steps
What problem is that? Genuinely asking.
Traceability.
Regular financial transfers, be they credit card, direct debit, straight-up written cheques, Interac/E-transfer (I am Canadian, that’s an us thing) are all inherently tracable.
XMR/Monero is not tracable, it’s specifically designed not to be, unlike Bitcoin and most other cryptocurrencies.
Of course, shitheads consider that to be a problem, but fuck them, they’re shitheads; it’s a solution, to the problem they cause.
For context, I say all this as someone who is vehemently opposed to prohibition; as far as I’m concerned every person who works for the DEA should be imprisoned or shot
Thanks for the info. That’s quite the way to end a comment though.
I mean it though.
The people working for the DEA now are no better than the people working to enforce alcohol prohibition in 1919. It’d be nice if humanity would learn, with a hundred years to think about it, but the ruling class at least haven’t. They enforce poorly thought out puritanical laws, and the world would be better off without them.
If I lived in America rather than Canada, which thank god I don’t, the DEA would happily kick down my door, shoot me, and then probably also shoot my wife, who doesn’t even partake of anything beyond alcohol, but would obviously be upset about my being shot.
All cops are bastards, and should be torched with molotovs at any available opportunity. If they didn’t want to be bastards, they shouldn’t have signed up as cops; it’s not like they’re conscripts
For me personally cryptocurrencies solve the problem of Russian money not being accepted anywhere because of one old megalomaniacal moron
In my personal opinion, it’s under-hyped. The average person has maybe heard about it on the news but not yet tried it. The models we have show the spark of wit, but are clearly limited. The news cycle moves on.
Even still, some huge changes are coming.
My reasoning is this - in David Epstein’s book “Range” he outlines how and why generalists thrive and why specialization has hurt progress. In narrow fields, specialization gives an advantage, but in complex fields, generalists or people from other disciplines can often see novel approaches and cause leaps ahead in the state of the art. There are countless examples of this in practice, and as technology has progressed, most fields are now complex.
Today, in every university, in every lab, there are smart, specialized people using ChatGPT to riff on ideas, to think about how their problem has been addressed in other industries, and to bring outsider knowledge to bear on their work. I have a strong expectation that this will lead to a distinct acceleration of progress. Conversely, an all-knowing oracle can assist a generalist in becoming conversant in a specialization enough to make meaningful contributions. A chat model is a patient and egoless teacher.
It’s a human progress accelerant. And that’s with the models we have today. With next generation models specialized behind corporate walls with fine tuning on all of their private research, or open source models tuned to specific topics and domains, the utility will only increase. Even for smaller companies, combining ChatGPT with a vector database of their docs, customer support chats, etc will give their rank and file employees better tools to work with
Simply put, what we have today can make average people better at their jobs, and gifted people even more extraordinary.
It’s overhyped but there are real things happening that are legitimately impressive and cool. The image generation stuff is pretty incredible, and anyone can judge it for themselves because it makes pictures and to judge it, you can just look at and see if it looks real or if it has freaky hands or whatever. A lot of the hype is around the text stuff, and that’s where people are making some real leaps beyond what it actually is.
The thing to keep in mind is that these things, which are called “large language models”, are not magic and they aren’t intelligent, even if they appear to be. What they’re able to do is actually very similar to the autocorrect on your phone, where you type “I want to go to the” and the suggestions are 3 places you talk about going to a lot.
Broadly, they’re trained by feeding them a bit of text, seeing which word the model suggests as the next word, seeing what the next word actually was from the text you fed it, then tweaking the model a bit to make it more likely to give the right answer. This is an automated process, just dump in text and a program does the training, and it gets better and better at predicting words when you a) get better at the tweaking process, b) make the model bigger and more complicated and therefore able to adjust to more scenarios, and c) feed it more text. The model itself is big but not terribly complicated mathematically, it’s mostly lots and lots and lots of arithmetic in layers: the input text will be turned into numbers, layer 1 will be a series of “nodes” that each take those numbers and do multiplications and additions on them, layer 2 will do the same to whatever numbers come out of layer 1, and so on and so on until you get the final output which is the words the model is predicting to come next. The tweaks happen to the nodes and what values they’re using to transform the previous layer.
