7 ms·

If it helps, this likely is coming. I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Y

If it helps, this likely is coming. I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Years ago this would have been a similar level of impossibility. Since all a codebase like that is is a kind of directed graph, then augmentations to the processing of the network to allow for the simultaneous parsing of and generation of this kind of code may not be as far off as you thinking. I say this as an ML researcher of coming up and around the bend towards 6 years of experience in the heavily technical side of the field. Strong negative skepticism is an easy way to bring confidence and the appearance of knowledge, but it also can have the downfall of what has happened in certain past technological revolutions -- and the threat is very much real here (in contrast to the group that believes you can get AGI from simply scaling LLMs, I think that is very silly indeed). Thank you for your comment, I really appreciate it and the discussion it generated and appreciate you posting it. Replying to it was fun, thank you.

morelisp 4y ago
Translating an idiomatic structured loop into assembly used to be an "L3" question (honestly, probably higher), yet compilers could do it with substantially fewer resources than and decades before any of these LLMs. While I wouldn't dare offer particular public prognostications about the effect transformer codegens will have on the industry, especially once filtered through a profit motive - the specific technical skill a programmer is called upon to learn at various points in their career has shifted wildly throughout the industry's history, yet the actual job has at best inflected a few times and never changed very dramatically since probably the 60s.
tedivm 4y ago
I've worked in ML for awhile (on the MLOps side of things) and have been in the industry for a bit, and one thing that I think is extremely common is for ML researchers to grossly underestimate the amount of work needed to make improvements. We've been a year away from full self driving cars for the last six years, and it seems like people are getting more cautious in their timing around that instead of getting more optimistic. Robotic manufacturing- driven by AI- was supposedly going to supplant human labor and speed up manufacturing in all segments from product creation to warehousing, but Amazon warehouses are still full of people and not robots. What I've seen again and again from people in the field is a gross underestimation of the long tail on these problems. They see the rapid results on the easier end and think it will translate to continued process, but the reality is that every order of magnitude improvement takes the same amount of effort or more. On top of that there is a massive amount of subsidies that go into training these models. Companies are throwing millions of dollars into training individual models. The cost here seems to be going up, not down, as these improvements are made. I also think, to be honest, that machine learning researchers tend to simplify problems more than is reasonable. This conversation started with "highly scalable system from scratch, or an ultra-low latency trading system that beats the competition" and turned into "the parsing of and generation of this kind of code"- which is in many ways a much simpler problem than what op proposed. I've seen this in radiology, robotics, and self driving as well. Kind of a tangent, but one of the things I do love about the ML industry is the companies who recognize what I mentioned above and work around it. The companies that are going to do the best, in my extremely bias opinion, are the ones that use AI to augment experts rather than try to replace them. A lot of the coding AI companies are doing this, there are AI driving companies that focus on safety features rather than driver replacement, and a company I used to work for (Rad AI) took that philosophy to Radiology. Keeping experts in the loop means that the long tail isn't as important and you can stop before perfection, while replacing experts altogether is going to have a much higher bar and cost.
- passwordoops 4y ago
  >We've been a year away from full self driving cars for the last six years Try at least 12 [0] (I would say 15 but my 45-second search didn't yield anything that far back) [0] https://spectrum.ieee.org/how-google-self-driving-car-works https://spectrum.ieee.org/how-google-self-driving-car-works
  - jodrellblank 4y ago
    https://www.youtube.com/watch?v=I39sxwYKlEE https://www.youtube.com/watch?v=I39sxwYKlEE was a self-driving van in 1986 which could detect obstacles by 1999 and drive in a convoy using that. This is a bit like seeing Steve Mann's wearable computers over the years ( https://cdn.betakit.com/wp-content/uploads/2013/08/Wearcompevolution2-1.jpg https://cdn.betakit.com/wp-content/uploads/2013/08/Wearcompe... ) and then today anyone with a smartphone and smart watch has more computing power and more features than most of his gear ever had, apart from the head mounted screen. More processing power, more memory, more storage, more face recognition, more motion sensing, more GPS, longer runtime on battery, more bandwidth and connectivity to e.g. mapping, more assistants like Google Now and Siri. And we still aren't at a level where you can be doing a physical task like replacing a laptop screen and have your device record what you're doing, with voice prompts for when you complete different stages, have it add markers to the recording, track objects in the scene like and solve for questions like 'where did that longer screw go?' or 'where did this part come from?' and have it jump to the video where you took that part out. Nor reflow the video backwards as an aide memoire to reassembling it. Or do that outside for something like garage or car work, or have it control and direct lighting on some kind of robot arm to help you see, or have it listen to the sound of your bike gears rattle as you tune them and tell you or show you on a graph when it identifies the least rattle. Anything a human assistant could easily do, we're still at the level of 'set a reminder' or 'add to calendar' rather than 'help me through this unfamiliar task'.
