There’s a shocking lack of introspection in the AI hype bubble — we are continually promised world-changing advances, but no one is very clear about which direction these advances are pushing us in. Even ‘AGI’ is nebulous and undefined at best.
Where is the focus on the foundational principles of growth? The author writes that endless growth is impossible, but that is only true in the absence of novel breakthroughs. We need creations and inventions which bring us the future we once imagined: limitless energy and abundance.
How have we completely lost focus on new physics or foundational sciences and devoted the smartest minds of our time to transformers and data scraping? There is some critical human component which is completely absent here.
Where is the renaissance, and how do we make it happen?
I think part of the issue is that a lot of the 'big' advances in physics nowadays aren't constrained by brain power as much as they are by cost (or rather, money allocation), and due to the corruption of the scientific funding system in most rich countries, waste is extremely high, severely limiting the money actually going into science. Eg to make radical progress in physics, we need more sensitive telescopes, larger particle accelerators, more efficient space missions, more actual iteration on fusion reactors etc.
All these things cost a lot of money, yet the majority of it gets eaten by systems designed to minimize the money actually going towards the research work. This isn't even really getting into how the researchers are paid.
I agree that AI researchers are concerningly blasé about what they're actually aiming to do. AGI is poorly defined, and the only negative externalities they seem to pay lip service to is culture war stuff. There's near zero consideration for the real negative externalities they're perpetrating upon the world. I think partly this also manifests in how they interact with fields where even older AI developments would be useful. At the physics lab I work at, I've found that many scientists have a negative impression of AI 'researchers', where they don't care to understand the problem they're trying to solve, preferring to just treat everything - even things where the solution space can be constrained by scientific understanding - as a black box. It's almost the opposite of science in approach.
It’s still surprising to me that not one decabillionaire has funded a good basic sciences institute. With a few hundred million dollars (paid primarily to salaries) and an extremely rigorous selection process, one could establish a highly prestigious research university and bring the brightest minds together in one place. Just go from there!
Arc is a cool concept, but these initiatives are ultimately handicapped by their reliance on the infrastructure of "grants".
Grants suck. What matters is having smart people together in one place.
If you pay smart people enough money, and offer them enough insulation and protection from academic politics, they will come to your institute. Grant-making to people at institutions which are already politically and socially captured does not fix the root issue.
I am studying physics now (with a CS and math background) and I feel obligated to get up to date on AI and to develop a good working philosophy of how it can be meaningfully used in scientific work. Not that all "apply machine learning to X" approaches aren't interesting, but I lack enough understanding to know whether these are popping up everywhere because people feel obligated to apply new methods. For example, the Fourier transform is deep and interesting and there are libraries and standards and ways to transform different objects over clusters, etc., but I wouldn't say good scientific research is about finding a place to apply a Fourier transform (maybe :)). I am new to this though.
I'm nearing the end of my PhD in computer engineering, but because my uni is associated with a big physics research lab, my work ended up being pretty involved with physics. I've had to go through a similar learning experience regarding how to meaningfully apply AI (and computation in general) to perform meaningful science.
I agree that simply applying a fourier transform isn't meaningful research, but there are fields like fourier optics, where you're effectively just doing that, with the goal of modeling wave optics.
The kinds of issues I had in mind wrt poor uses of AI are things like inverse problem solving methods which train a model as a black box and neglect to involve physics informed feedback, so the results are of questionable value, or hyperparameter estimators which need an unrealistic amount of data or produce blatantly unrealistic estimates because they're naive and the AI "researcher" has no interest in actually understanding the physical logic to the parameters.
My research hasn't been with AI, just with simulations of certain physical systems, and one thing that was constantly drilled into me by the physicists was to be really careful about how far I stretch computational techniques, because ultimately none of this digital stuff means much if it becomes detached from reality. It can even cause actual damage because sometimes expensive purchases will be made on the basis of simulations.
Thanks for the pointer to Fourier optics! I mentioned Fourier because it is something I still don't "get" even after studying and using these methods.
I am still learning, but I have so far only applied inverse methods to systems where the physics is well-defined and the model already accepted as "real". For example, CT scanner algorithms which model the photon counters and attenuation through matter (although this can get complicated...), with the output being data in well-defined format (like the distribution of matter). I do see people using inverse solvers or AI to derive something which isn't an "image" but a model of the physics (does that make sense?). The extreme version of this which I have seen once before, is a paper using AI to extract algebraic forms of PDEs from videos of fluids. To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way? Apologies if this is misinformed as I am still outside the research community, and don't very actively read papers.
Currently I am trying to learn background to understand electromagnetic properties of materials with an interest to apply this to computer graphics. I eventually want to learn what processes are currently done for extracting material properties from scans, etc., along the lines of the classic gonioreflectometers. In this space, from the papers I look at, it looks like AI is unavoidable and I am sure an extremely useful tool but I definitely want to be careful not to get lost in misunderstanding.
>To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way?
The conclusion that I reached is that you need to understand at least enough to be able to handle basic scrutiny from people in the same field. So, for example, if you come up with an AI for deriving some model of the physics of a CT scanner, you should at least understand the problem you're tackling enough to make meaningful comparisons to any popular competing approaches that might exist. That understanding should automatically encompass things like understanding how your variables are bounded/how they behave.
A contrived example could be with an AI intended to guess 2 prime factors of a given number. Imagine someone making such a tool without even knowing what a prime number is, where the only benchmark they use for correctness is if its outputs fit the dataset of products of prime numbers given. The tool would be clearly useless, because while to the AI researcher it'd seem fine that it outputs 36, which is close to the intended 37, to anyone who knows about prime numbers it'd be an immediate warning sign needing further elaboration and explanation. The researcher could protest that as an AI expert, they don't know anything about prime numbers, but that doesn't really matter in research.
Similarly, due to my non-physics background, while I was (and to an extent still am) given significant leeway in terms of my ability to respond to detailed physics questions about my work, by now I am expected to be able to handle questions about things that are common knowledge among physicists in this field (so, for example, knowing what performing a Fourier transform means in our context, understanding the high level functioning of the key components of the machines we work with, having some intuition for when a simulation result is unrealistic etc). If I were to talk about how I've simulated some of these components, but drew a complete blank when asked how my work improved upon the common knowledge means of simulating them, I'd obviously look like a complete idiot :P
It may seem like a pretty low and obvious bar, but surprisingly I have seen many cases of researchers who didn't even care about reaching that level.
This is the consequence of giving up privacy and agency very easily. At some point universities stopped encouraging independent exploration and started minting out industry ready vocational humans eager to join the rat race. That needs to be fixed but at this point there are not many direct beneficiaries who would want to address that problem so this is going to get worse for a while.
> At some point universities stopped encouraging independent exploration and started minting out industry ready vocational humans eager to join the rat race.
