FWIW, I'm not convinced that this piece will be right for more than a few years into the future. I think it's an interesting discussion piece, though.
Context matters a ton and a lot of programming is understanding context and requirements and goals and needs and economics and those, while trainable, will suffer from the slowness and lack of richness of the I/O between the real world and the model (this interface is not improving nearly as fast as the models themselves).
They make the (common) mistake of equating “programming” to implementing some algorithms, sure AI probably has down sooner than later. This is a small part of what “programming” has come to be though.
There's always going to be a programming layer, at least until AIs can self-improve, and at that point it's basically AGI. In other words, programmers will be needed until the "singularity" and then we have no idea what happens.
I tell you what will happen. Masses of people will worship it, just like people worship large companies and Tesla. The company will create a nice, lovable mascot for their AI, while it sucks the marrow out of people, but they will not care about it all, because it has an overwhelming smile, therefore it cannot be evil. :D
or
A committee is formed whose only purpose is to hit the AIs that have become too big with an oversized wrench and kick it back into a madmax style desert, so it can evolve in a different way.
At the bottom of the article is the blurb about the author:
> Matt Welsh (mdw@mdw.la) is the CEO and co-founder of Fixie.ai, a recently founded startup developing AI capabilities to support software development teams. He was previously a professor of computer science at Harvard University, a director of engineering at Google, an engineering lead at Apple, and the SVP of Engineering at OctoML. He received his Ph.D. from UC Berkeley back in the days when AI was still not playing chess very well.
It's still a definite conflict of interest; the author is making an argument that they have a financial interest to advance regardless of how true it is
If this article was some sort of “Don’t worry, the AI-enhanced world is gonna be just fine!” puff piece then the author’s credentials here would take be quite discrediting. I think it takes balls to start an AI startup and then post an article urging caution about AI and saying that recent developments should “scare the living daylights out of people like Nick Bostrom… who are (rightfully) concerned…”
Like sure, it’s in his interest to portray AI as being powerful. But this article felt pretty candid about what the effects of that power could be.
It's the exact opposite of take balls - it promotes a stance that is in direct alignment with his startup's business model, a pretty strident conflict of interest for an ACM article. It's a fire and brimstone treatise and he's the prophet that is here to shepard you to the promised land.
Seriously, this is the text from the home page of Fixie.ai
"We're setting out to change the way the world builds software, using AI as a foundation.
We're founded by a team from Google and Apple with expertise in AI, systems, and the web. We're funded by Zetta Venture Partners, SignalFire, Bloomberg Beta, and others.
We're hiring for multiple roles."
Yes, he is a highly competent and experienced individual, but so were legions leading up to the AI winter. Can't place much faith in someone who has something to sell with much as confidence as a ChatGPT response - an academic no less - in regards to the near-term future of this space.
As it stands I think this will entirely won't work. We can't still to this day define what we want a program to do. So much in successful software development happens in the journey of making it. The constant shaping and add features and product and engineering finding users do unexpected things so we then try and capitalize that.
Waiting for Monty PAIthon, when AI will develop a true sense of humor and we all will laugh about the programming bloopers which it will invent.
But there is also a great danger of the Killer Joke, which results in instant death of anyone, who hears that joke. A malicious AI can re-invent the Killer Joke, and exterminate the humanity.
Unless we get a good brain-computer interface you won't be communicating precisely and clearly to AI what program it needs to write. It would be like ordering a SmartPhone online and receiving PhoneSmart Teaching Telephone.
> 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, let alone the physics underlying transistor design.
I dont fully agree with this. A lot of folks in systems land have mechanical sympathy and deeply think about memory, IO and processors. Things are mostly built upon underlying abstractions. With AI becoming mainstream, some of the abstractions will be pushed down and some might evolve further.
Hm. I mean at the level of "you sandwich some doped semiconductors so that charge at one point controls the current through two other points" I was sort of thinking yes... but I do have a tendency to overestimate what people know and upon further reflection you're right, 50% is way too high. Can haz 30%?
This % goes waaay down if you're talking actual physics : just how quantum physics result in a semiconductor band gap, and what equations you need to write down to explain the electric behaviour of that sandwich.