    - mech422 4y ago
      Wow - Steve Mann - haven't checked what he's doing in ages - real blast from the past :-) I was really disappointed the AR/VR company he was with went under - I had really high hopes for it. RE: changing you laptop screen. My buddy wants an 'AR for Electronics' that can zoom in on components like a magnifying glass (he wants head mounted), identify components by marking/color/etc and call up schematics on demand. So far, nothing seems to be able to do that basic level of work.
phoehne 4y ago
I think you’re right in one sense, and we both agree LLMs are not sufficient. I think they are definitely the death knell for the junior python developer that slaps together common APIs by googling the answers. The same way good, optimizing C, C++, … compilers destroyed the need for wide-spread knowledge of assembly programming. 100% agreed on that. Those are the most precarious jobs in the industry. Many of those people might become LLM whisperers, taking their clients requests and curating prompts. Essentially becoming programmers over the prompting system. Maybe they’ll write a transpiler to generate prompts? This would be par of the course with other languages (like SQL) that were originally meant to empower end-users. The problem with current AI generated code from neural networks is the lack of an explanation. Especially when we’re dealing with anything safety critical or with high impact (like a stock exchange), we’re going to need an explanation of how the AI got to its solution. (I think we’d need the same for medical diagnosis or any high-risk activity). That’s the part where I think we’re going to need breakthroughs in other areas. Imagine getting 30,000-ish RISCV instructions out of an AI for a braking system. Then there’s a series of excess crashes when those cars fail to brake. (Not that human written software doesn’t have bugs, but we do a lot to prevent that.). We’ll need to look at the model the AI built to understand where there’s a bug. For safety related things we usually have a lot of design, requirement, and test artifacts to look at. If the answer is ‘dunno - neural networks, ya’ll’, we’re going to open up serious cans of worms. I don’t think an AI that self evaluates its own code is even on the visible horizon.
- jokethrowaway 4y ago
  I don't think chatgpt lacks an explanation. It can explain what it's doing. It's just that it can be completely wrong or the explanation may be correct and the code wrong. I gave some code to ChatGPT asking to simplify it and it returned the correct code but off by one. It was something dealing with dates, so it was trivial to write a loop checking for each day if the new code matched in functionality the old one. You will never have certainty the code makes any sense if it's coming from one of these high tech parrots. With a human you can at least be sure the intention was there.
  - scarface74 4y ago
Bukhmanizer 4y ago
I agree this would have been thought to be impossible a few years ago, but I don't think it's necessarily moving the goalposts. I don't think software engineers are really paid for their labour exactly. FAANG is willing to pay top dollar for employees, because that's how they retain dominance over their markets. Now you could say that LLMs enable Google to do what it does now with fewer employees, but the same thing is true for every other competitor to Google. So the question is how will Google try and maintain dominance over it's competitors now? Likely they will invest more heavily in AI and probably make some riskier decisions but I don't see them suddenly trying to cheap out on talent. I also think that it's not a zero sum game. The way that technology development has typically gone is the more you can deliver, the more people want. We've made vast improvements in efficiency and it's entirely possible that what an entire team's worth of people was doing in 2005 could be managed by a single person today. But technology has expanded so much since then that you need more and more people just to keep up pace.
- jeffbee 4y ago
  Google already published a paper claiming to have deployed an LLM for code generation at full scale to its tens of thousands of software engineers, years ago.
  - Linorm 4y ago
    Do you happen to have a link to this paper? I can't seem to find it.
LudwigNagasena 4y ago
> I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Years ago this would have been a similar level of impossibility. Define "we". There are all kinds of people with all kinds of opinions. I didn't notice any consensus on the questions of AI. There are people with all kinds of educations and backgrounds on the opposite sides and in-between.
- gptgpp 4y ago
