Disagree.
replyExamples:
- predict a coinflip: easy to verify, hard to learn
- earn $100: easy to verify, hard to learn
- increase paid subscriptions in an A/B test: easy to verify, hard to learn
I won't get into it, but there are many properties beyond verifiability that are needed to saturate a benchmark.
Doesn't "saturated" mean that essentially there won't be any more progress in the benchmarch? Also of note is that two of your points only mean something on an occidental capitalist system.
replythese just need more compute:
reply- earn $100: easy to verify, hard to learn
- increase paid subscriptions in an A/B test: easy to verify, hard to learn
but we both know these examples go against the spirit of my point
Perhaps, but I think a bigger problem than lack of compute is the cost of rewards. Games like Chess and Go were solved long before self-driving, partly because it's incredibly cheap to acquire the reward of a bad board game decision, relatively to how expensive it is to acquire the cost of a bad driving decision. With driving, acquiring the reward can cost you $20/hr for human supervisors to generate disengagements, or $100k if you crash, or $30B if you crash the car into a person in a way that causes your company to collapse (e.g., Cruise).
replyyeah but I think you may be underestimating the amount of capital available for compute. if AGI is possible through some 5 trillion of expenditure on computers, there will be money for it.
replyalso, you are underestimating how short a 10 year time frame is. we are close to self driving, the first neural net image model was in 2013. 13 years is a blink of an eye
You bring up an interesting point. Isn't the reward itself subjective in many domains?
replyYes, but not necessarily under tight budget constraints.
replytheres no budget constraints for AGI
replyYup. Only subjective taste remains.
replyNope that will be commodified in short order.
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