Back then we had industry research labs. There was Bell Labs, the Frythe, DuPont Experimental Station, BASF's Zentralforschung, and nowadays we have the 737-MAX.
Good catch. They definitely went the way of Boeing -- more interest in pocket lining than research. I do wonder if it went to zero or just a major scale back.
And to what extent are those labs doing the same kind of fundamental research that Bell Labs was doing back in the day?
Sure, they wanted to produce things that Ma Bell could monetize—but it was abundantly clear that they were not being micromanaged to that effect. Today, at least from the outside, it seems like any industry-funded research is cut off the moment it looks like it's not going to produce a clearly-profitable product or service within the next few years.
"10x more grad students" != Independent exploration. Limited opportunities means limited bandwidth to fail, this means a lot more incremental and conservative research. There is definitely a lot less research freedom and a lot more conformity and race to publications.
You appear to be conflating a few different things here.
The primary thesis of your original post appears to be universities' shift toward getting people ready for careers that require advanced knowledge (and away from pure learning/research).
That has very little to do with research freedom and the race to publications. Those are both real phenomena, but a) it is arguable whether the former is actually a problem, and b) they are only very loosely connected.
The number of grad students being trained, by contrast, is much more likely to be related to the degree to which universities still value and practice learning for learning's sake—although I think it is somewhat less so than vkou wished to imply, since there are, in fact, plenty of non-academic jobs that do require or prefer a Masters or even a PhD.
Your premise is that if universities are training more grad students they are valuing independent exploration and encouraging learning for the sake of learning. I don't think it is true. If I am training 100 people to think alike, it is not valuing independent research and exploration. It is herding. Current grad students are "trained" to align with interests of few research labs/few personalities with lot less eccentricity and originality.
The point I was trying to make is this, the fear driven conformal behavioral training or we can even call it draining in this case during the formative years is making many people lean towards their zombie mode and is killing their critical questioning faculty. Even simple decisions like what phone I would use are shaped by the green tick marks and peer perception.
Okay, everything you say is true, but that's not something under the control of universities.
Change everything about the research grant system, and increase funding for it by 10x, and it'll address your concerns, and universities will happily make even more grad students. They aren't currently the bottleneck, because they aren't the sources of research funding - they are just a landlord (with reasonably okay terms) for the research lab that spends that funding.
You seem to characterize universities as passive players. Who are the folks sitting on the grant committees? Who shaped the current system? Universities are there to shape societies and to lead in the first place. Not to seek a perfect system to play a passive role.
AI is just evolution of Big Data. It's not transformative, like the Internet was. The Internet was a new medium.
A more advanced Hadoop will not change your life. It will not change the way you read, listen, watch, communicate (fundamentally). The Internet did.
Especially at a time when the "legacy" internet is falling apart, a new way to find information is rather a necessity than some breakthrough.
The "good" AI needs to fight it out with the "bad" AI before we see if the net positive is even there. A podcast can be translated by AI into a different language, you say? The price is super-realistic fraudulent phone and video calls from your "family".
I'd argue this take is a bit dated, as if you're looking at ML/AI in terms of training of data.
If you look at the enablement of features, AI is a new (and, currently, often flaky) medium.
It would be analogous to looking at the Internet as limited to making it simpler to order from a catalog over the phone. While technically true, there is more that it can do besides.
Much like we don't use copper in our walls for dialup anymore, I don't anticipate the initial architectures of LLM and MoE to last terribly long, but they do enable proving that the concept works.
Current AI/ML is in fact a reflection on training data. Change the manifold, and change the response. It's unfortunate that of course the data is the entire internet.
GP mentioned that the current slate of transformer based AIs are not transformative in the same way the Internet was. Rather it's more of a triumph of data engineering practices.
OP disagrees with GP. OP's main thesis is that AI enables a lot new applications. OP claims that GP is simply looking at it as if it were training data.
I stated that current AI techniques ARE indeed just reflections of the data used in training. I agree with GP that the current "AI"s are simply not transformative in the same way the Internet was.
If you change the training data for the current generation of AI, you get different behaviours. The training data forms a manifold - which you can think of as a landscape with features forming valleys and hills. What the current generation of AI does is that it tries to find a shape that fits the landscape - think of it like taking a very large sheet of cloth to cover a landscape. The stiffer the cloth, the less well the cloth fits to the landscape. The "stiffness" of the cloth is the amount of parameters that a neural network has. Modern deep nets are highly overparameterized - imagine a very soft pliable cloth - of course it fits to a landscape well.
So if you have a different training data - the neural network will fit to this different landscape as well. Hence the response will be different.
It's unfortunate that the training data is the entire internet for a few reasons:
1. Only the rich can train a vaguely competent AI. You're at the whims of those well-resourced enough.
2. There's no "alternate" training dataset anymore. (Though a clever thing people at OpenAI are doing are Mixture of Experts models, where you train multiple NNs using different subsets of the full training set, so you get multiple competencies)
But you are specifically talking about one type of AI, which is a generative language model. There are tons of other AIs with different applications that do not need to be trained on the entire internet. You have computer vision which separates in object recognition, classification, OCR, etc; you have audio which has text-to-speech (and reverse), music generation, and all sorts of other things; machine translation; sentiment analysis (I won't list all the categories in hugging face but you get my point). These are not differentiated merely by 'training data' to my understanding, so that's why your comment didn't make sense to me.
Calling all AI LLMs is like calling all of the internet the web. Of course if I am mistaken, corrections are welcome.
> But you are specifically talking about one type of AI, which is a generative language model.
...Because that's easily and widely understood to be what people mean in recent times when they're talking about "AI", referring to the stuff that's in the news, without further qualifiers.
If you want to talk about something more specific, you are going to need to be explicit about it, rather than expecting everyone else to understand what you've got in your head without actually saying it.
This is like saying "but "crypto" means so much more than just cryptocurrency! there's a whole cryptography field out there that does lots of good stuff!" It's true, but it's not helpful, because it's ignoring the obvious (at least to the other participants in the discussion) context. In this particular case, the context should be even more obvious because it's so clear that's what the article is talking about.
It doesn't matter how knowledgeable and precise the people you're talking with are; you still need to communicate clearly about what you're actually talking about.
I disagree with your take here. While LLMs also enable significant functionality, we of all categories of service providers should be clearer when we are referencing the specificity of the LLM fad or the adoption of AI to enable services generally, which is the vision that drives the excitement behind the LLM fad.
When people read our comments in 5 years, they will read "AI" and have a much broader topical take than the present excitement about LLMs.
I agree. There are other types of AIs with different applications that do not need to be trained on the internet. The examples you have given however, are examples where the deep nets are extremely data hungry.
