7 ms·

The end of classical Computer Science is coming

37 points by mad 4y ago

oolveea 4y ago
The guy doesn’t know much DL, has recently learned it, and is simply hyped. Its that classic curve of how much you think you know vs how much you do, and all the excitement that comes with knowing little.
jmfldn 4y ago
This keeps me awake at night I must admit. How do I best future proof my career? I was sceptical about this until I started playing with GPT 3 and has it not only writen code for me, but also "explained" code to me. Sure, it's kind of limited right now, but it can only be a matter of time now before this all radically improves. Maybe I should focus on system design and translating the messy real world into systems. That's the hardest bit of my job currently. I was also thinking of moving down the stack and getting deeply into security engineering or something like that (not that this is immune from AI either!!).
- WheelsAtLarge 4y ago
  You can't guess the future so future proofing is next to impossible. Providing solutions to people wants and needs is the way to go. There you focus on the problem and then you look at what tools are available to provide the needed solution. People will always need food,recreation,sex, a place to live and religion to name a few. Figure how to fill needs in those areas and you'll be ok.
- fjfaase 4y ago
  It was in 1982 that I for the first time heard about the fourth generation programming languages in which you only had to specify the problem and you would get the system for free. But now 40 years later, most of the programming is still done in third generation programming languages. I am rather skeptical about the idea that AI is going to do away with programming soon. Yes, ML have shown some impressive results and will definitely show some improvements in the coming decades, but I think it will still take some time before the efficiency of electronic based ML systems will surpass that of organic based ML systems. Please note that this blog post is from a start-up that aims to work at ML systems that aim at replacing programming. So, this blog is also in a sense a kind of job advertisement and/or investor pitch.
  - tcbawo 4y ago
    Even if this were possible, the value of starting over in the problem domain would require considerable starting investment. In the meantime, your competitors will continue to iterate and improve. Depending on the domain, it might take many years to get to feature parity, even with vastly increased productivity. Many companies or industries will also shy away from such an investment because the benefit might not actually be worth the cost. Also, I think Joel Spolsky's lesson will still apply -- it's human nature to think it will be better the second time through: https://www.joelonsoftware.com/2000/04/06/things-you-should-never-do-part-i/ https://www.joelonsoftware.com/2000/04/06/things-you-should-...
- chowells 4y ago
  Just take a look at the generated code and explanations. A surprising amount of it is subtly but fundamentally wrong because gpt is just a regurgitation engine. The issues may look superficial, but when you start looking at why they happen, you realize the truth. The ml tools are usually great at writing boilerplate that's the same every time. The instant you do anything else, they fall over. They're statistical autocomplete, not any kind of important change to the process of programming. I don't see any reason to believe the current approaches can extend to something that actually changes programming. They're not based on understanding code, they're based on generating text that matches what they would expect to see given the context. They have no model of what code means, so they can't model why sometimes code is subtly different if there are no local contextual cues. And when there are your prompt would need to reproduce those contextual cues for it to key off of. In other words, you as the programmer still are directing the generation of the code. You're just doing it via an undocumented and somewhat unpredictable autocomplete. This doesn't remove the need to have someone who knows what they're doing in the loop. Best case is that it reduces the amount of time you spend typing by a little bit. As long as your job is to know what you're doing rather than to generate text, the current systems are no threat to it.
  - haolez 4y ago
    Sounds good, but... do we have models in our heads? Or are our minds simply huge and complicated neural networks?
    - chowells 4y ago
      Well, we absolutely have models in our heads. That's how we can understand what programs do. It's possible our minds are also huge and complicated neutral networks, though I suspect that description is incomplete at best. But the point is that current tools are trained on text generation. Something that would change programming would have to train on the meaning of programs. It's a rather different task, as it's no longer statistical. Doing it properly requires metacognition as well, to avoid falling into the trivial inconsistencies in most programming languages. And connecting that with real-world tasks that it hasn't seen before would require an understanding of the real world. I'd call something with all of those capabilities AGI. I honestly don't think any system short of that will ever be more than an autocomplete, because it can only ever fit things together based on some statistics.
- osigurdson 4y ago
  Don't stress. Do what you enjoy.
- mattnewport 4y ago
  An AI capable of replacing human programmers will have to be a full AGI. At that point worrying about your career is probably moot.
Rochus 4y ago
> Fast forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works The fact that many people do not know exactly enough what they are doing can be seen in the result. The people whose goal is to write as robust and efficient software as possible still do have to know and control the details. It's like driving a car; you do not have to be a engineer to drive one; but the more you want to push the limits of performance, the more you need to know about the details. And as far as AI is concerned, despite the predictions and full-bodied promises, we are obviously still a long way from replacing humans as drivers. I see no reason why software development should be any different. There are so many very complex issues involved that are not mentioned in the article. Just understanding the requirements of software will stretch the capabilities of AI for a few more decades.
- dwohnitmok 4y ago
  > we are obviously still a long way from replacing humans as drivers. This is only because we as a society have an extremely low tolerance for errors in automated driving and essentially require by default superhuman performance (a self-driving car with an error rate of the median human would never be allowed to be set loose by itself). In scenarios where a 0.1% error rate, 1% error rate, or maybe even 10% error rate are acceptable, AI is making huge strides. > Just understanding the requirements of software will stretch the capabilities of AI for a few more decades. I hope so. I'm not sure. And for a variety of reasons that's scary. What gives you a timeline of a few more decades?
  - shakow 4y ago
