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?

Kolanaki,
@Kolanaki@yiffit.net avatar

It’s really good at filling in gaps, or rearranging things, or aggregating data or finding patterns.

So if you need gaps filled, things rearranged, data aggregated or patterns found: AI is useful.

And that’s just what this one, dumb guy knows. Someone smarter can probably provide way more uses.

tara,
@tara@lemmy.blahaj.zone avatar

Hi academic here,

I research AI - better referred to as Machine Learning (ML) since it does away with the hype and more accurately describes what’s happening - and I can provide an overview of the three main types:

  1. Supervised Learning: Predicting the correct output for an input. Trained from known examples. E.g: “Here are 500 correctly labelled pictures of cats and dogs, now tell me if this picture is a cat or a dog?”. Other examples include facial recognition and numeric prediction tasks, like predicting today’s expected profit or stock price based on historic data.
  2. Unsupervised Learning: Identifying patterns and structures in data. Trained on unlabelled data. E.g: “Here are a bunch of customer profiles, group them by similarity however makes most sense to you”. This can be used for targeted advertising. Another example is generative AI such as ChatGPT or DALLE: “Here’s a bunch of prompt-responses/captioned-images, identify the underlying way of creating the response/image from the prompt/image.
  3. Reinforcement Learning: Decision making to maximise a reward signal. Trained through trial and error. E.g: “Control this robot to stand where I want, the reward is negative every second you’re not there, and very negative whenever you fall over. A positive reward is given whilst you are in the target location.” Other examples including playing board games or video games, or selecting content for people to watch/read/look-at to maximise their time spent using an app.
whataboutshutup,

What do you think on calling it AI?

tara,
@tara@lemmy.blahaj.zone avatar

So typically there are 4 main competing interpretations of what AI is:

  1. Acting like a human
  2. Thinking like a human
  3. Acting rationally
  4. Thinking rationally

These are from Norvig’s “AI: A Modern Approach”.

Alan Turing’s “Turing Test” tests whether a given agent is artificially intelligent (according to definition #1). The test involves a human conversing with the agent via text messages, and deciding whether the agent is human or not. Large language models, a form of machine learning, can produce chatbot agents which pass this test. Instances of GPT4 prompted sufficiently to text an assessor for example. The assessor occasionally interacts with humans so they are kept sufficiently uncertain.

By this point, I think that machine learning in the form of an LLM can achieve artificial intelligence according to definition #1, but that isn’t what most non-tech non-academic people mean by AI.

The mainstream definition of AI is what we would call Artificial General Intelligence (AGI). This is an agent that meets a given one of Norvig’s criteria for AI across multiple scenarios and situations that they have never encountered before.

Many would argue that LLMs like GPT4 do not meet the criteria for AGI because they are not general enough, unable to learn to play an Atari game for example, or to learn an entirely unseen language to fluency.

This is the difference between an LLM and a fictional AGI like Glados or Skynet.

Additionally forms of machine learning exist like k-means clustering, which identify related groups within a dataset as their only function. I would assert these are not AI, although a weak argument could be made that they are thinking “rationally” enough to meet definition #4.

Then there are forms of AI which are not machine learning, such as heuristic agents - agents that are hard coding with reasoning by humans - such as the chess playing Stockfish, or the AI found in most video games.

Ultimately AI can describe machine learning if “AI” is understood as something which meets one or more of Norvig’s definitions. But since most people say AI when they mean AGI, I think “machine learning” is a better term. Less undeserved hype, less marketing disinformation, and generally better at communicating what is being talked about.

whataboutshutup,

Thanks for taking your time and putting it in that laconic way.

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.

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.

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.

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

zumi,

Senior developer here. It is hard to overstate just how useful AI has been for me.

It’s like having a junior programmer on standby that I can send small tasks to–and just like the junior developer I have to review it and send it back with a clarification or comment about something that needs to be corrected. The difference is instead of making a ticket for a junior dev and waiting 3 days for it to come back, just to need corrections and wait another 3 days–I get it back in seconds.

Like most things, it’s not as bad as some people say, and it’s not the miracle others say.

This current generation was such a leap forward from previous AI’s in terms of usefulness, that I think a lot of people were looking to the future with that current rate of gains–which can be scary. But it turns out that’s not what happened. We got a big leap and now are back at a plateau again. Which honestly is a good thing, I think. This gives the world time to slowly adjust.

As far as similarities with crypto. Like crypto there are some ventures out there just slapping the word AI on something and calling it novel. This didn’t work for crypto and likely won’t work for AI. But unlike crypto there is actually real value being derived from AI right now, not some wild claims of a blockchain is the right DB for everything–which it was obviously not, and most people could see that, but hey investors are spending money so lets get some of it kind of mentality.

thelastknowngod,

Same. 5 minutes after installing Copilot I literally said out loud, “Well… I’m never turning this off.”

It’s one of the nicest software releases in years. And it’s instantly useful too… No real adjustment period at all.