Take computer vision for example - a "hello world" version of object recognition would use ImageNet, which is 14 million hand annotated images. Or Cifar10 which is 80 million images. That of course but sets the stage for training data differentiation. Google's image recognition algorithm is far superior to other search engines'. Why? Because of Google's data set.
Any Tom Dick and Harry can go create their own image recognition AI and train it based on all the public datasets (COCO, CIFAR, ImageNet) but that's considered pretty baseline nowadays. The differentiator is what _other_ datasets you have.
Different datasets yield different results. It doesn't matter the network. More data is better (usually).
In my opinion, your response is tautological and not related to the point I made that existing AI is good enough to start building applications and functionality.
It was good enough in 2015/2016 for me to run a startup that allowed people to program in natural language. We even had paying clients though eventually none could stomach the $2000 per month for incremental/on-line training costs.
The only real difference between then and now is that OpenAI's models are significantly better than my models from 2015, and they have that because well, they can afford to pile on more data. TBH, I never even considered using a large proportion of the whole internet as a training set as even remotely possible due to the sheer mind boggling costs.
Even now, to go through about 10% of The Pile would cost me way too much money.
And it's not really cost effective - as well as being an epicenter of culture wars. "Your AI is woke! Your AI is fascist!".
THIS part of the AI sector is just a giant pyramid scheme - impress the investors so they shovel trillions your way. That's not exactly new in Silicon Valley - keep the valuation of a hot potato going up until someone is left holding the bag.
AI's most useful applications are not being a generalist.
> How have we completely lost focus on new physics or foundational sciences and devoted the smartest minds of our time to transformers and data scraping?
I'm not smart enough to be a physicist, but I like listening to Eric Weinstein[1]. He thinks string theory is essentially a dead-end honeypot doing exactly what you describe with our smartest minds.
If Weinstein could tone down his conspiratorial edges and focus on the primary substance of what he’s saying, I think he would be more effective at achieving his goals.
I like his podcast appearances — they are fun to listen to — but the solution to political machinations destroying established institutions is not to focus on the politics! We need to escape that frame entirely, and focus instead on building new institutions that are sufficiently reverent of smart minds and brilliant people.
Yes, we need new physics! We get there by escaping the current career trap which stops brilliant people from trying new approaches. Give the top, boldest, most daring researchers an alternative to tenure — $10m vested in a secure position at a new research institute. Then they won’t have to be scared of string theory boogeymen.
Tech innovation doesn't really happen through grand planning and "focus on the foundational principles of growth." It happens through lots of people trying to make stuff.
Many people think about the diretion this is going but tend to get dismissed as singularuty cranks if they think far enough ahead.
This is a good insight, but it needs to scream the point louder. AI in the form of an LLM is a tool. We’re doing extractive things with all of these other tools as well. It is not AI that is the problem, we are the problem.
After social media anything goes; the rest are just extensions of the mind control social media made possible. Crypto nor AI would’ve been the hype they are without the current depressing iteration of social media and popular ‘influencers’ promoting their ‘businesses’ to somehow legally take your money. Of the only communities still out there that have some sort of free thought, like HN, even on HN, people are defending TikTok for being ‘great for discovery’. Even though it’s likely a Chinese state weapon and responsible for controlling a billion+ people their every waking (and probably therefore sleeping) moments. With seemingly smart people defending garbage like that, what does the rest matter? Who is going to listen to reason while scrolling through endless empty money grabs (which somehow they believe are adding actual value to their lives)?
There is an article on the HN homepage about leaded petrol lowering IQ; I bet that in 100 years TikTok etc will be considered far more detrimental than that.
I've come across countless great artisans and small businesses I would have never discovered otherwise, all thanks to social media. I've spent tens of thousands of dollars on products from these small businesses.
I will go so far as to say that millions of small businesses exist because social media provides them with global reach. As an example, if you forge, say, artisan chefs knives made out of meteorite, no one in your town/village/etc likely cares, but there are thousands of people worldwide that will gladly buy from you.
TikTok/Reels is simply the next evolution in information condensation. It's entertaining but also educative. I might not care to spend 20 minutes learning about the intricacies of, say, forging Damascus steel, but I'll gladly watch a couple TikToks on it.
The problem with condensed information is that it is extremely lossy. Sure, you may learn something with the bite-sized entertainment offered by a TikTok video or Instagram Reel, but the deeper substance is not there. It’s like saying that by eating 200 individual cheerios, or maybe 200 individual pieces of all kinds of cereal, you’ve eaten a full meal. But you haven’t, really.
Learning and understanding requires expending some kind of effort, and easy access to condensed information actually precludes you from experiencing that. If we don’t digest what we learn we are no better than LLMs — mindlessly regurgitating small bits of non-integrated information back and forth to each other.
That’s well said. I mostly use LLMs to help me learn faster, not to do my work for me. There’s a lot more value to applied learning but it does require a lot of time and effort.
There are plenty of topics you can get the gist of in 30-60 seconds.
When the alternative is not spending any time learning those topics at all, I'll take the 30 seconds.
I'm not going to spend minutes to hours learning something I'm barely interested in to begin with. As far as general knowledge goes, TikTok/Reels are great.
TikTok is another everything platform. If I used it, I imagine I could take any stream of thought and somehow warp it into a reason to use TikTok. It is very easy to pretend that the platform doesn't matter and it is just a carrier to provide the opportunity to engage in new thoughts or interactions. I made a huge mistake allowing reddit to be my go-to. I did not think twice about going from thinking "I want to study insects" to reading the top 500 posts of all time for insects hobbyists, learning almost nothing except apparent insider-knowledge and insect drama, feeling burned out, and continuing on from that thinly veiled meme-cynicism-comment-meme-article-comments new-tab new-tab new-tab cycle (in the context of insects) to the rest of the reddit machine. Maybe something is wrong with my brain but I'm going to assume that TikTok is actively trying to draw in every thought and intention into it's algorithm, and leave someone scrolling TikTok rather than continuing with whatever it is they intended to do.
> a compulsion to demonstrate the possibility of unending growth which is, frankly, impossible
It's only confusing when seen from the bottom. The purpose of growth, seen from the perspective of the wealth-weighted-people served by capitalism, is to deliver a return on capital, to pay them for being rich in proportion to how rich they are. It's possible for everyone to win if the growth is real, but in a pinch fake redistributive growth will work (see: Cantillon pump). In either case, the expectation of growth rationalizes the outcome and the pulling of any policy levers needed to get there.
First sentence of the article: "It's easy to pick on AI, because, well, it's costing a whole lot and providing, at best, dubious benefits."
"At best dubious benefits" ??? I feel like there is a whole essay missing there, because my analysis of the situation is that it provides at minimum minor benefits to million of people using it on a daily basis, and at best it saves lives to some of them.
