= 27d567bc2d48d9f40e9ccc20ea370b15)
Manfred comments on Open Thread, Jun. 22 - Jun. 28, 2015 - Less Wrong Discussion
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (203)
I've mostly been here for the sequences and interesting rationality discussion, I know very little about AI outside of the general problem of FAI, so apologies if this question is extremely broad.
I stumbled upon this facebook group (Model-Free Methods) https://www.facebook.com/groups/model.free.methods.for.agi/416111845251471/?notif_t=group_comment_reply discussion a recent LW post, and they seem to cast LW's "reductionist AI" approach to AI in a negative light compared to their "neural network paradigm".
These people seem confident deep learning and neural networks are superior to some unspecified LW approach. Can anyone give a high level overview of what the LW approach to AI is, possibly contrasted with theirs?
There isn't really a "LW approach to AI," but there are some factors at work here. If there's one universal LW buzzword, it's "Bayesian methods," though that's not an AI design, one might call it a conceptual stance. There's also LW's focus on decision theory, which, while still not an AI design, is usually expressed as short, "model-dependent" algorithms. It would also be nice for a self-improving AI to have a human-understandable method of value learning, which leads to more focus diverted away from black-box methods.
As to whether there's some tribal conflict to be worried about here, nah, probably not.