Useful in the way that it increases emissions and hopefully leads to our demise because that's what we deserve for this stupid technology.
Surely this is better than the crypto/NFT tech fad. At least there is some output from the generative AI that could be beneficial to the whole of humankind rather than lining a few people's pockets?
Unfortunately crypto is still somehow a thing. There is a couple year old bitcoin mining facility in my small town that brags about consuming 400MW of power to operate and they are solely owned by a Chinese company.
I recently noticed a number of bitcoin ATMs that have cropped up where I live - mostly at gas stations and the like. I am a little concerned by it.
I'm crypto neutral.
But it's really strange how anti-crypto ideologues don't understand that the system of states printing money is literally destroying the planet. They can't see the value of a free, fair, decentralized, automatable, accounting systems?
Somehow delusional chatbots wasting energy and resources are more worthwhile?
Printing currency isn't destroying the planet....the current economic system is doing that, which is the same economic system that birthed crypto.
Governments issuing currency goes back to a time long before our current consumption at all cost economic system was a thing.
I'm fine doing away with physical dollars printed on paper and coins but crypto seems to solve none of the problems that we have with a fiat currency but instead continues to consume unnecessary amounts of energy while being driven by rich investors that would love nothing more than to spend and earn money in an untraceable way.
While the consumption for AI train can be large, there are arguments to be made for its net effect in the long run.
The article's last section gives a few examples that are interesting to me from an environmental perspective. Using smaller problem-specific models can have a large effect in reducing AI emissions, since their relation to model size is not linear. AI assistance can indeed increase worker productivity, which does not necessarily decrease emissions but we have to keep in mind that our bodies are pretty inefficient meat bags. Last but not least, AI literacy can lead to better legislation and regulation.
The argument that our bodies are inefficient meat bags doesn't make sense. AI isn't replacing the inefficient meat bag unless I'm unaware of an AI killing people off and so far I've yet to see AI make any meaningful dent in overall emissions or research. A chatgpt query can use 10x more power than a regular Google search and there is no chance the result is 10x more useful. AI feels more like it's adding to the enshittification of the internet and because of its energy use the enshittification of our planet. IMO if these companies can't afford to build renewables to support their use then they can fuck off.
Using smaller problem-specific models can have a large effect in reducing AI emissions
Sure, if you consider anything at all to be "AI". I'm pretty sure my spellchecker is relatively efficient.
AI literacy can lead to better legislation and regulation.
What do I need to read about my spellchecker? What legislation and regulation does it need?
Is it me or is there something very facile and dull about Gartner charts? Thinking especially about the “””magic””” quadrants one (wow, you ranked competitors in some area along TWO axes!), but even this chart feels like such a mundane observation that it seems like frankly undeserved advertising for Gartner, again, given how little it actually says.
And it isn't even true in many cases. For example the internet with the dotcom bubble. It actually became much bigger and important than anyone anticipated in the 90s.
The graph for VR would also be quite interesting, given how many hype cycles it has had over the decades.
It's also false in the other direction: NFTs never got a "Plateau of Productivity".
A lot of tech hype are just convoluted scams or ponzi schemes.
To be fair, it is useful in some regards.
I'm not a huge fan of Amazon, but last time I had an issue with a parcel it was sorted out insanely fast by the AI assistant on the website.
Within literally 2 minutes I'd had a refund confirmed. No waiting for people to eventually pick up the phone after 40 minutes. No misunderstanding or annoying questions. The moment I pressed send on my message it instantly started formulating a reply.
The truncated version went:
"Hey I meant to get [x] delivery, but it hasn't arrived. Can I get a refund?"
"Sure, your money will go back into [y] account in a few days. If the parcel turns up in the meantime, you can send it back by dropping it off at [z]"
Done. Absolutely painless.
How is a chatbot here better, faster, or more accurate than just a "return this" button on a web page? Chat bots like that take 10x the programming effort and actively make the user experience worse.
Presumably there could be nuance to the situation that the chat bot is able to convey?