Nothing magical at all, and also nothing in there that would make you think “ah, yes, this will produce a conscious being if we do it enough”. It is designed to be sort of like how the brain works, with massively parallel connections between relatively simple neurons, but it’s only being trained on “what word should come next”, not anything about intelligence. If anything, it’ll get punished for being too original with its “thoughts” because those won’t match with the right answers. And while we don’t really know what consciousness is or where the lines are or how it works, we do know enough to be pretty skeptical that models of the size we are able to make now are capable of it.
But the thing is, we use text to communicate, and we imbue that text with our intelligence and ideas that reflect the rich inner world of our brains. By getting really, really, shockingly good at mimicking that, AIs also appear to have a rich inner world and get some people very excited that they’re talking to a computer with thoughts and feelings… but really, it’s just mimicry, and if you talk to an AI and interrogate it a bit, it’ll become clear that that’s the case. If you ask it “as an AI, do you want to take over the world?” it’s not pondering the question and giving a response, it’s spitting out the results of a bunch of arithmetic that was specifically shaped to produce words that are likely to come after that question. If it’s good, that should be a sensible answer to the question, but it’s not the result of an abstract thought process. It’s why if you keep asking an AI to generate more and more words, it goes completely off the rails and starts producing nonsense, because every unusual word it chooses knocks it further away from sensible words, and eventually it’s being asked to autocomplete gibberish and can only give back more gibberish.
You can also expose its lack of rational thinking skills by asking it mathematical questions. It’s trained on words, so it’ll produce answers that sound right, but even if it can correctly define a concept, you’ll discover that it can’t actually apply it correctly because it’s operating on the word level, not the concept level. It’ll make silly basic errors and contradict itself because it lacks an internal abstract understanding of the things it’s talking about.
That being said, it’s still pretty incredible that now you can ask a program to write a haiku about Danny DeVito and it’ll actually do it. Just don’t get carried away with the hype.
But the thing is, we use text to communicate, and we imbue that text with our intelligence and ideas that reflect the rich inner world of our brains. By getting really, really, shockingly good at mimicking that, AIs also appear to have a rich inner world and get some people very excited that they’re talking to a computer with thoughts and feelings… but really, it’s just mimicry, and if you talk to an AI and interrogate it a bit, it’ll become clear that that’s the case.
Does it, though? Where do you draw the line for real understanding? Most of the past tests for this have gotten overturned by the next version of GPT.
Seriously, it’s an open debate. A lot of people agree with you but I’m a bit uncomfortable with seeing it written as fact.
Admittedly this isn’t my main area of expertise, but I have done some machine learning/training stuff myself, and the thing you quickly learn is that machine learning models are lazy, cheating bastards who will take any shortcut they can regardless of what you are trying to get them to do. They are forced to get good at what you train them on but that is all the “effort” they’ll put in, and if there’s something easy they can do to accomplish that task they’ll find it and use it. (Or, to be more precise and less anthropomorphizing, simpler and easier approaches will tend to be more successful than complex and fragile ones, so those are the ones that will shake out as the winners as long as they’re sufficient to get top scores at the task.)
There’s a probably apocryphal (but stuff exactly like this definitely happens) story of early machine learning where the military was trying to train a model to recognize friendly tanks versus enemy tanks, and they were getting fantastic results. They’d train on pictures of the tanks, get really good numbers on the training set, and they were also getting great numbers on the images that they had kept out of the training set, pictures that the model had never seen before. When they went to deploy it, however, the results were crap, worse than garbage. It turns out, the images for all the friendly tanks were taken on an overcast day, and all the images of enemy tanks were in bright sunlight. The model hadn’t learned anything about tanks at all, it had learned to identify the weather. That’s way easier and it was enough to get high scores in the training, so that’s what it settled on.
