Is there anything actually useful or novel about "AI"?

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?

mo_lave,

I like to build up fictional settings. Not being limited to commissioning art/easy conceptualization without resorting to nicking images as-is from the internet is extremely useful.

Candid_Technology_66,

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.

SkepticElliptic,

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.

philluminati,

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.

charonn0,
@charonn0@startrek.website avatar

Several autonomous car companies operate in my city. They’re impressive technology, but they’re not nearly as good as an attentive human driver. In particular, they have problems coping with anything unexpected, such as road closures or emergency vehicles, and they do not understand gestures.

rustyricotta,

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.

zappy,

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.

ndguardian,

Focusing mostly on ChatGPT here as that is where the bulk of my experience is. Sometimes I’ll run into a question that I wouldn’t even know how best to Google it. I don’t know the terminology for it or something like that. For example, there is a specific type of connection used for lighting stands that looks like a plug but there is also a screw that you use to lock it in. I had no idea what to Google to even search for it to buy the adapter I needed.

I asked it again as I forgot what the answer was and I had deleted that ChatGPT conversation from my history, and asked it like this.

I have a light stand that at the top has a connector that looks like a plug. What is that connector called?

And it just told me it’s called a “spigot” or “stud” connection. Upon Googling it, that turned out to be correct, so I would know what to search for when it comes to searching for adapters. It also mentioned a few other related types of connections such as hot shoe and cold shoe connections, among others. They aren’t correct, but are very much related, and it told me as such.

To put it more succinctly, if you don’t know what to search for but have a general idea of the problem or question, it can take you 95% of the way there.

petenu,

My concern is that it feels like using Google to confirm the truth of what ChatGPT tells you is becoming less and less reliable, as so many of the pages indexed by Google are themselves created by similar models. But I suppose as long as your search took you to a site where you could actually buy the thing, that’s okay.

Or at least, it is until fake shopping sites start inventing products based on ChatGPT output.

flambonkscious,

Now there’s a money-spinner!!

Please note: I’m not being serious

ezmack,

Man that’d be useful I’m actually struggling to find a really niche electrical connector roght now

conditional_soup, (edited )

Yes, it is useful. I use ChatGPT heavily for:

  • Brainstorming meal plans for the week given x, y, and z requirements
  • Brainstorming solutions to abstract problems
  • Helping me break down complex tasks into smaller, more achievable tasks.
  • 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.

BestBunsInTown_,

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

Jase,
@Jase@lemmy.world avatar

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.

Majawat,

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?

Jase,
@Jase@lemmy.world avatar

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.

Karmmah,
@Karmmah@lemmy.world avatar

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.

CoderKat,

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.

ericskiff,

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.

MostlyGibberish,

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.

dnick,

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.

dtxer,

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.

zappy,

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

dtxer,

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]”

zappy,

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.

dtxer,

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.

zappy,

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.

nickwitha_k,

As a software engineer, I think it is beyond overhyped. I have seen it used once in my day job before it was banned. In that case, it hallucinated a function in a library that didn’t exist outside of feature requests and based its entire solution around it. It can not replace programmers or creatives and produce consistently equal quality.

I think it’s also extremely disingenuous for Large Language Models to be billed as “AI”. They do not work like human cognition and are basically just plagiarism engines. They can assemble impressive stuff at a rapid speed but are incapable of completely novel “ideas” - everything that they output is built from a statistical model of existing data.

If the hallucination problem could be solved in a local dataset, I could see LLMs as a great tool for interacting with databases and documentation (for a fictional example, see: VIs in Mass Effect). As it is now, however, I feel that it’s little more than an impressive parlor trick - one with a lot of future potential that is being almost completely ignored in favor of bludgeoning labor, worsening the human experience, and increasing wealth inequality.

unknowing8343,

You have not realised yet that… yes, it has all the right to be called AI. They are doing the same thing we do. Learn and then create thoughts based on those learnings.

I even asked them to make up words that are not related to any language, and they create them, entirely new, never-used words, that are not even composites of others. These are creative machines. They might fail at answering some questions, but that is partially why we call it Artificial Intelligence. It’s not saying that it is a machine of truth. Just a machine that “learns” and “knows”. Sometimes correctly, sometimes wrong. Just like us.

nickwitha_k,

Incorrect. An LLM COULD be a part of a system that implements AI but, itself, possesses no intelligence. Claiming otherwise is akin to claiming that the Pythagorean theorem is an AI because it “understands” geometry. Neither actually understands the data that they are fed but, are good at producing results that make it seem that way.