That it not to say there is 0 harm anywhere; we definitely need to do some cost/benefit analysis. But the initial premise is so obviously flawed that it's hard to consider the rest of the arguments.
LLMs/ChatGPT is saving me a lot of time, and it’s actually a very good tool to find a jumping off point for research.
Copilot saves me quite a bit of time writing documentation and boilerplate.
For hobbies AI is nice because I can ask my beginner questions and get results, where often you end up on low quality blogs or reddit threads arguing about things you shouldn’t bother thinking of when starting out.
I ask GPT things all the times and it tells me how dangerous they are. Like, everything I ask there is a warning about how I need to be careful doing it.
Maybe someone somewhere asked GPT how to cook pasta and had no idea boiling water was hot, and GPT's warning not to get boiling water on themselves saved them from massive burns?
I built a pretty complex home automation system using a stack of raspberry pis and python code that largely came out of GPT4 and copilot. I had no previous python experience. Just came up with the idea as a way to get GPT4 to teach me something.
This. I think the problem is that its hard to say that its increased all performance by 50%. But every day I hear of a new use case that's turned a single persons life around completely.
I know a number of people who find ChatGpt convenient and even fun. But I don't know of "life changing" instances. What are these instances?
I mean, the phrase sounds like "I started my business with ChatGpt", which is true of all the recent trendy things and so would be less unique than people imagine or "ChatGpt cured my depression", where someone really shouldn't do that.
I'm so done with this ridiculous techno-pessimism trend.
> AI [is] costing a whole lot and providing, at best, dubious benefits
A wild take. Technology like GPT-4 being available today was unimaginable to us just 5 years ago. Our company saves hundreds of thousands of engineering-hours a month using generative AI. It has let me automate the humdrum boilerplate bullshit out of my workflows, both professional and personal.
It's also essentially replaced Google for me. Just today I've already used GPT-4 at least 5-10 times. Immensely useful, the $20/mo is a drop in the bucket for the value it provides to me.
> Ride-hailing services? Those flouted regulations, hooked users on cheap prices, drove down wages and made employment more precarious.
Has this guy ever stepped foot outside a major city? Has he ever gotten drunk and needed a ride home without friends in the immediate vicinity? Uber/Lyft provide immense value that simply wasn't there before.
Has he even ever spoken to any Uber drivers, or ex-taxi Uber drivers? Does he realize that people often prefer working for Uber as opposed to a "regular, stable job" and that customers actually like using it over taxis? Compared to taxis, Ubers tend to be cleaner, the drivers tend to be nicer, and it's just a easier, better experience.
The taxi cartel is even more powerful and "extractive" than Uber in many cities, and they exert undue market power, especially at airports and resorts, not Uber.
> streaming platforms that are actively rolling back up into something analogous to cable
Spotify still provides me with far more value/$ than having to buy every single song from iTunes or whatever. Apple TV is still quite useful to me and I far prefer it to ad-riddled cable with set showtimes for what you want to see.
Tale as old as time: people seeing the past with rose-tinted glasses so they can have a pessimistic "hot take" about today vs the good old days.
> getting mad at the crowd for saying it is not likely to help
Except the emperor does have clothes, this pessimism simply isn't based in reality, it's based on a rose-tinted view of the past and a misunderstanding of why people use these services over the older alternatives.
The author seems to have such limited life experience that he can't even fathom why someone would prefer Uber over the taxis of old, or find great value in using generative AI every day, or prefer Spotify over something like the iTunes Store. The value proposition is so obvious and clear, and yet the author can't even see it.
More often than not, we do not try to solve the problem, we go with a shiny new thing that bring its own issues to the table. While we can discuss about tradeoff, in the currently unregulated tech landscape, new things have destructive impacts on society because of their scale. Facebook has been ignoring the effects of social medias on teens for a long time and everyone knows that only a lucky few makes money on Spotify.
Cabcharge and its associated monopolies in many cities is a blight that I am glad uber had a go at. "Flouted regulations" I hate when people associate words on paper, often well past their usefulness, with something physical or tangible or useful that can be damaged.
AI is doing so much to help a lot of people. Give it 5 years and hating on AI is going to go from mainstream to the most boomer of takes.
I would not like to think of "techno-" pessimism, just pessimism (we are past the point of making a sci-fi distinction between reality and computer-using reality, I think, especially since people walk around using a computer). I personally am not too focused on AGI or existential threats, or the loss of jobs, or the political changes, opportunity for exploitation, scams, AI grifting, misdirection of funding. It would be easy to name that as pessimism as it is just a little bit removed from daily life, you can go about your daily life and really forget about it unless you are ethically/politically or personally concerned.
One thing is obviously immediately concerning though. It is unbelievable how fast ChatGPT has been normalized. Within a year the new people have come to expect something like ChatGPT to always be provided for them, for the rest of their life. Of course people are rushing in to define what that something is, and provide an AI service which people will use/misuse like the first page of Google results. I think almost certainly this new "first page of Google" component of people's thoughts will be abused. Do you think the first page of Google is good and formed by good incentives and empowerment of the user, considering how much of a cultural force it is (Googling something being just a normal part of your thought process)?
(I do have limited life experiences. Anecdotally, I know a high school teacher who has been teaching for almost 40 years. Over time students went from hiding their phone in the back of the class to scrolling through it in class freely, vaguely attempting increasingly simplified homework, and have now jumped on the new cool AI website to complete even basic tasks. This is extremely normal, why would a kid not use it?)
> Do you think the first page of Google is good and formed by good incentives and empowerment of the user, considering how much of a cultural force it is (Googling something being just a normal part of your thought process)?
Yes. Google is an immensely useful everyday tool available to everyone for free. I think the negative externalities it causes are laughably trivial compared to the utility provided.
Ah yup I agree, I think I am wrong to make the comparison between Google and ChatGPT here. I am not nearly as concerned about the problems with Google as with potential problems with ChatGPT (or whatever the most popular equivalent is in the future). I do think an AI service will be as important as Google in the future. I think the negative externalities this time might not be trivial considering what ChatGPT promises the user (basic Google search still is in general just a way to find some webpages or immediate info, while an AI homepage is a way to create things or communicate with others...).
Infinite growth is not possible with finite resources. I'm not sure why this is so hard for some people (cough, economists) to understand. They treat it like an actual law of physics, but conveniently ignore every time a curve that looks exponential reveals itself to be sigmoidal on a long enough timescale.
Software can be re-written on the same hardware though. Is it all technically finite? Yes. Though it's infinite in terms of human ability to use software.
A casual glance at nuclear physics points to a world where we’re nowhere near our energy production capabilities. Automation sings along. And that argument would be salient if it was required - “economists say infinite growth is possible” is a strawman argument.