I can do that, at least for the basic layouts, but my own lack of (clear) understanding would be in the "middle" of the stack : starting up from logic gates, and bottom from scripting programming languages.
I understand how stuff works all the way down, but modern CPUs with register renumbering/coloring are mind breaking, then add JIT to things, there are a lot of tricks being used to make things faster that are beyond my stack depth.
Welsh was a tenured Harvard CA professor until 2010. While there may be some hyperbole, it seems like he’s speaking from some experience of understanding a top tier undergraduate curriculum (and if you don't consider Harvard top tier, he did his PhD at Berkeley before that). My guess is that his bar for “understanding” is higher than yours and the other commenter who suggests 50% of programmers understand CPU design. Even if you took an upper division HW design class 20 years ago, a fair bit has changed and there’s a good chance you've forgotten a bit since then.
He can be tenured all over the place but it doesn’t make his statements true. I think this is too charitable. I think he is clearly trying to make a very sloppy point that “99%” of programmers know nothing about hardware and are just making web apps or something to that effect. I think it’s too charitable to suggest his statement there is some ultra expertise bar he is clawing at. He’s not talking about quantum physics or anything like that; I am sure he is trying to convey that 99% of programmers don’t know surface things like branch prediction or what a super scalar pipeline is. He’s not talking about the intricacies if Sandy Brudge or anything like that. And, he’s wrong. He’s just trying to make a hand-wavy point abs relies on his pedigree to be taken seriously.
He may be right in saying that 99% of devs don't know what's under, but the 1% remaining is still a larger crowd that the total amount of devs of the 80s... and they are still essential to the industry.
I think it depends on how high our threshold for “understands how a CPU actually works” is. I mean sandybridge had 19 pipeline stages, I certainly couldn’t list them all off the top of my head and describe what they did!
Laws are very open to interpretation by a judge. Basically any adjective, unless defined elsewhere in the law text, is open to be interpreted. Judges use thick books of example cases from the past. I think it will take a long time before their skills of interpretation of the law and considering past example cases can be replaced.
I see my lawyer friends going through hell working 80+ hrs a week thanklessly doing those things. I just hope that it doesn't replace good lawyers, but rather actually allow them to have sane lives instead of burning out less than five years after graduation
First are jobs doing art, writing, and music. I never would have thought that a few years ago, but now they seem feasible and they stand out as fields where correctness isn't important and anyone can judge whether something feels right.
sure, thats why those systems take natural language and produce code. But the language is used to tell the system What the program does , not How to do it. AI systems tend to be good at filling the blanks with sane default behavior.
If this prediction is true (I’m skeptical), it could be a boost for personal computing, assuming everyone could run (or rent) such a model, because it then would become easy to design your own software or user interface by merely explaining what you want to the AI.
If anything it's rather the start of programming in my opinion, or rather the start of a new era.
We build endless higher level abstractions ontop of each other in programming, this is just another one.
I'm not bullish on AI actually understanding something in the near future and it'll rather continue to be something more akin to mimickery, albeit amazingly expressive and accurate.
I think this is rather going to become an amazing tool to help reduce repeating already solved problems. But humans would still be needed to plumb it together and adjust it to meet some final need.
If the AI can do the whole thing then whatever you're trying to create probably already exists and there's unlikely to be a need for it in my opinion.
If we do go to more ai contributed code fragments, we still need to test them for correctness. Given that we already see compelling looking but bug ridden solutions from chatgpt as well as from humans, I still see the test as crucial. If an ai writes the test, I'll skeptical. I'm skeptical of tests written by humans too.
Maybe the key is testing and coding can be seen as adversarial actions, and there is benefit in separating them. If the ai or code generator or whatever you want to call it writes the code and test, I'm even less likely to trust it.
Can someone that understands ML better than I tell me if there is a point where the AI can indefinitely train on data generated by other AI? If AI is trained on human development work product and then it eliminates human developers, will the capabilities of the AI be stuck indefinitely at the level of the software from which the models were trained? Not sure if I'm making sense, but the crux of my question is: can AI effectively generate their own training data sets? If not, then I don't see how it could replace an industry.
