I'm an Old Fart and AI Makes Me Sad
medium.com
medium.com
It's unnerving that such a revolutionary new technology in computing is fundamentally tethered to large binary blobs, usually proprietary or of uncertain provenance. Which is not like computing advances that we have hitherto enjoyed; even technologies for the operations of large data centres and distributed systems could at least be deployed and toyed with on virtually all common consumer hardware.
Perhaps it's because thus far the realm of Big Data wasn't alluring enough to draw in myself and the similarly minded. It's only now that a certain form of large data set has become tied to an interesting application that it's drawn my attention. Not to say that Big Data and AI are one and the same; only that they both deal with large-in-context data sets that are difficult to construct, acquire, and manipulate.
In many instances you don't even have the blob, you can just call an API that invokes it and runs the output thru multiple other hidden algorithms before sending it to you.
I'll admit that there's a definite and clear accessibility curve to building a CPU, but it's nowhere near as opaque as the binary blobs one can access for AI tooling.
I think what's different right now is the sudden hyper focus. It's a chaotic gold rush driven by speculators and prospectors as much as by technologists or scientists.
I wonder what we'll see on the other side when the fog starts to clear. I expect lots of "losers" i.e. frustrated prospectors who never found their gold mine. And probably some proprietary wins locked up by big investors. But, maybe we'll at least benefit from some new commodity products reaching out to a broader market after they initially targeted the rush itself...
At the same time, a group of guys trained a model that plays go, it can beat AlphaGo, and it probably is the strongest go player ever. No one from the group of people could have created LeelaZero by themselves alone, but collaboration is possible.
Whether those are "artistic choices" or random chance or a set of heuristics does not affect my enjoyment.
It does not mean I don't find knowing about the choices of an artist or their process interesting at times, but for the vast majority of art I see I have idea about them. They are separate to my enjoyment of a given image.
I don't know why you are skeptical - to me the notion that anything but the image itself should be necessary for me to enjoy it is a bizarre notion and I suspect it would be for a whole lot of people. Most people would not be able to name whomever created the vast majority of art they've seen, nor name the style, and do not spend time thinking about either.
Things can be visually interesting without intent or artistic meaning. Even if you've never stared into a fire, surely would agree that many people do it? Or the ocean? Or hills covered in snow? Or trees? Or other people having lunch? Or a pretty face? People have gazed contentedly at things that are just pretty without any worry about "artistic choices" since people have existed.
I can enjoy just looking at something, whether it's a painting or a generated image, or just the sky. It doesn't take away anything from the experience to me if I know there is no meaning or intent behind it.
To me the two are entirely separate, and e.g. when I make things myself, whether I draw, or play the piano, or write code, the outcome is often secondary (I'm shit on the piano, and mediocre at drawing, but it doesn't matter). When I learn about an artist, the person and ideas might be interesting even if I have no interest in their art (I pointed out my favourite part of the Matisse museum in Nice is not his art, but the olive garden outside it, elsewhere, but Matisse is fascinating even if I don't care about his art at all). But when I watch their art, it's purely about what I see then and there. It's not that knowing history behind it never affects me, or interests me, but that I don't need it to enjoy the work, nor will I always - or even most of the time - feel the slightest urge to learn about the work or the artist.
On a less theory-heavy notion, the mere fact that a certain image or certain parts of a given image enjoyed that much of an investment to look like it does, somewhat guarantees that it had been worth the effort to someone, that it was meant to convey something. A quality, which is now gone for ever. Meaning, even the barest spark of expression, is now just at random. Moreover, by the very definition, art will now be dogmatic, even where it's asked for aberrations and exceptions, and redundant, since it's just a product of weighted averages based on an existing library of expressions. At least applied art is pretty much over, as is visual media. (Just look at what happened to cinema, when the regulating factor of film stock and related production costs fell away.)
> the mere fact that a certain image or certain parts of a given image enjoyed that much of an investment to look like it does, somewhat guarantees that it had been worth the effort to someone, that it was meant to convey something
For reasons very similar to yours, I enjoy going to 2nd hand record stores: if someone has bought the record a 1st time, then someone else though it was worth buying and putting it on a shelf, it's more likely to bring enjoyment than a random new purchase
> Moreover, by the very definition, art will now be dogmatic, even where it's asked for aberrations and exceptions, and redundant, since it's just a product of weighted averages based on an existing library of expressions.
I don't know much about visual media except music videos, but if you want to discover non dogmatic art, I'd recommend you try out what's popular in any random country.
I love music and I've found russian rap and french pop to be extraordinary, maybe because they follow their own dogma, a dogma that feels very foreign to someone more used to north american music: whatever the russian (or french) weighted average may be, it's very far from the norm of what I'm used to, so it stands out.
But then from early on I found a lot of attempted analysis of art shallow and often outright insulting in it's insistence of knowing intent that was often not there.
E.g. I recall an interview with a Norwegian author where the interviewer was terribly invested in the symbolism of a scene, and the author though for a moment and answered that he just thought it sounded good, and wished he'd thought of that.
In other words, while there certainly is intent behind a lot of art, your interpretation is yours. It may or may not even intersect with any authorial intent.
So why does it matter?
I've written two novels. I don't give the slightest shit if people interpret things in them how I intended. For the most part I just wanted to evoke certain feelings. There's no intentional symbolism there. Many things in the setting that I know people will interpret as a positive outlook I consider depressing - from my perspective its a dystopia, but I don't want to make it feel like that. But how people take it is entirely up to them.
The artists investment has no relevance to or bearing on my enjoyment of a work. Nor would I expect or care if that is the case when people engage with my own. (I get that people who make a living of human art are worried, and that is valid)
I for one look forward to consuming AI art when it is pleasing. I also still look forward to consuming human art when it is pleasing. And hybrids.
I really don't care which is which if it looks good to me, sounds good to me, reads well to me, makes me think, makes me feel.
I feel like that's an attitude that's particularly common among software engineers: see an artifact as its surface presentation and nothing more. Maybe it's a result of thinking about abstractions like APIs too much.
When I appreciate an image, it typically has to be either a reflection of reality (this thing I'm seeing is a real thing that I now know about) or an actual person's expression (an act of communication) for me not to feel cheated.
It doesn't help that one of the biggest use-cases for "AI" image generation is the creation of clickbait bullshit masquerading as a reflection of reality.
Basically: the context is as important as the raw image itself.
I think there's no there there. Most of my time as a software engineer is spent understanding what someone else was thinking and trying to accomplish at the time through the lens of the code they ended up writing. Is that archaeological endeavor not strongly connected to if not exactly what we're talking about here?
It's just that I also know how to enjoy looking at things without any of that.
There is context to a storm, or a tree too. Many things have more, and more complex, context than human intent.
Intent is just one categorisation of data, and it can be interesting, but so are many other categorizations of data.
And we also often get authorial intent wrong, often embarrassingly so.
It also compels me to see the dismissive attitude to AI art as fundamentally flawed, in that while we're clearly not "there" yet, I see no fundamental conceptual difference between different forms of computation - including the human mind - so any dismissal of the "just statistics" kind to me is an attempt to imbue the human mind with religious characteristics I fundamentally reject.
At the same time, to me, that attempt denigrated human art, which to me is equally just a result of computation.
If you can't enjoy art unless you think there's some spark of something more behind it, then to me the only reason you fail to reject human art too is faith in something there's no reason to think I'd there.
Nothing we know suggests we are - or can be - anything more than automatons resulting from computation any more than the trees in a forest or waves on a beach.
Yet we still have intent, even if it is just a product of computation.
And we still produce beautiful patterns that I enjoy whether or not I recognize your intent, and whether or not there was any intent behind any given aspect I enjoy.
I thought about it, and while part of my overall dislike of procgen in games comes from it being lazy and repetitive (too easy to spot the patterns), another part comes just from realizing there's nothing behind it. No mind, no plan, just RNG that I can keep rolling to get variations of the same theme.
(There's also a sense of loneliness - when everyone is consuming unique content, it stops being a topic of conversation, because there's no shared experience anymore.)