"Nowhere near" is not "literally infinite". How many nuclear reactors can you build with a finite amount of matter? See other comment. I'm not sure why pointing out that economists say, believe, and rely on infinite growth is a strawman when it is a statement of fact.
Okay, then find me the economist who believes in "literally infinite" growth. If you have a hard time finding _one person_ who is both a published economist and has no grasp of the basic laws of thermodynamics, then that view is probably not representative. This is the weakest possible form of the argument, hence the "straw".
I kind of figured the rebuttal was going to be "we don't literally mean literally infinite growth forever, but our models do, and we believe our models, don't call it a strawman."
If the rate of economic growth merely slows, we get a recession. What kind of hell happens if the rate goes negative and the economy shrinks? For too long? I don't feel obligated to point at any one person who says "yes I believe growth can continue infinitely forever" because it's the most basic assumption baked into economic models on how prosperity works. An assumption of infinite growth, and assumption that "progress" is also eternal, that there is always something more.
It is absolutely not the most basic assumption based into economic models. Neither you nor I can find one single person who believes that. It is the weakest possible form of an argument you are intentionally misconstruing. This is the straw in the straw man. The steelman version of that argument is "can logarithmic growth continue for one hundred thousand times longer than the expected lifetime of our sun?". I think it's actually quite easy to say yes to that. One fun idea here is Von Neumann probes[1].
There is infinite distance between infinity and a negative number.
Assuming all growth came from logging sure. But efficiency gains are also growth. Assuming that accessible resources are a big counter down to zero is just neo malthusian thinking.
One does not need to consume additional resources for growth.
One can can instead use resources better to produce more valuable stuff (intensive growth).
The question is if there is a limit to the value of things we can produce with finite resources - and the answer is likely no - at least not in any kind of human timescale.
>a compulsion to demonstrate the possibility of unending growth which is, frankly, impossible.
I hate this saying.
Why is unending growth impossible? Nobody seems to every try to explain it.
Economic growth is generally improvement of society in some way. Usually it's about new inventions and ideas that spread more. Better ways to make things that people want, more efficient ways etc. if you come up with a more efficient fuel then that's still economic growth and the upside is that resource usage can decrease for the same benefit.
Math is essentially proven to be unending. There's always going to be something more to be discovered. Doesn't this mean that unending growth is possible?
Of course, in practice "unending" only means "for a very long time". We are nowhere near a point where we have exhausted new advancements.
Unless technological progress ends, we will continue to produce more and more valuable things. Even if the amount of money never changes or even if money disappears, growth still happens.
AI feels different to me because it's "retroactively" extractive.
Social media may be extractive if I give it my attention and data to resell. Crypto is extractive to the people buying into it and losing money. But I can avoid doing those things if I want.
AI these days takes anything I've written (from literature to code) or illustrated, and retroactively extracts value from it to be repackaged and redistributed. Even if I was proactive about securing the trademark or copyright, they can seemingly just ignore it without consequence, without me ever participating or opting in to it.
It feels massively different to have social media extract value from and image I uploaded willingly, vs AI extracting value (eg making things "in the style" of me) after training on my work against my will.
You create a highly useful website full of dense hard earned knowledge. Some people will just use that knowledge, but a few create their own sites, using what they learned.
A company sweeps up hard earned knowledge from millions of sites, with automation but not permission, and goes a long way to making those millions of informational sites redundant without any compensation.
Simplifying things obviously. But these two scenarios are not equivalent.
Why can't we agree there are differences between other human beings learning from other people and a multinational corporation learning from everyone at industrial scale, with industrial means?
Truly, your reply is ridiculous in its comparison. How you could fail to see the difference between these two things, their scales and also their qualitative differences is impressive.
I think if the ai were a free agent or something nobody would have an issue, but “the ai” isn’t really the best way to summarize the thing learning. It’s just a tool or service offered by a corporation which said corporation solely profits from.
In this case, the argument is indeed self evidently right. Trying to equate the two things after being able to observe both in the real world is like seriously claiming that a stove fire and a nuclear blast are pretty much the same thing because they both produce light and heat. Why would anyone bother convincing anyone out of such an obviously absurd claim in a practical context?
There's a clear qualitative difference. If I post a lecture on mine on YouTube and a million people watch it, great! If a training company takes my lecture without my permission and re-sells it to a million people, that's a clear distinction.
(Based on a true story, it took me 3 years and 2 law firms to recuperate royalties for IP theft)
Did you ask the people who wrote the book and articles you read to get knowledge on a subject how they feel about a YouTuber earning ad money by repackaging their content ?
Yeah, I'm unsure why learning without author consent is judged differently between humans & AI. Some folk are justifying the distinction "because scale", as if that explains anything. Why would scale matter? And what's the threshold for scale that turns freely learning into theft? I'm not being facetious; I genuinely don't see the connection.
Not lately - everything is extractive and has always been. This is the very foundation of economic development and creation. If we were against all extractive things then nobody would work for a living and our entire society would crumble.
Or maybe we'd just be dancing around fires fornicating and singing and hunting as intended. Wish "cities" were strictly medicinal hubs and the rest free to roam.
The idea of abandoning cities to frolic in the wilderness is naive and disconnected from historical reality. For most of human history, life was defined by grueling labor, scarcity, disease and servitude for all but a privileged elite.
Modern development, for all its flaws, has brought immense progress in living standards and quality of life that shouldn't be romanticized away.
I meant more of the sustainable earth home type than getting lime disease all day and walking barefoot in the amazon. What we have now is too much and disconnected from the nature around us. For example I like old japanese architecture because it was all done by hand (fun, commnunity, passion), with wood that grew locally so that it contracted and expanded with the weather and could stand it.
>Modern development, for all its flaws, has brought immense progress in living standards and quality of life that shouldn't be romanticized away.
Of course but it's imbalanced. The whole "infinite growth at any cost" thing combined with intense individualism is a recipe for solitude and mental health disaster. I'm not surprised populations are dropping.
Is there anything in humanity or the universe that isn't extractive at some level? Machinery, used oil or metal. Hell if you go all the way to solar or wood, you're still extracting energy from the sun which is extracting extracting energy from fusion.
Everything constructive is just refining entropy into something.
Not sure what he means by extractive. Googling a bit has
>extractive industry can be defined as a processes that involve different activities that lead to the extraction of raw materials from the earth (such as oil, metals, mineral and aggregates), processing and utilization by consumers.
AI seems the opposite. It takes nothing from the earth and outputs vast quantities of iffy writing photos and similar.
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[ 3.7 ms ] story [ 223 ms ] threadWhere is the focus on the foundational principles of growth? The author writes that endless growth is impossible, but that is only true in the absence of novel breakthroughs. We need creations and inventions which bring us the future we once imagined: limitless energy and abundance.