> [Ben Weber] set about organizing a tournament for StarCraft AI agents to compete against each other, hoping to kick-start progress and raise interest.
> The announcement for the tournament was made in November of 2009, and the word soon went out on gaming websites and blogs: the 2010 Artificial Intelligence and Interactive Digital Entertainment (AIIDE) Conference, to be held in October 2010 at Stanford University, would host the first ever StarCraft AI competition.
[...]
> the only way to really test and improve the agent would be to play against skilled human players. Flush with pride that the agent could defeat the built-in AI, we played a game during the class against John Blitzer, a post-doc in Dan’s group who played ranked ladder matches on International Cyber Cup (iCCup).
> It was a disaster.
[...]
> Manually iterating through parameters and making adjustments would take far too long, however.
> Instead, we let the Overmind learn to fight on its own.
> In Norse mythology, Valhalla is a paradise where warriors’ souls engage in eternal battle. Using StarCraft’s map editor, we built Valhalla for the Overmind, where it could repeatedly and automatically run through different combat scenarios. By running repeated trials in Valhalla and varying the potential field strengths, the agent learned the best combination of parameters for each kind of engagement.
[...]
> Recruiting Oriol as our “coach” helped us apply the final touches. Oriol had played StarCraft at the pro level before retiring and turning to a life of science, and he joined the team as our coach, designated opponent, and in-house StarCraft expert.
> With a high-level human expert to test against and all of the algorithms in place, the agent progressed rapidly in the last few weeks, culminating in that first victory against Oriol mere days before the final submission.
> Like OpenAI, DeepMind trains its AI agents against versions of themselves and at an accelerated pace, so that the agents can clock hundreds of years of play time in the span of a few months. That has allowed this type of software to stand on equal footing with some of the most talented human players of Go and, now, much more sophisticated games like Starcraft [2] and Dota [2].
Note that they are lucky there to have a controlled environment, meaning that experimentation is cheap, with clear goals - something that is not always the case in "real" life !
>Yet I think it is time to accept that this is a very likely future, and evolve our thinking accordingly, rather than just sit here waiting for the meteor to hit.
You know, I would love for all business app development to die in a wretched fire of scum and villainy. Not that it’s bad, but it’s probably some of the most mundane work that programmers could do. The people giving out busy work or bullshit jobs won’t be able to affect actual people. They’ll just have AI do it, which is great!
Fair, I was less thinking a physical dial and more a metaphorical one. It wouldn't be able to do the computation that is required to do the right thing, even if it can produce a program that can do the computation.
You could hook the program up to a python interpreter and approximate that, but then you're still generating code, not doing the task directly. In order to have an AI that will do the task directly, we would need to train a different model, not just plug the one we have directly in to the task.
This is actually why this whole topic is overblown. The AI can manipulate text, ergo it can replace programmer's because they can only manipulate text.
It won't replace a janitor because it has no hands but then again, if the model's only claim to replacing programmers is that it produces text, then the article is pretty weak.
I’ve always felt that the actual code writing is the least interesting part of coding. It find it fun, but feel that the real engineering is largely the stuff that comes before (and some after) the code is written.
I’m not yet sold on the idea that it will replace coding. It’s like autonomous cars. 99% is far far too low. But it’s also not to be dismissed. A brilliant tool with many use cases.
I think we have invented the Enterprise “ship’s computer.” It won’t answer “how do I solve this problem?” But it probably will answer things like, “if X and Y and Z, what might W be?” We get to be the big brains standing around a terminal! I can’t wait to have a holodeck where I can construct a world just by asking for things.
Yes, but I think writing code is more important than people make it sound. Because, writing something that is executed correctly is easy. But writing it so that others can understand it later, change it easily and safely, having a compiler to make sure nothing goes wrong - while still having the computer execute it correctly - THAT is the real challenge.
Which is also the problem with any AI. If the AI can program, then not only does it have to write code; it will also have to read existing code and make adjustments to it, based on request. This is the real challenge. "Hey, I have this 1MM line software, please change feature X to do A on top of B but keep C working". If the AI can do THAT, well THEN that's the end of programming by humans and then I'm gonna escape to a small island ASAP because the singularity will be soon after.