And if you do accept Intelligent Design, then arguably the RNG of AI images is also guided by it to the same extent.
(Except in context of biotech or dynamic systems; those are lenses that make me appreciate nature, but I realize this is an engineer's point of view - I'm excited about possibility of applying what we're learning, and/or repurposing what's already there.)
Whether there is intelligent design or not, you can see the effects of life existing and changing in a forest. You can detect choices, individual and collective.
And I've found many Minecraft landscapes beautiful too.
Part of it is that all current AI generated imagery is an imitation of something, but an imitation that doesn’t and can’t follow the rules of the original or understand the meaning of the original. It’s fake.
Nature feels like the opposite. It’s real and brutal and, for living things, it is driven by millions of years of evolution. It’s entirely grounded in the physical laws of our universe.
And all art is imitation - that is how we learn to follow and follow rules, how the rules come into being, and how we communicate.
It's reasonable to think that they are not good artists who consistently know when to follow rules and when and how to break them, but I don't see how this is any different than anyone else making mistakes while learning.
(As for understanding, I'd bet ChatGPT could give a more convincing analysis of an artwork than most people)
Maybe that's why we are destroying the biodiversity and most tech people seem to love it!
Because it wasn't what I wrote or read poems for.
Sometimes I'd even seek out a certain translation of a work because I enjoyed the beauty of the choice of word of the translator more than the authors underlying ideas.
(I've never gotten through the original of Whitman's Leaves of Grass, because I find it trite, but there's a particular Norwegian translation I loved - the authors intent was identical, but one presentation of it was beautiful to me in ways the other has never been because of the patterns of words rather than meaning)
Whether or not that intent or symbolism was there in a given poem, I found the process inherently destructive for my enjoyment of those poems, and I utterly detested the process because it felt like violence.
The one time I wrote a poem with a message was as a task in his class, and it was a sharp denunciation of the analysis of poetry. No analysis was necessary - the intent was brutally apparent and quite rudely expressed.
It is also the only poem of mine I've performed in 'public', which was a mistake of him, because it's perhaps the one thing that I have written that has met with the most universal approval among those who heard it, and it hardly improved the attitude towards the analysis of poetry.
I still find pulling art apart to often be brutally destructive and occasionally insulting in it's often shallow insistence of knowing intent that usually is without actual evidence and mired in dogma.
I don't mind people finding meaning in knowing more about how it was created when the creator of a piece of art wants that context to be known or part of the work, but I feel very strongly about assuming intent even for human art, because to me at least, for what I wrote, my intent usually was to write without any deeper meaning or symbolism, to evoke emotion.
For me, for what I wrote, picking it apart ruins that on every level in ways people rarely are able to undo for themselves.
It's like trying to reassemble a cadaver.
People can find their own meaning in anything, and make their own choices, but so much art snobbery revolves around that assignation of a dogmatic interpretation of intent as "correct" and objective that often feels outright disrespectful to assume to me.
If I know as a reader that there is no genuine expression behind this, no intent of evoking anything, that it's rather patched together, based on stochastic heuristics in order to mend seamlessly, this just doesn't work. Much the same, I mentioned earlier in another comment that I believe AI generated images to occupy pretty much the negative space of abstract expressionism and informal painting. (Both styles withstand quite robustly any attempts of simple analysis.) It's really about this particular stretch, transposing inner and outer impressions into expressions, what I'm concerned with. This sort of establishes kind of a net, in which we, as humans, may suspend in. (E.g., if you are enjoying yourself in nature, does it matter, whether this is organic, has eroded and grown in certain ways, in a polylog of beings and forces, or if this is just a generated prop made of plastic? I bet, it does.)
For a more concrete example, take light in figurative art. All images are modelled from and by light, and it's quite natural that we should explore an image along its lines. Light and the way it spreads emphasises the image, even if there was no intention of doing so, just by "how it works". These are choices, equally if made intentionally and consciously or not. It's the trace and trait of a human being. But, if there was no intent, if it's just patches mended to meet up, based on weighted averages, a transferred texture? Even if there are sculpted objects and dark and light patches to look at, is there light, at all? Is it even worth noting? How should this work for me? Is there any meaning in looking closely at this?
If I see an image that evokes a feeling, it won't diminish anything if I find out it is an AI image. Why would it? The image is the same.
The same is my attitude to my writing: I may have an intent, but whether the reader interprets it the same way is irrelevant - I've written it either way, and got my enjoyment out of writing it. It'd be a shame if they don't enjoy it, but that's all.
Why does it matter? It doesn't change my experience of writing it in any way. It has no bearing on my life at all.
Nor does an artists intent change what I see, or hear, or read in any way.
Your example at the end is just deeply depressing to me. Why does it matter? It looks the same. To lose our on enjoying something nice because of factors other than the art itself feels sad to me.
Furthermore, why do you think the human art is any less the result of computation? To me, that seems like a superstition, and meaningless. I don't feel like it's a distinction with any value.
And, for you, as a writer: As you can't compete with generators on economic grounds, the productions of the former will probably become prevalent (if they are not already, in some niches). If input rejection becomes the default mode of reception, this may affect you, as well.
PS: In other word, a representation is not the real thing, it's a thing of its own in its own realm, raising the question of "what realm?".
As an illustration, here are two illustrations of my own, both representing real objects, drawn in (pre-AI) Photoshop from scratch, for interface purpose. The first is an actual application – mind the focus and guidance of attention and varying detail, the second one is just a draft, still lacking any such focus. This second one is quite similar to an AI generated image, as it lacks any purpose, thus, any reason to be there at all. (Besides that bit of light that is already in that image, it is offending in its neutrality. And there is really no point in publishing it, outside of this context.)
I agree with you.
I basically stopped looking at things on /r/earthporn because nature itself is pretty and the bastardization that often happens on that subreddit isn't. They often play with the Hugh's to get deeper greens, more contrast, etc.
But then it stops looking natural and my reaction isn't one of awe or interest. It's not pretty to me. It's interesting the first time you see it, but not the 100th time you see it.
DALL-E is the disposable camera of AI images. There are plenty of artists working with more complex tool chains that take lots of effort and energy to create, and form their own type of AI art.
Low quality photography is not elevated to the status of art. Low quality AI generations are not elevated to the status of art.
The actual art will be made by people with something to say or show and it will take energy and effort - it won’t be made by typing your idea into DALL-E, it’ll happen with complex workflows on fine-tuned models.
It is just another tool with different strengths, weaknesses, and constraints.
I'm not convinced that this is what will happen long-term, but if it is, I'd completely agree that it's a new art form.
E.g. my son loves art, but gets exasperated at anything remotely abstract. Part of my enjoyment of taking him to art exhibitions is things like when he walked past a Picasso with a look of utter disdain and loud audible huff. Or the conversations we had about Matisse's sculptures, or the paper cutouts from his later years.
(I don't really like Matisse either - the thing I enjoy most of the visual experiences of the Matisse museum in Nice is the olive garden, despite the total lack of intent behind how the tree-branches grow)
My son is 14 now, and draws plenty of things I enjoy for their actual appearance, but I know most of them are scribbles or practice to him, and I don't try to analyze them for intent that I know usually isn't there.
Meanwhile, two of his pictures from when he was smaller that I know he did with intent, because he told us, and we wrote it down, are scribbles to me.
Shapeless blobs.
One of them I quite enjoy on a visual level, but while to him it was a fox, to me it is a red swirl that while visually pleasing in no way is anything like a fox.
While I wouldn't have it on the wall if it wasn't my son's, his intent behind it does not make it better.
The other is hideous, but his intent was to paint his mum and me, abd so I love it.
For that one the intent is what gives it value to me. That is a rare exception.
But only because he told us, and because of the emotional value of that.
He could've given us any random painting and said the same thing, and it's value would be the same. The "art" in that instance was his statement.
I also know from discussing his drawings with him, that whether he enjoyed a given drawing or I enjoyed seeing it, often correlates poorly - many of his that I enjoy are things he dismisses completely for reasons that does not affect my enjoyment of them at all.