    AI is making stride in comparatively easy environments, i.e. highways and squared suburbs – when they don't crash into white trucks. Now, what tends to be forgotten is that there AI-average vs. human average is that humans can also drive e.g. in Turin or Paris at rush hour, on mountain roads under the snow or in the Cornwalls roads while under tempest rains. It's not that I believe that self-driving AI will never progress to this level, but let us be honest when comparing; they still drive themselves into fully-visible plots by daylight or run over cyclists at night.
hintymad 4y ago
It'll be interesting to see who's right: Matt or Brooks. Matt essentially argues that AI can be trained to take the responsibility of specifying a system, which is the opposite of what Brooks argues in his essay No Silver Bullet. Of course, I'm assuming that we are writing programs to specify what and how a system should work. It could be that AI (but not AGI) is so advanced that specifying a system can be compressed into training a model.
lmm 4y ago
Writing a program is still the most efficient way to explain a lot of things, even to another human - I've been in plenty of meetings where hours of explaination and examples only added confusion, whereas 20 minutes of pseudocode or 5 minutes of real code made it very clear what we were talking about. If you just want to do the same thing you currenty do but faster, AI will handle that. But modelling a business process properly and making it explicit will still bring huge value to those who care to put the effort in.
- KirillPanov 4y ago
  This is a great point. Look at how many IETF standards contain pseudocode. Also, in mathematics, many proofs contain a decision procedure or a parametric algorithm for constructing a counterexample.
bigbillheck 4y ago
> 99% of people who are writing software have almost no clue how a CPU actually works, let alone the physics underlying transistor design. My undergraduate education was in the early 90s, and at no point in my life have I ever had much of a clue regarding the physics underlying transistor design. EDIT: also at one time I probably did have a reasonably solid grasp on how CPUs work, there's been an awful lot of advancement in the field over the decades, and I won't describe my understanding as anything more than a cartoon model.
- angry_octet 4y ago
  For a CS person transistor design would be useless, but for EE it’s still core knowledge. But CPU design constraints are still the same now as they were in the 90s: speed of light, cache and coherency architecture, Amdahl’s law, pipelining. You’re way ahead of most coders if you understand even the basics of memory hierarchy.
  - bigbillheck 4y ago
    I didn't learn anything about caches, and NUMA postdates my education.
    - angry_octet 4y ago
      NUMA was around but it's an advanced topic. Never to late to learn about caches and memory hierarchy.
chatterhead 4y ago
There is a reason we are getting robot dogs with guns on their backs at the same time AI is advancing; because once AI crosses a certain line it is going to be powerful enough to nullify most jobs. It's not science fiction. What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention? That's where we get machines building machines. What happens when we start using AI to find the best recreational drugs? How about recreational drugs designed for specific kind of Overdoses - like crumple zones on a car? Or using AI to find the best cocktail of psychedelics that allow us all to work stoned and to maximum benefit all day long without diminishing returns? Finally, what happens when these AI can layer themselves together through transfer protocols and problem solving distribution without us telling them to? A self-analyzing, self-correcting and self-improving system can be considered a kind of life. We really are very close now.
- SrslyJosh 4y ago
  > What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention? You're going to have to define "understands" and explain how to get there from where the technology is right now, because a model is a statistical artifact and doesn't "understand" anything, including its inputs and outputs. > How about recreational drugs designed for specific kind of Overdoses - like crumple zones on a car? Why would anyone want an overdose? > Or using AI to find the best cocktail of psychedelics that allow us all to work stoned and to maximum benefit all day long without diminishing returns? Who benefits from this? Because it doesn't sound like it would benefit the people doing the work. > We really are very close now. Just a few more puffs and I'm sure you'll have the solution.
  - chatterhead 4y ago
    >> Understand means to sense, interpret, analyze, articulate and affect the real world. Why would anyone want a car accident? Not quite following me huh? Seems like maybe you just want to argue. Who benefits from humans working on stimulants and psychedelic drugs? How about the entire 20th/21st century? Are you seriously pretending everything from caffeine to opium hasn't been a driving force of production? The sensing machines we are building are paralleled in their design and utility to the functional parts of our brains. We've networked them all together already, and all we need is a simple spark to set it all in motion. Cognition rhymes with ignition for a reason. exhales
SrslyJosh 4y ago
> CEO and Co-Founder Fixie.ai. Okay, so this is an advertisement.
- karmasimida 4y ago
  Exactly, I have to honest I think I am being fooled.
- paulcole 4y ago
  What’s wrong with an advertisement? HN is fundamentally a marketing project of a VC firm.
  - jslakro 4y ago
    So true
- FrontierPsych 4y ago
  Right. My thoughts too. He said they are in "stealth mode." Yeah, so stealthy that he is publishing information on his company. Kinda anti-stealth if you ask me.
RomanPushkin 4y ago
When the full car auto-pilot is getting released you said? Oh, I forget about foreign language translation. As a person who speaks multiple languages, we still cannot translate from one language to another so it doesn't look ugly.
- uup 4y ago
  Machine translation works pretty well though. At least into English.
  - RomanPushkin 4y ago
    From what language? Do you speak two languages fluently, so you can compare translations?
    - uup 4y ago
      I’ve used it a lot from German and Russian. I lived in Germany for a few years. Auto translation was absolutely essential for navigating official things like government websites, banking, flat renting, etc. Now, all of my in-laws are exclusive Russian speakers. When my partner isn’t around to translate, we use Google Translate’s conversation feature. It works great. My partner overhears a lot of our conversation. She’s never needed to clarify anything.
      - RomanPushkin 4y ago
        Google translate: === One day in the spring, at the hour of an unprecedentedly hot sunset, two citizens appeared in Moscow, at the Patriarch's Ponds. The first of them, dressed in a summer gray pair, was short, well-fed, bald, carried his decent hat with a pie in his hand, and on his well-shaven face were glasses of supernatural size in black horn-rimmed. The second, a broad-shouldered, reddish, swirling young man in a checkered cap twisted at the back of his head, was in a cowboy shirt, chewed white trousers and black slippers. === Human: === At the sunset hour of one warm spring day two men were to be seen at Patriarch's Ponds. The first of them--aged about forty, dressed in a greyish summer suit--was short, dark-haired, well-fed and bald. He carried his decorous pork-pie hat by the brim and his neatly shaven face was embellished by black hornrimmed spectacles of preternatural dimensions. The other, a broad-shouldered young man with curly reddish hair and a check cap pushed back to the nape of his neck, was wearing a tartan shirt, chewed white trousers and black sneakers. === "шляпу пирожком" has been auto-translated to "hat with a pie" - ridiculous and inaccurate AI translation. It is only one of many, many examples. The example above was from a random book. I knew AI is going to fail.