How have we completely lost focus on new physics or foundational sciences and devoted the smartest minds of our time to transformers and data scraping? There is some critical human component which is completely absent here.
Where is the renaissance, and how do we make it happen?
All these things cost a lot of money, yet the majority of it gets eaten by systems designed to minimize the money actually going towards the research work. This isn't even really getting into how the researchers are paid.
I agree that AI researchers are concerningly blasé about what they're actually aiming to do. AGI is poorly defined, and the only negative externalities they seem to pay lip service to is culture war stuff. There's near zero consideration for the real negative externalities they're perpetrating upon the world. I think partly this also manifests in how they interact with fields where even older AI developments would be useful. At the physics lab I work at, I've found that many scientists have a negative impression of AI 'researchers', where they don't care to understand the problem they're trying to solve, preferring to just treat everything - even things where the solution space can be constrained by scientific understanding - as a black box. It's almost the opposite of science in approach.
If Leland Stanford could do it…
Grants suck. What matters is having smart people together in one place.
If you pay smart people enough money, and offer them enough insulation and protection from academic politics, they will come to your institute. Grant-making to people at institutions which are already politically and socially captured does not fix the root issue.
I agree that simply applying a fourier transform isn't meaningful research, but there are fields like fourier optics, where you're effectively just doing that, with the goal of modeling wave optics.
The kinds of issues I had in mind wrt poor uses of AI are things like inverse problem solving methods which train a model as a black box and neglect to involve physics informed feedback, so the results are of questionable value, or hyperparameter estimators which need an unrealistic amount of data or produce blatantly unrealistic estimates because they're naive and the AI "researcher" has no interest in actually understanding the physical logic to the parameters.
My research hasn't been with AI, just with simulations of certain physical systems, and one thing that was constantly drilled into me by the physicists was to be really careful about how far I stretch computational techniques, because ultimately none of this digital stuff means much if it becomes detached from reality. It can even cause actual damage because sometimes expensive purchases will be made on the basis of simulations.
I am still learning, but I have so far only applied inverse methods to systems where the physics is well-defined and the model already accepted as "real". For example, CT scanner algorithms which model the photon counters and attenuation through matter (although this can get complicated...), with the output being data in well-defined format (like the distribution of matter). I do see people using inverse solvers or AI to derive something which isn't an "image" but a model of the physics (does that make sense?). The extreme version of this which I have seen once before, is a paper using AI to extract algebraic forms of PDEs from videos of fluids. To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way? Apologies if this is misinformed as I am still outside the research community, and don't very actively read papers.
Currently I am trying to learn background to understand electromagnetic properties of materials with an interest to apply this to computer graphics. I eventually want to learn what processes are currently done for extracting material properties from scans, etc., along the lines of the classic gonioreflectometers. In this space, from the papers I look at, it looks like AI is unavoidable and I am sure an extremely useful tool but I definitely want to be careful not to get lost in misunderstanding.
The conclusion that I reached is that you need to understand at least enough to be able to handle basic scrutiny from people in the same field. So, for example, if you come up with an AI for deriving some model of the physics of a CT scanner, you should at least understand the problem you're tackling enough to make meaningful comparisons to any popular competing approaches that might exist. That understanding should automatically encompass things like understanding how your variables are bounded/how they behave.
A contrived example could be with an AI intended to guess 2 prime factors of a given number. Imagine someone making such a tool without even knowing what a prime number is, where the only benchmark they use for correctness is if its outputs fit the dataset of products of prime numbers given. The tool would be clearly useless, because while to the AI researcher it'd seem fine that it outputs 36, which is close to the intended 37, to anyone who knows about prime numbers it'd be an immediate warning sign needing further elaboration and explanation. The researcher could protest that as an AI expert, they don't know anything about prime numbers, but that doesn't really matter in research.
Similarly, due to my non-physics background, while I was (and to an extent still am) given significant leeway in terms of my ability to respond to detailed physics questions about my work, by now I am expected to be able to handle questions about things that are common knowledge among physicists in this field (so, for example, knowing what performing a Fourier transform means in our context, understanding the high level functioning of the key components of the machines we work with, having some intuition for when a simulation result is unrealistic etc). If I were to talk about how I've simulated some of these components, but drew a complete blank when asked how my work improved upon the common knowledge means of simulating them, I'd obviously look like a complete idiot :P
It may seem like a pretty low and obvious bar, but surprisingly I have seen many cases of researchers who didn't even care about reaching that level.
Universities absolutely encourage independent exploration.
Every year, they mint ~10x more grad students than there are research professor positions for them.
You want universities to produce even more of them?
https://cra.org/cra-wp/research-labs/
Just because Boeing sucks these days doesn't mean there isn't good research happening in industry.
Sure, they wanted to produce things that Ma Bell could monetize—but it was abundantly clear that they were not being micromanaged to that effect. Today, at least from the outside, it seems like any industry-funded research is cut off the moment it looks like it's not going to produce a clearly-profitable product or service within the next few years.
I can not disagree more with you on this.
The primary thesis of your original post appears to be universities' shift toward getting people ready for careers that require advanced knowledge (and away from pure learning/research).
That has very little to do with research freedom and the race to publications. Those are both real phenomena, but a) it is arguable whether the former is actually a problem, and b) they are only very loosely connected.
The number of grad students being trained, by contrast, is much more likely to be related to the degree to which universities still value and practice learning for learning's sake—although I think it is somewhat less so than vkou wished to imply, since there are, in fact, plenty of non-academic jobs that do require or prefer a Masters or even a PhD.
My premise is, purely and simply, that you are conflating multiple things that are not the same.
Change everything about the research grant system, and increase funding for it by 10x, and it'll address your concerns, and universities will happily make even more grad students. They aren't currently the bottleneck, because they aren't the sources of research funding - they are just a landlord (with reasonably okay terms) for the research lab that spends that funding.
A more advanced Hadoop will not change your life. It will not change the way you read, listen, watch, communicate (fundamentally). The Internet did.
Especially at a time when the "legacy" internet is falling apart, a new way to find information is rather a necessity than some breakthrough.
The "good" AI needs to fight it out with the "bad" AI before we see if the net positive is even there. A podcast can be translated by AI into a different language, you say? The price is super-realistic fraudulent phone and video calls from your "family".
If you look at the enablement of features, AI is a new (and, currently, often flaky) medium.
It would be analogous to looking at the Internet as limited to making it simpler to order from a catalog over the phone. While technically true, there is more that it can do besides.
Much like we don't use copper in our walls for dialup anymore, I don't anticipate the initial architectures of LLM and MoE to last terribly long, but they do enable proving that the concept works.
OP disagrees with GP. OP's main thesis is that AI enables a lot new applications. OP claims that GP is simply looking at it as if it were training data.