It's good the kids know...
Yes, photography was similar in how it overwhelmed painting, but the difference is that photography, from day one, offered a gloriously rich set of tools for making pictures while DALL-E is a peg-board toddler toy by comparison and the results speak for themselves. Just compare the first few years of photography to the middling pablum we have that's spit out from these generative AI models. Despite the models stealing the entire history of world art and putting all of that at the fingertips of untrained artists, the results are still embarrassingly naive and mostly boring. The reason is that art isn't about the tools, it's about the mind and people with no training and no experience in art cannot make consistently good art without training, even with tools that let them create sophisticated collages of other people's real art.
This is like saying that post-photography there was no reason to look at paintings anymore. And while the camera certainly disrupted art, it also led to a burst of creativity and gave us Picasso and Pollock.
Humanity will adapt to these new technologies. We'll begin to trust what we see less and less. Which will prevent us from being fooled, but we also lose a massive part of the human experience in the process.
Soon the days of being able to view a photograph, a video, artwork, and appreciate it as human ability at its pinnacle will be gone.
On top of that, real artists will often find that they surprise themselves, and those surprises will drive the eventual creation. So this is a similar process, except it goes through AI instead of the unconscious part of the brain of a real artist.
I believe that when thinking about AI art, it's helpful to keep photography in mind as a source of analogies. For instance, both give anyone the ability to create images easily. Both gave existing artists heart attacks and were dismissed because they didn't require artistic training to use. Both resulted in a flood of images being created without thought or effort. The main difference is that we skipped straight to the smartphone camera age with AI.
Given the demand for AI art, it's safe to say that most people disagree. Personally, I like looking at pretty pictures.
Also realize that death of the author was proposed many decades ago.
Also, crucially, those representing the demand for AI art are not those who are meant to consume it. It's still too soon for any systemic feedback, other than economic factors and incentives.
Furthermore, jump on some Discord groups and subreddits like /r/localllama or /r/stablediffusion you will see a vibrant AI hacker community that is alive and well, working very hard to build tools for the masses. Don't resign yourself just because you have not mastered this new thing by default, regardless of whether that's the world-wide-web in the 90's or tensors in the 20's.
>Don't resign yourself just because you have not mastered this new thing by default, regardless of whether that's the world-wide-web in the 90's or tensors in the 20's.
Might be my favorite part of what you said. It's so true and I meet so many people who are used to things just being something they understand so they approach a more complex topic they don't have context on and start "feeling stupid" without realizing how vast the world is.
All in all, well said!
it's not the tools that are the obstacles; it's the training data and training resources (why do you think OpenAI had to sell itself to Microsoft?)
I would strongly strongly strongly recommend starting with karpathy's from 0 to hero neural networks youtube course - it starts with building a tensor library and back propagation, explaining it in a way that finally clicked for me https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThs...
jeremy howard also has a fantastic video that is more around how to use LLMs and such called a hacker's guide to language models - https://www.youtube.com/watch?v=jkrNMKz9pWU&t=607s
as i've dove more and more into it, i would strongly recommend trying to run things on your local machine too (llama.cpp, ollama, LM studio). that has helped me fight that feeling of like "are we all just going to be open AI developers in the end" and made me feel like you _can_ integrate these things into stuff you build by your self. I can't imagine how fucked we'd all be if llama was never opensourced. being old does not mean that you can't continue to grow, and remember that it's okay to feel overwhelmed about all this - many people are.
>“If we open up ChatGPT or a system like it and look inside, you just see millions of numbers flipping around a few hundred times a second,” says AI scientist Sam Bowman. “And we just have no idea what any of it means.”
>To me as an engineer, that is just incredibly unsatisfying. Without understanding how something works, we are doomed to be just users.
AI aren't complicated. They aren't sophisticated math that you can poke at and understand.
They're fucking million dollar spaghetti code that happen to work (for values of 'work').
Those videos are teaching people "This is an if statement! This is a CPU!" And then you can look at 5.8 billion lines of spaghetti code and say "Gee! I understand how this works now! Yay!"
LLMs don't have anywhere near that much code. The algorithms for training and inference are not that complicated; the "intelligent" behavior is entirely due to the weights.
Aside from annoying people who want to understand how things work, it also means you can't ever know if you have a fully optimal or correct solution, all you can do is keep throwing money into the training furnace and hope a better solution falls out next time. The whole nature of it gatekeeps out anyone who doesn't have enormous amounts of money to burn.
Totally agreed that the process of generating and evaluating weights is opaque and not very accessible.
You don't see any meaningful, understandable code. For example, if prompt begins with draw me a picture then jump to layer X.
I'm no expert but I can imagine that to be the problem when one attempts to debug an Algorithmic Intelligence black box.
We know the rules they play by thoroughly - we made those ourselves(the math/model structure). But the outputs we are getting in many cases were never explicitly outlined in the rule set. We can follow the prompts step-by-step but quickly end up on seemingly non-nonsensical paths that explode into a web of what appears to be completely unrelated concepts. It could be that our meat brains simply don't have the working memory necessary to track these meta and meta-meta emergent systems at play that arrive at an output.
But your last paragraph resonates with me pretty deeply, and suggests to me that there might be a way forward for me when this becomes unavoidable, which it will.
Frankly I would rather direct my energies away from the accelerating face of dehumanising technologies and towards rehumanising technology through education, but I do recognise I'll eventually have to engage with this just to educate.
LLMs are a very new technology and we can't predict where they'll be in 50 years, but I, for one, I'm optimistic about it. Mostly because it is very much not GAI so its impact will be lower. I don't think it will be more revolutionary than transistors or personal computers, but it will be impactful for sure. There's a large cost of entry right now but that has almost always been the case for bleeding-edge technologies. And I'm not too worried about these private companies having total control over it in the long run. They, so far, have no moat but $$$ to spend on training, that's it. In this case it really is just math.
I think market forces will drive a breakthrough in training at some point, either mathematical (better algorithms) or technological (better training hardware). And that will reduce the moat of these companies and open the space up even more.
I think a lot of people are jaded by how fast technology changed between 1980 -> 2010. But, a lot of that is because the technology was easy to learn, understand, and manipulate.
I suspect that AI will take a lot longer to evolve and perfect than the World Wide Web and smartphones.
I don’t know about LLMs… it might hit a performance peak and stay there, same as CPUs have been 3+ GHz for the past 10 years. Or there might come a breakthrough that will make them incredibly better, or obsolete them. We don’t know! And I find that exciting.
But with AI? Your comparison to going to the moon feels apt. We're deep into the age of the hyper scalers, but AI has done far more to fill me with dread & make me think maybe Watson was right when he said:
> I think there is a world market for maybe five computers
This has none of the appeal of computing that drew me in & for decades fed my excitement.
As for breakthroughs, I have much doubts; there seems to be a great conflagration of material & energy being poured in. Maybe we can eek out some magnitudes of efficiency & cost, but those gains will be mostly realized & used by existing winners, and the scope and scale will only proportionately increase. Humanity will never catch up to the hyper-ai-ists.
I feel the same way. Developments in computing have evolved and improved incrementally until now. Networks and processors have gotten faster, languages more expressive and safer, etc but it’s all been built on what preceded it. Gen AI is new-new in general purpose computing - the first truly novel concept to arrive in my nearly 30 years in the field.
When I’m working in Python, I can “peer down the well” past the runtime, OS and machine code down to the transistors. I may not understand everything about each layer but I know that each is understandable. I have stable and useful abstractions for each layer that I use to benefit my work at the top level.
With Gen AI you can’t peer down the well. Just a couple of feet down there’s nothing but pitch black.
I think this bit is really not the case, FWIW.
