And I think this energy-shaming whether that's AI crypto or something else is just a wrong approach. If somebody buys energy he can use it for mindless entertainment if he so pleases, why focus on datacenters and not Disneyland?
I think we can leave Disneyland aside and talk about the content of the article, and whether AI companies should receive the gentle treatment they have received: premium access to water, power, and money. Sure, they can buy it, but what about the people who have to absorb the externalities?
Then you end up with luxury villas with swimming pools in the desert while farm land dries out and people die of thirst. If you grow a system against stiff bounds you will get hard saturation effects. And that means you can't have it all in such an environment.
Now using that drought-example I think it is reasonable to say your pools have to stay empty, priority 1 is drinking water and priority 2 is farm land. And if you are the type that would like to fill a pool in that hypothetical situation you probably deserve to be shamed.
Thanks.
Is every word generated by ChatGPT a word which would otherwise have been typed by a human writer?
Where is it all taking us, friend? Most people are not honest enough with themself to understand the bigger picture, nor are they selfless enough to give a darn to sacrifice something of their own benefit to help the whole.
My mistake is mistaking high karma for intelligence. Wrong again!
~1-3% of global energy consumption is a paltry price to pay for what digital technology can offer us. Do we waste a lot of it clicking ads and watching Netflix? Yeah, a lot of people do, and I take issue with that, not... data centers.
GPT: Did you know that octopuses have three hearts and their blood is blue? Two of their hearts pump blood to the gills, while the third pumps it to the rest of the body. Interestingly, when an octopus swims, the heart that supplies blood to the body actually stops beating, which is one reason they prefer crawling to swimming—it’s less stressful on their system! Their blue blood is due to hemocyanin, a copper-based molecule that is more efficient than hemoglobin in cold, low-oxygen environments.
Me, after researching and corroborating each claim, which you should always do for any source: Wow, I didn't know some of that! Thanks for sharing!
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This was no different than googling "interesting facts about octopuses" and skimming the first few links. And if I'd done that, you wouldn't be claiming that I didn't "gain knowledge". But by all means, commence the mental gymnastics that "prove" I haven't gained knowledge. Perhaps because I had to corroborate the information? No, you should always do that for anything. But I'm eager to hear your explanation for what really happened.
If only there was a test to find out if the person using it was a moron. Then it might be useful. Of course, Dunning-Kruger dictates that no one using such a system would ask such an important question. Also, the world is obviously well short of the necessary training data.
I've got yer real intelligence right here, dude. Ask away. I even know how to tell you that your question is a waste of time.
I can't tell if your deliberately pretending like I didn't give you a toy example or if you truly think I sit around querying animal facts all day.
> I even know how to tell you that your question is a waste of time.
The only waste of time has been attempting to engage with you intellectually, when all you're really interested in doing is "proving" that you're right. But if you're just going to keep moving goalposts we can just end this discussion here.
There is a force inside us that seeks to convince us to undermine our own selves.
Wetware >>> software
But who cares? I'd gladly pay for something to do in minutes what would've taken me hours. It doesn't need to be as efficient as my body in the same time frame.
Meanwhile, a decent portion of my own peers echo my sentiment, mainly programmers. I don't have to argue with them. Instead I can analyze and improve methodologies with them, collaborate, and generally have positive, stimulating and thought-provoking conversations. You can imagine which of the two kinds of people I typically choose to engage with. Especially on a forum like HN, where meandering circular arguments are discouraged. Maybe the happy medium is just blogging about it. I really do want to blog and I'm trying to learn about it and make more time to write, but HN does already scratch a lot of that itch for me.
All in all... the only self-proclaimed AI critics left at this point are people who have either chosen to be deliberately ignorant about the benefits of this technology, or who lack the depth of skill or interest to find useful things for these models to do for them.
I've taken on increasingly ambitious projects because I now have little programmable assistants embedded into the UX of various tools that I work with. They're not perfect, sometimes we argue, sometimes I just have to turn them off for a few hours, roll up my sleeves and work through the muck. But at a high level, the benefits are huge. Modern adequately-trained LLMs are amazing at project scaffolding, architecture, design, documentation and generally being a backboard for my thoughts when I have no one else around to converse with. My typical day is highly varied and while I have specialist friends, none of them match the generalist abilities of some LLMs.
I am generally skilled enough with informatics to know when I need to corroborate or discard information (basic research skills learned independently of LLMS) and so "what if it's wrong / how will you know?" has always been an irrelevant, false paradox parroted by people lacking those basic skills which should have been taught in school. These same people already struggle with finding accurate information with search engines or libraries, it's no wonder they struggle with something like an LLM which can be very confidently wrong. With programming, it's easier. You can keep the model focused, break down tasks into tiny pieces, solve and write tests for them one by one. It encourages you to write clean, modular, self-documenting code.
With other topics like science, art and mathematics, I'm still consistently impressed by 4o and o1's capabilities, despite glaring shortcomings. I know you want very specific instances but I'm really ADHD and so I am not exaggerating when I say my conversations with LLMs are highly multidisciplinary and before a just a couple years ago, I had a hard time organizing some of these more esoteric and complex systems in my head. My typical conversations would probably be of little use to someone.
I'm now working in parallel on several scientific and mathematical inquiries, as well as a few ambitious engineering projects. My time spent researching has greatly reduced, as I can get up to speed very quickly by dumping some articles into an LLM conversation and asking it detailed questions, asking it to provide thought models, etc., getting maximal value out of the information by immediately dialing into areas of interest.
