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?
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
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’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.
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.
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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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.
Yes. What a strange question…as if hivemind fads are somehow relevant to the merits of a technology.
There are plenty of useful, novel applications for AI just like there are PLENTY of useful, novel applications for crypto. Just because the hivemind has turned to a new fad in technology doesn’t mean that actual, intelligent people just stop using these novel technologies. There are legitimate use-cases for both AI and crypto. Degenerate gamblers and Do Kwan/SBF just caused a pendulum swing on crypto…nothing changed about the technology. It’s just that the public has had their opinions shifted temporarily.
So I’m a reasearcher in this field and you’re not wrong, there is a load of hype. So the area that’s been getting the most attention lately is specifically generative machine learning techniques. The techniques are not exactly new (some date back to the 80s/90s) and they aren’t actually that good at learning. By that I mean they need a lot of data and computation time to get good results. Two things that have gotten easier to access recently. However, it isn’t always a requirement to have such a complex system. Even Eliza, a chatbot was made back in 1966 has suprising similar to the responses of some therapy chatbots today without using any machine learning. You should try it and see for yourself, I’ve seen people fooled by it and the code is really simple. Also people think things like Kalman filters are “smart” but it’s just straightforward math so I guess the conclusion is people have biased opinions.
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.
I find it useful in a lot of ways. I think people try to over apply it though. For example, as a software engineer, I would absolutely not trust AI to write an entire app. However, it’s really good at generating “grunt work” code. API requests, unit tests, etc. Things that are well trodden, but change depending on the context.
I also find they’re pretty good at explaining and summarizing information. The chat interface is especially useful in this regard because I can ask follow up questions to drill down into something I don’t quite understand. Something that wouldn’t be possible with a Wikipedia article, for example. For important information, you should obviously check other sources, but you should do that regardless of whether the writer is a human or machine.
Basically, it’s good at that it’s for: taking a massive compendium of existing information and applying it to the context you give it. It’s not a problem solving engine or an artificial being.
I feel like it won’t be AI until we figure out how to point it back at itself, have it review its own answers and then be ‘happy’ when it’s answers are right. Not necessarily like if the user gives it a good score, but if it recognizes an answer it had given was actually used, or a prediction it makes if proved true (if I answer this way, the user is likely to ask this as its next question, etc) and it starts changing its behaviour, and asking itself questions to get better at that.
As a senior developer I see it unlocking so much more power in computing than a regular coder can muster.
There are literally cars in America driving around on their own, interacting with other traffic , navigating problems and junctions, following gestures and laws. It’s incredible and more impressive than chatgpt is. We are on our way to self-driving cars and lorries, self-service checkouts, delivery services and taxis, more efficient machines in agriculture and so many other things. It’s touching every facet of life.
we’re at a point where we’ve seen so many wonderful benefits of AI it’s time to apply it to everything and see what sticks.
Of course some people who invest in the stock market lose money but the technology is more than a step forward, it’s a leap forward.
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.
AI != chatGPT
There are other ML models out there for all kinds of purposes. I heard someone made one at one point that could detect certain types of cancer from a cough
Copilot is pretty useful when programming as it is basically like what IDEs normally do (automatically generating boilerplate) but supercharged
As far as generating code is concerned it’s never going to beat actually knowing what you’re doing in a language for more complex stuff but it allows you to generate code for languages you’re not familiar with
I use it all the time at work when I’m asked to write DAX because it’s not particularly complex logic but the syntax makes me want to impale my face with a screwdriver
To the second question it’s not novel at all. The models used were invented decades ago. What changed is Moores Law striked and we got stronger computational power especially graphics cards. It seems that there is some resource barrier that when surpassed turns these models from useless to useful.
