Crosspost from my blog.  

If you spend a lot of time in the blogosphere, you’ll find a great deal of people expressing contrarian views. If you hang out in the circles that I do, you’ll probably have heard of Yudkowsky say that dieting doesn’t really work, Guzey say that sleep is overrated, Hanson argue that medicine doesn’t improve health, various people argue for the lab leak, others argue for hereditarianism, Caplan argue that mental illness is mostly just aberrant preferences and education doesn’t work, and various other people expressing contrarian views. Often, very smart people—like Robin Hanson—will write long posts defending these views, other people will have criticisms, and it will all be such a tangled mess that you don’t really know what to think about them.

For...

My understanding is that pilot wave theory (ie Bohmian mechanics) explains all the quantum physics with no weirdness like "superposition collapse" or "every particle interaction creates n parallel universes which never physically interfere with each other". It is not fully "local" but who cares?

Is there any reason at all to expect some kind of multiverse? Why is the multiverse idea still heavily referenced (eg in acausal trade posts)?

Edit April 11: I challenge the properly physics brained people here (I am myself just a Q poster) to prove my guess wrong: Can you get the Born rule with clean hands this way? 

They also implicitly claim that in order for the Born rule to work [under pilot wave], the particles have to start the sim following the psi^2 distribution.

The history of science has tons of examples of the same thing being discovered multiple time independently; wikipedia has a whole list of examples here. If your goal in studying the history of science is to extract the predictable/overdetermined component of humanity's trajectory, then it makes sense to focus on such examples.

But if your goal is to achieve high counterfactual impact in your own research, then you should probably draw inspiration from the opposite: "singular" discoveries, i.e. discoveries which nobody else was anywhere close to figuring out. After all, if someone else would have figured it out shortly after anyways, then the discovery probably wasn't very counterfactually impactful.

Alas, nobody seems to have made a list of highly counterfactual scientific discoveries, to complement wikipedia's list of multiple discoveries.

To...

Summary: Evaluations provide crucial information to determine the safety of AI systems which might be deployed or (further) developed. These development and deployment decisions have important safety consequences, and therefore they require trustworthy information. One reason why evaluation results might be untrustworthy is sandbagging, which we define as strategic underperformance on an evaluation. The strategic nature can originate from the developer (developer sandbagging) and the AI system itself (AI system sandbagging). This post is an introduction to the problem of sandbagging.

The Volkswagen emissions scandal

There are environmental regulations which require the reduction of harmful emissions from diesel vehicles, with the goal of protecting public health and the environment. Volkswagen struggled to meet these emissions standards while maintaining the desired performance and fuel efficiency of their diesel engines (Wikipedia). Consequently, Volkswagen...

Epistemic – this post is more suitable for LW as it was 10 years ago

Thought experiment with curing a disease by forgetting

Imagine I have a bad but rare disease X. I may try to escape it in the following way:

1. I enter the blank state of mind and forget that I had X.

2. Now I in some sense merge with a very large number of my (semi)copies in parallel worlds who do the same. I will be in the same state of mind as other my copies, some of them have disease X, but most don’t.  

3. Now I can use self-sampling assumption for observer-moments (Strong SSA) and think that I am randomly selected from all these exactly the same observer-moments. 

4. Based on this, the chances that my next observer-moment after...

About a year ago I decided to try using one of those apps where you tie your goals to some kind of financial penalty. The specific one I tried is Forfeit, which I liked the look of because it’s relatively simple, you set single tasks which you have to verify you have completed with a photo.

I’m generally pretty sceptical of productivity systems, tools for thought, mindset shifts, life hacks and so on. But this one I have found to be really shockingly effective, it has been about the biggest positive change to my life that I can remember. I feel like the category of things which benefit from careful planning and execution over time has completely opened up to me, whereas previously things like this would be largely down to the...

In short: Training runs of large Machine Learning systems are likely to last less than 14-15 months. This is because longer runs will be outcompeted by runs that start later and therefore use better hardware and better algorithms. [Edited 2022/09/22 to fix an error in the hardware improvements + rising investments calculation]

Larger compute budgets and a better understanding of how to effectively use compute (through, for example, using scaling laws) are two major driving forces of progress in recent Machine Learning.

There are many ways to increase your effective compute budget: better hardware, rising investments in AI R&D and improvements in algorithmic efficiency. In this article...