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Something like building Linux is more akin to managing a McDonald's than it is to a 10 page technical proof in Algebraic Groups. Programming is more multimodal

Something like building Linux is more akin to managing a McDonald's than it is to a 10 page technical proof in Algebraic Groups. Programming is more multimodal than math. Something like performance engineering might be free lunch though

slopinthebag 7mo ago
Yeah, it's hard to compare management and programming but they're both multimodal in very different ways. But there's gonna be entire domains in which AI dominates much like stockfish, but stockfish isn't managing franchises and there is no reason to expect that anytime soon. I feel like something people miss when they talk about intelligence is that humans have incredible breadth. This is really what differentiates us from artificial forms of intelligence as well as other animals. Plus we have agency, the ability to learn, the ability to critically think, from first principles, etc.
- vatsachak 7mo ago
  Exactly. It's what the execs are missing. Also animals thrive in underspecified environments, while AIs like very specific environments. Math is the most specified field there is lol
  - slopinthebag 7mo ago
    Oooh yeah that's really good framing. Humans have been building machines that outperform humans for hundreds of years at this point, but all in problems which are extremely well specified. It's not surprising LLM's are also great in these well specified domains. One difference between intelligence and artificial intelligence is that humans can thrive with extremely limited training data, whereas AI requires a massive amount of it. I think if anybody is worried about being replaced by AI, they should look at maximising their economic utility in areas which are not well specified.
    - vatsachak 7mo ago
      Exactly. I would not want to have a pure math career or a performance engineering career in 10 years.
  - gottheUIblues 7mo ago
    So specified .. that it can actually prove it can't be completely specified by any single specification
    - vatsachak 7mo ago
      All mathematical statements we care about fall out of the purview of incompleteness
      - gottheUIblues 7mo ago
        To the contrary (as summarised by Gemini): Gödel showed that arithmetic cannot prove everything about itself. Turing showed that computers cannot predict everything about themselves. Rice showed that we cannot automatically verify what programs will do. Chaitin showed that mathematics is full of random, unprovable facts. Lawvere showed that they are all failing for the exact same structural reason! These are not fringe issues. They define the absolute boundaries of human and machine intelligence.
- cindyllm 7mo ago
  [dead]
bitwize 7mo ago
But LLMs have proven themselves better at programming than most professional programmers. Don't argue. If you think Hackernews is a representative sample of the field then you haven't been in the field long enough. What LLMs have actually done is put the dream of software engineering within reach. Creativity is inimical to software engineering; the goal has long been to provide a universal set of reusable components which can then be adapted and integrated into any system. The hard part was always providing libraries of such components, and then integrating them. LLMs have largely solved these problems. Their training data contains vast amounts of solved programming problems, and they are able to adapt these in vector space to whatever the situation calls for. We are already there. Software engineering as it was long envisioned is now possible. And if you're not doing it with LLMs, you're going to be left behind. Multimodal human-level thinking need only be undertaken at the highest levels: deciding what to build and maybe choosing the components to build it. LLMs will take care of the rest.
- abcde666777 7mo ago
  A bit optimistic I'd say. It's put some software engineering within reach of some people who couldn't do it prior. Where 'some' might be a lot, but still far from all. I was thinking the other day of how things would go if some of my less tech savvy clients tried to vibe code the things I implement for them, and frankly I could only imagine hilarity ensuing. They wouldn't be able to steer it correctly at all and would inevitably get stuck. Someone needs to experiment with that actually: putting the full set of agentic coding tools in the hands of grandma and recording the outcome.
  - bitwize 7mo ago
    It's still going to take a knowledgeable person to steer an LLM. The point is that code written entirely by humans is finished as a concept in professional work—if you're writing it yourself you're not working efficiently or employing industry best practice.
    - slopinthebag 7mo ago
      That is akin to saying if you aren't using an IDE you are not working efficiently or employing industry best practice, which is insane when you consider people using Vi often run rings around people using IDEs. AI usage is a useless metric, look at results. Thus far, results and AI usage are uncorrelated.
hodgehog11 7mo ago
> Programming is more multimodal than math I have no idea how you come to this conclusion, when the evidence on the ground for those training models suggests it is precisely the opposite. We are much further along the path of writing code than writing new maths, since the latter often requires some degree of representational fluency of the world we live in to be relevant. For example, proving something about braid groups can require representation by grid diagrams, and we know from ARC-AGI that LLMs don't do great with this. Programming does not have this issue to the same extent; arguably, it involves the subset of maths that is exclusively problem solving using standard representations. The issues with programming are primarily on the difficulty with handling large volumes of text reliably.
- slopinthebag 7mo ago
  Nah, LLM's are solving unique problems in maths, whereas they're basically just overfitting to the vast amounts of training data with writing code. Every single piece of code AI writes is essentially just a distillation of the vast amounts of code it's seen in it's training - it's not producing anything unique, and it's utility quickly decays as soon as you even move towards the edge of the distribution of it's training data. Even doing stuff as simple as building native desktop UI's causes it massive issues.
- zeroonetwothree 7mo ago
  I guess the comment you are replying to really meant to say “software engineering” not “programming”.
- vatsachak 7mo ago
  Grid Diagrams can be specified (hopefully) through algebraic equations. The way that most math is currently done is that someone provides an extremely specified problem and then one has to answer that extremely specified problem. The way that programming is currently done is through constructing abstractions and trying to create a specification of the problem. Of course I'm not saying we're close to creating a silicon Grothendieck (I think that Bourbaki actually reads like a codebase) but I'm saying that we're much closer to constructing algorithms that can solve specified programs as opposed to specifying underspecified problems Think about the difference in specificity of Prove Fermat's last theorem vs Build a web browser

slopinthebag 7mo ago

Yeah, it's hard to compare management and programming but they're both multimodal in very different ways. But there's gonna be entire domains in which AI dominates much like stockfish, but stockfish isn't managing franchises and there is no reason to expect that anytime soon. I feel like something people miss when they talk about intelligence is that humans have incredible breadth. This is really what differentiates us from artificial forms of intelligence as well as other animals. Plus we have agency, the ability to learn, the ability to critically think, from first principles, etc.