  I mean, you can just as easily make the claim that researchers shift goalposts as a "self-defense" mechanism. For example... Hows that self-driving going? Got all those edge-cases ironed out yet? Oh, by next year? Wierd, that sounds very familiar... Remember about Tesla's autopilot was released 9 years ago, and the media began similar speculation about how all of the truckers were going to get automated out of a job by AI? And then further speculation about how Taxi drivers were all going to be obsolete? Those workers are the ones shifting the goal posts though as a "self-defense mechanism", sure, sure... lol.
  - lostmsu 4y ago
    Well, there's a difference between the situation with self-driving and with language models. With self-driving, we barely ever saw anything obviously resembling human abilities, but there was a lot of marketing promising more. With language models when GPT-2 came out everyone was still saying it is a "stochastic parrot" and even GPT-3 was one. But now there's ChatGPT, and every single teenager is aware that that tool is capable of replacing them with their school assignments. And as a dev I am aware that it can write code. And yet not many people expected any of this to happen this year, neither were those capabilities promised at any point in the past. So if anything, self-driving was always overhyped, while the LLMs are quite underhyped.
    - quonn 4y ago
      We actually saw a lot resembling human abilities. It just turns out that it‘s not enough to blindly rely on it in all situations and so here we are. And it‘s quite similar with LLMs. One difference, though, is that it‘s economically not much use to have self-driving if the backup driver has to be in the car or present. While partially automating programming would make it possible to use far less programmers for the same amount of work.
CommieBobDole 4y ago
I don't think ChatGPT or its successors will be able to do large-scale software development, defined as 'translating complex business requirements into code', but the actual act of programming will become more one of using ML tools to create functions, and writing code to link them together with business logic. It'll still be programming, but it will just start at a higher level, and a single programmer will be vastly more productive. Which, of course, is what we've always done; modern programming, with its full-featured IDEs, high level languages, and feature-rich third-party libraries is mostly about gluing together things that already exist. We've already abstracted away 99% of programming over the last 40 years or so, allowing a single programmer today to build something in a weekend that would have taken a building full of programmers years to build in the 1980s. The difference is, of course, this is going to happen fairly quickly and bring about an upheaval in the software industry to the detriment of a lot of people. And of course, this doesn't include the possibility of AGI; I think we're a very long way from that, but once it happens, any job doing anything with information is instantly obsolete forever.
- SketchySeaBeast 4y ago
  That's my assumption as well - the human programmers will far more productive, but they'll still be required because there's no way we can take the guard rails off and let the AI build - it'll build wrong unit tests for wrong functions which create wrong programs and will require humans to get it back on track.
  - brookst 4y ago
    Is your opinion time bounded to 5 years? 20 years? 100 years? Forever?
    - SketchySeaBeast 4y ago
      I don't think anyone can say what's going to happen in 10 years, but what I do know is if you look back people have been saying programmers will be obsolete in 10 years for way longer than a decade.
  - 4y ago
saurik 4y ago
I've been hearing this "you're moving the goalposts" argument for over 20 years now, ever since I was a college student taking graduate courses in Cognitive Science (which my University decided to cobble together at the time out of Computer Science, Psychology, Biology, and Geography), and I honestly don't think it is a useful framing of the argument. In this case, it could be that you are just talking to different people and focusing on their answers. I am more than happy to believe that Copilot and ChatGPT, today, cause a bunch of people fear. Does it cause me fear? No. And if you had asked me five years ago "if I built a program that was able to generate simple websites, or reconfigure code people have written to solve problems similar to ones solved before, would that cause you to worry?" I also would have said "No", and I would have looked at you as crazy if you thought it would. Why? Because I agree with the person you are replying to (though I would have used a slightly-less insulting term than "trash engineers", even if mentally it was just as mean): the world already has too many "amateur developers" and frankly most of them should never have learned to program in the first place. We seriously have people taking month or even week long coding bootcamps and then thinking they have a chance to be a "rock star coder". Honestly, I will claim the only reason they have a job in the first place is because a bunch of cogs--many of whom seem to work at Google--massively crank the complexity of simple problems and then encourage us all to type ridiculous amounts of boilerplate code to get simple tasks done. It should be way easier to develop these trivial things but every time someone on this site whines about "abstraction" another thousand amateurs get to have a job maintaining boilerplate. If anything, I think my particular job--which is