Not the specific models unless I’ve been missing out on some key papers. The 90s models were a lot smaller. A “deep” NN used to be 3 or more layers and that’s nothing today. Data is a huge component too
The specifics are a bit different, but the main ideas are much older than this, I’ll leave here the Wikipedia
"Frank Rosenblatt, who published the Perceptron in 1958,[10] also introduced an MLP with 3 layers: an input layer, a hidden layer with randomized weights that did not learn, and an output layer.[11][12] Since only the output layer had learning connections, this was not yet deep learning. It was what later was called an extreme learning machine.[13][12]
The first deep learning MLP was published by Alexey Grigorevich Ivakhnenko and Valentin Lapa in 1965, as the Group Method of Data Handling.[14][15][12]
The first deep learning MLP trained by stochastic gradient descent[16] was published in 1967 by Shun’ichi Amari.[17][12] In computer experiments conducted by Amari’s student Saito, a five layer MLP with two modifiable layers learned internal representations required to classify non-linearily separable pattern classes.[12]
In 1970, Seppo Linnainmaa published the general method for automatic differentiation of discrete connected networks of nested differentiable functions.[3][18] This became known as backpropagation or reverse mode of automatic differentiation. It is an efficient application of the chain rule derived by Gottfried Wilhelm Leibniz in 1673[2][19] to networks of differentiable nodes.[12] The terminology “back-propagating errors” was actually introduced in 1962 by Rosenblatt himself,[11] but he did not know how to implement this,[12] although Henry J. Kelley had a continuous precursor of backpropagation[4] already in 1960 in the context of control theory.[12] In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard.[6][12] In 1985, David E. Rumelhart et al. published an experimental analysis of the technique.[7] Many improvements have been implemented in subsequent decades.[12]"
The idea of NN or the basis itself is not AI. If you had actual read D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation.” Sep. 01, 1985. then you would understand this bc that paper is about a machine learning technique not AI. If you had done your research properly instead of just reading wikipedia, then you would have also come across autoassociative memory which is the precursor to autoencoders and generative autoencoders which is the foundation of a lot of what we now think of as AI models. H. Abdi, “A Generalized Approach For Connectionist Auto-Associative Memories: Interpretation, Implication Illustration For Face Processing,” in In J. Demongeot (Ed.) Artificial, University Press, 1988, pp. 151–164.
I thank you for your critic but I’m not writing a research paper here and therefore wikipedia is a good ressource for the uniniated public. This is also why I think it’s sufficient to know a) what an artificial neural network is by talking about the simplest examples b) this field of research didn’t initiate 10 years ago as often conceived by public, when first big headlines were made. These tradeoffs are always made: correctness vs simplification. I see your disagreeing with this PoV but that’s no reason to be condescending.
You don’t get to complain about people being condescending to you when you are going around literally copy and pasting wikipedia. Also you’re not right, major progress in this field started in the 80s although the concepts were published earlier, they were basically ignored by researchers. You’re making it sound like the NNs we’re using now are the same as the 60s when in reality our architectures and just even how we approach the problem have changed significantly. It’s not until the 90s-00s that we started getting decent results that could even match older ML techniques like SVM or kNN.
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.
It’s insanely useful.
Take ChatGPT for instance.
You can essentially use it as an interactive docs when learning something new.
You can paste in a large text document and get it summarize it.
You can paste in a review and get it to do sentiment analysis and generate scores out of 100 for different things (actively pursuing this at work and it looks great)
I use it all the time to write simple regex and code snippets.
Machine learning has many massive applications. Many phone cameras use it to get the quality of photos up massively.
It’s used all over the place without you even realising.
As others have said, in it’s current state, it can be useful in the early stages of anything you do, such as brainstorming. ChatGPT (I have most experience with) and other LLM excel at organizing, formating, explaining, etc the information of the internet. In almost all cases (at the moment) whatever they spit out needs to be fact checked and refined.
Just from personally dinking around with chatGPT a little, it does give you that “scarily good” feeling at first. You do start seeing it’s flaws after a while, and you get to learn that it’s quite fallible. The information it can spit out can be good for additional ideas and brainstorming.
What I want it do (and it might already, if not soon) is that I when I program something up and for the life of me can’t find the cause of some bug, just be able to give it my entire code and my problem and see what’s deal.
I am super amateur with python and I don’t work in IT, but I’ve used it to write code for me that allows me to significantly save time in my work flow.
Like something that used to take me an hour to do now takes 15-20 minutes.
So as a nonprogrammer, im able to get it to write enough code that I can tweak until it works instead of just not having that tool.