bigbillheck 4y ago
> A time traveller from even 20 years ago would have a hard time making sense of the three sentences in the (75-page-long!) GPT-3 paper that describe the actual software that was built for the model First off, the only one of those three sentences that a 2002 researcher would be stumped by is the first, and that solely due to the unfamiliar nouns. The other two sentences are perfectly classical, and the only difficulty one of the ancients would have is putting their eyes back in after they popped out on seeing the model sizes. Second, isn't that good? It means the field has advanced, and there are new concepts being used, which I'd have thought is exactly what we want. Third, how different is this than the past? Would a time traveller from 1982 be equally stymied by a paper from 2002? How about 1962 to 1982?
palata 4y ago
Feels like the typical extrapolation: "Look how fast we've done the 80%! It is only a matter of time until we reach 100%". Except that it never works like that, does it? The further we go, the harder it becomes. It's impressive how fast they went from nothing to almost-fully-autonomous cars, but actually-fully-autonomous cars may never happen, who knows? As a developer, I honestly feel more threatened by the energy crisis to come (end of fossil fuels in the next couple of decades) than with AI replacing my job.
- mcphage 4y ago
  I think even 80% is a bit much…
  - bombcar 4y ago
    Yeah, as we get to know what a computer (say) can do we start to change what we expect it to do.
- worthless443 4y ago
  I hope people understand that this process is non-linear.
sys_64738 4y ago
There will always be a specialization of CS when other segments splinter from it. It might not be as large but it will be there. The reference to programming being in a death spiral is a bit of a stretch. There will be common mechanisms for like process but business always needs customization so I would worry about the Linux kernel dying anytime soon. What I’d expect is that programming tools become even more forgiving than today allowing almost anybody to do programming tasks with computer aid.
jacknews 4y ago
Progress in deep learning has been quite astounding the last few years, but the output is still very dreamy, fuzzy, inexact etc, as though eg for image generation, the pixels are representing individual neurons, and you're viewing the 'dream state' of the network. I think actual programming requires something more concrete; the 'atoms' of a program are not text letters or pixels, but something more abstract, more exact. I think once deep learning incorporates a symbolic or logic system of some kind, that might be a solution, but then that will apply not only to programming. All IMHO.
fungiblecog 4y ago
When I was 17 (I’m now 50) I was at a careers fair at my school. I was told “don’t go into computer programming, there won’t be any jobs because the computers will program themselves”
- tejohnso 4y ago
  Similar here. It was a computer store sales guy telling preteen me not to bother with the MS-DOS book I had in hand because in a few years people will just verbally instruct their computers in plain language.
  - bombcar 4y ago
    Hey, we’re getting very close! I can get my phone to turn off a light after asking it three or four times.
    - Brian_K_White 4y ago
      one career later
brundolf 4y ago
I agree with the gist, and I actually just started doing a course on AI today as a result of not wanting to get left behind However, this bit reads as needlessly hyperbolic to me: > The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and them some), ready to be given any task required of the machine. I mean okay, sure, eventually. But people were predicting hand-wavy everything-solutions like this sixty years ago in Star Trek. It's not very imaginative. Not to mention- this four-quintillion-parameter model will be hugely inefficient for simple tasks. I think it'll be a long time before we care that little about efficiency. But here's a much more near-term scenario I'm imagining: You need to stand up a new microservice. You have an off-the-shelf "learned microservice" web framework that you reach for. You write a small training set of example request/response JSON, not unlike unit-tests. Maybe the training set includes DB mutations too. You start testing out the service by hand, find corner-cases it doesn't handle correctly, add more training examples until it does everything you need. Now, in addition to saved effort vs hand-coding (which may or may not be the case, depending on how simple the logic is), you've got what I've started to think of as a "squishy" system component. Maybe, because AI is fuzzy, this service can handle malformed data. Maybe it won't choke on a missing JSON key, or a weird status code. Maybe it can be taught to detect fishy or malicious requests and reject them. Maybe it can tolerate typos. Maybe it can do reasonable things with unexpected upstream errors (log vs hard-fail vs etc). This is the really compelling thing for me: so much of what makes software hard is its fragility. Things have to be just so or everything blows up. What if instead, the component pieces of our digital world were a little squishy? Like human beings?
- capitalsigma 4y ago
  Excited for squishy logic to calculate the balance of my PayPal account and enforce GDPR compliance
  - brundolf 4y ago
    > calculate the balance of my PayPal account Yeah, fair. Maybe we'll have to change the level of abstraction to where the neural net determines a math equation, instead of openly translating to and from different numbers? > enforce GDPR compliance I actually think this would be an excellent use-case because it's such a sprawling problem (and because law is already "squishy" in this sense, because it has to be, because it's all about the messy real-world). Imagine watchdogs being able to hand companies a neural-net that continuously audits them for compliance, which works across different companies and systems (you'd probably have a human make the final ruling once a company is flagged, but still)
rgmerk 4y ago
Bull. At best, you'll reduce the time spent writing code at the cost of greatly increasing the time spent writing tests to give you sufficient confidence that your autogenerated code actually does what it is supposed to. And if it doesn't, good luck fixing it.
- darepublic 4y ago
  Better and better code gen might mean you can review and give the ok on ai created PRs. If something goes wrong you can either intervene manually or turn to the AI again for a fix
fardo 4y ago