I stated that current AI techniques ARE indeed just reflections of the data used in training. I agree with GP that the current "AI"s are simply not transformative in the same way the Internet was.
If you change the training data for the current generation of AI, you get different behaviours. The training data forms a manifold - which you can think of as a landscape with features forming valleys and hills. What the current generation of AI does is that it tries to find a shape that fits the landscape - think of it like taking a very large sheet of cloth to cover a landscape. The stiffer the cloth, the less well the cloth fits to the landscape. The "stiffness" of the cloth is the amount of parameters that a neural network has. Modern deep nets are highly overparameterized - imagine a very soft pliable cloth - of course it fits to a landscape well.
So if you have a different training data - the neural network will fit to this different landscape as well. Hence the response will be different.
It's unfortunate that the training data is the entire internet for a few reasons:
1. Only the rich can train a vaguely competent AI. You're at the whims of those well-resourced enough. 2. There's no "alternate" training dataset anymore. (Though a clever thing people at OpenAI are doing are Mixture of Experts models, where you train multiple NNs using different subsets of the full training set, so you get multiple competencies)
Calling all AI LLMs is like calling all of the internet the web. Of course if I am mistaken, corrections are welcome.
...Because that's easily and widely understood to be what people mean in recent times when they're talking about "AI", referring to the stuff that's in the news, without further qualifiers.
If you want to talk about something more specific, you are going to need to be explicit about it, rather than expecting everyone else to understand what you've got in your head without actually saying it.
This is like saying "but "crypto" means so much more than just cryptocurrency! there's a whole cryptography field out there that does lots of good stuff!" It's true, but it's not helpful, because it's ignoring the obvious (at least to the other participants in the discussion) context. In this particular case, the context should be even more obvious because it's so clear that's what the article is talking about.
When people read our comments in 5 years, they will read "AI" and have a much broader topical take than the present excitement about LLMs.
Take computer vision for example - a "hello world" version of object recognition would use ImageNet, which is 14 million hand annotated images. Or Cifar10 which is 80 million images. That of course but sets the stage for training data differentiation. Google's image recognition algorithm is far superior to other search engines'. Why? Because of Google's data set.
Any Tom Dick and Harry can go create their own image recognition AI and train it based on all the public datasets (COCO, CIFAR, ImageNet) but that's considered pretty baseline nowadays. The differentiator is what _other_ datasets you have.
Different datasets yield different results. It doesn't matter the network. More data is better (usually).
The only real difference between then and now is that OpenAI's models are significantly better than my models from 2015, and they have that because well, they can afford to pile on more data. TBH, I never even considered using a large proportion of the whole internet as a training set as even remotely possible due to the sheer mind boggling costs.
Even now, to go through about 10% of The Pile would cost me way too much money.
THIS part of the AI sector is just a giant pyramid scheme - impress the investors so they shovel trillions your way. That's not exactly new in Silicon Valley - keep the valuation of a hot potato going up until someone is left holding the bag.
AI's most useful applications are not being a generalist.
This can be seen as a “shift left” in data capabilities. The rise of Data Products ultimately democratizes access to data.
I'm not smart enough to be a physicist, but I like listening to Eric Weinstein[1]. He thinks string theory is essentially a dead-end honeypot doing exactly what you describe with our smartest minds.
[1]: https://youtu.be/eOvqJwgY8ow
I like his podcast appearances — they are fun to listen to — but the solution to political machinations destroying established institutions is not to focus on the politics! We need to escape that frame entirely, and focus instead on building new institutions that are sufficiently reverent of smart minds and brilliant people.
Yes, we need new physics! We get there by escaping the current career trap which stops brilliant people from trying new approaches. Give the top, boldest, most daring researchers an alternative to tenure — $10m vested in a secure position at a new research institute. Then they won’t have to be scared of string theory boogeymen.
Many people think about the diretion this is going but tend to get dismissed as singularuty cranks if they think far enough ahead.
There is an article on the HN homepage about leaded petrol lowering IQ; I bet that in 100 years TikTok etc will be considered far more detrimental than that.
I will go so far as to say that millions of small businesses exist because social media provides them with global reach. As an example, if you forge, say, artisan chefs knives made out of meteorite, no one in your town/village/etc likely cares, but there are thousands of people worldwide that will gladly buy from you.
TikTok/Reels is simply the next evolution in information condensation. It's entertaining but also educative. I might not care to spend 20 minutes learning about the intricacies of, say, forging Damascus steel, but I'll gladly watch a couple TikToks on it.
Learning and understanding requires expending some kind of effort, and easy access to condensed information actually precludes you from experiencing that. If we don’t digest what we learn we are no better than LLMs — mindlessly regurgitating small bits of non-integrated information back and forth to each other.
When the alternative is not spending any time learning those topics at all, I'll take the 30 seconds.
I'm not going to spend minutes to hours learning something I'm barely interested in to begin with. As far as general knowledge goes, TikTok/Reels are great.
It's only confusing when seen from the bottom. The purpose of growth, seen from the perspective of the wealth-weighted-people served by capitalism, is to deliver a return on capital, to pay them for being rich in proportion to how rich they are. It's possible for everyone to win if the growth is real, but in a pinch fake redistributive growth will work (see: Cantillon pump). In either case, the expectation of growth rationalizes the outcome and the pulling of any policy levers needed to get there.
"At best dubious benefits" ??? I feel like there is a whole essay missing there, because my analysis of the situation is that it provides at minimum minor benefits to million of people using it on a daily basis, and at best it saves lives to some of them.
That it not to say there is 0 harm anywhere; we definitely need to do some cost/benefit analysis. But the initial premise is so obviously flawed that it's hard to consider the rest of the arguments.
Copilot saves me quite a bit of time writing documentation and boilerplate.
For hobbies AI is nice because I can ask my beginner questions and get results, where often you end up on low quality blogs or reddit threads arguing about things you shouldn’t bother thinking of when starting out.
Maybe someone somewhere asked GPT how to cook pasta and had no idea boiling water was hot, and GPT's warning not to get boiling water on themselves saved them from massive burns?
I mean, the phrase sounds like "I started my business with ChatGpt", which is true of all the recent trendy things and so would be less unique than people imagine or "ChatGpt cured my depression", where someone really shouldn't do that.
> AI [is] costing a whole lot and providing, at best, dubious benefits
A wild take. Technology like GPT-4 being available today was unimaginable to us just 5 years ago. Our company saves hundreds of thousands of engineering-hours a month using generative AI. It has let me automate the humdrum boilerplate bullshit out of my workflows, both professional and personal.
It's also essentially replaced Google for me. Just today I've already used GPT-4 at least 5-10 times. Immensely useful, the $20/mo is a drop in the bucket for the value it provides to me.