When humans approach the task of finishing a sentence, they read the words, turn them into abstract concepts in their minds, manipulate and react to those concepts, then put the resulting thoughts back into words that make sense after the previous words. There’s no reason to think a computer is incapable of the same thing, but we aren’t training them to do that. We’re training them on “what’s the next word going to be?” and that’s it. You can do that by developing intelligence and learning to turn thoughts into words, but if you’re just being graded on predicting one word at a time, you can get results that are nearly as good by just developing a mostly statistical model of likely words without any understanding of the underlying concepts. Training for true intelligence would almost certainly require a training process that the model can only succeed at by developing real thoughts and feelings and analytical skills, and we don’t have anything like that yet.
It is going to be hard to know when that line gets crossed, but we’re definitely not there yet. Text models, when put to the test with questions that require synthesizing abstract ideas together precisely, quickly fall short. They’ve got the gist of what’s going on, in the same way a programmer can get some stuff done by just searching for everything and copy-pasting what they find, but that approach doesn’t scale and if they never learn what they’re doing, they’ll get found out when confronted with something that requires actual understanding. Or, for these models, they’ll make something up that sounds right but definitely isn’t, because even the basic understanding of “is this a real thing or is it fake” is beyond them, they just “know” that those words are likely and that’s what got them through training.
I agree with all your examples and experience. Anyone who knows machine learning would, I think. The controversial bit is here:
Training for true intelligence would almost certainly require a training process that the model can only succeed at by developing real thoughts and feelings and analytical skills, and we don’t have anything like that yet.
Maybe, or maybe not. How do we know we ourselves aren’t just very complicated statistical models? Different people will have different answers to that.
Personally, I’d venture that any human concept can be expressed with some finite string of natural language. At least to a philosophical pragmatist, being able to work flawlessly with any finite string of natural language should be equivalent to perfectly understanding the concepts contained within, then. LLMs don’t do that, but they’re getting closer all the time.
Others take a different view on epistemology that require more than just competence, or dispute that natural language is as expressive as I claim. I’m just some rando, so maybe they have a point, but I do think it’s not settled.
I would agree that we are also very complicated statistical models, there’s nothing magical going on in the human brain either, just physics which as far as we know is math that we could figure out eventually. It’s a massively huge order of magnitude leap in complexity from current machine learning models to human brains, but that’s not to say that the only way we’ll get true artificial intelligence is by accurately simulating a human brain, I’d guess that we’ll have something that’s unambiguously intelligent by any definition well before we’re capable of that. It’ll be a different approach from the human brain and may think and act in alien or unusual ways, but that can still count.
Where we are now, though, there’s really no reason to expect true intelligence to emerge from what we’re currently doing. It’s a bit like training a mouse to navigate a maze and then wondering whether maybe the mouse is now also capable of helping you navigate your cross-country road trip. “Well, you don’t know how it’s doing it, maybe it has acquired general navigation intelligence!” It can’t be disproven, I guess, but there’s no reason to think that it picked up any of those skills because it wasn’t trained to do any of that, and although it’s maybe a superintelligent mouse packing a ton of brainpower into a tiny little brain, all our experience with mice would indicate that their brains aren’t big enough or capable of that regardless of how much you trained them. Once we’ve bred, uh, mice with brains the size of a football, maybe, but not these tiny little mice.
So I was thinking that that’s about all that needs to be discussed, but I do actually have one thing to add. It sounds like you are just fundamentally less impressed with language than me. I wouldn’t buy any hype about a maze-navigating neural net, but I do buy it (with space for doubt) about a natural language AI. I literally thought “this is 90% of the GAI problem solved, it just needs something for that last 10%” the first time I played with a transformer, and I think it was GPT-2. That might sound lame now but it was just such a fundamental advance on what was around before.
Time will tell I guess if it makes me a sucker like some consumers of past chatbots, or if there is something fundamentally different this time.