I think the more realistic model of the future of programming given programs like Copilot is what’s happened in the SQL and compiler domain. SQL and Compilers changed the goal of many programmers from writing custom extractors or serving as computer translators, to writing useful intents for extraction and actions. You still need to know WHY you’re extracting or adding your code in both cases, often need to understand the substructure enough to dive in and debug when results are unexpected or “not optimal enough”, and ultimately much of the cut content caused by the gained efficiency was ultimately boilerplate that, once eliminated, allowed programmers to take on more ambitious project scope than they did previously, because they weren’t spending their time rewriting a new data storage system or translating actions into machine language for the umpteenth time. I feel we’ll see a similar progression - these code generators, very optimistically assuming a world where they work deterministically “good enough” to trust with even core business logic, will be treated as black box valid action generators, but in a world where action generation is free, but under-specification or incorrect specification is wrong, we still have something curiously resembling programming - the art of programming becomes one of maintainably chaining assemblages of black boxes into cohesive, maintainable superstructures. I suspect we’ll say the same thing about code which was of simple and safe enough structure that we could trust it to black box generators that we currently say about SQL, > Thank god I don’t have to redo all that work every time I start a new project And, as with SQL, the project requirement boundaries will move to match your increased output capacity. Much like the old saying “What Andy Giveth, Bill Taketh away”, it is perhaps modernized, “What Copilot giveth, Your PM Taketh Away”
jillesvangurp 4y ago
I think humans being marginalized by their own inventions, might be a longer term consequence. Short term, we're still dealing with a growing demand for things where people with skills are more effective than any AI. And ironically, there's a lot of demand right now for people that can do some productive but low level stuff with AI. Ten years ago, you needed a team with phd propeller heads to do anything with AI. These days, what you need is a lot of data engineers capable of moving data around via scripts efficiently and people that can use the off the shelf stuff coming out of a handful of AI companies. It's like database technology. You don't have to have a deep understanding of them in order to use them. I can get productive with this stuff pretty quickly. And I need a working knowledge of what's out there in order to lead others to do this stuff. The consequences of a general AI, or even something close enough to that, coming online would be that, pretty soon after, we'd put that to use to do things currently done by really smart humans. Including programming things. The analogy is maybe that as an executive of a large tech company, you don't necessarily have to be a hard core techie yourself. You can delegate that stuff to "human resources". Adding AI resources to the mix is going to naturally happen. But it will be a while before that's cheap and good enough to replace everybody. For the foreseeable future, we'll have a growing number of AI resources but it will be relatively expensive to use them and we'll use them sparingly until that changes.
FrontierPsych 4y ago
Meh. I read about neural networking in the 1990s, and it has been around as a concept from the 1940s. in 1943, Warren McCulloch and Walter Pitts created a computational model for neural networks. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence. The author stated that a 3 sentence passage would not be comprehensible 20 years ago, but not true. Anyone could understand what they were saying from the context. Code that writes code, which he is essentially talking about, heck, I wrote one when I was a freshman back in 1980. Because code that writes code is what it all boils down to. Using fancy words might get someone a job promotion for using the current buzzwords, but that's just buzzwords. Color me not impressed.
Brian_K_White 4y ago
At least 2 opposing forms of self-deception are both true and a risk to try to do your best to watch out for: "I'm not a classical style developer and that's fine because because programming is over." "I'm a classical style developer and I'm safe because AI can never do this." At the moment, either might be true and either might be false, and both might be merely some percent true and false at the same time. The only real mistake is probably jumping to either assumption at this time. Well I'm sure an argument can be made about fence sitting too so whatever.
dark_angel22 4y ago
Imagine AI dealing with Web development and CSS for a client
osigurdson 4y ago
I'd have more faith that we can eventually automate programming if we actually succeeded at automating a very well scoped problem domain such as accounting.
huijzer 4y ago
As a great example of what "classical" computer science can do, take a look at the driverless metro introduced in Lille in 1983 [1]. Researchers in France have used formal systems to prove system correctness and this shows in the reliability and safety of the metro. I like to think that this is a better way to handle complex problems than to just throw data and algorithms against and hope that it will work correctly for new data. [1] https://en.m.wikipedia.org/wiki/Lille_Metro https://en.m.wikipedia.org/wiki/Lille_Metro
awill88 4y ago
> I don’t think this idea is crazy. And other things the author likes to tell themselves.. or perhaps they enjoy building clout for saying outrageous things.. yawn Sure there will be obsolete concepts, algorithms, and plenty of AI assistance, but programming is building a state machine, or like a house in an abstract space that powers machinery to accomplish tasks of value using a general-purpose computation device.. computer science is informs and is informed by a craft (programming) and that can only be replaced by another craft (whatever this is rests in the imagination the author). You’re still doing creative work, and you will only be as effective as your abilities in practice of applying theory.. the theory will not be “use AI” or “don’t learn computer architecture lmao what a nerd” Imho that is.
kkfx 4y ago
I totally disagree... Seeing actual development instead I think the era of end-user programming will came back. Really. Try see ANY large enough project: no matter if it's a kernel or a GUI desktop application, at a certain point ALL of them try to integrate, this, that, that other etc becoming monsters. Original desktops was designed as a single OS-application-framework where "applications" where just "code added to the core image". That's the missing level of integration we can't achieve in modern systems and that's why all complex software became monsters trying to circumvent the lack of integration adding features directly. Unix at first succeed over classic systems stating they are too complex and expensive, separating "the system" and "users" is the best cheap and quick solution. They they backpedal violating unix KISS logic with X11 and GUIs, libraries, frameworks etc because the KISS principle do not scale. Widget based GUIs born and succeed over document-oriented UIs stating those are too complex and expensive. The modern web prove they were wrong. In another ten years I think we will came back to Xerox...
peter-m80 4y ago
A computer program must produce correct outputs 100% of the time. Most AI assisted things I know are buggy black boxes. I wouldn't rely my bussines on that

