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(1) is just simply wrong. People with domain expertise in software are going to be amplified 10x using ChatGPT and curating the results. Likewise with any fiel

(1) is just simply wrong. People with domain expertise in software are going to be amplified 10x using ChatGPT and curating the results. Likewise with any field that ChatGPT has adequate training data in. Further models will be created that are more specialized to specific fields that way their prediction model spews out things that are much more sophisticated and useful

vidarh 4y ago
I think you're right. I noted on another thread that I got ChatGPT to produce a mostly right DNS server in ~10 minutes that it took me just a couple of corrections to make work. It worked great for that task, because I've written a DNS server before (a simple one) and I've read the RFCs, so it was easy for me to find the few small bugs without resorting to a line by line cross-check with a spec that might have been unfamiliar to others. I expect using it to spit out boilerplate for things you could do just as well yourself will be a lot more helpful than using it to try to avoid researching new stuff (though you might well be able to use it to help summarise and provide restatements of difficult bits to speed up your research/learning as well).
- tsimionescu 4y ago
  How often has the solution to a business problem you faced been "write a simple DNS server"? Or are you claiming that it produced a fully featured and world-scale fast DNS server?
  - vidarh 4y ago
    Several times. If that was the only thing I got it to do it wouldn't be very interesting, but that it answered the first problem I threw at it and several subsequent expansions with quite decent code was. Writing a "world-scale fast DNS server" is a near trivial problem if what you look up in is fast to query. Most people don't know that, because most people don't know how simple the protocol is. As such it's surprisingly versatile. E.g. want to write a custom service-discovery mechanism? Providing a DNS frontend is easy. How that domain knowledge interacts with ChatGPT's "mostly right" output was the point of my comment, not specifically a DNS server. If you need to implement something you know well enough, odds are ChatGPT can produce a reasonable outline of it that is fast for someone who already knows the domain well enough to know what is wrong with, and what needs to be refined. E.g. for fun I asked it right now to produce a web server that supports the Ruby "Rack" interface that pretty much all Ruby frameworks supports. It output one that pretty much would work, but had plenty of flaws that are obvious to anyone versed in the HTTP spec (biggest ones: what it output was single threaded, and the HTTP parser is too lax). As a starting point for someone unaware of the spec it'd be awful, because they wouldn't know what to look for. As a starting point for someone who has read the spec, it's easy enough to ask for refinements ("split the request parsing from the previous answer into a separate method"; "make the previous answer multi-threaded" - I tried them; fascinatingly, when I asked it to make it multi-threaded it spit out a better request parsing function, likely because it then started looking more like Rack integrations it's "seen" during training; it ran on the first try, btw. and served up requests just fine). EDIT: Took just "Make it work with Sinatra" followed by fixing a tiny issue by asking to "Add support for rack.input" to get to a version that could actually serve up a basic Sinatra app.
- soiler 4y ago
  In what way is this development loop: 1. Read technology background thoroughly 2. Read technology documentation thoroughly 3. Practice building technology 4. Ask ChatGPT to create boilerplate for basic implementation 5. Analyze boilerplate for defects 10x fast than this development loop: 1. Read technology background thoroughly 2. Read technology documentation thoroughly 3. Practice building technology 4. Manually create boilerplate for basic implementation 5. Analyze boilerplate for defects
  - vidarh 4y ago
    It isn't. My exact point was that it isn't and accordingly ChatGPT produces the best benefits for someone who has already done 1, 2, 3 for a given subject. It was in agreement with the comment above that suggest people with domain expertise will be faster with it. In those cases, ChatGPT will do 4 far faster, and 5 will be little different.
  - Madmallard 4y ago
    For new technologies coming out it won't be effective until newer models are made. Notice how I said it's going to make developers with existing domain knowledge faster. But even to your point, I've never used Excel VBA before and I had ChatGPT generate some VBA macros to move data with specific headers and labels from one sheet to another and it wrote a script to do exactly that for me in ~1 minute, and just reading what it wrote it's immediately helping me clearly understand how it works. The scripts also work. The computer science and server infrastructure technology fundamental background is what matters. Then the implementations will be quickly understandable by those that use it. I asked it to make a 2D fighting game in Phaser 3 and specified what animations it will be using, the controls each player will have, the fact that there's a background with X name, what each of the moves do to the momentum of each player, and the type of collisions it will do and it spat out something in ~15 minutes (mainly because of all the 'continue' commands I had to give) that gets all the major bullet points right and I just have to tweak it a bit to make it functional. The moves are simplified of course but uhh yeah. This is kinda insane. I think you can be hyper specific about even complex technology and as long as there has been good history of it online in github and stack overflow and documentation it will give you something useful quickly. https://www.youtube.com/watch?v=pspsSn_nGzo https://www.youtube.com/watch?v=pspsSn_nGzo Here's a perspective from a guy that used to work at Microsoft on every version of windows from the beginning to XP.
