Open Thread June 2010, Part 3

Kevin

This thread is for the discussion of Less Wrong topics that have not appeared in recent posts. If a discussion gets unwieldy, celebrate by turning it into a top-level post.

The thrilling conclusion of what is likely to be an inaccurately named trilogy of June Open Threads.

This thread is for the discussion of Less Wrong topics that have not appeared in recent posts. If a discussion gets unwieldy, celebrate by turning it into a top-level post.

The thrilling conclusion of what is likely to be an inaccurately named trilogy of June Open Threads.

I see Yoreth's version of the majoritarian argument as ahistorical. The US Government did put a lot of money into AI research and became disillusioned. Daniel Crevier wrote a book AI: The tumultuous history of the search for artificial intelligence. It is a history book. It was published in 1993, 17 years ago.

There are two possible responses. One might argue that time has moved on, things are different now, and there are serious reasons to distinguish today's belief that AI is around the corner from yesterday's belief that AI is around the corner. Wrong then, right now, because...

Alternatively one might argue that scaling died at 90 nanometers, practical computer science is just turning out Java monkeys, the low hanging fruit has been picked, there is no road map, theoretical computer science is a tedious sub-field of pure mathematics, partial evaluation remains an esoteric backwater, theorem provers remain an esoteric backwater, the theorem proving community is building the wrong kind of theorem provers and will not rejuvenate research into partial evaluation,...

The lack of mainstream interest in explosive developments in AI is due to getting burned in the past. Noticing that the scars are not fading is very different from being unaware of AI.

I know of partial evaluation in the context of optimization, but I hadn't previously heard of much connection between that and AI or theorem provers. What do you see as the connection?

Or, more concretely: what do you think would be the right kind of theorem provers?

2SilasBarta16y

I'm reminded of a historical analogy from reading Artificial Addition. Think of it this way: a society that believes addition is the result of adherence to a specific process (or a process isomorphic thereto), and understands part of that process, is closer to creating "general artificial addition" than one that tries to achieve "GAA" by cleverly avoiding the need to discover this process. We can judge our own distance to artificial general intelligence, then, by the extent to which we have identified constraints that intelligent processes must adhere to. And I think we've seen progress on this in terms of more refined understanding of e.g. how to apply Bayesian inference. For example, the work by Sebastian Thrun on how to seamlessly aggregate knowledge across sensors to create a coherent picture of the environment, which has produced tangible results (navigating the desert).