37 points by mad 4y ago

The guy doesn’t know much DL, has recently learned it, and is simply hyped. Its that classic curve of how much you think you know vs how much you do, and all the excitement that comes with knowing little.

jmfldn 4y ago

This keeps me awake at night I must admit. How do I best future proof my career? I was sceptical about this until I started playing with GPT 3 and has it not only writen code for me, but also "explained" code to me. Sure, it's kind of limited right now, but it can only be a matter of time now before this all radically improves. Maybe I should focus on system design and translating the messy real world into systems. That's the hardest bit of my job currently. I was also thinking of moving down the stack and getting deeply into security engineering or something like that (not that this is immune from AI either!!).

WheelsAtLarge 4y ago
You can't guess the future so future proofing is next to impossible. Providing solutions to people wants and needs is the way to go. There you focus on the problem and then you look at what tools are available to provide the needed solution. People will always need food,recreation,sex, a place to live and religion to name a few. Figure how to fill needs in those areas and you'll be ok.
fjfaase 4y ago
It was in 1982 that I for the first time heard about the fourth generation programming languages in which you only had to specify the problem and you would get the system for free. But now 40 years later, most of the programming is still done in third generation programming languages. I am rather skeptical about the idea that AI is going to do away with programming soon. Yes, ML have shown some impressive results and will definitely show some improvements in the coming decades, but I think it will still take some time before the efficiency of electronic based ML systems will surpass that of organic based ML systems. Please note that this blog post is from a start-up that aims to work at ML systems that aim at replacing programming. So, this blog is also in a sense a kind of job advertisement and/or investor pitch.
- tcbawo 4y ago
  Even if this were possible, the value of starting over in the problem domain would require considerable starting investment. In the meantime, your competitors will continue to iterate and improve. Depending on the domain, it might take many years to get to feature parity, even with vastly increased productivity. Many companies or industries will also shy away from such an investment because the benefit might not actually be worth the cost. Also, I think Joel Spolsky's lesson will still apply -- it's human nature to think it will be better the second time through: https://www.joelonsoftware.com/2000/04/06/things-you-should-never-do-part-i/ https://www.joelonsoftware.com/2000/04/06/things-you-should-...
chowells 4y ago
Just take a look at the generated code and explanations. A surprising amount of it is subtly but fundamentally wrong because gpt is just a regurgitation engine. The issues may look superficial, but when you start looking at why they happen, you realize the truth. The ml tools are usually great at writing boilerplate that's the same every time. The instant you do anything else, they fall over. They're statistical autocomplete, not any kind of important change to the process of programming. I don't see any reason to believe the current approaches can extend to something that actually changes programming. They're not based on understanding code, they're based on generating text that matches what they would expect to see given the context. They have no model of what code means, so they can't model why sometimes code is subtly different if there are no local contextual cues. And when there are your prompt would need to reproduce those contextual cues for it to key off of. In other words, you as the programmer still are directing the generation of the code. You're just doing it via an undocumented and somewhat unpredictable autocomplete. This doesn't remove the need to have someone who knows what they're doing in the loop. Best case is that it reduces the amount of time you spend typing by a little bit. As long as your job is to know what you're doing rather than to generate text, the current systems are no threat to it.
- haolez 4y ago
  Sounds good, but... do we have models in our heads? Or are our minds simply huge and complicated neural networks?
  - chowells 4y ago
    Well, we absolutely have models in our heads. That's how we can understand what programs do. It's possible our minds are also huge and complicated neutral networks, though I suspect that description is incomplete at best. But the point is that current tools are trained on text generation. Something that would change programming would have to train on the meaning of programs. It's a rather different task, as it's no longer statistical. Doing it properly requires metacognition as well, to avoid falling into the trivial inconsistencies in most programming languages. And connecting that with real-world tasks that it hasn't seen before would require an understanding of the real world. I'd call something with all of those capabilities AGI. I honestly don't think any system short of that will ever be more than an autocomplete, because it can only ever fit things together based on some statistics.
osigurdson 4y ago
Don't stress. Do what you enjoy.
mattnewport 4y ago
An AI capable of replacing human programmers will have to be a full AGI. At that point worrying about your career is probably moot.

Rochus 4y ago

> Fast forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works The fact that many people do not know exactly enough what they are doing can be seen in the result. The people whose goal is to write as robust and efficient software as possible still do have to know and control the details. It's like driving a car; you do not have to be a engineer to drive one; but the more you want to push the limits of performance, the more you need to know about the details. And as far as AI is concerned, despite the predictions and full-bodied promises, we are obviously still a long way from replacing humans as drivers. I see no reason why software development should be any different. There are so many very complex issues involved that are not mentioned in the article. Just understanding the requirements of software will stretch the capabilities of AI for a few more decades.

dwohnitmok 4y ago
> we are obviously still a long way from replacing humans as drivers. This is only because we as a society have an extremely low tolerance for errors in automated driving and essentially require by default superhuman performance (a self-driving car with an error rate of the median human would never be allowed to be set loose by itself). In scenarios where a 0.1% error rate, 1% error rate, or maybe even 10% error rate are acceptable, AI is making huge strides. > Just understanding the requirements of software will stretch the capabilities of AI for a few more decades. I hope so. I'm not sure. And for a variety of reasons that's scary. What gives you a timeline of a few more decades?
- shakow 4y ago
  AI is making stride in comparatively easy environments, i.e. highways and squared suburbs – when they don't crash into white trucks. Now, what tends to be forgotten is that there AI-average vs. human average is that humans can also drive e.g. in Turin or Paris at rush hour, on mountain roads under the snow or in the Cornwalls roads while under tempest rains. It's not that I believe that self-driving AI will never progress to this level, but let us be honest when comparing; they still drive themselves into fully-visible plots by daylight or run over cyclists at night.

hintymad 4y ago

It'll be interesting to see who's right: Matt or Brooks. Matt essentially argues that AI can be trained to take the responsibility of specifying a system, which is the opposite of what Brooks argues in his essay No Silver Bullet. Of course, I'm assuming that we are writing programs to specify what and how a system should work. It could be that AI (but not AGI) is so advanced that specifying a system can be compressed into training a model.

lmm 4y ago

Writing a program is still the most efficient way to explain a lot of things, even to another human - I've been in plenty of meetings where hours of explaination and examples only added confusion, whereas 20 minutes of pseudocode or 5 minutes of real code made it very clear what we were talking about. If you just want to do the same thing you currenty do but faster, AI will handle that. But modelling a business process properly and making it explicit will still bring huge value to those who care to put the effort in.