If you look at what computer magazines were like in the 1980s it's very clear that people were already imagining what photorealism might look like, from the very earliest first-person-perspective 3D games (which date back to the early 1980s if not earlier)
no it's not; it's math + a *ckton of data + massive compute resources
training will likely be made more efficient over time, reducing required resources, but training data will always be a major obstacle
It's almost less about software and more about how our society just seems to not give a damn about expertise because it costs someone more money. I have a good career built on that expertise along with emotional intelligence, but it's a bitter pill to swallow knowing that everyone's trying to deleverage themselves from needing to pay me for my expertise. I've avoided this to some degree by focusing more on fundamentals than BS like AWS service invocations, but I'm doubting that this strategy will continue to work long-term with AI around.
The real sad part is it feels like everyone's happy to suck every last bit of humanity out of work for ok-ish results that come from ingesting the whole Internet and not compensating people for it.
Yeah, to me almost all the "old programmer sadness" is actually cultural, it has to do with a feared difference-in/failure-of values.
Maybe that's also why "enshittification" is in the zeitgeist right now: It represents a sense of disappointment or betrayal with certain industries/companies/products--but it's with fallible people, rather than with fragile computers.
I can finally say I’ve started to transmute that into energy towards being a solopreneur. It feels like the equivalent of “f this industry I’m taking my ball and going home” but I don’t see any other way that lets me feel agency at this time.
Regarding AI: it is a tangible manifestation of the phantoms that keep us running hard on the economic treadmill. Very familiar feeling, really.
With image generation, you can generate seemingly infinite images with nothing but a prompt, but you’ll never get what you really want. You’ll get an approximation at best. You have all this agency, but also no real agency that matters.
But of course if you’re a visual professional of some kind then I imagine it’s even worse, a net decrease in agency. Not only can you never quite get what you want, but having to go in and edit things is tedious and dull. Even if you’re saving time this way, the lack of enthusiasm and flow makes it feel like the job takes much longer than before.
Likewise, with programming, at the end of the day it feels as if I’d be more productive and just wrote down my thoughts as code rather than delegate to a copilot or ChatGPT and waste time making sure this actually works or solving for the occasional bug that came from a chunk of mystery code.
At the end of the day, you aren’t creating the image or the video, the AI is and you make adjustments where you can but otherwise accept the results. Extrapolating this to software, I don’t imagine a future of where people are empowered to write their own software, but simply a future where people ask for software from an AI, it gives it to them, and they do an awkward back and forth to try and get the details right until they inevitably accept what’s been given to them.
It’s depressing, but also so goofy that it’s hard to imagine this being the final outcome.
AI can now generate images that are not as good, but the average people won't really realize. Therefore it not only makes artists more productive (as in: "make more profit faster, even with lower quality"), but it makes the best artists less relevant because "anyone" can replace them and make worse content that's more profitable.
Same with code Copilots. To me it's killing the job. I take care into crafting good code, and I believe I am better than average at it. but the Copilots enables worse developers to be more productive than me (not that their code is more maintainable, but they can produce so much that they help making more money). But I don't want to do their job (which is basically debugging what the Copilot wrote).
I believe that AI is lowering the quality of everything it does well, but people love it because it's increasing the profit. What a great time to be alive.
A year ago I shared exactly this concern. Today I'm not nearly as worried about it.
If you haven't tried running local, openly licensed models yet I strongly recommend giving them a go. Mistral 7B and Mixtral both run on my laptop (Mistral 7B runs on my iPhone!) and they are very capable.
Those options didn't exist even six months ago.
There are increasing numbers of good closed competitors to OpenAI now as well. We aren't stuck with a single vendor any more.
It all has its right for existence. But I really wish the hype ends soon and people become more realistic again.
It will have a big impact on the world, but people don’t get that it’s not there yet and there is a ton of research we still need to do. That’s burning me out. There is so much more work to do compared to the expectations- and even in the scientific community there is such a big volume of junk papers coming out (sometimes even by big companies) that it feels like most of the time wading through all the BS and marketing hype is all I do.
Sure with a good amount of time and effort you could fully understand every single minute facet and detail that goes into basic x86 CPU. You wouldn't be able to grok every single operation that happens inside when it produces some output but you can definitely understand the fundamentals and have an intuitive understanding of the internals. You the average hobbyist or programmer would be able with significant investment create a photolithography setup at home and even create one of your designs. None of this would be anywhere near the scale, performance or quality of the products coming out of big companies. You just dont have the expertise or hardware to make a product anywhere near a current gen CPU. Does that make you sad?
Obviously theres a difference here in that AI is "software" but the comparison is apt I feel. It is clearly a different class of software than fun webpages and video games from the 90s. Just like the CPUs and GPUs of today are a different class of hardware than what you can build at home. Even with much education and resources, both are out of reach.
You can write software and it will always work, and it will be composable. It's kind of "disappointing" that for so many problems the "winning" approach seems to be "just download internet.tar.gz".
First, I've gone back to school. I have a PhD in computer science from 1983, in which I explored spatial query processing. (General purpose ideas, but mostly applied to database systems.) My PhD has basically expired, the amount of new stuff to learn is daunting, and for a variety of other reasons, I thought it would be better to go back to school rather than study on my own. So I'm enrolled in a CS MS program to learn about AI and cognitive psychology.
Second: To a first approximation, I view the systemsy parts of computer science: CPUs, compilers, operating systems, data structures and algorithms, as having served their purpose, which was to enable AI in its current form. AI is just a different discipline. I happen to be familiar with some parts of those foundations, but I'm basically starting over with AI.
Finally: I recall what I now realize was a fork in the road. I took an AI course in 1980, I think, and we learned about A*, and simple feature detection in images, and alpha-beta pruning, and so on. Symbolic AI was up and coming, and looked to be the way to go. I remember learning about perceptrons and being intrigued. And then I learned about Minsky's proof that a perceptron can't compute XOR, so this idea of computing with a neurons was basically a dead end. Also, I liked the precision of algorithms: the answer is right or not. It has such and such worst case running time. I disliked the wishy washiness of AI, with uncertain and only probably correct answers. I distrusted the idea of tall stacks of probability calculations yielding anything other than a guess. And so I picked my path (into data structures, algorithms, databases).
Now, of course, I realize how wrong it was to dismiss the path of neural models of computation, and the usefulness of statistical methods. I have no regrets, I've had a very satisfying career. But if I were starting out now, I would study a lot more math, and hell yes, get deep into machine learning.
I demonstrated this with Hebbian learning on a Hopfield network finding learning of up to 6 dimensional XORs. This was a replication of work by Elman and reported in Rethinking Innateness[0].
A more provocative claim: layers and back propagation, despite providing us with fantastic advancements, are unnecessary.
[0] https://mitpress.mit.edu/9780262050524/rethinking-innateness...
Just kidding, but I’ve actively encouraged my 8yr old daughter to learn how to code (using Scratch) and beginning to think that might be a dead-end… hope she will enjoy math!
I'm not at all sure that's a wise decision, right now, especially with the glut of software engineers, with far more experience, being laid off by FAANG.
She's leaving EPIC, and taking a several months long road trip with her boyfriend, and will find a job when she comes off the road. She's a smart kid, she'll figure it out. I just don't think she will end up in software.
They want an adventure, and they don't like Epic much, so they are leaving shortly. She has definitely ruled out grad school, after doing research summers in college, and observing how miserable the grad students were. And how glacially slow progress is. (She spent all summer analyzing data from a telescope only to find out, oopsie, the telescope was pointed the wrong way, never mind.)
I'm an old fart too, blah, blah, blah, since the days of 2400bps modem. For anyone that wants to level up, go take Andrew Ng's ML course, watch some online lectures on neural networks, deep learning, reinforcement learning. Never in the history of the world have we had so much learning resources at our finger tips, plenty of youtube videos, blogs, articles, free books, free courses, software libraries to get into it.
Stop with the self pity, get up and dance. Start learning, at some point pick up scikit learn for shallow learning and pytorch for DL. GPUs are super cheap. You can pick up a used 3060 RTX for < $250. Or you can just rent for even cheaper. If you are technical and find yourself agreeing with the author, please snap out of it. You are going to feel lost for quite a while, but I assure you, you will find your way if you keep at it.
AI gets me excited, computing has been too boring for far too long!
