3 ms·

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 be

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.
  - Davidzheng 1y ago
    OK I, like the other commenter, also feel stupid to reply to zingers--but here goes. First of all, I think a lot of the issue here is this sense of baggage over this word intelligence--I guess because believing machines can be intelligent goes against this core belief that people have that humans are special. This isn't meant as a personal attack--I just think it clouds thinking. Intelligence of an agent is a spectrum, it's not a yes/no. I suspect most people would not balk at me saying that ants and bees exhibits intelligent behavior when they look for food and communicate with one another. We infer this from some of the complexity of their route planning, survival strategies, and ability to adapt to new situations. Now, I assert that those same strategies can not only be learned by modern ML but are indeed often even hard-codable! As I view intelligence as a measure of an agent's behaviors in a system, such a measure should not distinguish the bee and my hard-wired agent. This for me means hard-coded things can be intelligent as they can mimic bees (and with enough code humans). However, the distribution of behaviors which humans inhabit are prohibitively difficult to code by hand. So we rely on data-driven techniques to search for such distributions in a space which is rich enough to support complexities at the level of the human brain. As such I certainly have no reason to believe, just because I can train one, that it must be less intelligent than humans. On the contrary, I believe in every verifiable domain RL must drive the agent to be the most intelligent (relative to RL award) it can be under the constraints--and often it must become more intelligent than humans in that environment.

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.
- Davidzheng 1y ago
  OK I, like the other commenter, also feel stupid to reply to zingers--but here goes. First of all, I think a lot of the issue here is this sense of baggage over this word intelligence--I guess because believing machines can be intelligent goes against this core belief that people have that humans are special. This isn't meant as a personal attack--I just think it clouds thinking. Intelligence of an agent is a spectrum, it's not a yes/no. I suspect most people would not balk at me saying that ants and bees exhibits intelligent behavior when they look for food and communicate with one another. We infer this from some of the complexity of their route planning, survival strategies, and ability to adapt to new situations. Now, I assert that those same strategies can not only be learned by modern ML but are indeed often even hard-codable! As I view intelligence as a measure of an agent's behaviors in a system, such a measure should not distinguish the bee and my hard-wired agent. This for me means hard-coded things can be intelligent as they can mimic bees (and with enough code humans). However, the distribution of behaviors which humans inhabit are prohibitively difficult to code by hand. So we rely on data-driven techniques to search for such distributions in a space which is rich enough to support complexities at the level of the human brain. As such I certainly have no reason to believe, just because I can train one, that it must be less intelligent than humans. On the contrary, I believe in every verifiable domain RL must drive the agent to be the most intelligent (relative to RL award) it can be under the constraints--and often it must become more intelligent than humans in that environment.