> Ride-hailing services? Those flouted regulations, hooked users on cheap prices, drove down wages and made employment more precarious.
Has this guy ever stepped foot outside a major city? Has he ever gotten drunk and needed a ride home without friends in the immediate vicinity? Uber/Lyft provide immense value that simply wasn't there before.
Has he even ever spoken to any Uber drivers, or ex-taxi Uber drivers? Does he realize that people often prefer working for Uber as opposed to a "regular, stable job" and that customers actually like using it over taxis? Compared to taxis, Ubers tend to be cleaner, the drivers tend to be nicer, and it's just a easier, better experience.
The taxi cartel is even more powerful and "extractive" than Uber in many cities, and they exert undue market power, especially at airports and resorts, not Uber.
> streaming platforms that are actively rolling back up into something analogous to cable
Spotify still provides me with far more value/$ than having to buy every single song from iTunes or whatever. Apple TV is still quite useful to me and I far prefer it to ad-riddled cable with set showtimes for what you want to see.
Tale as old as time: people seeing the past with rose-tinted glasses so they can have a pessimistic "hot take" about today vs the good old days.
If you are tired of hearing the emperor has no clothes, getting mad at the crowd for saying it is not likely to help.
Except the emperor does have clothes, this pessimism simply isn't based in reality, it's based on a rose-tinted view of the past and a misunderstanding of why people use these services over the older alternatives.
The author seems to have such limited life experience that he can't even fathom why someone would prefer Uber over the taxis of old, or find great value in using generative AI every day, or prefer Spotify over something like the iTunes Store. The value proposition is so obvious and clear, and yet the author can't even see it.
Cabcharge and its associated monopolies in many cities is a blight that I am glad uber had a go at. "Flouted regulations" I hate when people associate words on paper, often well past their usefulness, with something physical or tangible or useful that can be damaged.
AI is doing so much to help a lot of people. Give it 5 years and hating on AI is going to go from mainstream to the most boomer of takes.
One thing is obviously immediately concerning though. It is unbelievable how fast ChatGPT has been normalized. Within a year the new people have come to expect something like ChatGPT to always be provided for them, for the rest of their life. Of course people are rushing in to define what that something is, and provide an AI service which people will use/misuse like the first page of Google results. I think almost certainly this new "first page of Google" component of people's thoughts will be abused. Do you think the first page of Google is good and formed by good incentives and empowerment of the user, considering how much of a cultural force it is (Googling something being just a normal part of your thought process)?
(I do have limited life experiences. Anecdotally, I know a high school teacher who has been teaching for almost 40 years. Over time students went from hiding their phone in the back of the class to scrolling through it in class freely, vaguely attempting increasingly simplified homework, and have now jumped on the new cool AI website to complete even basic tasks. This is extremely normal, why would a kid not use it?)
Yes. Google is an immensely useful everyday tool available to everyone for free. I think the negative externalities it causes are laughably trivial compared to the utility provided.
I would argue software is an infinite resource. Therefore, we can have infinite growth in a world of software.
If the rate of economic growth merely slows, we get a recession. What kind of hell happens if the rate goes negative and the economy shrinks? For too long? I don't feel obligated to point at any one person who says "yes I believe growth can continue infinitely forever" because it's the most basic assumption baked into economic models on how prosperity works. An assumption of infinite growth, and assumption that "progress" is also eternal, that there is always something more.
There is infinite distance between infinity and a negative number.
1. https://en.wikipedia.org/wiki/Self-replicating_spacecraft#Vo...
One can can instead use resources better to produce more valuable stuff (intensive growth).
The question is if there is a limit to the value of things we can produce with finite resources - and the answer is likely no - at least not in any kind of human timescale.
I hate this saying.
Why is unending growth impossible? Nobody seems to every try to explain it.
Economic growth is generally improvement of society in some way. Usually it's about new inventions and ideas that spread more. Better ways to make things that people want, more efficient ways etc. if you come up with a more efficient fuel then that's still economic growth and the upside is that resource usage can decrease for the same benefit.
Math is essentially proven to be unending. There's always going to be something more to be discovered. Doesn't this mean that unending growth is possible?
Of course, in practice "unending" only means "for a very long time". We are nowhere near a point where we have exhausted new advancements.
Unless technological progress ends, we will continue to produce more and more valuable things. Even if the amount of money never changes or even if money disappears, growth still happens.
I think people are satisfied as long as stuff they dont like remains absent from their new sources.
Social media may be extractive if I give it my attention and data to resell. Crypto is extractive to the people buying into it and losing money. But I can avoid doing those things if I want.
AI these days takes anything I've written (from literature to code) or illustrated, and retroactively extracts value from it to be repackaged and redistributed. Even if I was proactive about securing the trademark or copyright, they can seemingly just ignore it without consequence, without me ever participating or opting in to it.
It feels massively different to have social media extract value from and image I uploaded willingly, vs AI extracting value (eg making things "in the style" of me) after training on my work against my will.
You create a highly useful website full of dense hard earned knowledge. Some people will just use that knowledge, but a few create their own sites, using what they learned.
A company sweeps up hard earned knowledge from millions of sites, with automation but not permission, and goes a long way to making those millions of informational sites redundant without any compensation.
Simplifying things obviously. But these two scenarios are not equivalent.
It's basically a store-brand argumentum ad verecundiam, casting yourself in the position of authority.
It should be obvious by now that there are many, many people who disagree with you without their arguments necessarily being "ridiculous".
I mean, I too would enjoy it if everyone agreed with all my opinions all the time, but I don't expect that to actually happen.
No, it absolutely is not.
> obviously absurd claim
You are obviously and absurdly wrong.
It's clear that nothing productive is going to come of this conversation.
(Based on a true story, it took me 3 years and 2 law firms to recuperate royalties for IP theft)
Not lately - everything is extractive and has always been. This is the very foundation of economic development and creation. If we were against all extractive things then nobody would work for a living and our entire society would crumble.
Modern development, for all its flaws, has brought immense progress in living standards and quality of life that shouldn't be romanticized away.
>Modern development, for all its flaws, has brought immense progress in living standards and quality of life that shouldn't be romanticized away.
Of course but it's imbalanced. The whole "infinite growth at any cost" thing combined with intense individualism is a recipe for solitude and mental health disaster. I'm not surprised populations are dropping.
Everything constructive is just refining entropy into something.
Only 37% of people surveyed in the US believe AI has more benefits than drawbacks
AI is curiously well-received in countries with high levels of corruption
>extractive industry can be defined as a processes that involve different activities that lead to the extraction of raw materials from the earth (such as oil, metals, mineral and aggregates), processing and utilization by consumers.
AI seems the opposite. It takes nothing from the earth and outputs vast quantities of iffy writing photos and similar.