These are the empty calories of research. Focus on them and you will end up fat (rich) and useless (no publications worth a damn)
Prove me wrong, but from the pavement. You don’t need to be on my lawn.
Toxic and empty. Full of promises and delusions about saving time and effort.
[1] https://www.lesswrong.com/posts/nmxzr2zsjNtjaHh7x/actually-o...
DALL-E, Stable Diffusion, Midjourney and the new Sora video model are diffusion models, which work differently from GANs.
Maybe you meant "generative AI" rather than GANs? That's an umbrella term that covers ChatGPT-style language models and image/video/audio generation as well.
(I am typing on my phone on a train in between connections, both rail and internet, and I am grateful for your correction)
Everyone knows what I mean by “empty calories” anyway. If they think I am wrong, they are in denial. ;-)
It won't be long (4-5 years) before AI starts appearing everywhere.
I use chatgpt almost daily. I use it like I used to use google in a lot of situations. Now GOOGLE frustrates me to use as a search engine. "How many hours ago was june 1st 1972" and you get links to time/date calculators instead of the answer. Then I'll click through a few and they won't even be what I need. Then I sigh and type it into chatgpt and it answers it.
I don't assume any code is perfect, but I talk to it like it's my rubber duck and it helps me figure out different ways to do something, or sometimes even hand holds me.
And now I don't have to ever do regex.
And hey my past engineering teachers, guess what, I haven't had to mathematically slice a subnet up without a subnet calculator either in my entire career.
Payload of the answer: "So, approximately 455,796 hours have passed since June 1, 1972, as of February 16, 2024."
Today (as I write this) minus 455,796 hours: https://www.timeanddate.com/date/dateadded.html?m1=2&d1=16&y...
Which is Thursday, February 17, 1972. Since it just did the year and ignored the month you were asking about. (I accidentally deleted my first conversation instead of sharing it but it gave this answer twice.)
The real question though isn't whether ChatGPT is wrong, the real question is, can you detect it? That's going to be the important question here going forward.
https://chat.openai.com/share/d5264b01-3be5-4d59-b175-34ad90...
"June 1st, 1972 was approximately 453,304 hours ago."
Code it generated and ran:
from datetime import datetime
# Current date and time
now = datetime.now()
# Date and time for June 1st, 1972
then = datetime(1972, 6, 1)
# Calculate the difference in hours
difference = (now - then).total_seconds() / 3600
How are regular users meant to know that they should hint it to use Code Interpreter mode for a question like this?
I need to dig more into how much more I can do.
"= 453288 hours" as the first result, above any links.
I feel like my account is gonna look like a Kagi shill pretty soon but it really is so much better than Google now. The Kagi + GPT4 combo is so much better than Google alone 1.5 years ago.
while in other media the results are very interesting and impactful, most corporate scenarios are getting a lot more excited about text. but the weight of text and its correctness goes way beyond other forms of media, and the current approach is being overstated for its effectiveness.
I do think the idea of 'prompt engineering' isn't 'programming' in the low-level sense, but it's close enough for some peoples' needs to qualify, and will be a useful skill. But I think it'll be more like "being good at searching google" was a few years back. There was a period where you could be very productive understanding a few things about searching (filtering/keyword stuff, mostly) but that 'skill' isn't as useful today as google continues to put less emphasis on keeping those tools useful.
Similarly, being good with Excel. That's extremely powerful for a lot of people in their day to day jobs. Is it 'programming' in the classical hacker-at-a-desktop sense? No, but allowing people to get value from the computers in a way that's under their control (broad definition, I know) does, imo, fall under a large banner of 'programming'.
That said I don't believe using ChatGPT to hobble together some python code to call the GPT API would constitute "learning to program" any more than nailing two boards together makes you a capable carpenter.
But it must feel amazing for that 2-3x guy who is just trying to get his company off the ground, and that's great!
Aside: I recently went down a rabbit hole exploring fast food training videos from the 80s and 90s. It makes you appreciate the engineering (culinary, mechanical, and industrial) that allows a company to make a consistent product at scale, only requiring interchangeable, unskilled labor. Did you know that McDonald's claims that 1 in every 8 Americans have worked at the chain? You can get a fairly pricey jacket celebrating this fact[1]!
Perhaps we're finally at that point with computing. We know the externalities that have resulted from McDonalds and similar chains and, good or bad, we've accepted them. Over the next decade we get to watch the same with commodity knowledge work.
[1] https://goldenarchesunlimited.com/products/1-in-8-alumni-jac...
I‘m 42 and I‘m v busy building a library for barcode scanning in web apps: https://strich.io To this day I enjoy coding and especially low-level computer vision stuff.
Maybe I just have to find the time and dig deep to understand everything better, found some great pointers in the discussion here already. Thanks for the encouragement!
How is this a legitimate argument? We have invented so many algorithms and used so much math to build our software along the years. They are not easy to understand. Understanding them requires tons of effort, including reading papers. People may have forgotten that 30 years ago, the "host stuff" was still systems, and people did read "deep" papers. People still do nowadays, except that only the very experts do so.
Besides, the math is really not that hard -- merely college level. In contrast, go read an introductory book on program analysis or type system or distributed algorithms. Those maths can be harder as they are more abstract. In addition, the amount of code in a model is orders of magnitude than a compiler or a distributed system or game engine and etc. I'd argue that it's actually easier to understand how a model works.
I won’t say the math behind AI is simple, but it’s mostly undergrad level. You can get up to speed on it if you really want to. The hard part is writing fast implementations, but many others are already doing this for us.
We do not have a grand theory of AI or a deep understanding, but every year we make improvements in machine understandability, and you can “debug” models if need be.
Lastly, the author is right, the best models are closed source, but open source is hot on its tail. There are plenty of good local LLMs and they get better every month. Unfortunately it still is out of reach for a hobbyist to train a good LLM from scratch, but open source pretrained models can mitigate this for now.
Beyond that, it is up to us to guide AI development and deployment. If we use it to crush the human spirit (and it sure seems like we're hell-bent on doing that right now), that's more of an indictment on AI leaders, and in a broader sense all of humanity, than it is on the technology itself. Nothing is inevitable, despite what some in the industry want to have you believe.
In Roger Williams's "The Metamorphosis of Prime Intellect", a scientist didn't understand the statement made by his pet AI, so he opened up the debugger to see the decision tree and set of axioms that led to the decision, and was able to debug it, prune the logic and make adjustments.
I wish I could do this with ChatGPT. The way human beings reason is as opaque as ChatGPT.
When creating learning systems, I think it should be required to include the capability to visualize the thought process which leads to a result. As much to debug the system as to glean insights into reasoning in general.
The picture there sums it up well: It just seems impossible to keep up with the pace that AI is moving. So many papers coming out on a daily basis. So many new techniques. It's hard to see how people can keep their skills up-to-date.
Some kind of alive software will get mass attention & be appealing, and there'll be some crashing wave of interest in actually bringing human and computer closer together rather than building higher higher towers of dead software.
It feels like we are flitting further away from that which makes people grand, making man a toolmaker & owner. Technology's Prescriptive application keeps being used to leverage people while it's Holistic side that allows symbiotic growth is ignored, to use Ursala Franklin terms (https://en.wikipedia.org/wiki/Ursula_Franklin#Holistic_and_p...). We're drifting away from what should be empowering us, from the greatest potential source of liberty & thought we have access to.
I can acknowledge that AI has some ability to offer people means and knowledge, that it can be used to make things. But like the author, I mainly see it as sad and unfortunate, something utterly out of reach & obscure & indecipherable. Monkeys beating on monoliths shit.
What always excited me about software was that I always had renew myself, learn a new thing, change the way I think about something, reflect on what is now possible that wasn't before, etc. I.e. it was never stagnant.
In addition I could try out everything myself, and (more of less) understand what it is doing and why. When I wanted to understand RSA, I read the paper and implemented a PoC myself, or a BTree, or an LSM tree, same for Paxos and Raft.