But that nuance is probably limited to a paragraph or two of text. There's nothing the chatbot knows about the returns process at a specific company that isn't contained in that paragraph. The question is just whether that paragraph is shown directly to the user, or if it's filtered through an LLM first. The only thing I can think of is that chatbot might be able to rephrase things for confused users and help stop users from ignoring the instructions and going straight to human support.
That has nothing to do with AI and is strictly a return policy matter. You can get a return in less than 2 minutes by speaking to a human at Home Depot.
Businesses choose to either prioritize customer experience, or not.
There’s a big claim from Klarna - that I am not aware has been independently verified – that customers prefer their bot.
The cynic might say they were probably undertraining a skeleton crew of underpaid support reps. More optimistically, perhaps so many support inquiries are so simple that responding to them with a technology that can type a million words per minute should obviously be likely to increase customer satisfaction.
Personally, I'm happy with environmentally-acceptable and efficient technologies that respect consumers… assuming they are deployed in a world with robust social safety nets like universal basic income. Heh
You can just go to the order and click like 2 buttons. Chat is for when a situation is abnormal, and I promise you their bot doesn't know how to address anything like that.
I like using it to assist me when I am coding.
Do you feel like elaborating any? I'd love to find more uses. So far I've mostly found it useful in areas where I'm very unfamiliar. Like I do very little web front end, so when I need to, the option paralysis is gnarly. I've found things like Perplexity helpful to allow me to select an approach and get moving quickly. I can spend hours agonizing over those kinds of decisions otherwise, and it's really poorly spent time.
I've also found it useful when trying to answer questions about best practices or comparing approaches. It sorta does the reading and summarizes the points (with links to source material), pretty perfect use case.
So both of those are essentially "interactive text summarization" use cases - my third is as a syntax helper, again in things I don't work with often. If I'm having a brain fart and just can't quite remember the ternary operator syntax in that one language I never use....etc. That one's a bit less impactful but can still be faster than manually inspecting docs, especially if the docs are bad or hard to use.
With that said I use these things less than once a week on average. Possible that's just down to my own pre-existing habits more than anything else though.
An example I did today was adjusting the existing email functionality of the application I am working on to use handlebars templates. I was able to reformat the existing html stored as variables into the templates, then adjust their helper functions used to distribute the emails to work with handlebars rather than the previous system all in one fell swoop. I could have done it by hand, but it is repetitive work.
I also use it a lot when troubleshooting issues, such as suggesting how to solve error messages when I am having trouble understanding them. Just pasing the error into the chat has gotten me unstuck too many times to count.
It can also be super helpful when trying to get different versions of the packages installed in a code base to line up correctly, which can be absolutely brutal for me when switching between multiple projects.
Asking specific little questions that may take up the of a coworker or the Sr dev lets me understand the specifics of what I am looking at super quickly without wasting peoples time. I work mainly with existing code, so it is really helpful for breaking down other peoples junk if I am having trouble following.
LLMs need to get better at saying "I don't know." I would rather an LLM admit that it doesn't know the answer instead of making up a bunch of bullshit and trying to convince me that it knows what it's talking about.
LLMs don't "know" anything. The true things they say are just as much bullshit as the falsehoods.
I work on LLM's for a big tech company. The misinformation on Lemmy is at best slightly disingenuous, and at worst people parroting falsehoods without knowing the facts. For that reason, take everything (even what I say) with a huge pinch of salt.
LLM's do NOT just parrot back falsehoods, otherwise the "best" model would just be the "best" data in the best fit. The best way to think about a LLM is as a huge conductor of data AND guiding expert services. The content is derived from trained data, but it will also hit hundreds of different services to get context, find real-time info, disambiguate, etc. A huge part of LLM work is getting your models to basically say "this feels right, but I need to find out more to be correct".
With that said, I think you're 100% right. Sadly, and I think I can speak for many companies here, knowing that you're right is hard to get right, and LLM's are probably right a lot in instances where the confidence in an answer is low. I would rather a LLM say "I can't verify this, but here is my best guess" or "here's a possible answer, let me go away and check".