vatsachak 7mo ago
Exactly. It's what the execs are missing. Also animals thrive in underspecified environments, while AIs like very specific environments. Math is the most specified field there is lol
- slopinthebag 7mo ago
  Oooh yeah that's really good framing. Humans have been building machines that outperform humans for hundreds of years at this point, but all in problems which are extremely well specified. It's not surprising LLM's are also great in these well specified domains. One difference between intelligence and artificial intelligence is that humans can thrive with extremely limited training data, whereas AI requires a massive amount of it. I think if anybody is worried about being replaced by AI, they should look at maximising their economic utility in areas which are not well specified.
  - vatsachak 7mo ago
    Exactly. I would not want to have a pure math career or a performance engineering career in 10 years.
- gottheUIblues 7mo ago
  So specified .. that it can actually prove it can't be completely specified by any single specification
  - vatsachak 7mo ago
    All mathematical statements we care about fall out of the purview of incompleteness
    - gottheUIblues 7mo ago
      To the contrary (as summarised by Gemini): Gödel showed that arithmetic cannot prove everything about itself. Turing showed that computers cannot predict everything about themselves. Rice showed that we cannot automatically verify what programs will do. Chaitin showed that mathematics is full of random, unprovable facts. Lawvere showed that they are all failing for the exact same structural reason! These are not fringe issues. They define the absolute boundaries of human and machine intelligence.
cindyllm 7mo ago
[dead]

bitwize 7mo ago

But LLMs have proven themselves better at programming than most professional programmers. Don't argue. If you think Hackernews is a representative sample of the field then you haven't been in the field long enough. What LLMs have actually done is put the dream of software engineering within reach. Creativity is inimical to software engineering; the goal has long been to provide a universal set of reusable components which can then be adapted and integrated into any system. The hard part was always providing libraries of such components, and then integrating them. LLMs have largely solved these problems. Their training data contains vast amounts of solved programming problems, and they are able to adapt these in vector space to whatever the situation calls for. We are already there. Software engineering as it was long envisioned is now possible. And if you're not doing it with LLMs, you're going to be left behind. Multimodal human-level thinking need only be undertaken at the highest levels: deciding what to build and maybe choosing the components to build it. LLMs will take care of the rest.

abcde666777 7mo ago
A bit optimistic I'd say. It's put some software engineering within reach of some people who couldn't do it prior. Where 'some' might be a lot, but still far from all. I was thinking the other day of how things would go if some of my less tech savvy clients tried to vibe code the things I implement for them, and frankly I could only imagine hilarity ensuing. They wouldn't be able to steer it correctly at all and would inevitably get stuck. Someone needs to experiment with that actually: putting the full set of agentic coding tools in the hands of grandma and recording the outcome.
- bitwize 7mo ago
  It's still going to take a knowledgeable person to steer an LLM. The point is that code written entirely by humans is finished as a concept in professional work—if you're writing it yourself you're not working efficiently or employing industry best practice.
  - slopinthebag 7mo ago
    That is akin to saying if you aren't using an IDE you are not working efficiently or employing industry best practice, which is insane when you consider people using Vi often run rings around people using IDEs. AI usage is a useless metric, look at results. Thus far, results and AI usage are uncorrelated.

hodgehog11 7mo ago

> Programming is more multimodal than math I have no idea how you come to this conclusion, when the evidence on the ground for those training models suggests it is precisely the opposite. We are much further along the path of writing code than writing new maths, since the latter often requires some degree of representational fluency of the world we live in to be relevant. For example, proving something about braid groups can require representation by grid diagrams, and we know from ARC-AGI that LLMs don't do great with this. Programming does not have this issue to the same extent; arguably, it involves the subset of maths that is exclusively problem solving using standard representations. The issues with programming are primarily on the difficulty with handling large volumes of text reliably.

slopinthebag 7mo ago
Nah, LLM's are solving unique problems in maths, whereas they're basically just overfitting to the vast amounts of training data with writing code. Every single piece of code AI writes is essentially just a distillation of the vast amounts of code it's seen in it's training - it's not producing anything unique, and it's utility quickly decays as soon as you even move towards the edge of the distribution of it's training data. Even doing stuff as simple as building native desktop UI's causes it massive issues.
zeroonetwothree 7mo ago
I guess the comment you are replying to really meant to say “software engineering” not “programming”.
vatsachak 7mo ago
Grid Diagrams can be specified (hopefully) through algebraic equations. The way that most math is currently done is that someone provides an extremely specified problem and then one has to answer that extremely specified problem. The way that programming is currently done is through constructing abstractions and trying to create a specification of the problem. Of course I'm not saying we're close to creating a silicon Grothendieck (I think that Bourbaki actually reads like a codebase) but I'm saying that we're much closer to constructing algorithms that can solve specified programs as opposed to specifying underspecified problems Think about the difference in specificity of Prove Fermat's last theorem vs Build a web browser