I worked a lot on databases and "BigData" and on the re-convergence of the two. Much of this is open source, so I could play with it, change it, etc.
In that AI is indeed different. I can install (say) Ollama on my machine and play with it, look at the source code (llama.cpp), etc. And, yet, when I get a response to a prompt, even locally on my machine, I feel blind.
And I used to work on neural networks in the late 90s, when their use was limited, so I understand what they do and how they work.
How exactly was that model trained? On what data? What did it actually learn?
(Aside: Here I am reminded of early usage of neural networks to detect enemy tanks. It worked perfectly in the lab, would correctly classify enemy vs friendly tanks, and in a field test it failed terribly - worse than random. What happened? Well it turned out that the set of photos with enemy tanks mostly showed a particular weather pattern, whereas the friendly photos predominantly showed another. So what the neural network had actually learned was to classify the weather. You might laugh about this now... But that's what I mean.)
So, yeah, I can related to OP, even though I am excited about what AI might bring.
Another aspect I think which makes us down on AI in particular is that it's the first thing which readily seems able to threaten our job security as programmers.
I'd propose a thought experiment where we imagine LLMs and other AI model types don't exist but everything else in computing stays the same (shift to cloud, increasingly asynchronous and interconnected systems). That world actually seems pretty bleak to me. AI upends a lot of industries and yes, it will upend some of our careers. But a world in which it exists seems a lot more interesting than one in which it doesn't.
Instead of solving some deeply rooted problems, people chase shiny and new, while society stays more or less the same.
However, I am apprehensive about how society will navigate these new AI advancements. I believe we won't be able to adapt concepts and cultural techniques as quickly as the reality shifts due to ubiquitous AI. These social changes are beyond my comprehension and overwhelm me.
My engineering education has always helped me explain technology to both my parents and my children. But for now, it's just a matter of "fasten your seat belts."
First, no paper will make you understand why prompt X made result Y. But you can understand the architecture of these systems and try to understand the prompt answer relations somewhat intuitively (e.g., by learning your own models with various text collections).
I think we need to accept these AIs as creatures of their own kind. As complex actors that we don't fully understand.
Humans have been breeding dogs since tens of thousands of years and we don't understand them fully. But we understand enough to employ them in useful ways and mitigate the risks.
And if we only focus on the unreliability, look at many bugs in our systems today. Why does this crash with some input? Why does a new enough processor have security vulnerabilities, so someone can steal passwords? All might as well be magic, even though a few humans, somewhere, might know the reasoning.
And if AI feels difficult, imagine biotech. I could, theoretically, mess with weights and retry a prompt over and over again, seeing what changes. It's a lot of work, but it can be done. See how much fun we have figuring out what a single nucleotide polymorphism does phenotypically, and why it does it: It's a research project by an expert in the easiest of cases! We are only a little ahead of the new C programmer changing things at random to see where his pointer math went wrong.
We don't understand how anything works. We are just sometimes satisfied with our degree of ignorance, and decide to stop asking questions.
I can press a key on my keyboard and work through the system to understand exactly how the physical press up to the letter on the screen works, and then apply it to all the other keys no problem.
AI does not work that way at all. The steps are seemingly random, meandering, and nonsensical, yet it still ends up with these well structured chains of tokens on the output.
It's like a world where Windows and a non-unix based Mac are the only OSs. There is no possibility for something comparable to Linux (FreeBSD etc. etc.) could emerge as viable alternatives free of large corporate control.
So it's not just sad, it's a major problem. Not right now because it's more of a novel toy than anything, but as more systems are built on these LLMs we will be increasingly at the mercy of the companies that control the LLMs.
I tend to see this as a larger societal trend than just computing technology, so maybe my own age is approaching "old fart-hood," too.
Training ML models and data cleanup was already tedious and boring, and "prompt engineering" makes me want to blow my brains out (I work in a company with enough resources and tooling so those are not a problem). I'd rather go debug a memory leak in an old unmaintained C codebase ;)
So many cool things can be built with these tools we have now, so much faster. And while doing this, our experience will be useful in companies wanting to integrate these AI tools.
Checkout what's happening with open / local LLMs, tiny LLMs running on RaspberryPIs, LLama3 about to drop any minute now, Google just released a 1-million context model.
Feels incredibly exciting, I'm not able to relate to these posts.
I think its likely AI will continue to expand. I hope that I can utilize it well enough that I'll still be employable in the future when it can do my current job. Or at least that I know how to get it to pump out endless amounts of enjoyable entertainment so I wont mind not having a job.
There are some academics working on this, but it pales in comparison with how much money is being poured into generative AI.
So today's state-of-the-art models are trained with trial and error, and experts who are building some intuition why some methods work and others don't.
AI itself is a pretty scientific, neutral subject. On the other hand, the use we're making of it currently and the use we will probably make of it in the future is something certainly depressing.
edit. Looks like someone made almost exactly the same comment at the same time.
This is a predictable response but just doesn't hold up. The path through abstractions for a modern CPU/GPU is more straightforward and linear than AI, and you don't have to go very deep to be back to 50-yr-old principles.
>> when all that matters is the observed behavior for day to day usage.
The entire point of the post is that observed behaviour is NOT what matters, vs understanding how we get to these outcomes.
I don’t mind getting into the nitty gritty details of the how transformers work, all the different types of models, etc. because they are so empowering for me. I have never felt that way about a programming language, framework, or anything else in the digital space before.
It’s resolved countless questions of jr devs at work, helped me migrate a C++ project to Python, and continues to help me solve problems in personal and professional projects.
This post is very confusing. It is an amazing utility for engineering (to say nothing of other applications - it gave me a great recipe for dinner the other night, too)
AI will become better exponentially. Extrapolate the theme of this post, what do you get? A significant portion of humanity that are so mad at AI they'll do something about it.
AI is fragile. It needs chips, power, supply chain, maintenance. If enough people are anti AI, our path to AGI can be greatly hindered.
This point hits the nail in the head. If it can happen, and it will increase someone's profit, it will happen. It's not a matter of if, but when.
Maybe true in theory, but in practice, less so.
“I've come up with a set of rules that describe our reactions to technologies:
1. Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.
2. Anything that's invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.
3. Anything invented after you're thirty-five is against the natural order of things.”
-- Douglas Adams
With retirement on the horizon, I cannot wait to close my laptop lid and never touch it again unless it is an absolute necessity.
I'm not criticizing your desire - it's just that for me, the learning and exploration and building is the fun part. As long as it's still either useful for me, or I have the memories, it doesn't matter if it's still used by others, though that can be fun too.
I couldn't imagine having made this my career otherwise.
I just want to get to a time where I am not glued to the internet and do not carry a cellphone. The absolute freedom it brings to me is something that a lot of people are not familiar with today.
I don't want to get into too many details, but a side gig I have, when I bring people out into the field, I take their phones from them. And to watch the withdrawal that they have from the lack of dopamine hits is just sad.
Happy to provide my anecdote. In my 40s and I still live to build/create.
It is my soul. If it is not satisfied, who are you to tell me different? We all have to chop wood, carry water.
>it is not satisfying to _the_ soul.
Instead of writing:
>it is not satisfying to _my_ soul.
The first could be interpreted as you speaking generally (i.e. you think no ones soul can be satisfied by building tech or whatever), the second makes it clear that you are talking about specifically your soul not being satisfied.
I'm getting ready to explore ML applications on Apple systems, but first, I need to finally get around to learning SwiftUI as a shipping app system (I have only been playing with it, so far. Doing ship work is an order of magnitude more than the simple apps that are featured in "Learn SwiftUI" courses).
I'll probably do that, switching over to using SwiftUI for all of my test harnesses. My test harnesses tend to be fairly robust systems.
So far, it looks like I may not be using it to actually ship stuff, for a while. Auto Layout is a huge pain, but it is very, very powerful. I can basically do anything I want, in UI, with it. SwiftUI seems to make using default Apple UI ridiculously easy, but the jury is out, as to how far off the beaten path I can go.