tsimionescu 4y ago
Expertise in software is about understanding the problem domain, understanding the constraints imposed by the hardware, understanding how to translate business logic to code. None of these are significantly helped by AI code assistance, as they currently exist. The AI only helps with the coding part, usually helping generate boilerplate tailored to your code. That may help 1.1x your productivity, but nowhere near 10x.
- broast 4y ago
  I'm surprised you haven't been able to leverage the AI for the analysis of a problem domain and constraints in order to engineer a novel solution. This is generally what I use it for, and not actual code generation.
mariusor 4y ago
Domain knowledge resolves into intuition about solving particular types of problems. All ChatGPT can do about that is offer best guess approximations of what is already out there in the training corpus. I doubt very much that this exercise is anything but wasted time, so I think that people with domain knowledge (in a non trivial domain) are using ChatGPT instead of applying that knowledge, they are basically wasting time 10x not being more productive.
mrtranscendence 4y ago
What, precisely, about (1) is "simply wrong"? You've made a prediction about the usefulness of ChatGPT, but you haven't described why it's wrong to analogize GPT-type models to the language center of a brain.
- Madmallard 4y ago
  "To put it in code assistant terms, I expect people to be increasingly amazed at how well they seem to be coding, until you put the results together at scale and realize that while it kinda, sorta works, it is a new type of never-before-seen crap code that nobody can or will be able to debug short of throwing it away and starting over." This part
jerf 4y ago
I expect ChatGPT to be in a sort of equivalent of the uncanny valley, where any professional who gets to the point that they can routinely use it will also be in a constant war with their own brain to remind it that the output must be carefully checked. In some ways, the 99.99% reliable process used at scale is more dangerous than the 50% reliable process; everyone can see the latter needs help. It's the former where it's so very, very tempting to just let it go. I'm not saying ChatGPT is 99.99% reliable, just using some numbers for concreteness. If you were setting out to design an AI that would slip the maximum amount of error into exactly the places human brains don't want to look, it would look like ChatGPT. You can see this in the way that as far as I know, literally all the ads for GPT-like search technologies included significant errors in their ad copy, which you would think everyone involved would have every motivation to error check. This is not merely a "ha ha, silly humans" story... this means something. In a weird sort of way it is a testament to the technology... no sarcasm! But it makes it dangerous for human brains. Human brains are machines for not spending energy on cognitive tasks. They are very good at it, in all senses of the phrase. We get very good bang-for-the-buck with our shortcuts in the real world. But GPT techs are going to make it really, really easy to not spend the energy to check after a little while. This is a known problem with human brains. How many people can tell the story of what may be the closest human equivalent, where they got some intern, paid a ton of attention to them for the first two weeks, got to the point where they flipped the "OK they're good now" bit on them, and then came back to a complete and utter clusterfuck at the end of their internship because the supervisor got "too lazy" (although there's more judgment in that phrase than I like, this is a brain thing you couldn't survive without, not just "laziness") to check everything closely enough? They may even have been glancing at the PRs the whole time and only put together how bad the mess is at the end. I'm not going to invite a technology like this into my life. The next generation, we'll see when it gets here. But GPT is very scary because its in the AI uncanny valley... very good, very good at hiding the problems from human brains, and not quite good enough to actually do the job. And you know, since we're not talking theory here, we'll be running this experiment in the real world. You use ChatGPT to build your code, and I won't. You and I personally of course won't be comparing notes, but as a group, we sure will be. I absolutely agree there will be a point where ChatGPT seems to be pulling ahead in the productivity curve in a short term, but I predict that won't hold and it will turn net negative at some point. But I don't know right now, any more than you do. We can but put our metaphorical money down and see how the metaphorical chips fall.