I have done some experiments with text-davinci-003 that given a program update and directory list can select the files, then do the requested update. Its not a million lines.
I generally agree. I think that today it’s a brilliant tool for throwaway code. Like when you just want the output. Especially if the output can be verifiable or is okay to be fuzzy.
I think, if possible, the faster way is to skip lexing and parsing all together for the AI input+output, it's a waste of resource. Probably start fresh with a new kind of assembly and AST, so the AI could have a better learning data and approach.
True. I would imagine training AI to understand assembly first, make it write a higher level simplest building block like `foldl`. And make the language pipe-only. No closure or anything like that, just pipe it all the way down, the machine doesn't need "naming" or name based id, now refactoring is easier not to break thing. etc.
As someone who tried to feed a 50 line code base into ChatGPT only to watch it fail with very hard errors (no I don't mean it answered incorrectly, it crashed), I am not very impressed.
"We are no longer particularly in the business of writing software to perform specific tasks. We now teach the software how to learn, and in the primary bonding process it molds itself around the task to be performed. The feedback loop never really ends, so a tenth year polysentience can be a priceless jewel or a psychotic wreck, but it is the primary bonding process--the childhood, if you will--that has the most far-reaching repercussions."
Bad'l Ron, Wakener, Morgan Polysoft
Accompanies the Digital Sentience technology
"'Abort, Retry, Fail?' was the phrase some wormdog scrawled next to the door of the Edit Universe project room. And when the new dataspinners started working, fabricating their worlds on the huge organic comp systems, we'd remind them: if you see this message, always choose 'Retry.'"
Bad'l Ron, Wakener, Morgan Polysoft
Accompanies the Matter Editation technology
I will wax a touch philosophical here, but I believe perfect systems do not exist unless they are purely within thought. Implementations will experience failure due to both its physical and logical components, logical in the sense due to unforeseen n-th degree effects. This is when expert knowledge is needed, unless you have already designed such a generalized model that captures, taxonomizes, reacts, and optimizes for all events until the end of time. From an educational standpoint, who cares if you know how to add a node to a binary tree using C++. It's not the technical details but the struggle to understand recursion until it finally clicks. It's the sculpting of the computational mind. Until the essence of controlling and directing computation by computational machines themselves is satisfactorily solved (for then you've become god), then no, expert humans will always be needed, just not in abundance.
Throwback to Keynes forecasting that we'd all be working 15 hour work weeks.
But if you can’t understand how to add a node to a binary tree that what does having a “computational mind” even mean. It sounds like feel-good happy-talk nonsense.
The point I'm trying to make is that for certain technical concepts (mathematics, programming, etc.), it's not the technical detail that must be remembered. Sure, you forget exactly the syntactical details of implementation, but you do remember (assuming you understood it at the start):
1 - How do I traverse the tree?
2 - Is there any ordering required among the nodes so that my traversal is 'correct'?
3 - How do I maintain such an ordering and establish correctness after my new node is added?
4 - Can I make any improvements so that locating the position of the to-be-added node doesn't take forever?
You don't begin to touch on these higher level "computationally minded" ideas until you can understand the primitive action of adding a node. And once you've grasped it, you forget the primitive details, only the essential concept remains.
The author seems to suggest that the next generation doesn't need to make an attempt to understand the fundamentals. I argue that unless you're a genius, you need to first add a node to a binary tree before labeling yourself a computational thinker.
Nah, these tools will empower programming. Almost no one writes assembly any more. We've got high-powered tools that already abstract away a ton of software engineering complexity so people can do what they want to do quickly and inexpensively. But I have a very hard time believing that "programming" — the act of writing out precise textual instructions in a file for a computer to read and execute — is going anywhere. It's a very elegant and powerful means of interacting with a machine. Similar to how the written word remains one of the most powerful mechanisms for interpersonal communication despite the massive and powerful media tools at our disposal.
These requirements need to be precise, unambiguous, and complete.