I thought the tuning procedures, such as RLHF, kind of messes up the probabilities, so you can't really tell how confident the model is in the output (and I'm not sure how accurate these probabilities were in the first place)?
Also, it seems, at a certain point, the more context the models are given, the less accurate the output. A few times, I asked ChatGPT something, and it used its browsing functionality to look it up, and it was still wrong even though the sources were correct. But, when I disabled "browsing" so it would just use its internal model, it was correct.
It doesn't seem there are too many expert services tied to ChatGPT (I'm just using this as an example, because that's the one I use). There's obviously some kind of guardrail system for "safety," there's a search/browsing system (it shows you when it uses this), and there's a python interpreter. Of course, OpenAI is now very closed, so they may be hiding that it's using expert services (beyond the "experts" in the MOE model their speculated to be using).
I hate to break this to everyone who thinks that “AI” (LLM) is some sort of actual approximation of intelligence, but in reality, it’s just a fucking fancy ass parrot.
Our current “AI” doesn’t understand anything or have context, it’s just really good at guessing how to say what we want it to say… essentially in the same way that a parrot says “Polly wanna cracker.”
A parrot “talking to you” doesn’t know that Polly refers to itself or that a cracker is a specific type of food you are describing to it. If you were to ask it, “which hand was holding the cracker…?” it wouldn’t be able to answer the question… because it doesn’t fucking know what a hand is… or even the concept of playing a game or what a “question” even is.
It just knows that it makes it mouth, go “blah blah blah” in a very specific way, a human is more likely to give it a tasty treat… so it mushes its mouth parts around until its squawk becomes a sound that will elicit such a reward from the human in front of it… which is similar to how LLM “training models” work.
Oversimplification, but that’s basically it… a trillion-dollar power-grid-straining parrot.
And just like a parrot - the concept of “I don’t know” isn’t a thing it comprehends… because it’s a dumb fucking parrot.
The only thing the tech is good at… is mimicking.
It can “trace the lines” of any existing artist in history, and even blend their works, which is indeed how artists learn initially… but an LLM has nothing that can “inspire” it to create the art… because it’s just tracing the lines like a child would their favorite comic book character. That’s not art. It’s mimicry.
It can be used to transform your own voice to make you sound like most celebrities almost perfectly… it can make the mouth noises, but has no idea what it’s actually saying… like the parrot.
You get it?
Useful for scammers and spam
We should be using AI to pump the web with nonsense content that later AI will be trained on as an act of sabotage. I understand this is happening organically; that's great and will make it impossible to just filter out AI content and still get the amount of data they need.
That sounds like dumping trash in the oceans so ships can't get through the trash islands easily anymore and become unable to transport more trashy goods. Kinda missing the forest for the trees here.
My shitposting will make AI dumber all on its own; feedback loop not required.
Alternatively, and possibly almost as useful, companies will end up training their AI to detect AI content so that they don't train on AI content. Which would in turn would give everyone a tool to filter out AI content. Personally, I really like the apps that poison images when they're uploaded to the internet.
hot take: chatbots are actually kinda useful for problem solving but its not the best at it
Third, we see a strong focus on providing AI literacy training and educating the workforce on how AI works, its potentials and limitations, and best practices for ethical AI use. We are likely to have to learn (and re-learn) how to use different AI technologies for years to come.
Useful?!? This is a total waste of time, energy, and resources for worthless chatbots.
I use it all the time at work, generative ai is very useful. I don't know vba coding but I was able to automate all my excel reports by using chatgpt to write me vba code to automate everything. I know sql and I'm a novice at it. Chatgpt can fix all the areas in weak at in SQL. I end up asking it about APIs and was able to integrate another source of data giving everyone in my department new and better reporting.
There are a lot of limitations and you have to ask it to fix a lot of the errors it creates but it's very helpful for someone like me who doesn't know programming but it can enable me to use programming to be more efficient.
So if I were to get this straight, the entire logic is that due to big hype, it fits the pattern or other techs becoming useful… that’s sooo not a guarantee, so many big hype stuff have died.
This is a most excellent place for technology news and articles.