KirillPanov 4y ago
This is a great point. Look at how many IETF standards contain pseudocode. Also, in mathematics, many proofs contain a decision procedure or a parametric algorithm for constructing a counterexample.

bigbillheck 4y ago

> 99% of people who are writing software have almost no clue how a CPU actually works, let alone the physics underlying transistor design. My undergraduate education was in the early 90s, and at no point in my life have I ever had much of a clue regarding the physics underlying transistor design. EDIT: also at one time I probably did have a reasonably solid grasp on how CPUs work, there's been an awful lot of advancement in the field over the decades, and I won't describe my understanding as anything more than a cartoon model.

angry_octet 4y ago
For a CS person transistor design would be useless, but for EE it’s still core knowledge. But CPU design constraints are still the same now as they were in the 90s: speed of light, cache and coherency architecture, Amdahl’s law, pipelining. You’re way ahead of most coders if you understand even the basics of memory hierarchy.
- bigbillheck 4y ago
  I didn't learn anything about caches, and NUMA postdates my education.
  - angry_octet 4y ago
    NUMA was around but it's an advanced topic. Never to late to learn about caches and memory hierarchy.

chatterhead 4y ago

There is a reason we are getting robot dogs with guns on their backs at the same time AI is advancing; because once AI crosses a certain line it is going to be powerful enough to nullify most jobs. It's not science fiction. What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention? That's where we get machines building machines. What happens when we start using AI to find the best recreational drugs? How about recreational drugs designed for specific kind of Overdoses - like crumple zones on a car? Or using AI to find the best cocktail of psychedelics that allow us all to work stoned and to maximum benefit all day long without diminishing returns? Finally, what happens when these AI can layer themselves together through transfer protocols and problem solving distribution without us telling them to? A self-analyzing, self-correcting and self-improving system can be considered a kind of life. We really are very close now.

SrslyJosh 4y ago
> What happens when you have a NN that understands how to integrate new physical input and render usable actions for creating outputs without human intervention? You're going to have to define "understands" and explain how to get there from where the technology is right now, because a model is a statistical artifact and doesn't "understand" anything, including its inputs and outputs. > How about recreational drugs designed for specific kind of Overdoses - like crumple zones on a car? Why would anyone want an overdose? > Or using AI to find the best cocktail of psychedelics that allow us all to work stoned and to maximum benefit all day long without diminishing returns? Who benefits from this? Because it doesn't sound like it would benefit the people doing the work. > We really are very close now. Just a few more puffs and I'm sure you'll have the solution.
- chatterhead 4y ago
  >> Understand means to sense, interpret, analyze, articulate and affect the real world. Why would anyone want a car accident? Not quite following me huh? Seems like maybe you just want to argue. Who benefits from humans working on stimulants and psychedelic drugs? How about the entire 20th/21st century? Are you seriously pretending everything from caffeine to opium hasn't been a driving force of production? The sensing machines we are building are paralleled in their design and utility to the functional parts of our brains. We've networked them all together already, and all we need is a simple spark to set it all in motion. Cognition rhymes with ignition for a reason. exhales

SrslyJosh 4y ago

> CEO and Co-Founder Fixie.ai. Okay, so this is an advertisement.

karmasimida 4y ago
Exactly, I have to honest I think I am being fooled.
paulcole 4y ago
What’s wrong with an advertisement? HN is fundamentally a marketing project of a VC firm.
- jslakro 4y ago
  So true
FrontierPsych 4y ago
Right. My thoughts too. He said they are in "stealth mode." Yeah, so stealthy that he is publishing information on his company. Kinda anti-stealth if you ask me.

RomanPushkin 4y ago

When the full car auto-pilot is getting released you said? Oh, I forget about foreign language translation. As a person who speaks multiple languages, we still cannot translate from one language to another so it doesn't look ugly.

uup 4y ago
Machine translation works pretty well though. At least into English.
- RomanPushkin 4y ago
  From what language? Do you speak two languages fluently, so you can compare translations?
  - uup 4y ago
    I’ve used it a lot from German and Russian. I lived in Germany for a few years. Auto translation was absolutely essential for navigating official things like government websites, banking, flat renting, etc. Now, all of my in-laws are exclusive Russian speakers. When my partner isn’t around to translate, we use Google Translate’s conversation feature. It works great. My partner overhears a lot of our conversation. She’s never needed to clarify anything.
    - RomanPushkin 4y ago
      Google translate: === One day in the spring, at the hour of an unprecedentedly hot sunset, two citizens appeared in Moscow, at the Patriarch's Ponds. The first of them, dressed in a summer gray pair, was short, well-fed, bald, carried his decent hat with a pie in his hand, and on his well-shaven face were glasses of supernatural size in black horn-rimmed. The second, a broad-shouldered, reddish, swirling young man in a checkered cap twisted at the back of his head, was in a cowboy shirt, chewed white trousers and black slippers. === Human: === At the sunset hour of one warm spring day two men were to be seen at Patriarch's Ponds. The first of them--aged about forty, dressed in a greyish summer suit--was short, dark-haired, well-fed and bald. He carried his decorous pork-pie hat by the brim and his neatly shaven face was embellished by black hornrimmed spectacles of preternatural dimensions. The other, a broad-shouldered young man with curly reddish hair and a check cap pushed back to the nape of his neck, was wearing a tartan shirt, chewed white trousers and black sneakers. === "шляпу пирожком" has been auto-translated to "hat with a pie" - ridiculous and inaccurate AI translation. It is only one of many, many examples. The example above was from a random book. I knew AI is going to fail.