Essentially... I've always felt naked without a phone or computer around to google the random questions and ideas I have throughout the day, and I consider the internet and search engines integral to who I am and the knowledge and skill I've attained. Now, instead of a search engine, I feel naked if I don't have a well-trained chat model around. I have developed a similar dependence as I have to search engines (I panic when I have a question, slap my leg and realize my phone isn't on my person) but the tradeoff is that I feel my thinking and doing has been augmented.
And even if transformers/LLMs hit a dead end in our lifetime... after all, many of these techniques are rooted in ideas from the 60s-90s which simply "didn't work" at the time, only to become relevant again after sufficient compute is accessible... I am eager to see what the next 40-odd years of technology brings us.
Regarding reproducibility, I think it would be extremely beneficial for people to be shown how AI can help them with day-to-day things, but I have a hard time recommending LLMs to people who I know aren't equipped with enough research skill to avoid harming or hindering themselves from incorrect advice. And my experience with these models is mainly academic and related to engineering, so I just don't have much to offer normies, but I'm sure other people have made lots of ground on that.
Personally I think chat models in their current form just aren't natural or functional enough to appeal to the average person. I think the average person will see the biggest benefits from tooling built around modern models. I'm working on one right now, a grid-based component system which essentially lets you create tiny little tools that can speak with each other, and orchestrate them into one or more domain-specific UIs. Users can share and adapt apps or individual components, or create production-ready in-house creative or business tooling. Social productivity programs like this, as well as calendars, sheets, etc (all buildable within my app in minutes with a handful of stock components, with built-in multi-user support) will unlock the full power of advanced natural language models. Hoping to debut it sometime next year.
I’ve used ChatGPT to teach myself several programming languages, guided me through learning how to design circuits for my own projects, among many other uses. Professionally I use LLMs to scale processes for detecting fraud, financial crimes such as money laundering and sanctions evasion, amongst other things at a much greater precision and recall than other models and human investigators at a much larger scale (we still use those other vectors as well but the major break through for quality and scale was LLM hierarchical agents). We’ve done the analysis and it’s demonstrably improved our business and made the financial system in our corner safer and more stable. So, I dunno my friend. I think you’ve perhaps got a bias based on your inexperience ?
I don't really have an answer to my own efficiency question and will likely never will. But for every company like yours, there are companies who blindly introduce LLMs into their product for no other reason than hype. Spotify, your recommendations ("DJ") are no better because you used LLMs; collaborative filtering worked just fine for music. Meta/FB, your value prop to me as a social network hits rock bottom if my friends are not even sharing real-life experiences. Google, you're already on thin ice with all the SEO spam but your value decreases even more if you give me hallucinations for summaries. Hence, sorry but my outlook isn't rosy.
However you need to understand that what you said is wrong. Many of these things can’t be solved with a simple query because a query returns a blob of data and the human has to search for the information in a set of documents possibly containing it.
The UX of “I have a question” and the answer of “ok here’s a likely direct answer that’s comprehensive and comprehensible to which you can ask any follow up” in unbeatable. It’s what AskJeaves.com was as one of the first search engines but couldn’t achieve because NLP was so shitty. The fact it might hallucinate - which is very unlikely in a frontier model on a pedestrian question about common knowledge that can likely be answered by a search query since it’s training set likely contains a lot of examples of the answer and it can supplement with a basic RAG against a traditional search engine - is not even that relevant because unless you’ve not used modern search engines recently it’s almost impossible to get a straight answer from search due to SEO.
So the reality to your rhetorical question is “every single one of those questions only an LLM could have solved.” Because they’re the first real NLP system we’ve invented that can give coherent answers to the question.
Very few search engine queries are seeking documents. Most are seeking an answer to a question. A search engine has been the best we could do until a few years ago to answer questions and the user experience is fairly shit and rapidly deteriorating due to economics. It’s like asking a professor a question and being answered with a bibliography but over time realizing the professor is being paid under the table to stuff the bibliography with advertisements and the rest of the bibliography was replaced by other ads pretending to be source material and the professor just looked at the title to select them. Not only is the answer not an answer - it’s a list of stuff to read which might contain the answer if you read it all - it’s increasingly unlikely the answer is even there at all. You can argue “but the professor answering with a bibliography ensures you learn a lot!” Which is fair but people usually just want a direct answer and would sooner go to the cool professor down the hall who answers your question and gives the bibliography as citations (as modern frontier LLMs like ChatGPT and Claude do).
The fact the cook professor dabbles in mushrooms too much during office hours is unfortunate and the school administration is working on that. But it’s useful enough people literally don’t care.
For use cases where the cost >> value like collaborative filtering - well, fine. LLMs won’t be used there because the value doesn’t hold. We are only two or three years into this and there’s a lot of just dumb stuff because no one knows what will stick. And LLMs will not be the answer to everything. But I think they’re going to be increasingly more powerful simply because we will defer the right algorithm to the problem and the LLMs will be the glue of language and abductive reasoning that makes something useful, such as information retrieval, usable. And that alone is more valuable than Google’s search engine. And that is not the only use - as I’ve seen directly. So yes LLMs won’t be grand masters at chess beating alpha blue. They don’t have to be. They can be the user interface to Alpha blue and alpha blue becomes more usable immediately and much more valuable.
The hardest problem in CS isn’t cache coherency or naming things or off by one errors it’s making useful things usable, and among all the other useful things LLMs have done, they have absolutely solved the hardest problem of all - making all the useful things usable.