I'm an Old Fart and AI Makes Me Sad(medium.com)
medium.com
I'm an Old Fart and AI Makes Me Sad
https://medium.com/@alex.suzuki/im-an-old-fart-and-ai-makes-me-sad-06003bfb6750
302 comments
Another reason why AI makes me sad: take for example images. There is really no reason anymore to look at any posted art, image or article illustration, since, while it may look like an image, it may not be an image in the sense of an opus operatum. There is no sense in investigating and uncovering the decisions and choices that made the image, its composition, style, etc., thus opening the image to an experience, as a projection of human experience and thought, since there were no such choices made. If there are no (strong) con-textual hints at this being an actual image, there is really no sense in looking at it, anymore. And now much the same for video. – For an ardent fan of visual media, it's just depressing.
I understand that AI is especially academic right now, but I'm not sure OP appreciates just how impenetrable everything else was to the masses before him. Nothing about DOS or SMTP have ever felt accessible to the vast majority of people before now. If anything this should spark some empathy.
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.
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.
I understand the sadness around not understanding it, it's fucking hard. however, there are more and more resources online getting published for how to get started understanding it that help with understanding the math at an abstraction that helps with learning how to build with it.
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.
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 you had witnessed the Wright brothers' first flight you would have had a hard time predicting that within 60 years we would be landing on the moon. And if you were playing computer games in the 1980s in a slow DOS box you could not have even imagined modern GPUs and modern games. Nor could we have predicted how far modern CPUs have come since the 1960s.
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 $$$ to spend on training, that's it. In this case it really is just math.
I think market forces will drive a breakthrough in training at some point, either mathematical (better algorithms) or technological (better training hardware). And that will reduce the moat of these companies and open the space up even more.
LLMs are a very new technology and we can't predict where they'll be in 50 years, but I, for one, I'm optimistic about it. Mostly because it is very much not GAI so its impact will be lower. I don't think it will be more revolutionary than transistors or personal computers, but it will be impactful for sure. There's a large cost of entry right now but that has almost always been the case for bleeding-edge technologies. And I'm not too worried about these private companies having total control over it in the long run. They, so far, have no moat but $$$ 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.
My biggest worry with AI and software dev is the degree to which people are okay with things that sort of work. I mean, we're already there with code that humans write. I'm not sure that humans who can't write the code themselves can always fix bugs that sneak into AI-generated code.
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.
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.
Something I take issue with concerning AI, which is also incidentally why I remain skeptical of it, is that it feels like it doesn’t increase agency in any meaningful way, and sometimes decreases it.
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.
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.
"If I build an app that needs persistence, I might use Postgres and S3 for storing data. If those are no longer available, I’ll use another relational database, key-value store, distributed filesystem, whatever. But what if OpenAI decides to revoke access to that API feature I’m using? What if they change pricing and make it uneconomical to run?"
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.
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.
I am working in ai all my professional life and have to admit that right now it’s both exciting as well as burning me out.
I get tired when I scroll through LinkedIn and see all the dall-e images. I am annoyed by all the snake oil sellers who say that they will change the world - and then there is just another prompt underneath.
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.
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.
OK this kind of reminds of CPUs.
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.
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.
I have a lot of these moments nowadays where I try to figure out if I am becoming the old fart, or if my skepticism is actually legitimate. It’s a difficult exercise, often futile, but it’s a good first step. It always felt like my parents never really asked themselves “why do we feel like technology X is bad”…they just had a knee jerk reaction to it and banned me from it.
I think I'm an even older fart, and a lot of this resonates with me. So a few thoughts.
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.
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.
This defeatist mentality is plain stupid. There are under 20yrs old working on AI today, they went from 0 to their current knowledge in most cases 2-3 years. So what to do? Level up.
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!
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!
GANs are ruining AI research the way cryptocurrency ruins cryptography research.
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.
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.
I'm having trouble seeing AI as anything more than the next bitcoin - frightening because of the people who are adamant it will do things it really can't. In particular, the hallucination glitch seems impossisble to resolve, and no one appears to care. It's like the Emperor's New Clothes.
I'm sorry to hear that it makes you sad. Maybe it's not empowering for many programmers, but stuff like GPT has inspired many non-programmers to learn programming just to use it. Which is pretty empowering.
Author here. I never expected my rant to blow up like this, probably I‘ve hit a nerve.
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!
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!
> I want to understand how things work. AI feels like a black box to me. The amount of papers I’d have to read and mathematics that I’d have to ingest to really understand why a certain prompt X results in a certain output Y feels overwhelming.
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.
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'm at the cusp of becoming an old fart, and AI makes me happy. It brought back a lot of the fun and wonder I had with computers that is long gone now. I often prefer using Stable Diffusion and local LLMs over computer games now, because it's more fun to me.
I wouldn’t worry too much on a lot of these points.
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.
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.
Many of the points made in this article apply to generative AI specifically. Which makes sense, because it's the flavor of the decade. But I think it's worth pointing that out, because AI does not inherently have to be inaccessible and unexplainable.
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.
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.
I share some of the same sentiment.
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.
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.
We old farts are going to have a blast in our nursing homes with AI goggles.
> The amount of papers I’d have to read and mathematics that I’d have to ingest to really understand why a certain prompt X results in a certain output Y feels overwhelming. Even some top scientists in the field admit that we don’t really understand how AI works.
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.
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.
There have been few signs for hope and yet my hope persists, that eventually computing will open up more.
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.
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.
I'm an old fart too. First computer what the ZX81 with 1K of RAM :)
That's how it all started, and made a carrier out of it.
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.
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.
I know AI makes you sad for a different reason but I always find amusement when reading a title like that and seeing an AI generated image.
A lot of downers on AI and I can understand it, part of it is the response to things invented when we're older - new music, new movies, new technologies. I guess most of our brains are less plastic as we age and we resist incorporating these new, unfamiliar things in our lives and instead reaffirm the old attributes that make up who we think we are and what we already identify with.
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.
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.
I disagree with the author. Insights and opportunities to understand new technological developments increase every year. In the early years of these technologies, they were always less approchable to me. While in the 80s, I had to rely on outdated books from the library for superficial knowledge about PCs, now I can actively engage with AI developments in near real-time, experimenting with it as a service, running it on my own machine, or even training smaller models.
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."
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."
> I want to understand how things work. AI feels like a black box to me. The amount of papers I’d have to read and mathematics that I’d have to ingest to really understand why a certain prompt X results in a certain output Y feels overwhelming.
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.
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.
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.