bigbillheck 4y ago

> A time traveller from even 20 years ago would have a hard time making sense of the three sentences in the (75-page-long!) GPT-3 paper that describe the actual software that was built for the model First off, the only one of those three sentences that a 2002 researcher would be stumped by is the first, and that solely due to the unfamiliar nouns. The other two sentences are perfectly classical, and the only difficulty one of the ancients would have is putting their eyes back in after they popped out on seeing the model sizes. Second, isn't that good? It means the field has advanced, and there are new concepts being used, which I'd have thought is exactly what we want. Third, how different is this than the past? Would a time traveller from 1982 be equally stymied by a paper from 2002? How about 1962 to 1982?

palata 4y ago

Feels like the typical extrapolation: "Look how fast we've done the 80%! It is only a matter of time until we reach 100%". Except that it never works like that, does it? The further we go, the harder it becomes. It's impressive how fast they went from nothing to almost-fully-autonomous cars, but actually-fully-autonomous cars may never happen, who knows? As a developer, I honestly feel more threatened by the energy crisis to come (end of fossil fuels in the next couple of decades) than with AI replacing my job.

mcphage 4y ago
I think even 80% is a bit much…
- bombcar 4y ago
  Yeah, as we get to know what a computer (say) can do we start to change what we expect it to do.
worthless443 4y ago
I hope people understand that this process is non-linear.

sys_64738 4y ago

There will always be a specialization of CS when other segments splinter from it. It might not be as large but it will be there. The reference to programming being in a death spiral is a bit of a stretch. There will be common mechanisms for like process but business always needs customization so I would worry about the Linux kernel dying anytime soon. What I’d expect is that programming tools become even more forgiving than today allowing almost anybody to do programming tasks with computer aid.

jacknews 4y ago

Progress in deep learning has been quite astounding the last few years, but the output is still very dreamy, fuzzy, inexact etc, as though eg for image generation, the pixels are representing individual neurons, and you're viewing the 'dream state' of the network. I think actual programming requires something more concrete; the 'atoms' of a program are not text letters or pixels, but something more abstract, more exact. I think once deep learning incorporates a symbolic or logic system of some kind, that might be a solution, but then that will apply not only to programming. All IMHO.

fungiblecog 4y ago

When I was 17 (I’m now 50) I was at a careers fair at my school. I was told “don’t go into computer programming, there won’t be any jobs because the computers will program themselves”

tejohnso 4y ago
Similar here. It was a computer store sales guy telling preteen me not to bother with the MS-DOS book I had in hand because in a few years people will just verbally instruct their computers in plain language.
- bombcar 4y ago
  Hey, we’re getting very close! I can get my phone to turn off a light after asking it three or four times.
  - Brian_K_White 4y ago
    one career later

brundolf 4y ago

I agree with the gist, and I actually just started doing a course on AI today as a result of not wanting to get left behind However, this bit reads as needlessly hyperbolic to me: > The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and them some), ready to be given any task required of the machine. I mean okay, sure, eventually. But people were predicting hand-wavy everything-solutions like this sixty years ago in Star Trek. It's not very imaginative. Not to mention- this four-quintillion-parameter model will be hugely inefficient for simple tasks. I think it'll be a long time before we care that little about efficiency. But here's a much more near-term scenario I'm imagining: You need to stand up a new microservice. You have an off-the-shelf "learned microservice" web framework that you reach for. You write a small training set of example request/response JSON, not unlike unit-tests. Maybe the training set includes DB mutations too. You start testing out the service by hand, find corner-cases it doesn't handle correctly, add more training examples until it does everything you need. Now, in addition to saved effort vs hand-coding (which may or may not be the case, depending on how simple the logic is), you've got what I've started to think of as a "squishy" system component. Maybe, because AI is fuzzy, this service can handle malformed data. Maybe it won't choke on a missing JSON key, or a weird status code. Maybe it can be taught to detect fishy or malicious requests and reject them. Maybe it can tolerate typos. Maybe it can do reasonable things with unexpected upstream errors (log vs hard-fail vs etc). This is the really compelling thing for me: so much of what makes software hard is its fragility. Things have to be just so or everything blows up. What if instead, the component pieces of our digital world were a little squishy? Like human beings?

capitalsigma 4y ago
Excited for squishy logic to calculate the balance of my PayPal account and enforce GDPR compliance
- brundolf 4y ago
  > calculate the balance of my PayPal account Yeah, fair. Maybe we'll have to change the level of abstraction to where the neural net determines a math equation, instead of openly translating to and from different numbers? > enforce GDPR compliance I actually think this would be an excellent use-case because it's such a sprawling problem (and because law is already "squishy" in this sense, because it has to be, because it's all about the messy real-world). Imagine watchdogs being able to hand companies a neural-net that continuously audits them for compliance, which works across different companies and systems (you'd probably have a human make the final ruling once a company is flagged, but still)

rgmerk 4y ago

Bull. At best, you'll reduce the time spent writing code at the cost of greatly increasing the time spent writing tests to give you sufficient confidence that your autogenerated code actually does what it is supposed to. And if it doesn't, good luck fixing it.