vidarh 4y ago

I think you're right. I noted on another thread that I got ChatGPT to produce a mostly right DNS server in ~10 minutes that it took me just a couple of corrections to make work. It worked great for that task, because I've written a DNS server before (a simple one) and I've read the RFCs, so it was easy for me to find the few small bugs without resorting to a line by line cross-check with a spec that might have been unfamiliar to others. I expect using it to spit out boilerplate for things you could do just as well yourself will be a lot more helpful than using it to try to avoid researching new stuff (though you might well be able to use it to help summarise and provide restatements of difficult bits to speed up your research/learning as well).

tsimionescu 4y ago
How often has the solution to a business problem you faced been "write a simple DNS server"? Or are you claiming that it produced a fully featured and world-scale fast DNS server?
- vidarh 4y ago
  Several times. If that was the only thing I got it to do it wouldn't be very interesting, but that it answered the first problem I threw at it and several subsequent expansions with quite decent code was. Writing a "world-scale fast DNS server" is a near trivial problem if what you look up in is fast to query. Most people don't know that, because most people don't know how simple the protocol is. As such it's surprisingly versatile. E.g. want to write a custom service-discovery mechanism? Providing a DNS frontend is easy. How that domain knowledge interacts with ChatGPT's "mostly right" output was the point of my comment, not specifically a DNS server. If you need to implement something you know well enough, odds are ChatGPT can produce a reasonable outline of it that is fast for someone who already knows the domain well enough to know what is wrong with, and what needs to be refined. E.g. for fun I asked it right now to produce a web server that supports the Ruby "Rack" interface that pretty much all Ruby frameworks supports. It output one that pretty much would work, but had plenty of flaws that are obvious to anyone versed in the HTTP spec (biggest ones: what it output was single threaded, and the HTTP parser is too lax). As a starting point for someone unaware of the spec it'd be awful, because they wouldn't know what to look for. As a starting point for someone who has read the spec, it's easy enough to ask for refinements ("split the request parsing from the previous answer into a separate method"; "make the previous answer multi-threaded" - I tried them; fascinatingly, when I asked it to make it multi-threaded it spit out a better request parsing function, likely because it then started looking more like Rack integrations it's "seen" during training; it ran on the first try, btw. and served up requests just fine). EDIT: Took just "Make it work with Sinatra" followed by fixing a tiny issue by asking to "Add support for rack.input" to get to a version that could actually serve up a basic Sinatra app.
soiler 4y ago
In what way is this development loop: 1. Read technology background thoroughly 2. Read technology documentation thoroughly 3. Practice building technology 4. Ask ChatGPT to create boilerplate for basic implementation 5. Analyze boilerplate for defects 10x fast than this development loop: 1. Read technology background thoroughly 2. Read technology documentation thoroughly 3. Practice building technology 4. Manually create boilerplate for basic implementation 5. Analyze boilerplate for defects
- vidarh 4y ago
  It isn't. My exact point was that it isn't and accordingly ChatGPT produces the best benefits for someone who has already done 1, 2, 3 for a given subject. It was in agreement with the comment above that suggest people with domain expertise will be faster with it. In those cases, ChatGPT will do 4 far faster, and 5 will be little different.
- Madmallard 4y ago
  For new technologies coming out it won't be effective until newer models are made. Notice how I said it's going to make developers with existing domain knowledge faster. But even to your point, I've never used Excel VBA before and I had ChatGPT generate some VBA macros to move data with specific headers and labels from one sheet to another and it wrote a script to do exactly that for me in ~1 minute, and just reading what it wrote it's immediately helping me clearly understand how it works. The scripts also work. The computer science and server infrastructure technology fundamental background is what matters. Then the implementations will be quickly understandable by those that use it. I asked it to make a 2D fighting game in Phaser 3 and specified what animations it will be using, the controls each player will have, the fact that there's a background with X name, what each of the moves do to the momentum of each player, and the type of collisions it will do and it spat out something in ~15 minutes (mainly because of all the 'continue' commands I had to give) that gets all the major bullet points right and I just have to tweak it a bit to make it functional. The moves are simplified of course but uhh yeah. This is kinda insane. I think you can be hyper specific about even complex technology and as long as there has been good history of it online in github and stack overflow and documentation it will give you something useful quickly. https://www.youtube.com/watch?v=pspsSn_nGzo https://www.youtube.com/watch?v=pspsSn_nGzo Here's a perspective from a guy that used to work at Microsoft on every version of windows from the beginning to XP.