a combination of achieving low-level stunts no one has done before, dreaming up new abstractions no one has considered before, and finding mistakes in code other people have written--is going to just be in even more demand from the current generation of these tools, as I think this stuff is mostly going to encourage more people to remain amateurs for longer and, as far as anyone has so far shown, the generators are more than happy to generate slightly buggy code as that's what they were trained on, and they have no "taste". Can you fix this? Maybe. But are you there? No. The reality is that these systems always seem to be missing something critical and, to me, obvious: some kind of "cognitive architecture" that allows them to think and dream possibilities, as well as a fitness function that cares about doing something interesting and new instead of being "a conformist": DALL-E is sometimes depicted as a robot in a smock dressed up to be the new Pablo Picasso, but, in reality, these AIs should be wearing business suits as they are closer to Charles Schmendeman. But, here is the fun thing: if you do come for my job even in the near future, will I move the goal post? I'd think not, as I would have finally been affected. But... will you hear a bunch of people saying "I won't be worried until X"? YES, because there are surely people who do things that are more complicated than what I do (or which are at least different and more inherently valuable and difficult for a machine to do in some way). That doesn't mean the goalpost moved... that means you talked to a different person who did a different thing, and you probably ignored them before as they looked like a crank vs. the people who were willing to be worried about something easier. And yet, I'm going to go further: if the things I tell you today--the things I say are required to make me worry--happen and yet somehow I was wrong and it is the future and you technically do those things and somehow I'm still not worried, then, sure: I guess you can continue to complain about the goalposts being moved... but is it really my fault? Ergo: was it me who had the job of placing the goalposts in the first place? The reality is that humans aren't always good at telling you what you are missing or what they need; and I appreciate that it must feel frustrating providing a thing which technically implements what they said they wanted and it not having the impact you expected--there are definitely people who thought that, with the tech we have now long ago pulled off, cars would be self-driving... and like, cars sort of self-drive? and yet, I still have to mostly drive my car ;P--then I'd argue the field still "failed" and the real issue is that I am not the customer who tells you what you have to build and, if you achieve what the contract said, you get paid: physics and economics are cruel bosses whose needs are oft difficult to understand.
ryanjshaw 4y ago
I think OP set relatively simple goals. How long until AI can architect, design, build, test, deploy and integrate commercial software systems from scratch, and handle users submitting bug reports that say "The OK button doesn't work when I click it!"?
jrockway 4y ago
I'm kind of interested in how AI is going to interface with the world. Humans have a lot of autonomy to change the physical world they're in; from rearranging furniture, to building structures, to visiting other worlds. Why isn't AI doing any of that stuff? As programmers, we keep talking about programming jobs and how AI will eliminate them all. But nobody is talking about eliminating other jobs. When will a robot vacuum be able to clean my apartment as quickly as I? Why isn't there a robot that takes my garbage out on Tuesday night? When will AI plan and build a new tunnel under the Hudson River for trains? When will airliners be pilotless? If AI can't do this stuff, what makes software so different? Why will AI be good at that but not other things? It seems like the only goal is to eliminate jobs doing things people actually like (art, music, literature, etc.), and not eliminate any tedium or things that is a waste of humanity's time whatsoever. (On the software front, when will AI decide what software to build? Will someone have to tell it? Will it do it on its own? Why isn't it doing this right now?) My takeaway is that this all raises a lot of questions for me on how far along we actually are. Language models are about stringing together words to sound like you have understanding, but the understanding still isn't there. But, I suppose we won't know understanding until we see it. Do we think that true understanding is just a year or two away? 10? 50? 100? 1000?
- mLuby 4y ago
  Household tasks can involve a robot moving with enough kinetic energy to maim or kill a human (or pet) in unlucky circumstances. And we'll quickly become habituated to their presence and so careless around them. Even a Roomba could knock granny down the stairs if it isn't careful about its environment. You could make the same argument as with self-driving cars, that people already get hurt this way and maybe the robot is in fact safer. But it's still a hard sell that Sunny-01 has only accidentally killed 1/10 as many children as parents have—the number has to be more like zero. Let's solve automating trains first then we can do airliners.
solumunus 4y ago
So you've drank the industry kool aid.
water-your-self 4y ago
I think there's a real story here behind the ownership and usage of proprietary data.
deleted 4y ago
[deleted]