I hope I don’t come across as too cynical about it :) It’s pretty amazing, and the things these things can do in, what, a few gigabytes of weights and a beefy GPU are many, many times better than I would’ve expected if you had outlined the approach for me 2 years ago. But there’s also a long history of GAI being just around the corner, and we do keep turning corners and making useful progress, but it’s always still a ways off after each leap. I remember some people thinking that chess was the pinnacle of human intelligence, requiring creativity and logic to succeed, and when computers blew past humans at chess, it became clear that no, that’s still impressive but you can get good at chess without really getting good at anything else.
It might be possible for an ML model to assemble itself into general intelligence based solely on being fed words like we’re doing, it does seem like the data going in contains enough to do that, but getting that last 10% is going to be hard, each percentage point much harder than the last, and it’s going to require more rigorous training to stop them from skating by with responses that merely come close when things get technical or precise. I’d expect that we need more breakthroughs in tools or techniques to close that gap.
It’s also important to remember that as humans, we’re inclined to read consciousness and intent into everything, which is why pretty much every pantheon of gods includes one for thunder and lightning. Chatbots sound human enough that they cross the threshold for peoples’ brains to start gliding over inaccuracies or strange thinking or phrasing, and we also unconsciously help our conversation partner by clarifying or rephrasing things if the other side doesn’t seem to be understanding. I suppose this is less true now that they’re giving longer responses and remaining coherent, but especially early on, the human was doing more work than they realized keeping the conversation on the rails, and once you started seeing that it removed a bit of the magic. Chatbots are holding their own better now but I think they still get more benefit of the doubt than we realize we’re giving them.
The Turing test was never meant to be a test of a machine’s ability to think. It was meant to boil that question down into a question that can actually be answered, but the original question remains unanswered.
In my opinion, when general AI arrives it will not be an “open debate”, the consequences will be dramatic, far-reaching and rapid.
I’m not even thinking of the Turing test, I’m thinking of the counter-example ones. Like asking how many eyes a ruler or desk has. Earlier GPTs would answer “one eye” or something, and it was used by the Chinese-room people as an example of why it was just a mimic. Now it correctly objects to the implicit assumption in the question.
You’re right, “ChatGPT is currently our overlord” would be the strongest proof of intelligence. But absence of proof is not proof of absence. What is proof of absence, or a strong enough proof of presence is where the debate is.
My perspective is that consciousness isn’t a binary thing, or even a linear scale. It’s an amalgamation of a bunch of different independent processes working together; and how much each matters is entirely dependent on culture and beliefs. We’re artificially creating these independent processes piece by piece in a way that doesn’t line up with traditional ideas of consciousness. Conversation and being able to talk about concepts one hasn’t personally experienced are facets of consciousness and intelligence, ones that the latest and greatest LLMs do have. Of course there others too that they don’t: logic, physical presence, being able to imagine things in their mind’s eye, memory, etc.
It’s reductive to dismiss GPT4 as nothing more than mimicry; saying it’s just a mathematical text prediction model is like saying your brain is just a bunch of neurons. Both statements are true, but it doesn’t change what they can do. If someone could accurately predict the moves a chess master would make, we wouldn’t say they’re just good at statistics, we’d say they’re a chess master. Similarly, regardless of how rich someone’s internal world is, if they’re unable to express the intelligent ideas they have in any intelligible way we wouldn’t consider them intelligent.
So what we have now with AI are a few key parts of intelligence. One important thing to consider is how language can be a path to other types of intelligence; here’s a blog post I stumbled across that really changed my perspective on that: http://www.asanai.net/2023/05/14/just-a-statistical-text-predictor/. Using your example of mathematics, as we know it falls apart doing anything remotely complicated. But when you help it approach the problem step-by-step in the way a human might - breaking it into small pieces and dealing with them one at a time - it actually does really well. Granted, the usefulness of this is limited when calculators exist and it requires as much guidance as a child to get correct answers, but even matching the mathematical intelligence of a ten year old is nothing to sneeze at.