The AI could help the requirements writer to “fill in the gaps,” but the main onus is still on the author of the requirements.
As mentioned, we don’t program in machine code, anymore. Maybe the result of an AI-assisted construction would be machine code, but it would take an AI to test, debug, and maintain it.
I know that every C-Suite denizen has been dreaming of getting rid of “annoying engineers,” for my entire career, but that won’t happen, as those requirements will look a lot like … code … and I guarantee that the C-Suiters will have zero patience for writing it.
AI will write the requirements. In fact, I wouldn’t be surprised if AI comes for high cost centers like programming and high information decision-making (the C-Suite) at roughly the same time.
We could spend years in a lopsided state where groups of investors fund an AI that operates on an investment thesis, delivers commands to humans who manage physical labor in areas that have been tough to automate (like the remaining Amazon warehouse jobs), and handles on its own all of the work that would normally be done by office employees.
With any project, the first step is the customer describing their business needs. Analysts turn those requirements into a complete set of specs, then programmers write code that performs the specs.
There's no way AI is going to take the place of the customer, since the AI doesn't know what the customer needs, nor will it take the place of the analyst since even the smartest AI can't deal with a customer who is unable to clearly articulate their requirements. Hence the need for human analysts.
The AI might be able to help the programmer turn the specs into code (see Copilot) but it will always be hamstrung by not fully understanding (as a human would) the actual requirements.
With a sufficiently advanced and independent AI, the business need would be "make money", or perhaps hopefully "make money while obeying the laws, acting ethically, and doing something publicly beneficial".
I would love this to happen because finally I can have my own one man paramilitary industry like Tony Stark.
Then I just need to procure the right assault weapons. Then I will be (at least my bunker will be) unstoppable. No need to hire mercenaries anymore.
Maybe one day it will get even better, that I can have my own attack units like robot dogs/personal tanks equipped with insane amounts of assault weapons and javelins. Then I can mount an attack against anything. A person, an organization, a city, a small government. A true one man army, with AI controlling everything.
I’m guessing this is a troll or are you suggesting you would be the only one with this AI? If it’s a troll then great because I think people are leaving their brains out if these conversations about AI / chatgpt.
I think people are making pretty wild assumptions. But, ok if the AIs are these as advanced then why would there be investiture funding AI, the AI systems in the world would be just be capable of solving any problem abs no investment. Why would there be investment? Presumably, anyone with access to an AI (and presumably “open AI” will exist), therefore anyone could easily have any conceivable software written in seconds (if that long). Furthermore, what would be the need for software as the AI would have taken all the jobs for needing software on the first place. Would it just be to run the farm equipment for our “post scarcity” whatever?
159 comments
[ 3.2 ms ] story [ 194 ms ] threadContext matters a ton and a lot of programming is understanding context and requirements and goals and needs and economics and those, while trainable, will suffer from the slowness and lack of richness of the I/O between the real world and the model (this interface is not improving nearly as fast as the models themselves).
or
A committee is formed whose only purpose is to hit the AIs that have become too big with an oversized wrench and kick it back into a madmax style desert, so it can evolve in a different way.
> Matt Welsh (mdw@mdw.la) is the CEO and co-founder of Fixie.ai, a recently founded startup developing AI capabilities to support software development teams. He was previously a professor of computer science at Harvard University, a director of engineering at Google, an engineering lead at Apple, and the SVP of Engineering at OctoML. He received his Ph.D. from UC Berkeley back in the days when AI was still not playing chess very well.
Don't the arguments stand on their own, independent of who made them?
Like sure, it’s in his interest to portray AI as being powerful. But this article felt pretty candid about what the effects of that power could be.
Seriously, this is the text from the home page of Fixie.ai
"We're setting out to change the way the world builds software, using AI as a foundation. We're founded by a team from Google and Apple with expertise in AI, systems, and the web. We're funded by Zetta Venture Partners, SignalFire, Bloomberg Beta, and others. We're hiring for multiple roles."
Yes, he is a highly competent and experienced individual, but so were legions leading up to the AI winter. Can't place much faith in someone who has something to sell with much as confidence as a ChatGPT response - an academic no less - in regards to the near-term future of this space.