morelisp 4y ago

Translating an idiomatic structured loop into assembly used to be an "L3" question (honestly, probably higher), yet compilers could do it with substantially fewer resources than and decades before any of these LLMs. While I wouldn't dare offer particular public prognostications about the effect transformer codegens will have on the industry, especially once filtered through a profit motive - the specific technical skill a programmer is called upon to learn at various points in their career has shifted wildly throughout the industry's history, yet the actual job has at best inflected a few times and never changed very dramatically since probably the 60s.

tedivm 4y ago

I've worked in ML for awhile (on the MLOps side of things) and have been in the industry for a bit, and one thing that I think is extremely common is for ML researchers to grossly underestimate the amount of work needed to make improvements. We've been a year away from full self driving cars for the last six years, and it seems like people are getting more cautious in their timing around that instead of getting more optimistic. Robotic manufacturing- driven by AI- was supposedly going to supplant human labor and speed up manufacturing in all segments from product creation to warehousing, but Amazon warehouses are still full of people and not robots. What I've seen again and again from people in the field is a gross underestimation of the long tail on these problems. They see the rapid results on the easier end and think it will translate to continued process, but the reality is that every order of magnitude improvement takes the same amount of effort or more. On top of that there is a massive amount of subsidies that go into training these models. Companies are throwing millions of dollars into training individual models. The cost here seems to be going up, not down, as these improvements are made. I also think, to be honest, that machine learning researchers tend to simplify problems more than is reasonable. This conversation started with "highly scalable system from scratch, or an ultra-low latency trading system that beats the competition" and turned into "the parsing of and generation of this kind of code"- which is in many ways a much simpler problem than what op proposed. I've seen this in radiology, robotics, and self driving as well. Kind of a tangent, but one of the things I do love about the ML industry is the companies who recognize what I mentioned above and work around it. The companies that are going to do the best, in my extremely bias opinion, are the ones that use AI to augment experts rather than try to replace them. A lot of the coding AI companies are doing this, there are AI driving companies that focus on safety features rather than driver replacement, and a company I used to work for (Rad AI) took that philosophy to Radiology. Keeping experts in the loop means that the long tail isn't as important and you can stop before perfection, while replacing experts altogether is going to have a much higher bar and cost.

passwordoops 4y ago
>We've been a year away from full self driving cars for the last six years Try at least 12 [0] (I would say 15 but my 45-second search didn't yield anything that far back) [0] https://spectrum.ieee.org/how-google-self-driving-car-works https://spectrum.ieee.org/how-google-self-driving-car-works
- jodrellblank 4y ago
  https://www.youtube.com/watch?v=I39sxwYKlEE https://www.youtube.com/watch?v=I39sxwYKlEE was a self-driving van in 1986 which could detect obstacles by 1999 and drive in a convoy using that. This is a bit like seeing Steve Mann's wearable computers over the years ( https://cdn.betakit.com/wp-content/uploads/2013/08/Wearcompevolution2-1.jpg https://cdn.betakit.com/wp-content/uploads/2013/08/Wearcompe... ) and then today anyone with a smartphone and smart watch has more computing power and more features than most of his gear ever had, apart from the head mounted screen. More processing power, more memory, more storage, more face recognition, more motion sensing, more GPS, longer runtime on battery, more bandwidth and connectivity to e.g. mapping, more assistants like Google Now and Siri. And we still aren't at a level where you can be doing a physical task like replacing a laptop screen and have your device record what you're doing, with voice prompts for when you complete different stages, have it add markers to the recording, track objects in the scene like and solve for questions like 'where did that longer screw go?' or 'where did this part come from?' and have it jump to the video where you took that part out. Nor reflow the video backwards as an aide memoire to reassembling it. Or do that outside for something like garage or car work, or have it control and direct lighting on some kind of robot arm to help you see, or have it listen to the sound of your bike gears rattle as you tune them and tell you or show you on a graph when it identifies the least rattle. Anything a human assistant could easily do, we're still at the level of 'set a reminder' or 'add to calendar' rather than 'help me through this unfamiliar task'.
  - mech422 4y ago
    Wow - Steve Mann - haven't checked what he's doing in ages - real blast from the past :-) I was really disappointed the AR/VR company he was with went under - I had really high hopes for it. RE: changing you laptop screen. My buddy wants an 'AR for Electronics' that can zoom in on components like a magnifying glass (he wants head mounted), identify components by marking/color/etc and call up schematics on demand. So far, nothing seems to be able to do that basic level of work.

