LessWrong 2.0 Reader

There are likely multiple detectors of risk of falling. Being on shaky ground is for sure one. In amusement parks, there are sometimes thingies that share and wobble and can also give these kind of feeling. Also, it could be a learned (prediction by the though assessor) reaction, as you mention too.

These original warnings were always written from a framework that assumed the only way to make intelligence is RL. They are still valid for RL, but thankfully it seems that at least for the time being, pure RL is not popular; I imagine that might have something to do with how obvious it is to everyone who tries pure RL that it's pretty hard to get it to do useful things, for reasons that can be reasonably called alignment problems.

Imagine trying to get an AI to cure cancer entirely by RLHF, without even letting it learn language first. That's how bad they thought it would be.

But RL setups do get used, and they do have generalization issues that do have connection to these issues.

contrarianism is not what lead people to be right about those things.

H5N1 has spread to cows. Should I be worried?

I'd guess that you have to rely a lot more on persuasion and positive reinforcement - if you want them to do something, it's probably not going to happen unless they willingly agree to do it.

I wasn't really like this until I was about 12-13 years old, though; as a younger child I often went into violent rages instead of displaying submissive behavior. I eventually did grow out of hitting peopIe and now only rarely feel genuine anger (as opposed to anger-adjacent feelings such as frustration), but 15-year-old me was still willing to passively resist by laying in a limp ball and enduring the consequences for as long as I needed to!

Measuring the composition of fryer oil at different times certainly seems like a good way to test both the original hypothesis and the effect of altitude.

I've also realized that it might explain the anomalous (i.e. after adjusting for confounders) effects of living at higher altitude. The lower the atmospheric pressure, the less oxygen available to oxidize the PUFAs. Of course some foods will be imported already full of oxidized FAs and that will be too late, but presumably a McDonalds deep fryer in Colorado Springs is producing less PUFAs/hour than a correspondingly-hot one in San Francisco.

This feels too crazy to put in the original post but it's certainly interesting.

That's extremely cool, seems worth adding to the main post IMHO!

Yes, thank you, it's less than 1000 parameters for both

We have been able to scale to 79% accuracy on a balanced dataset of n119 and non-n119, with networks each less than three convolution layers and less than 1000 neurons, compared to pure deep-learning which does 92% on 1000 parameters and three convolution layers

Is the "1000 parameters" a typo, should it be "1000 neurons"? Otherwise, this would be a strange comparison (since 1000 parameters is a much smaller network than 1000 neurons)