To be clear I don’t think pursuing LLMs endlessly will be the key to a widely accepted ‘general intelligence’; it’ll require a multitude of different processes and approaches working together for that to ever happen, and we’re a long way from that. But it’s also not just getting carried away with the hype to say the past few years have yielded massive steps towards ‘true’ artificial intelligence, and that current LLMs have enough use cases to change a lot of people’s lives in very real ways (good or bad).
Thanks for that article, it was a very interesting read! I think we’re mostly agreeing about things :) This stood out to me from there as an encapsulation of the conversation:
I don’t think LLMs will approach consciousness until they have a complex cognitive system that requires an interface to be used from within – which in turn requires top-down feedback loops and a great deal more complexity than anything in GPT4. But I agree with Will’s general point: language prediction is sufficiently challenging that complex solutions are called for, and these involve complex cognitive stratagems that go far beyond anything well described as statistics.
“Statistics” is probably an insufficient term for what these things are doing, but it’s helpful to pull the conversation in that direction when a lay person using one of those things is likely to assume quite the opposite, that this really is a person in a computer with hopes and dreams. But I agree that it takes more than simply consulting a table to find the most likely next word to, to take an earlier example, write a haiku about Danny DeVito. That’s synthesizing two ideas together that (I would guess) the model was trained on individually. That’s very cool and deserving of admiration, and could lead to pretty incredible things. I’d expect that the task of predicting words, on its own, wouldn’t be stringent enough to force a model to develop “true” intelligence, whatever that means, to succeed during training, but I suppose we’ll find out, and probably sooner than we expect.
Well put! I think I kinda misunderstood what you were saying, I guess we sort of reached the same conclusion from different directions. And yeah, it does seem like we’re hitting the limits of what can be achieved from the current underlying word-prediction mechanisms alone, with how diminishing the returns are from dumping more data in. Maybe something big will happen soon, but it looks to me like LLMs will stagnate for a while until they’re taken in a fundamentally new direction.
Either way, what they can do now is pretty incredible, and equally interesting to me is how it’s making us reevaluate our ideas of consciousness and intelligence on a large scale; it’s one thing to theorize about what could happen with an ‘intelligent’ AI, but the reality of these philosophical questions being so thoroughly challenged and dissected in mundane legal and practical matters is wild.
I will give you just one example. Pharmaceutical companies often create aggregate reports where they have to process a large number of cases. Say, 5000. Such processing sometimes includes analysis of x-Ray or other images. Very specialized and highly paid people (radiologists) do this. It is expensive and is part of the reason why medicine prices are high. One company recently had a trial - if AI can do that job. Turns out it can. Huge savings for the company. And the radiologist lost their job. This is just one example of good and bad things that will and already are happening in our society due to AI.
You know this personally or did you just read an article? My wife works in a pharmaceutical company. And if I learned one thing by her stories: there will always be some person responsible for decisions! I doubt the radiologist lost her/ his job. I mean who’s going to jail if the quality was poor and people die?
I rather think AI downsized her/ his engagement. Either just doing an supervision and sanity check or used the tool by itself and increased productivity.
Yes, personally. They did the trials for precision of processing.
Good luck to them. Very brave to put their business critical decisions into the AI basket. FDA isn’t known for being humorous.
Every large aggregate report contains errors. As long as the errors are small and do not impact conclusions, there is no “business critical” element. And of course, they are going to check the accuracy with real human beings, constantly. But I have no doubt that AI is capable to do this kind of work as good or even better than human beings. So yes, some radiologists will be remained employed, but you need like what? 20% of them? Less, as time goes?
In various jobs, AI can do the less important and easier work for you, so you can focus on the more important work. For example, you’re doing some kind of research which needs a specific kind of data you have collected, but all of that data is cluttered and messy. AI can sort the data for you, so you can focus on your research instead of spending a lot of your time on sorting the data into something more understandable. Or in programming, AI can write the easy part of a program for you, and you do the harder and more important part, which saves you time.
I work at a small business and we use it to write out dumb social media post. I hated doing it before. Sometimes I’ll write it myself still and ask chatgpt to add all the relevant emojis. I also think ai had the chance to be what we’ve always wanted from Alexa, assistant, and Siri. Deep system integration with the os will allow it to actually do what we want it to do with way less restrictions. Also, try using chatgpts voice recognition in the app. It blows the one built into your phone out of the water.
