4 ms·

A lot of people here are responding to the message but not to the meaning. It would be a good idea for young people to deeply know how these programs work. Not

A lot of people here are responding to the message but not to the meaning. It would be a good idea for young people to deeply know how these programs work. Not so that they can spend their career building them, but so that they can approach the next class of problems we'll all start trying to solve, with intuition all the way down to the weights and underlying mathematics. And also, to develop a healthy intuition of when "Just LLM it" will not be the right choice. "Build an OS" wasn't a common university project because we were all expected to go out and work on Windows, but because understanding the bare-metal firmware for a computer helps you deeply understand how to intuit building for a whole class of problems.

Joel_Mckay 1mo ago
Depends, the assumption things are predictable always negatively affects both Market Bears and Bulls alike. Indeed, if credulous folks look to the world expecting people to bestow success upon them... than the disillusionment with reality will hit their savings harder. The Shrek movie market correction correlations are undeniably funny, and a new film is due July 2027. OpenAI may be going public in the next few months while still losing $2.25 for every $1 of customer revenue, and with 6 other firms sharing over $4Tn in debt disclosed to investors in a footnote. There is only one direction things can go at the Peak of inflated expectations. Popcorn ready. =3 https://en.wikipedia.org/wiki/Gartner_hype_cycle https://en.wikipedia.org/wiki/Gartner_hype_cycle
skydhash 1mo ago
> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems. Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.
cpt_sobel 1mo ago
> understanding the bare-metal firmware for a computer IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS
- therepanic 1mo ago
  As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.
dofm 1mo ago
> A lot of people here are responding to the message but not to the meaning. Well it is framed as quite specific advice. (I'm done with mining PG tweets for meaning)
- aaron695 1mo ago
  [dead]
jen20 1mo ago
> It would be a good idea for young people to deeply know how these programs work. It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit. But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.
andy_ppp 1mo ago
I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.
- pwdisswordfishs 1mo ago
  So computer science actually involves science now.
- Creamsicle47 1mo ago
  I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after. But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems. And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it." So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
  - deleted 1mo ago
    [deleted]
  - tadfisher 1mo ago
    This is no longer funny.
  - kunley 1mo ago
    You apparently have no intuition on what the intuition is
kazinator 1mo ago
[dead]
chermi 1mo ago
Couldn't have said it better
flockonus 1mo ago
Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.
nialv7 1mo ago
The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.
- michael0church 1mo ago
  [dead]
  - seanmcdirmid 1mo ago
    Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming). If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
  - libraryofbabel 1mo ago
    > there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives Mechanistic Interpretability has entered the chat. For a classic example, see https://www.anthropic.com/research/tracing-thoughts-language-model https://www.anthropic.com/research/tracing-thoughts-language... The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
- aschobel 1mo ago
  Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"? Lot's of neat stuff to learn.
BodyCulture 1mo ago
The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.
- potatoking 1mo ago
  Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
  - rchaud 1mo ago
    35 years ago, the enormously popular animated series Captain Planet taught kids about protecting the ozone layer, using biodegradable materials and renewable energy. Absolutely none of that mattered in the long run because capital markets think environmental destruction is a small price to pay as long as some people get rich. What kids learn about personal cooling tech and what not won't matter when the supply of the raw materials required to make them is reserved for some VC-funded trillion-dollar 'startup' trying to synthesize an undetectable chemical weapon to get past the bureaucratic red tape of international treaties.
- TheOtherHobbes 1mo ago
  The next wave is already in view and it's going to surprise and horrify the survivors in equal amounts. Some people will be delighted. Most are going to have a very hard time adjusting. YC and adjacent isn't it.
mcculley 1mo ago
What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?
sebastianconcpt 1mo ago
That depth requires math and traditional computing depth. Where is the time to be young and have that depth?
ta93754829 1mo ago
I think you're conflating complex and complicated. An OS is very complicated but is predictable. An LLM is complex and not necessarily understandable.
aandreic 1mo ago
[dead]

Joel_Mckay 1mo ago

Depends, the assumption things are predictable always negatively affects both Market Bears and Bulls alike. Indeed, if credulous folks look to the world expecting people to bestow success upon them... than the disillusionment with reality will hit their savings harder. The Shrek movie market correction correlations are undeniably funny, and a new film is due July 2027. OpenAI may be going public in the next few months while still losing $2.25 for every $1 of customer revenue, and with 6 other firms sharing over $4Tn in debt disclosed to investors in a footnote. There is only one direction things can go at the Peak of inflated expectations. Popcorn ready. =3 https://en.wikipedia.org/wiki/Gartner_hype_cycle https://en.wikipedia.org/wiki/Gartner_hype_cycle

skydhash 1mo ago

> "Build an OS" wasn't a common university project because we were all expected to go […] helps you deeply understand how to intuit building for a whole class of problems. Sure, but it was for a specific degree with a syllabus that taught you the foundational knowledge. It was not expected from the law students to learn how to build one.

cpt_sobel 1mo ago

> understanding the bare-metal firmware for a computer IMO this is still relevant, everything surrounding the LLMs needs such a vast infrastructure that I don't know if I would find it more useful to learn the maths behind ML than CS

therepanic 1mo ago
As a 17 year old, I agree with this. Ofc I'm against all the hate directed at PG, I believe that all knowledge has value regardless of its economic utility, but I understand where the hate is coming from. Personally, I find LLMs boring for now, and I'm more focused on CS and electrical engineering.

