Useful in the way that it increases emissions and hopefully leads to our demise because that’s what we deserve for this stupid technology.
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?
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.
It takes living with a broken system to understand the fix for it. There are millions of people who have been saved by Bitcoin and the freedom that it brings, they are just mainly in the 2nd and 3rd worlds, so to many people they basically don’t exist.
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.
Theoretically we could slow down training and coast on fine-tuning existing models. Once the AI’s trained they don’t take that much energy to run.
Everyone was racing towards “bigger is better” because it worked up to GPT4, but word on the street is that raw training is giving diminishing returns so the massive spending on compute is just a waste now.
It’s a bit more complicated than that.
New models are sometimes targeting architecture improvements instead of pure size increases. Any truly new model still needs training time, it’s just that the training time isn’t going up as much as it used to. This means that open weights and open source models can start to catch up to large proprietary models like ChatGPT.
From my understanding GPT 4 is still a huge model and the best performing. The other models are starting to get close though, and can already exceed GPT 3.5 Turbo which was the previous standard to beat and is still what a lot of free chatbots are using. Some of these models are still absolutely huge though, even if not quite as big as GPT 4. For example Goliath is 120 billion parameters. Still pretty chonky and intensive to run even if it’s not quite GPT 4 sized. Not that anyone actually knows how big GPT 4 is. Word on the street is it’s a MoE model like Mixtral which run faster than a normal model for their size, but again no one outside Open AI actually can say with certainty.
You generally find that Open AI models are larger and slower. Wheras the other models focus more on giving the best performance at a given size as training and using huge models is much more demanding. So far the larger Open AI models have done better, but this could change as open source models see a faster improvement in the techniques they use. You could say open weights models rely on cunning architectures and fine tuning versus Open AI uses brute strength.
We should’ve known this fact, when we still have those input prompt voice operators that still can’t for the life of it, understand some of the shit we tell it. That’s the direction I saw this whole AI thing going and had a hunch that it was going to plummet because the big new shiny tech isn’t all that it was cracked up to be.
To call it ‘ending’ though is a stretch. No, it’ll be improved in time and it’ll come back when it’s more efficient. We’re only seeing the fundamental failures of expectancy vs reality in the current state. It’s too early to truly call it.
It’s on the falling edge of the hype curve. It’s quite expected, and you’re right about where it’s headed. It can’t do everything people want/expect but it can do some things really well. It’ll find its niche and people will continue to refine it and find new uses, but it’ll never be the threat/boon folks have been expecting.
People are using it for things it’s not good at thinking it’ll get better. And it has to an extent. It is technically very capable of writing prose or drawing pictures, but it lacks any semblance of artistry and it always will. I’ve seen trained elephants paint pictures, but they are interesting for the novelty, not for their expression. AI could be the impetus for more people to notice art and what makes good art special.
To call it ‘ending’ though is a stretch.
That’s only the title and it is only referring to the hype ending, not development of the technology:
Eighteen months later, generative AI is not transforming business. Many projects using the technology are being cancelled, such as an attempt by McDonald’s to automate drive-through ordering which went viral on TikTok after producing comical failures. Government efforts to make systems to summarise public submissions and calculate welfare entitlements have met the same fate.
If you read the article you’ll find the author is not claiming we have universally reached the end of AI or its hype cycle yet:
A Gartner report published in June listed most generative AI technologies as either at the peak of inflated expectations or still going upward.
There’s a whole section of the article dedicated to answering why this is the case, too. I recommended you read the article, as you seem to have misinterpreted it based on the title.
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.
So how “intelligent” do you think the amazon returns bot is? As smart as a choose-your-own-adventure book, or a gerbil, or a human or beyond? Has it given you any useful life advice or anything?
Doesn’t need to be “intelligent”, it needs to be fit for purpose, and it clearly is.
The closest comparison you made was to the cyoa book, but that’s only for the part where it gives me options. It has to have the “intelligence” to decipher what I’m asking it and then give me the options.
The fact it can do that faster and more efficiently than a human is exactly what I’d expect from it. Things don’t have to be groundbreaking to be useful.
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.
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.
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.
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.
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.
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.