phoehne 4y ago

I think you’re right in one sense, and we both agree LLMs are not sufficient. I think they are definitely the death knell for the junior python developer that slaps together common APIs by googling the answers. The same way good, optimizing C, C++, … compilers destroyed the need for wide-spread knowledge of assembly programming. 100% agreed on that. Those are the most precarious jobs in the industry. Many of those people might become LLM whisperers, taking their clients requests and curating prompts. Essentially becoming programmers over the prompting system. Maybe they’ll write a transpiler to generate prompts? This would be par of the course with other languages (like SQL) that were originally meant to empower end-users. The problem with current AI generated code from neural networks is the lack of an explanation. Especially when we’re dealing with anything safety critical or with high impact (like a stock exchange), we’re going to need an explanation of how the AI got to its solution. (I think we’d need the same for medical diagnosis or any high-risk activity). That’s the part where I think we’re going to need breakthroughs in other areas. Imagine getting 30,000-ish RISCV instructions out of an AI for a braking system. Then there’s a series of excess crashes when those cars fail to brake. (Not that human written software doesn’t have bugs, but we do a lot to prevent that.). We’ll need to look at the model the AI built to understand where there’s a bug. For safety related things we usually have a lot of design, requirement, and test artifacts to look at. If the answer is ‘dunno - neural networks, ya’ll’, we’re going to open up serious cans of worms. I don’t think an AI that self evaluates its own code is even on the visible horizon.

jokethrowaway 4y ago
I don't think chatgpt lacks an explanation. It can explain what it's doing. It's just that it can be completely wrong or the explanation may be correct and the code wrong. I gave some code to ChatGPT asking to simplify it and it returned the correct code but off by one. It was something dealing with dates, so it was trivial to write a loop checking for each day if the new code matched in functionality the old one. You will never have certainty the code makes any sense if it's coming from one of these high tech parrots. With a human you can at least be sure the intention was there.
- scarface74 4y ago

Bukhmanizer 4y ago

I agree this would have been thought to be impossible a few years ago, but I don't think it's necessarily moving the goalposts. I don't think software engineers are really paid for their labour exactly. FAANG is willing to pay top dollar for employees, because that's how they retain dominance over their markets. Now you could say that LLMs enable Google to do what it does now with fewer employees, but the same thing is true for every other competitor to Google. So the question is how will Google try and maintain dominance over it's competitors now? Likely they will invest more heavily in AI and probably make some riskier decisions but I don't see them suddenly trying to cheap out on talent. I also think that it's not a zero sum game. The way that technology development has typically gone is the more you can deliver, the more people want. We've made vast improvements in efficiency and it's entirely possible that what an entire team's worth of people was doing in 2005 could be managed by a single person today. But technology has expanded so much since then that you need more and more people just to keep up pace.

jeffbee 4y ago
Google already published a paper claiming to have deployed an LLM for code generation at full scale to its tens of thousands of software engineers, years ago.
- Linorm 4y ago
  Do you happen to have a link to this paper? I can't seem to find it.