dofm 1mo ago

> A lot of people here are responding to the message but not to the meaning. Well it is framed as quite specific advice. (I'm done with mining PG tweets for meaning)

aaron695 1mo ago
[dead]

jen20 1mo ago

> It would be a good idea for young people to deeply know how these programs work. It would be a good idea for _everyone in the industry_ to deeply know how LLM training, inference and "agents" work, not least because it removes the ability of shysters to bamboozle with bullshit. But, as much as a good idea it is for the young to understand this, it's the elderly who will be really taken advantage of if they do not keep up - just look at Facebook for good examples of why.

andy_ppp 1mo ago

I'm not sure it's possible to have intuition about systems that work in thousands of orthogonal dimensions. In fact I'm pretty sure most of the research is people trying fairly arbitrary things and testing them and then post rationalising implied understanding of what is really happening on top of good outcomes.

pwdisswordfishs 1mo ago
So computer science actually involves science now.
Creamsicle47 1mo ago
I'd have to push back, though not on the part you'd expect. Your description of human researchers is roughly right: a lot of the field is try-things-and-narrativize-after. But the load-bearing assumption is that intuition has to be human-shaped intuition. Humans can't intuit thousands of orthogonal directions because we project everything down into a 3D metaphor and hope it holds. That's a fact about our hardware, not about the systems. And the reason why is the most interesting part: nothing requires the compression step. A model or an agent can operate over the actual objects, holding thousands of runs and ablations in context and noticing regularities in the native dimensionality, without translating them into a picture of a ball rolling down a hill. No bottleneck at "can you visualize it." So the narrower claim: it's not that intuition here is impossible full-stop, it's that human intuition is unreliable. Your post-hoc rationalization point is evidence for that, not against it. The story exists because a person needs something to hold in their head. Drop that requirement and the failure mode goes with it.
- deleted 1mo ago
  [deleted]
- tadfisher 1mo ago
  This is no longer funny.
- kunley 1mo ago
  You apparently have no intuition on what the intuition is

kazinator 1mo ago

[dead]

chermi 1mo ago

Couldn't have said it better

flockonus 1mo ago

Agreed. During comp sci we got to re-implement various algos of networks, OS, database, firmware.. and it all gave complimentary intuitions that were useful when tackling practical implementations and bottlenecks.

nialv7 1mo ago

The funny thing about LLM is, you can build one from scratch and yet you still won't understand how it works. You would understand what kind of matrix multiplications the neural network performs (in fact that's not that hard. An OS is orders of magnitudes more complex), but you would still have no idea why it does what it does.

michael0church 1mo ago
[dead]
- seanmcdirmid 1mo ago
  Learning how to build emergent systems is also a skill kids should learn these days. The closest I got was coding up game of life for CSE 142 (intro programming). If stochastic gradient descent isn't taught in whatever CS Theory 101 is now, it really should be these days.
- libraryofbabel 1mo ago
  > there’s no way to “crack open” an LLM and see precisely where each skill or tendency lives Mechanistic Interpretability has entered the chat. For a classic example, see https://www.anthropic.com/research/tracing-thoughts-language-model https://www.anthropic.com/research/tracing-thoughts-language... The spirit of your point stands, though. This kind of research is interesting to read about, but it's very hard, more like neuroscience or biology than computer science ("LLMs are grown, not made"). You're dealing with a lot of extremely _messy_ complexity, for which organic life is really the only good point of comparison. Most of us here are't really equipped for that kind of work; it's not at all like, say, reverse-engineering a piece of software written by humans. And of course the only people who can do it on frontier models from Anthropic and OpenAI are people within the labs themselves. (But I'm optimistic we'll see more of this work on open weights models...)
aschobel 1mo ago
Yah, "building" it is not sufficient. But a lot of times when I build I want to know the why. "Why does gradient Descent have some clever tricks that easily translate to matrix math"? Lot's of neat stuff to learn.

BodyCulture 1mo ago

The next class of problems to solve for future generations is how to survive on a hot planet and how to recover from the damage done by reckless lunatic profiteering assholes.

potatoking 1mo ago
Maybe people want to take it as cynicism but I think you are factually correct that personal cooling, infectious disease, atypical weather and fire breaks are all more realistic topics for people too young to already have patents, connections or their own oil well.
- rchaud 1mo ago
  35 years ago, the enormously popular animated series Captain Planet taught kids about protecting the ozone layer, using biodegradable materials and renewable energy. Absolutely none of that mattered in the long run because capital markets think environmental destruction is a small price to pay as long as some people get rich. What kids learn about personal cooling tech and what not won't matter when the supply of the raw materials required to make them is reserved for some VC-funded trillion-dollar 'startup' trying to synthesize an undetectable chemical weapon to get past the bureaucratic red tape of international treaties.
TheOtherHobbes 1mo ago
The next wave is already in view and it's going to surprise and horrify the survivors in equal amounts. Some people will be delighted. Most are going to have a very hard time adjusting. YC and adjacent isn't it.

mcculley 1mo ago

What corpus would one be advised to use for training? Should one limit oneself to legally downloadable documents or should one follow the examples of Altman, Amodei, and Zuckerberg and use LibGen and Anna's Archive?

sebastianconcpt 1mo ago

That depth requires math and traditional computing depth. Where is the time to be young and have that depth?

ta93754829 1mo ago

I think you're conflating complex and complicated. An OS is very complicated but is predictable. An LLM is complex and not necessarily understandable.

aandreic 1mo ago

[dead]