Human cognition does not work that way; it is much more complex and squishy. Association of current experiences with remembered experiences is only a fraction of what is going on in a brain related to cognition.

unknowing8343,

I am not saying it works exactly like humans inside of the black box. I just say it works. It learns and then creates thoughts. And it works.

You talk about how human cognition is more complex and squishy, but nobody really knows how it truly works inside.

All I see is the same kind of blackbox. A kid trying many, many times to stand up, or to say “papa”, until it somehow works, and now the pathway is setup in the brain.

Obviously ChatGPT is just dealing with text. But does it make it NOT intelligent? I think it makes it very text-intelligent. Just add together all the AI pieces we are building and you got yourself a general AI that will do anything we do.

Yeah, maybe it does not work like our brain. But is a human brain structure the only possible structure for intelligence? I don’t think so.

stsquad,

If you consider the amount of text an LLM has to consume to replicate something approaching human like language you have to appreciate there is something else going on with our cognition. LLM’s give responses that make statistical sense but humans can actually understand why one arrangement of words might not make sense over the other.

unknowing8343,

Yes, it’s inefficient… and OpenAI and Google are losing exactly because of that.

There’s open source models already out there that are rivaling ChatGPT and that you can train on your 10 year-old laptop in a day.

And this is just the beggining.

Also… maybe we should check how many words of exposure a kid gets throughout their life to get to the point to develop arguments such as ChatGPT’s… because the thing is that… ChatGPT does know way more about many things than any human being will ever do. Like, easily thousands of times more.

nickwitha_k,

And this is just the beggining.

Absolutely agreed, so long as protections are put in place to defang it as a weapon against labor (if few have leisure time or income to support tech development, I see great danger of stagnation). LLMs do clearly seem an important part in advancing towards real AI.

nickwitha_k,

It does not create “thoughts”, it is very good at tricking humans into believing that it does, though.

You talk about how human cognition is more complex and squishy, but nobody really knows how it truly works inside.

It is not that there is no understanding, but rather that we have incomplete understanding. We know, for example, that human cognition is not purely storing recorded stimuli and performing associative analysis against them when meeting other stimuli.

All I see is the same kind of blackbox. A kid trying many, many times to stand up, or to say “papa”, until it somehow works, and now the pathway is setup in the brain.

This is a bit of a logical fallacy here, unfortunately, specifically false equivalency (ie. Thing A and Thing B both have characteristic C, therefore Thing A and Thing B are the same). This is exactly the sort of “dangerous” fallacy that a number of AI academics have warned about as well. LLMs are great at producing outputs that our socially-oriented brains can interpret as sentient thought and mistakenly anthropomorphize.

However, LLMs, as the word “model” in the name suggests, are statistical modeling software. They do not understand context or abstract meaning; only statistical occurrence of data in their stack, compared to the inputs. They are physically incapable of developing the Theory of the Mind due to the limitations in how they work.

But does it make it NOT intelligent?

No. The fact that they literally cannot actually understand anything or undertake contemplative, abstract thoughts is what makes them not intelligent. They do not understand the meaning of language; it is just data to them that has no context but how it relates to other parts of language.

Yeah, maybe it does not work like our brain.

I absolutely think that LLMs could be a component in AI but, alone, they are just like saying that a tire is a car because both can travel linear distances using rotation movements. By themselves, LLMs fail to fulfill what we tend to define as intelligence.

But is a human brain structure the only possible structure for intelligence? I don’t think so.

I certainly hope that the human brain isn’t the only possible structure for intelligence and find it very unlikely because our meat-computers aren’t really that special, even if we can’t entirely understand how they work yet (we’ve only really been trying for a relatively short time, compared to our species’ existence). We seem to agree there. I absolutely want AI as well as other non-human intelligence to be a thing because the idea of a universe in which humanity is the only sentience is very lonely and sad to me.

captain_samuel_brady,

As a non-software engineer, it’s basically magic for programming. Can it handle your workload? Probably not based on your comment. I have, however, coaxed it to write several functional web applications and APIs. I’m sure you can do better, but it’s very empowering for someone that doesn’t have the same level of knowledge.

TORFdot0,

Don’t ask LLMs about how to do something in power shell because there’s a good chance it will tell you to use a module or function that just doesn’t plain exist. I did use an outline ChatGPT created for a policy document and it did a pretty good job. And if you give it a compsci 100 level task or usually can output functional code faster than I can type.

Lmaydev,

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.

kratoz29,

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.

Aux,

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.

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