BTW: I have been "retired," since 2017. I wanted to keep working, but no one wanted me, so I set up a small company to buy my toys, and kept coding. I also love learning new stuff.
SwiftUI is very bad at working with maps, and they still have a long way to go. Since most of the stuff I'm doing, these days, is highly location-dependent, I can't compromise.
I have been reading about people hitting these types of walls for a couple of years, and thought that Apple has worked around it. I suspect that the issue is with trying to use UIKit stuff inside of SwiftUI, and I was trying to avoid UIViewRepresentable (because it's a kludge).
Back to UIKit. This stinks. :(
And while some of the above had reasons, I am itching to rewrite more of my stack, and mostly for fun or out of curiosity than any need.
Things looking radically different is more likely to affect my enjoyment of it as a job than in general, though, because for my side projects I can build things as I please, using the tools I want (and mostly have written myself), and can ignore everything I don't like.
I absolutely agree things change, but I've been doing this for 42 years now, and so far it doesn't feel any different, so I'm going to guess I'll stick to this for some time still.
:-)
Side gig? Still do consulting for various entertainment companies on a particular subject.
It's incredibly sad how badly most programmers especially feel about tech. If I'll editorialize for a moment, it seems to most programmers actually hate software. Something about programming just makes people hate software.
I guess I was responding to the part about "closing the laptop forever", which I took to mean closing it off to all the other amazing things you can do with a computer today. But in context, they probably mean just stopping programming.
But it still drives me crazy that god forbid a programmer would actually do something as low as open an Adobe product and actually make something that someone that's not another programmer could actually enjoy. Appreciate the creative good our industry has accomplished for godsake.
It was expected to be empowering, democratizing, censorship resistant, decentralising. The reality is disillusioning.
I'd argue the latter two conflict with the first two. Making something decentralized makes it inherently harder to use (less empowering), making it censorship resistant runs counter to companies interests and companies fund everything (less democratized, i.e., harder to make a living).
60-something here. I know exactly how you feel. I can't point at anything I've worked on in the last 30+ years that's still in use. It's very demotivating. When you first get into tech it's all shiny and new and exciting. But nothing lasts.
“Meaningless! Meaningless!” says the Teacher. “Utterly meaningless! Everything is meaningless.”
What do people gain from all their labors at which they toil under the sun?
Generations come and generations go, but the earth remains forever.
The sun rises and the sun sets, and hurries back to where it rises.
The wind blows to the south and turns to the north; round and round it goes, ever returning on its course.
All streams flow into the sea, yet the sea is never full. To the place the streams come from, there they return again.
All things are wearisome, more than one can say. The eye never has enough of seeing, nor the ear its fill of hearing.
What has been will be again, what has been done will be done again; there is nothing new under the sun.
Is there anything of which one can say, “Look! This is something new”? It was here already, long ago; it was here before our time.
No one remembers the former generations, and even those yet to come will not be remembered by those who follow them.
What a heavy burden God has laid on mankind! I have seen all the things that are done under the sun; all of them are meaningless, a chasing after the wind.
Time marches on, and our great efforts are as nothing once they are replaced by the next big thing.
1. https://support.avaya.com/elmodocs2/cajun/docs/p333t24ug.pdf
However, even though I don't program, nothing in his list prior to AI is beyond me yet, if I wanted to learn it. I am happy being a 25 years Linux power user who climbed the Emacs learning curve to use org-mode and then gradually added email,rss, irc, web, and gopher modalities to it.
These are the low level operations. From a higher level mathematical standpoint? I can prove to you analytically how a SGD optimizer on a convex surface will converge to the global minimum at an exponential rate, starting from either set theory or dependent type theory and the construction of real numbers from sets of rational numbers.
None of these tell me how and why LLM works.
It seems you are both missing the point of article, and jumping on a stereotype.
Said point being: while the author clearly does like learning new stuff, and rolling up his sleeves to deal with all the fiddly bits -- AI is categorically different. In that the complexity is simply off the page compared to most (choosing my words carefully) technologies one is used to geeking out on. And that even experts in the field admit they don't really understand it.
(That, and the sheer resources required to do something interesting, the perpetual lock-in with the sociopathic entities that provide said resources, etc).
Maybe just evaluate things for what they are.
We could also present them as if the person is wrong in all cases:
(1) We tend to accept whatever is already there when we grow up, even though it might be the worst crap and detrimental to ourselves or even society.
(2) We tend to adopt whether "new and revolutionary" appears when we're younger and starting our careers, without often questioning whether it's actually because of marketing hype and whatever it is a regression over what existed.
(3) We tend to dislike new technology when we're older, even though it might be great and improve things.
I was happier then, though. And definitely less cynical. Ignorance is bliss, I guess, though my heart doesn't accept that, so I'm doomed to be forever whiny.
I'd imagine after seeing what Social media has done to us, a lot of people are fearful AI will take us far more down that path than the path to salvation the people trumpeting AI claim it will. I honestly see a lot of parallels between what early social media supposedly would bring to the world, and where we ended up.
It isn't the technology that broke. It's the people.
Not all technologies go to shit. Only those we give to the eternal Septemberists
EVERYTHING about social media is designed to keep you addicted to it. The fact that human nature can be exploited isn't the human's fault, it's the corporation doing the exploiting...
I've seen absolutely nothing coming from the AI sector other than handwavy "we need to be responsible" excuses. Meanwhile we've got deepfakes of Taylor Swift and Joe Biden that Grandma Marge can't tell from the real thing and absolutely nothing can or will be done about it.
I respectfully disagree. People haven’t changed in ten thousand years. It’s technology and culture that have changed. Many changes in culture have been amazing, wonderful breakthroughs (such as human rights). Similarly for technology (green revolution, modern medicine, electricity).
Social media though? That’s a technology that has found its niche exploiting human psychology for profit. It’s on the same dark branch as advertising and “big lie” propaganda/fascism. We were much better off before it!
The quote is about our _reactions_ to technologies, but it is constantly used to dismiss _actual concerns_ about technologies. It doesn't engage with the content of the discussion, but instead dresses up "lol, OP is old" in the clothing of a brilliant writer. Then, if you're lucky enough to have the conversation continue at all, the discussion tends to become about ageism instead of the original concerns.
Social media has made conspiracy theories into somthing akin to a virus, and every year more people start to subscribe to them. Even if the theories have been throughly debunked, they're still gaining a larger audience with every year.
While I'd pretty much never be willing to give up my phone at this point, anyone with a critical mind will have to admit that our helpful distraction device has very real impact on the well being of the society at large. And not in a good way -- at the very least if you're looking at the society as a whole.
Discourse keeps getting more polarizing and what went for "populism" pre 2000 is pretty much table stakes for what goes for a discussion, currently.
Really, he made that statement in the time when people were talking about how "the internet will improve [everything]". In early 2000s, nobody i heard of realized how the internet would actually influence us as a society
The black box aspect is a direct result of AI being a learning technology. A higher order technology. It learns to do things we have not explicitly taught it, or might not even know how to do ourselves.
We will eventually find better ways to analyze and interpret how models work. Why a model produced a specific answer to a given prompt.
But the models will keep getting more powerful too. Today they help coders over the speed bumps of unfamiliar language syntax, or esoteric library conventions. In a few years they will be actively helping researchers with basic problems.
I.e. the hurdles for getting to the front of AI as a field as a technology contributor, and the resources needed, are going to get steeper. Of course there will be many people who do, but it won't be in the same way that most technically savvy people have had many years to learn programming languages, apply them, and even contribute to them, without language's progressing out from under them. (Except for C++ of course! /h)
EDIT: It is worth distinguishing between AI like GPT, as in very flexible and powerful models that will be used across disciplines by all kinds of people regardless of technical chops, vs. the overlapping AI algorithms (partly subset, partly different) applied to smaller learning tasks, which has been a commonplace tool for many years and will continue to have its place.
I'm in exactly the right stage of my life to take advantage of generative AI, but I want nothing to do with it, and somehow an entire industry with millions of people working in it can only focus on one thing at a time.