GarlicBender,

I tried it for a couple months and it was alright but eventually it got too frustrating. I did love how well it did some really repetitive things. But rarely did it actually get anything complex 100% right. In computing, “almost right” is wrong. But because it was so close, it was hard to spot the mistakes.

There were cases where my IDE knew the right answer but Copilot did not. Realizing that Copilot was messing up my IDE enhancements to produce code I was painfully babysitting, I cancelled it.

sLLiK,

This is the most insidious conundrum related to AI usage. At the end of the day, a LLM’s top priority is to ensure that your question is answered in a way that satisfies that model. The accuracy of its answers are a secondary concern. If forced to choose between making up BS so it can have a response that looks right versus admitting it doesn’t have enough information to answer, it can and often will choose the former. Thus the “hallucination” problem was born.

The chance of getting your answer lightly sprinkled with made up stuff is disturbingly high. This transfers the cognitive load of the AI user from “what is the answer” to “I must repeatedly go verify everything in this answer because I can’t trust it”.

Not an insurmountable obstacle, and they will likely solve it sooner rather than later, but AI right now is arguably the perfect extension of the modern internet - take absolutely everything you read with at least a grain of salt… and keep a pile of salt cubes close by.

evanuggetpi,

I’ve been a web developer for 22 years. For the last 13 years I’ve been working self employed from home. I cannot express how useful AI has become. As a lone wolf, where most of my job is problem solving, having an AI that can help troubleshoot issues has been hugely useful.

It also functions as a junior developer, doing the grunt programming work.

I also run a bunch of e-commerce sites around the world and I use it for content generation, SEO, business plans, marketing strategies and multi-lingual customer support.

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.

mim,

I don’t think the comparison with crypto is fair.

People are actually using these models in their daily lives.

PeepinGoodArgs,

I’m one of those that use it in my daily life.

The current top comment says it’s “really good at filling in gaps, or rearranging things, or aggregating data or finding patterns.”

So, I use Perplexity.ai like you would use Google. Except I don’t have to deal with shitty ads and a bunch of filler content. It summarizes links for me, so I can more quickly understand whatever I’m searching for. However, I personally believe it’s important to look directly at the sources once I get the summary, if only to verify the summary. So, in this instance, I find AI makes understanding a topic easier and faster than alternatives.

As a graduate student, I use ChatGPT extensively, but ethically. I’m not writing essays with it. I am, however, downloading lecture notes as PDFs and having ChatGPT rearrange that information into outline. Or I copy whole chapters from a book and have it do the same. Suddenly, my reading time is cut down by like 45 minutes because it takes me 15 minutes to get output that I just copy and paste into my notes, which I take digitally.

Honestly, using it like I do, it’s pretty clear that AI is both as scary as it sounds in some instances and not, in others. The concern with disinformation during the 2024 election is a real concern. I could generate essays with it with whatever conclusions I wanted. In contrast, the concern that AI is scary smart and will take over the world is nonsense. It’s not smart in any meaningful sense and doesn’t have goals. Smart bombs are just dumb bombs with the ability to hone in better on the target, it’s still has the mission of blowing shit up given to it by some person and inherent in its design. AI is the same way.

ShaggyDemiurge,
@ShaggyDemiurge@lemmy.blahaj.zone avatar

Perplexity.ai

Huh, this one looks pretty cool. Is it good enough to use as a default search engine, or would it still be better to leave google for it?

PeepinGoodArgs,

It’s useful for when you want to go down a rabbit hole. It’s less useful for super specific stuff, like where to go if you want your nails done.

Blaze,
@Blaze@sopuli.xyz avatar

Thank you for perplexity.ai, didn’t know about this one

Komplekx,

I’m currently working on my bachelor thesis and checked perplexity.ai out after I saw your comment. This is incredibly useful, thanks for sharing!

dismalnow,
@dismalnow@kbin.social avatar

I love revisiting comments like these every 4 years.

mim,

And yet, people still don’t use crypto in their daily lives. How many years has it been?

can,

Reddit just tied karma to the blockchain lol

Not saying it’s a good use, but lots of people are going to be using it now.

hglman,

People have actually used crypto to make payments. Crypto is valuable, but only when it’s widely adopted. Before you say something like “use a database,” you might take the time to understand what decentralized blockchains are accomplishing and namely removing a class of corruption from any information coordination tasks.

beatle,

Why bother with the overhead of blockchain when users centralise on a handful of banks exchanges.

hglman,

Exchanges only exist to convert away from the crypto. If that’s the standard money, they don’t live. They aren’t the banks of the blockchain. They are the intersection of fiat banks and the blockchain.

beatle,

Strongly disagree, some exchanges don’t even have fiat on-ramps.

Blockchain is inefficient and pointless when users centralise on coinbase and binance.

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.

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.

MxM111,
@MxM111@kbin.social avatar

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.

DrunkenPirate,

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.

MxM111,
@MxM111@kbin.social avatar

Yes, personally. They did the trials for precision of processing.

DrunkenPirate,

Good luck to them. Very brave to put their business critical decisions into the AI basket. FDA isn’t known for being humorous.

MxM111,
@MxM111@kbin.social avatar

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?

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.

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.

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.

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