It’s not bullshit. It routinely does stuff we thought might not happen this century. The trick is we don’t understand how. At all. We know enough to build it and from there it’s all a magical blackbox. For this reason it’s hard to be certain if it will get even better, although there’s no reason it couldn’t.
Coming from CNC I don’t think I’d just send it with some chatgpt code.
That goes back to the “not knowing how it works” thing. ChatGPT predicts the next token, and has learned other things in order to do it better. There’s no obvious way to force it to care if it’s output is right or just right-looking, though. Until we solve that problem somehow, it’s more of an assistant for someone who can read and understand what it puts out. Kind of like a calculator but for language.
Honestly crypto wasn’t totally either. It was a marginally useful idea that turned into a Beanie-Babies-like craze. If you want to buy or sell illegal stuff (which could be bad or could be something like forbidden information on democracy) it’s still king.
There’s no obvious way to force it to care if it’s output is right or just right-looking, though
Putting some expert system in front of LLMs seems to be working pretty well. Basically modeling how a human agent would interact with it.
We’ll see how that goes, I guess. I’m not involved enough to comment.
I’m guessing the expert system would be a classical algorithm?
I never interacted with any AI until ChatGPT started to get popular, and I could say I’m a bit of a tech guy (I like tech news, I selfhost some stuff on my NAS, I used Linux on my teenage days etc etc) but when I first interacted with it it was really jaw dropping for me.
Maybe the information isn’t 100% real, but the way it paraphrases stuff is amazing to me.
We’ve been using it at my day job to help us outline ideas for our content writers. It writes garbage content on its own, but it is a decent tool for organizing ideas.
At least that is what we use it for. I’m sure there are other valuable uses, but it is not as valuable (to me at least) as it has been made out to be.
Would you say it is comparable for summarizing ideas as a spelling/grammar checker is at checking spelling/grammar?
Helpful, but not close to perfect?
I think that is a great way to look at it.
The thing I’m most excited for is the removal of FUD from our daily lives. Everything on our would is designed around the preconceived notions of a small group of people from the past.
You can see this most obviously in traffic and urban planning. They had limited technology and time to make decisions 100 years ago that have serious negative affects today.
AI will soon be able to run its own complex models and decisions can be fact based, rather than emotional.
As a programmer, I think it’s scary how AI is now able to write functioning programs out of natural language input now. Sure, it’s not perfect. It’s still pretty mediocre at the task. But a few years ago this was way outside the realm of possibility.
It can even correct the code it has written if there’s any error (with varying results).
What will happen in five years time? Ten years? My fear is that it will only need to be “good enough” to replace most of the programmer’s work. Unlike self driving cars, where “good enough” isn’t good enough.
It’s a language model, it can’t even do math reliably. Yes, it produces code that works sometimes, but it also hallucinates functions that don’t exist or can introduce bugs you won’t notice at first glance.
And writing a script is different than extending an existing code base. How often do you really start a greenfield project?
I wouldn’t even know how to input a code base into ChatGPT to extend, do you just throw in hundreds of files with a 100k+ lines of code?
I guess LLM with plugins can solve most of the problems. ChatGPT can already interact with Wolfram Alpha to do math.
I can imagine similar plugins for code. Like it knows what kind of function it needs, so it interacts with a plugin that searches the code base to see if it exists. It might get back a snippets of candidates and examples how they’re used in the code already.
This is probably a difficult thing to achieve, but I don’t think it’s impossible. It’s probably going to take some time until something like this is made.
What regular people see as AI/ML is only a tip of an iceberg, that’s why it feels kind of useless. There are ML systems which design super strong yet lightweight geometries, there are systems which track legal documents of large companies making lawyers obsolete, heck even cameras in mobile phones today are hyper dependent on ML and AI. ChatGPT and image generators are just toys for consumers so that public can get slowly familiar with current tech.