But ultimately a pat argument. Why? Because it is simply isn't so. The "old world" contains plenty of stupid inefficiencies and annoyances -- we've just rightfully forgotten about them. Everything from tape-based answering machines, to all those tattered paper maps bursting out of your glove compartment (itself an anachronism as few people wear gloves these day), etc.
Survivorship bias, as another commenter mentioned. There's a grain of truth in what he's saying, but not enough to carry the day.
This is why, after all his books were placed in the humor section of your local bookstore (back when people still read physical books, and knew what a bookstore was).
Could someone recommend a starting point to start learning more (book, how-to-series etc) for someone with a non-AI software engineering background?
Which I suppose means I’m less sad about the technology itself. It’s very cool in many respects. I’m sad about the incentives behind and around it which will inevitably make it exhausting at best and predatory at worst.
It will.
> I wonder if it's feasible to classify AI generated text and images with much less computing power.
It won't, at least not for text.
The only hope is that people value good information highly enough that a diverse range of centralized authoritative sources (basically old-school media) become economically viable again.
However this technology means they'll now have to resist the temptation of burning their reputation for short-term profits by sneaking in "AI"-generated crap. My experience with ads being introduced to previously ad-free subscription services and shoehorned into devices I literally own does not give me much hope there.
> But what if OpenAI decides to revoke access to that API feature I’m using?
This starts with personal computers, and why would computers at that time be called "personal"? Part of the problem with this essay is it starts with personal computers - personal computers were called personal because before that was mainframes, which were the same kind of gatekeeping that a cluster of H100s in a data center would be today.
Computers started out as these centralized IBM mainframes, but in 1975 people could buy an Altair kit, which is the same year the MOS 6502 was released. There is some centralization in neural networks now, if that displeases people they can work to do the same kind of thing that MITS and MOS and Apple and even Microsoft did.
> Flipping all those numbers to get the result (inference), and especially determining those numbers in the first place (training), requires a vast amounts of resources, data and skill.
Using a Stable Diffusion model as my base, and a number of pictures of a friend, and a day or two's work on my relatively not-so-powerful Nvidia desktop card, I can now make Stable Diffusion creations with my friend in the mix now. This can be done by different methods - textual inversion, hypernetworks, dreambooth (I have been also told LoRa works, but have not tried it myself).
On the same relatively unpowerful Nvidia card I can run the Llama LLM - with only a few billion parameters, and with quantized less precision - but results are decent enough. I have been told people are fine-tuning these types of LLMs as well.
There's nothing inherently centralized about neural networks - although OpenAI, Nvidia, Google, Facebook, Anthropic and the like tend to have the people who know the most about them, and have enormous resources to put behind development. I'm sure something with the power of an H100 will become cheaper in the coming years. I'm sure tricks will develop to allow inference and even training without the need for a massive amount of VRAM - I see this in all types of places already.
If you don't want some centralized neural network monolith - do what the people at MITS and MOS and Apple did - do what people are already doing, figuring out how to use LLMs on weaker cards with quantization, figuring out how offload some VRAM to RAM for various Pytorch operations. Centralization isn't inherent to neural networks, if you want things more decentralized then there's plenty of things that can be done to achieve that in many areas, and getting to work on that is how you achieve it.
I am only worried of ClosedAI highjacking my killer app, like with Amazon Essentials
Bankruptcy is only a "ClosedGPT whos been using you a lot and for what?" away...
The rise of IT in 80s was so to say prepared in 70s. But the economy has changed drastically since then. There's just no real base for such rise of AI. Just the market cap.
For instance, the financial sector somewhere after the 1945 was about 5% of GDP. Now it's 70-75%.
2. While what you say may perhaps be true on a larger timescale, at the scale of a human lifetime I don't think this is generally true. During the industrialization period in the 18th century, many people lost their jobs and were unhappy.
Human psychology is dominated by getting stuck in emotion-behavior cycles, and those cycles wind up in local maxima.
Change is scary. Self-actualization is hard.
I highly, highly, highly doubt what you’re saying would be true for most people. Regardless of social class. I think most people would view retirement as freedom.
Would they immediately feel secure, content, and know their next direction to take? Probably the fuck not, but hey that’s just being human.
I would feel sad though to see someone give up on retirement and personal development to go back to working on business web apps. You have so much potential, don’t be afraid
People rejoiced at the idea that you could have a magic media-machine computer in your pocket that provided tools for every aspect of your life.
AI seems to be a lot of AI-bros running around highfiving each other over their new startups and everyone else looking around nervously wondering who will lose their jobs first, all while their managers have a new tool to push people harder with because of all the infinite promises made by AI-bros.
These freebies coming from companies like Stability and Mistral are going to dry up real quick once their investors start getting antsy about seeing actual returns on their $billions.
Modern deep learning is almost wholly built off of partial derivatives, the chain rule, and matrix multiplication. It looks complicated from the outside because there are so many people publishing variations on this same basic formula, but it is the same basic formula over and over and over again. I may be a "new fart" myself, but when I set out to become an AI researcher I was honestly kind of surprised how transparent the mechanisms underlying AI actually are! I had expected to need to spend years to learn what I needed to understand it, but in reality it took me about 6 months of dedicated study to get to a point where I felt more-or-less comfortable with how it all worked. Granted, learning how Transformers and attention worked specifically took me a little longer (not sure why, was just some kind of mental block)
> AI is Not Approachable
I would argue that AI is more approachable than it ever has been. A used RTX 3090 with 24GB of VRAM can get you shockingly far for ~800 bucks. Will you be training GPT-5 at home? No. But there is still so much fascinating fundamental research waiting to be done in this field. Personally, I really enjoy using my home PC to implement and train funky novel architecture designs and see if they work on common toy problems. My hope is that one day I'll stumble onto one that becomes a Transformer-killer and then I could get a compute grant or something to try scaling it, but that's likely a pipe dream. Still, I find the amount of cool stuff you can do with consumer hardware today astounding.
> AI is Not Open
At the risk of sounding like a broken record, I think AI is more open than it ever has been. Yes, the state of the art is closed-source right now, but Open Source AI has never been as active and exciting as it is right now. Mistral LLMs, CLIP, Whisper, TheBloke and his crazy quantizations, DINOv2, LLaMA-2, Stable Diffusion, and so much more have been released open in just the past year or two. Local LLMs are slowly (but surely) catching up to GPT-4, even if they are lagging behind by about 6-8 months. The best open LLMs these days are (anecdotally) above the level of ChatGPT-3.5 when it launched. That's exciting! Of course all the coolest, most sexy AI research is happening behind closed doors, but to see that and conclude that AI is "not open" feels shortsighted.
In conclusion: What a time to be alive! I think it's perfectly valid to feel nervous about the future, because things, they are a-changing! And change is scary! But I really do believe that there is more to be excited about than there is to be worried about, and that everything will Work Out™ in the end.
It's just another tool in the toolbox. Personally, I think we've reached the limits of "computers do exactly what you ask them to do, to a fault." I'm interested to see how the opposite direction works out for us.
Good point, maybe this is the dawn of a new kind of computer engineering, a higher level, fundamentally social one.
We already see people "hacking" chatGPT to reveal its system prompts or get around its given boundaries using nothing but clever conversational logic tricks.
This is really, really funny in the context of age and perspective. But not in the way you meant.
https://en.m.wikipedia.org/wiki/The_Monkey%27s_Paw
The Simpsons borrowed from that.
I mean, I'm a software developer and I don't really understand how a complier works.
I'm sure there is a non-zero benefit to my abilities if I did understand more about how a complier works, but since I work on higher level software and nothing that is really so resource constrained, I just don't think I really need to understand the low level to be useful and successful.
What’s worse is that it isn’t any one thing. AI will eventually learn any skill any human can do, and do it far better, more consistently and for less cost than any human could. It’s hard for me to justify the expense (time, money, effort) on learning anything new when some LLM or model can and will do it better.
Why learn video production? Why learn 3D modeling? Why learn coding? Why learn any kind of art?
Why learn anything anymore? It’s not a sound financial decision.