4 ms·

Saying the best way to understand LLMs is by building one is like saying the best way to understand compilers is by writing one. Technically true, but most peop

by pyman 1y ago

Saying the best way to understand LLMs is by building one is like saying the best way to understand compilers is by writing one. Technically true, but most people aren't interested in going that deep.

hackable_sand 1y ago
It's not that deep
benreesman 1y ago
I don't know, I've heard that meme too but it doesn't track with the number of cool compiler projects on GitHub or that frontpage HN, and while the LLM thing is a lot newer, you see a ton of useful/interesting stuff at the "an individual could do this on their weekends and it would mean they fundamentally know how all the pieces fit together" type stuff. There will always be a crowd that wants the "master XYZ in 72 hours with this ONE NEAT TRICK" course, and there will always be a..., uh, group of people serving that market need. But most people? Especially in a place like HN? I think most people know that getting buff involves going to the gym, especially in a place like this. I have a pretty high opinion of the typical person. We're all tempted by the "most people are stupid" meme, but that's because bad interactions are memorable, not because most people are stupid or lazy or whatever. Most people are very smart if they apply themselves, and most people will work very hard if the reward for doing so is reasonably clear. https://www.youtube.com/shorts/IQmOGlbdn8g https://www.youtube.com/shorts/IQmOGlbdn8g
wickedsight 1y ago
The best way to understand a car is to build a car. Hardly anyone is going to do that, but we still all use them quite well in our daily lives. In large part because the companies who build them spend time and effort to improve them and take away friction and complexity. If you want to be an F1 driver it's probably useful to understand almost every part of a car. If you're a delivery driver, it probably isn't, even if you use one 40+ hours a week.
- benreesman 1y ago
  Your example / analogy is useful in the sense that its usually useful to establish the thought experiment with the boundary conditions. But in between someone commuting in a Toyota and an F1 driver are many, many people, the best example from inside the extremes is probably a car mechanic, and even there, there's the oil change place with the flat fee painted in the window, and the Koenigsberg dealership that orders the part from Europe. The guy who tunes those up can afford one himself. In the use case segment where just about anyone can do it with a few hours training, yeah, maybe that investment is zero instead of a week now. But I'm much more interested in the one where F1 cars break the sound barrier now.
- eclecticfrank 1y ago
  It might make sense to split the car analogy into different users: 1. For the majority of regular users the best way to understand the car is to read the manual and use the car. 2. For F1 drivers the best way to understand the car is to consult with engineers and use the car. 3. For a mechanic / engineer the best way to understand the car is to build and use the car.
- Davidzheng 1y ago
  yes except intelligence isn't like a car, there's no way to break the complicated emergent behaviors of these models into simple abstractions. you can understand a LLM by training one the same amount you can understand a brain by dissection.
  - LtWorf 1y ago
    I think making one would help you understand that they're not intelligent.
    - benreesman 1y ago
      Your reply is enough of a zinger that I'll chuckle and not pile on, but there is a very real and very important point here, which is that it is strictly bad to get mystical about this. There are interesting emergent behaviors in computationally feasible scale regimes, but it is not magic. The people who work at OpenAI and Anthropic worked at Google and Meta and Jump before, they didn't draw a pentagram and light candles during onboarding. And LLMs aren't even the "magic. Got it." ones anymore, the zero shot robotics JEPA stuff is like, wtf, but LLM scaling is back to looking like a sigmoid and a zillion special cases. Half of the magic factor in a modern frontier company's web chat thing is an uncorrupted search index these days.
deleted 1y ago
[deleted]

by pyman 1y ago

hackable_sand 1y ago

It's not that deep

benreesman 1y ago

I don't know, I've heard that meme too but it doesn't track with the number of cool compiler projects on GitHub or that frontpage HN, and while the LLM thing is a lot newer, you see a ton of useful/interesting stuff at the "an individual could do this on their weekends and it would mean they fundamentally know how all the pieces fit together" type stuff. There will always be a crowd that wants the "master XYZ in 72 hours with this ONE NEAT TRICK" course, and there will always be a..., uh, group of people serving that market need. But most people? Especially in a place like HN? I think most people know that getting buff involves going to the gym, especially in a place like this. I have a pretty high opinion of the typical person. We're all tempted by the "most people are stupid" meme, but that's because bad interactions are memorable, not because most people are stupid or lazy or whatever. Most people are very smart if they apply themselves, and most people will work very hard if the reward for doing so is reasonably clear. https://www.youtube.com/shorts/IQmOGlbdn8g https://www.youtube.com/shorts/IQmOGlbdn8g

wickedsight 1y ago

The best way to understand a car is to build a car. Hardly anyone is going to do that, but we still all use them quite well in our daily lives. In large part because the companies who build them spend time and effort to improve them and take away friction and complexity. If you want to be an F1 driver it's probably useful to understand almost every part of a car. If you're a delivery driver, it probably isn't, even if you use one 40+ hours a week.

benreesman 1y ago
Your example / analogy is useful in the sense that its usually useful to establish the thought experiment with the boundary conditions. But in between someone commuting in a Toyota and an F1 driver are many, many people, the best example from inside the extremes is probably a car mechanic, and even there, there's the oil change place with the flat fee painted in the window, and the Koenigsberg dealership that orders the part from Europe. The guy who tunes those up can afford one himself. In the use case segment where just about anyone can do it with a few hours training, yeah, maybe that investment is zero instead of a week now. But I'm much more interested in the one where F1 cars break the sound barrier now.
eclecticfrank 1y ago
It might make sense to split the car analogy into different users: 1. For the majority of regular users the best way to understand the car is to read the manual and use the car. 2. For F1 drivers the best way to understand the car is to consult with engineers and use the car. 3. For a mechanic / engineer the best way to understand the car is to build and use the car.
Davidzheng 1y ago
yes except intelligence isn't like a car, there's no way to break the complicated emergent behaviors of these models into simple abstractions. you can understand a LLM by training one the same amount you can understand a brain by dissection.
- LtWorf 1y ago
  I think making one would help you understand that they're not intelligent.
  - benreesman 1y ago
    Your reply is enough of a zinger that I'll chuckle and not pile on, but there is a very real and very important point here, which is that it is strictly bad to get mystical about this. There are interesting emergent behaviors in computationally feasible scale regimes, but it is not magic. The people who work at OpenAI and Anthropic worked at Google and Meta and Jump before, they didn't draw a pentagram and light candles during onboarding. And LLMs aren't even the "magic. Got it." ones anymore, the zero shot robotics JEPA stuff is like, wtf, but LLM scaling is back to looking like a sigmoid and a zillion special cases. Half of the magic factor in a modern frontier company's web chat thing is an uncorrupted search index these days.

deleted 1y ago

[deleted]