tsimionescu 4y ago

Expertise in software is about understanding the problem domain, understanding the constraints imposed by the hardware, understanding how to translate business logic to code. None of these are significantly helped by AI code assistance, as they currently exist. The AI only helps with the coding part, usually helping generate boilerplate tailored to your code. That may help 1.1x your productivity, but nowhere near 10x.

broast 4y ago
I'm surprised you haven't been able to leverage the AI for the analysis of a problem domain and constraints in order to engineer a novel solution. This is generally what I use it for, and not actual code generation.

mariusor 4y ago

Domain knowledge resolves into intuition about solving particular types of problems. All ChatGPT can do about that is offer best guess approximations of what is already out there in the training corpus. I doubt very much that this exercise is anything but wasted time, so I think that people with domain knowledge (in a non trivial domain) are using ChatGPT instead of applying that knowledge, they are basically wasting time 10x not being more productive.

mrtranscendence 4y ago

What, precisely, about (1) is "simply wrong"? You've made a prediction about the usefulness of ChatGPT, but you haven't described why it's wrong to analogize GPT-type models to the language center of a brain.

Madmallard 4y ago
"To put it in code assistant terms, I expect people to be increasingly amazed at how well they seem to be coding, until you put the results together at scale and realize that while it kinda, sorta works, it is a new type of never-before-seen crap code that nobody can or will be able to debug short of throwing it away and starting over." This part

jerf 4y ago

I expect ChatGPT to be in a sort of equivalent of the uncanny valley, where any professional who gets to the point that they can routinely use it will also be in a constant war with their own brain to remind it that the output must be carefully checked. In some ways, the 99.99% reliable process used at scale is more dangerous than the 50% reliable process; everyone can see the latter needs help. It's the former where it's so very, very tempting to just let it go. I'm not saying ChatGPT is 99.99% reliable, just using some numbers for concreteness. If you were setting out to design an AI that would slip the maximum amount of error into exactly the places human brains don't want to look, it would look like ChatGPT. You can see this in the way that as far as I know, literally all the ads for GPT-like search technologies included significant errors in their ad copy, which you would think everyone involved would have every motivation to error check. This is not merely a "ha ha, silly humans" story... this means something. In a weird sort of way it is a testament to the technology... no sarcasm! But it makes it dangerous for human brains. Human brains are machines for not spending energy on cognitive tasks. They are very good at it, in all senses of the phrase. We get very good bang-for-the-buck with our shortcuts in the real world. But GPT techs are going to make it really, really easy to not spend the energy to check after a little while. This is a known problem with human brains. How many people can tell the story of what may be the closest human equivalent, where they got some intern, paid a ton of attention to them for the first two weeks, got to the point where they flipped the "OK they're good now" bit on them, and then came back to a complete and utter clusterfuck at the end of their internship because the supervisor got "too lazy" (although there's more judgment in that phrase than I like, this is a brain thing you couldn't survive without, not just "laziness") to check everything closely enough? They may even have been glancing at the PRs the whole time and only put together how bad the mess is at the end. I'm not going to invite a technology like this into my life. The next generation, we'll see when it gets here. But GPT is very scary because its in the AI uncanny valley... very good, very good at hiding the problems from human brains, and not quite good enough to actually do the job. And you know, since we're not talking theory here, we'll be running this experiment in the real world. You use ChatGPT to build your code, and I won't. You and I personally of course won't be comparing notes, but as a group, we sure will be. I absolutely agree there will be a point where ChatGPT seems to be pulling ahead in the productivity curve in a short term, but I predict that won't hold and it will turn net negative at some point. But I don't know right now, any more than you do. We can but put our metaphorical money down and see how the metaphorical chips fall.