Your probabilities are not independent, your estimates mostly flow from a world model which seem to me to be flatly and clearly wrong.
The plainest examples seem to be assigning
We invent a way for AGIs to learn faster than humans
40%
AGI inference costs drop below $25/hr (per human equivalent)
16%
despite current models learning vastly faster than humans (training time of LLMs is not a human lifetime, and covers vastly more data) and the current nearing AGI and inference being dramatically cheaper and plummeting with algorithmic improvements. There is a general factor of progress, where progress leads to more progress, which you seem to be missing in the positive factors. For the negative, derailment that delays enough to push us out that far needs to be extreme, on the order of a full-out nuclear exchange, given more reasonable models of progress.
I’ll leave you with Yud’s preemptive reply:
Taking a bunch of number and multiplying them together causes errors to stack, especially when those errors are correlated.
despite current models learning vastly faster than humans (training time of LLMs is not a human lifetime, and covers vastly more data)
Some models learning some things faster than humans does not imply AGI will learn all things faster than humans. Self-driving cars, for example, are taking much longer to learn to drive than teenagers do.
Disagree with example. Human teenagers spend quite a few years learning object recognition and other skills necessary for driving before driving, and I’d bet at good odds that a end-to-end training run of a self-driving car network is shorter than even the driving lessons a teenager goes through to become proficient at a similar level to the car. Designing the training framework, no, but the comparator there is evolution’s millions of years so that doesn’t buy you much.
The end-to-end training run is not what makes learning slow. It’s the iterative reinforcement learning process of deploying in an environment, gathering data, training on that data, and then redeploying with a new data collection strategy, etc. It’s a mistake, I think, to focus only the narrow task of updating model weights and omit the critical task of iterative data collection (i.e., reinforcement learning).
Your probabilities are not independent, your estimates mostly flow from a world model which seem to me to be flatly and clearly wrong.
The plainest examples seem to be assigning
despite current models learning vastly faster than humans (training time of LLMs is not a human lifetime, and covers vastly more data) and the current nearing AGI and inference being dramatically cheaper and plummeting with algorithmic improvements. There is a general factor of progress, where progress leads to more progress, which you seem to be missing in the positive factors. For the negative, derailment that delays enough to push us out that far needs to be extreme, on the order of a full-out nuclear exchange, given more reasonable models of progress.
I’ll leave you with Yud’s preemptive reply:
Taking a bunch of number and multiplying them together causes errors to stack, especially when those errors are correlated.
Some models learning some things faster than humans does not imply AGI will learn all things faster than humans. Self-driving cars, for example, are taking much longer to learn to drive than teenagers do.
Disagree with example. Human teenagers spend quite a few years learning object recognition and other skills necessary for driving before driving, and I’d bet at good odds that a end-to-end training run of a self-driving car network is shorter than even the driving lessons a teenager goes through to become proficient at a similar level to the car. Designing the training framework, no, but the comparator there is evolution’s millions of years so that doesn’t buy you much.
The end-to-end training run is not what makes learning slow. It’s the iterative reinforcement learning process of deploying in an environment, gathering data, training on that data, and then redeploying with a new data collection strategy, etc. It’s a mistake, I think, to focus only the narrow task of updating model weights and omit the critical task of iterative data collection (i.e., reinforcement learning).