darepublic 4y ago
Better and better code gen might mean you can review and give the ok on ai created PRs. If something goes wrong you can either intervene manually or turn to the AI again for a fix

fardo 4y ago

I think the more realistic model of the future of programming given programs like Copilot is what’s happened in the SQL and compiler domain. SQL and Compilers changed the goal of many programmers from writing custom extractors or serving as computer translators, to writing useful intents for extraction and actions. You still need to know WHY you’re extracting or adding your code in both cases, often need to understand the substructure enough to dive in and debug when results are unexpected or “not optimal enough”, and ultimately much of the cut content caused by the gained efficiency was ultimately boilerplate that, once eliminated, allowed programmers to take on more ambitious project scope than they did previously, because they weren’t spending their time rewriting a new data storage system or translating actions into machine language for the umpteenth time. I feel we’ll see a similar progression - these code generators, very optimistically assuming a world where they work deterministically “good enough” to trust with even core business logic, will be treated as black box valid action generators, but in a world where action generation is free, but under-specification or incorrect specification is wrong, we still have something curiously resembling programming - the art of programming becomes one of maintainably chaining assemblages of black boxes into cohesive, maintainable superstructures. I suspect we’ll say the same thing about code which was of simple and safe enough structure that we could trust it to black box generators that we currently say about SQL, > Thank god I don’t have to redo all that work every time I start a new project And, as with SQL, the project requirement boundaries will move to match your increased output capacity. Much like the old saying “What Andy Giveth, Bill Taketh away”, it is perhaps modernized, “What Copilot giveth, Your PM Taketh Away”

jillesvangurp 4y ago

I think humans being marginalized by their own inventions, might be a longer term consequence. Short term, we're still dealing with a growing demand for things where people with skills are more effective than any AI. And ironically, there's a lot of demand right now for people that can do some productive but low level stuff with AI. Ten years ago, you needed a team with phd propeller heads to do anything with AI. These days, what you need is a lot of data engineers capable of moving data around via scripts efficiently and people that can use the off the shelf stuff coming out of a handful of AI companies. It's like database technology. You don't have to have a deep understanding of them in order to use them. I can get productive with this stuff pretty quickly. And I need a working knowledge of what's out there in order to lead others to do this stuff. The consequences of a general AI, or even something close enough to that, coming online would be that, pretty soon after, we'd put that to use to do things currently done by really smart humans. Including programming things. The analogy is maybe that as an executive of a large tech company, you don't necessarily have to be a hard core techie yourself. You can delegate that stuff to "human resources". Adding AI resources to the mix is going to naturally happen. But it will be a while before that's cheap and good enough to replace everybody. For the foreseeable future, we'll have a growing number of AI resources but it will be relatively expensive to use them and we'll use them sparingly until that changes.

FrontierPsych 4y ago

Meh. I read about neural networking in the 1990s, and it has been around as a concept from the 1940s. in 1943, Warren McCulloch and Walter Pitts created a computational model for neural networks. One approach focused on biological processes while the other focused on the application of neural networks to artificial intelligence. The author stated that a 3 sentence passage would not be comprehensible 20 years ago, but not true. Anyone could understand what they were saying from the context. Code that writes code, which he is essentially talking about, heck, I wrote one when I was a freshman back in 1980. Because code that writes code is what it all boils down to. Using fancy words might get someone a job promotion for using the current buzzwords, but that's just buzzwords. Color me not impressed.

Brian_K_White 4y ago

At least 2 opposing forms of self-deception are both true and a risk to try to do your best to watch out for: "I'm not a classical style developer and that's fine because because programming is over." "I'm a classical style developer and I'm safe because AI can never do this." At the moment, either might be true and either might be false, and both might be merely some percent true and false at the same time. The only real mistake is probably jumping to either assumption at this time. Well I'm sure an argument can be made about fence sitting too so whatever.

dark_angel22 4y ago

Imagine AI dealing with Web development and CSS for a client

osigurdson 4y ago

I'd have more faith that we can eventually automate programming if we actually succeeded at automating a very well scoped problem domain such as accounting.

huijzer 4y ago

As a great example of what "classical" computer science can do, take a look at the driverless metro introduced in Lille in 1983 [1]. Researchers in France have used formal systems to prove system correctness and this shows in the reliability and safety of the metro. I like to think that this is a better way to handle complex problems than to just throw data and algorithms against and hope that it will work correctly for new data. [1] https://en.m.wikipedia.org/wiki/Lille_Metro https://en.m.wikipedia.org/wiki/Lille_Metro

awill88 4y ago

> I don’t think this idea is crazy. And other things the author likes to tell themselves.. or perhaps they enjoy building clout for saying outrageous things.. yawn Sure there will be obsolete concepts, algorithms, and plenty of AI assistance, but programming is building a state machine, or like a house in an abstract space that powers machinery to accomplish tasks of value using a general-purpose computation device.. computer science is informs and is informed by a craft (programming) and that can only be replaced by another craft (whatever this is rests in the imagination the author). You’re still doing creative work, and you will only be as effective as your abilities in practice of applying theory.. the theory will not be “use AI” or “don’t learn computer architecture lmao what a nerd” Imho that is.

kkfx 4y ago

I totally disagree... Seeing actual development instead I think the era of end-user programming will came back. Really. Try see ANY large enough project: no matter if it's a kernel or a GUI desktop application, at a certain point ALL of them try to integrate, this, that, that other etc becoming monsters. Original desktops was designed as a single OS-application-framework where "applications" where just "code added to the core image". That's the missing level of integration we can't achieve in modern systems and that's why all complex software became monsters trying to circumvent the lack of integration adding features directly. Unix at first succeed over classic systems stating they are too complex and expensive, separating "the system" and "users" is the best cheap and quick solution. They they backpedal violating unix KISS logic with X11 and GUIs, libraries, frameworks etc because the KISS principle do not scale. Widget based GUIs born and succeed over document-oriented UIs stating those are too complex and expensive. The modern web prove they were wrong. In another ten years I think we will came back to Xerox...

peter-m80 4y ago

A computer program must produce correct outputs 100% of the time. Most AI assisted things I know are buggy black boxes. I wouldn't rely my bussines on that