LudwigNagasena 4y ago

> I think we have a tendency to mentally move the goalposts when it comes to this kind of thing as a self-defense mechanism. Years ago this would have been a similar level of impossibility. Define "we". There are all kinds of people with all kinds of opinions. I didn't notice any consensus on the questions of AI. There are people with all kinds of educations and backgrounds on the opposite sides and in-between.

gptgpp 4y ago
I mean, you can just as easily make the claim that researchers shift goalposts as a "self-defense" mechanism. For example... Hows that self-driving going? Got all those edge-cases ironed out yet? Oh, by next year? Wierd, that sounds very familiar... Remember about Tesla's autopilot was released 9 years ago, and the media began similar speculation about how all of the truckers were going to get automated out of a job by AI? And then further speculation about how Taxi drivers were all going to be obsolete? Those workers are the ones shifting the goal posts though as a "self-defense mechanism", sure, sure... lol.
- lostmsu 4y ago
  Well, there's a difference between the situation with self-driving and with language models. With self-driving, we barely ever saw anything obviously resembling human abilities, but there was a lot of marketing promising more. With language models when GPT-2 came out everyone was still saying it is a "stochastic parrot" and even GPT-3 was one. But now there's ChatGPT, and every single teenager is aware that that tool is capable of replacing them with their school assignments. And as a dev I am aware that it can write code. And yet not many people expected any of this to happen this year, neither were those capabilities promised at any point in the past. So if anything, self-driving was always overhyped, while the LLMs are quite underhyped.
  - quonn 4y ago
    We actually saw a lot resembling human abilities. It just turns out that it‘s not enough to blindly rely on it in all situations and so here we are. And it‘s quite similar with LLMs. One difference, though, is that it‘s economically not much use to have self-driving if the backup driver has to be in the car or present. While partially automating programming would make it possible to use far less programmers for the same amount of work.

CommieBobDole 4y ago

I don't think ChatGPT or its successors will be able to do large-scale software development, defined as 'translating complex business requirements into code', but the actual act of programming will become more one of using ML tools to create functions, and writing code to link them together with business logic. It'll still be programming, but it will just start at a higher level, and a single programmer will be vastly more productive. Which, of course, is what we've always done; modern programming, with its full-featured IDEs, high level languages, and feature-rich third-party libraries is mostly about gluing together things that already exist. We've already abstracted away 99% of programming over the last 40 years or so, allowing a single programmer today to build something in a weekend that would have taken a building full of programmers years to build in the 1980s. The difference is, of course, this is going to happen fairly quickly and bring about an upheaval in the software industry to the detriment of a lot of people. And of course, this doesn't include the possibility of AGI; I think we're a very long way from that, but once it happens, any job doing anything with information is instantly obsolete forever.

SketchySeaBeast 4y ago
That's my assumption as well - the human programmers will far more productive, but they'll still be required because there's no way we can take the guard rails off and let the AI build - it'll build wrong unit tests for wrong functions which create wrong programs and will require humans to get it back on track.
- brookst 4y ago
  Is your opinion time bounded to 5 years? 20 years? 100 years? Forever?
  - SketchySeaBeast 4y ago
    I don't think anyone can say what's going to happen in 10 years, but what I do know is if you look back people have been saying programmers will be obsolete in 10 years for way longer than a decade.
- 4y ago

