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
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.
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.
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.
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.
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.
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.
As someone who works in machine learning (ML) research the use of ML has hit almost every scientific discipline you can imagine and it’s been tremendously helpful in pushing research forward.
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.
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.
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’m currently building a Jungian shadow work (a kind of psycho therapy) web app using local machine learning and it’s doing a decent enough job to continue developing it.
ChatGPT 4.0 is also quite helpful in making my python code less terrible and it’s good at guiding me through wherever I’m facing challenges, since I’m more of an ops person instead of a developer. Can’t complain, though the coding quality of GPT4.0 has declined noticably within the last weeks.
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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.