And then it would also be handy for it to design a programming language for both readability and writability that leveraged those libraries.
But there is also a great danger of the Killer Joke, which results in instant death of anyone, who hears that joke. A malicious AI can re-invent the Killer Joke, and exterminate the humanity.
I dont fully agree with this. A lot of folks in systems land have mechanical sympathy and deeply think about memory, IO and processors. Things are mostly built upon underlying abstractions. With AI becoming mainstream, some of the abstractions will be pushed down and some might evolve further.
You think 50% of SWEs understand the physics of transistor design???
I can do that, at least for the basic layouts, but my own lack of (clear) understanding would be in the "middle" of the stack : starting up from logic gates, and bottom from scripting programming languages.
You know, kind of like those who said you won't need to know what a relational database is if you use ORMs.
We build endless higher level abstractions ontop of each other in programming, this is just another one.
I'm not bullish on AI actually understanding something in the near future and it'll rather continue to be something more akin to mimickery, albeit amazingly expressive and accurate.
I think this is rather going to become an amazing tool to help reduce repeating already solved problems. But humans would still be needed to plumb it together and adjust it to meet some final need.
If the AI can do the whole thing then whatever you're trying to create probably already exists and there's unlikely to be a need for it in my opinion.
Maybe the key is testing and coding can be seen as adversarial actions, and there is benefit in separating them. If the ai or code generator or whatever you want to call it writes the code and test, I'm even less likely to trust it.
https://arstechnica.com/gaming/2011/01/skynet-meets-the-swar...
> [Ben Weber] set about organizing a tournament for StarCraft AI agents to compete against each other, hoping to kick-start progress and raise interest.
> The announcement for the tournament was made in November of 2009, and the word soon went out on gaming websites and blogs: the 2010 Artificial Intelligence and Interactive Digital Entertainment (AIIDE) Conference, to be held in October 2010 at Stanford University, would host the first ever StarCraft AI competition.
[...]
> the only way to really test and improve the agent would be to play against skilled human players. Flush with pride that the agent could defeat the built-in AI, we played a game during the class against John Blitzer, a post-doc in Dan’s group who played ranked ladder matches on International Cyber Cup (iCCup).
> It was a disaster.
[...]
> Manually iterating through parameters and making adjustments would take far too long, however.
> Instead, we let the Overmind learn to fight on its own.
> In Norse mythology, Valhalla is a paradise where warriors’ souls engage in eternal battle. Using StarCraft’s map editor, we built Valhalla for the Overmind, where it could repeatedly and automatically run through different combat scenarios. By running repeated trials in Valhalla and varying the potential field strengths, the agent learned the best combination of parameters for each kind of engagement.
[...]
> Recruiting Oriol as our “coach” helped us apply the final touches. Oriol had played StarCraft at the pro level before retiring and turning to a life of science, and he joined the team as our coach, designated opponent, and in-house StarCraft expert.
> With a high-level human expert to test against and all of the algorithms in place, the agent progressed rapidly in the last few weeks, culminating in that first victory against Oriol mere days before the final submission.
[...]
https://www.theverge.com/2019/10/30/20939147/deepmind-google...
> Like OpenAI, DeepMind trains its AI agents against versions of themselves and at an accelerated pace, so that the agents can clock hundreds of years of play time in the span of a few months. That has allowed this type of software to stand on equal footing with some of the most talented human players of Go and, now, much more sophisticated games like Starcraft [2] and Dota [2].
Note that they are lucky there to have a controlled environment, meaning that experimentation is cheap, with clear goals - something that is not always the case in "real" life !
You know, I would love for all business app development to die in a wretched fire of scum and villainy. Not that it’s bad, but it’s probably some of the most mundane work that programmers could do. The people giving out busy work or bullshit jobs won’t be able to affect actual people. They’ll just have AI do it, which is great!
You could hook the program up to a python interpreter and approximate that, but then you're still generating code, not doing the task directly. In order to have an AI that will do the task directly, we would need to train a different model, not just plug the one we have directly in to the task.