saurik 4y ago

I've been hearing this "you're moving the goalposts" argument for over 20 years now, ever since I was a college student taking graduate courses in Cognitive Science (which my University decided to cobble together at the time out of Computer Science, Psychology, Biology, and Geography), and I honestly don't think it is a useful framing of the argument. In this case, it could be that you are just talking to different people and focusing on their answers. I am more than happy to believe that Copilot and ChatGPT, today, cause a bunch of people fear. Does it cause me fear? No. And if you had asked me five years ago "if I built a program that was able to generate simple websites, or reconfigure code people have written to solve problems similar to ones solved before, would that cause you to worry?" I also would have said "No", and I would have looked at you as crazy if you thought it would. Why? Because I agree with the person you are replying to (though I would have used a slightly-less insulting term than "trash engineers", even if mentally it was just as mean): the world already has too many "amateur developers" and frankly most of them should never have learned to program in the first place. We seriously have people taking month or even week long coding bootcamps and then thinking they have a chance to be a "rock star coder". Honestly, I will claim the only reason they have a job in the first place is because a bunch of cogs--many of whom seem to work at Google--massively crank the complexity of simple problems and then encourage us all to type ridiculous amounts of boilerplate code to get simple tasks done. It should be way easier to develop these trivial things but every time someone on this site whines about "abstraction" another thousand amateurs get to have a job maintaining boilerplate. If anything, I think my particular job--which is a combination of achieving low-level stunts no one has done before, dreaming up new abstractions no one has considered before, and finding mistakes in code other people have written--is going to just be in even more demand from the current generation of these tools, as I think this stuff is mostly going to encourage more people to remain amateurs for longer and, as far as anyone has so far shown, the generators are more than happy to generate slightly buggy code as that's what they were trained on, and they have no "taste". Can you fix this? Maybe. But are you there? No. The reality is that these systems always seem to be missing something critical and, to me, obvious: some kind of "cognitive architecture" that allows them to think and dream possibilities, as well as a fitness function that cares about doing something interesting and new instead of being "a conformist": DALL-E is sometimes depicted as a robot in a smock dressed up to be the new Pablo Picasso, but, in reality, these AIs should be wearing business suits as they are closer to Charles Schmendeman. But, here is the fun thing: if you do come for my job even in the near future, will I move the goal post? I'd think not, as I would have finally been affected. But... will you hear a bunch of people saying "I won't be worried until X"? YES, because there are surely people who do things that are more complicated than what I do (or which are at least different and more inherently valuable and difficult for a machine to do in some way). That doesn't mean the goalpost moved... that means you talked to a different person who did a different thing, and you probably ignored them before as they looked like a crank vs. the people who were willing to be worried about something easier. And yet, I'm going to go further: if the things I tell you today--the things I say are required to make me worry--happen and yet somehow I was wrong and it is the future and you technically do those things and somehow I'm still not worried, then, sure: I guess you can continue to complain about the goalposts being moved... but is it really my fault? Ergo: was it me who had the job of placing the goalposts in the first place? The reality is that humans aren't always good at telling you what you are missing or what they need; and I appreciate that it must feel frustrating providing a thing which technically implements what they said they wanted and it not having the impact you expected--there are definitely people who thought that, with the tech we have now long ago pulled off, cars would be self-driving... and like, cars sort of self-drive? and yet, I still have to mostly drive my car ;P--then I'd argue the field still "failed" and the real issue is that I am not the customer who tells you what you have to build and, if you achieve what the contract said, you get paid: physics and economics are cruel bosses whose needs are oft difficult to understand.

ryanjshaw 4y ago

I think OP set relatively simple goals. How long until AI can architect, design, build, test, deploy and integrate commercial software systems from scratch, and handle users submitting bug reports that say "The OK button doesn't work when I click it!"?

jrockway 4y ago

I'm kind of interested in how AI is going to interface with the world. Humans have a lot of autonomy to change the physical world they're in; from rearranging furniture, to building structures, to visiting other worlds. Why isn't AI doing any of that stuff? As programmers, we keep talking about programming jobs and how AI will eliminate them all. But nobody is talking about eliminating other jobs. When will a robot vacuum be able to clean my apartment as quickly as I? Why isn't there a robot that takes my garbage out on Tuesday night? When will AI plan and build a new tunnel under the Hudson River for trains? When will airliners be pilotless? If AI can't do this stuff, what makes software so different? Why will AI be good at that but not other things? It seems like the only goal is to eliminate jobs doing things people actually like (art, music, literature, etc.), and not eliminate any tedium or things that is a waste of humanity's time whatsoever. (On the software front, when will AI decide what software to build? Will someone have to tell it? Will it do it on its own? Why isn't it doing this right now?) My takeaway is that this all raises a lot of questions for me on how far along we actually are. Language models are about stringing together words to sound like you have understanding, but the understanding still isn't there. But, I suppose we won't know understanding until we see it. Do we think that true understanding is just a year or two away? 10? 50? 100? 1000?

mLuby 4y ago
Household tasks can involve a robot moving with enough kinetic energy to maim or kill a human (or pet) in unlucky circumstances. And we'll quickly become habituated to their presence and so careless around them. Even a Roomba could knock granny down the stairs if it isn't careful about its environment. You could make the same argument as with self-driving cars, that people already get hurt this way and maybe the robot is in fact safer. But it's still a hard sell that Sunny-01 has only accidentally killed 1/10 as many children as parents have—the number has to be more like zero. Let's solve automating trains first then we can do airliners.

solumunus 4y ago

So you've drank the industry kool aid.

water-your-self 4y ago

I think there's a real story here behind the ownership and usage of proprietary data.

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