It won't replace a janitor because it has no hands but then again, if the model's only claim to replacing programmers is that it produces text, then the article is pretty weak.
https://youtu.be/Ybk8hxKeMYQ
I’m not yet sold on the idea that it will replace coding. It’s like autonomous cars. 99% is far far too low. But it’s also not to be dismissed. A brilliant tool with many use cases.
I think we have invented the Enterprise “ship’s computer.” It won’t answer “how do I solve this problem?” But it probably will answer things like, “if X and Y and Z, what might W be?” We get to be the big brains standing around a terminal! I can’t wait to have a holodeck where I can construct a world just by asking for things.
Which is also the problem with any AI. If the AI can program, then not only does it have to write code; it will also have to read existing code and make adjustments to it, based on request. This is the real challenge. "Hey, I have this 1MM line software, please change feature X to do A on top of B but keep C working". If the AI can do THAT, well THEN that's the end of programming by humans and then I'm gonna escape to a small island ASAP because the singularity will be soon after.
"We are no longer particularly in the business of writing software to perform specific tasks. We now teach the software how to learn, and in the primary bonding process it molds itself around the task to be performed. The feedback loop never really ends, so a tenth year polysentience can be a priceless jewel or a psychotic wreck, but it is the primary bonding process--the childhood, if you will--that has the most far-reaching repercussions."
Bonus :https://www.youtube.com/watch?v=-aUcvswVJ58&t=3852s
"'Abort, Retry, Fail?' was the phrase some wormdog scrawled next to the door of the Edit Universe project room. And when the new dataspinners started working, fabricating their worlds on the huge organic comp systems, we'd remind them: if you see this message, always choose 'Retry.'"
Throwback to Keynes forecasting that we'd all be working 15 hour work weeks.
The point I'm trying to make is that for certain technical concepts (mathematics, programming, etc.), it's not the technical detail that must be remembered. Sure, you forget exactly the syntactical details of implementation, but you do remember (assuming you understood it at the start):
1 - How do I traverse the tree? 2 - Is there any ordering required among the nodes so that my traversal is 'correct'? 3 - How do I maintain such an ordering and establish correctness after my new node is added? 4 - Can I make any improvements so that locating the position of the to-be-added node doesn't take forever?
You don't begin to touch on these higher level "computationally minded" ideas until you can understand the primitive action of adding a node. And once you've grasped it, you forget the primitive details, only the essential concept remains.
The author seems to suggest that the next generation doesn't need to make an attempt to understand the fundamentals. I argue that unless you're a genius, you need to first add a node to a binary tree before labeling yourself a computational thinker.
These requirements need to be precise, unambiguous, and complete.
The AI could help the requirements writer to “fill in the gaps,” but the main onus is still on the author of the requirements.
As mentioned, we don’t program in machine code, anymore. Maybe the result of an AI-assisted construction would be machine code, but it would take an AI to test, debug, and maintain it.
I know that every C-Suite denizen has been dreaming of getting rid of “annoying engineers,” for my entire career, but that won’t happen, as those requirements will look a lot like … code … and I guarantee that the C-Suiters will have zero patience for writing it.
We could spend years in a lopsided state where groups of investors fund an AI that operates on an investment thesis, delivers commands to humans who manage physical labor in areas that have been tough to automate (like the remaining Amazon warehouse jobs), and handles on its own all of the work that would normally be done by office employees.
There's no way AI is going to take the place of the customer, since the AI doesn't know what the customer needs, nor will it take the place of the analyst since even the smartest AI can't deal with a customer who is unable to clearly articulate their requirements. Hence the need for human analysts.
The AI might be able to help the programmer turn the specs into code (see Copilot) but it will always be hamstrung by not fully understanding (as a human would) the actual requirements.
Then I just need to procure the right assault weapons. Then I will be (at least my bunker will be) unstoppable. No need to hire mercenaries anymore.
Maybe one day it will get even better, that I can have my own attack units like robot dogs/personal tanks equipped with insane amounts of assault weapons and javelins. Then I can mount an attack against anything. A person, an organization, a city, a small government. A true one man army, with AI controlling everything.