All of Charlie Sanders's Comments + Replies

I'd like to propose a test to objectively quantify the average observer's reaction with regards to skepticism of doomsday prophesizing present in a given text. My suggestion is this: take a text, swap the subject of doom (in this case AGI) with another similar text spelling out humanity's impending doom - for example, a lecture on Scientology and Thetans or the Jonestown massacre - and present these two texts to independent observers, in the same vein as a Turing test. 
 

If an outside independent observer cannot reliably identify which subject of ... (read more)

I think this post would be stronger if it covered at least basic metrology and statistics. 

It's incorrect to say that billions of variables aren't affecting a sled sliding down a hill - of course they're affecting the speed, even if most are only by a few planck-lengths per hour. But, crucially, they're mostly not affecting it to a detectable amount. The detectability threshold is the key to the argument. 

For detectability, whether you notice the effects of outside variables is going to come down to the precision of the instrument that you're usi... (read more)

2TLW
As an aside, this is one of the reasons why some sensing systems deliberately inject random noise. If it turns out that, for instance, your system's actual states are always X.4 MPH, you have a systematic bias if you use a radar gun that actually gives readings to the nearest MPH. If, however, you inject ±0.5 MPH random noise, you don't have a systematic bias any more. (Of course, this requires repeated sampling to pick up on.) As an extreme example of that, consider: f(x)→SHA512(x)=SHA512(someRandomLongConstant) Under blackbox testing, this function is indistinguishable from f(x)→false. It is important to differentiate between billions of: uncorrelated variables, correlated variables, and anticorrelated variables. A grain of sand on the hill may not detectably influence the sled. A truckload of sand, on the other hand, will very likely do so. You are correct in the case of uncorrelated variables with a mean of zero; it is interesting in the real world that almost all variables appear to fall into this category.
  1. The size of the community working on the alignment problem can be assumed to be at least somewhat proportional to the likelihood of successfully solving the alignment problem.
  2. Eliezer, being the most public face of the alignment problem community, wields outsized influence in shaping public perception of the community.
  3. Eliezer's writing is distinctly condescending and polemical, and has at least a hypothetical possibility of causing reputational harm to the community (as evidenced by your comment).

Based on this, there absolutely exists a hypothetical point w... (read more)

5Rob Bensinger
My first order response to this is in https://www.lesswrong.com/posts/Js34Ez9nrDeJCTYQL/politics-is-way-too-meta 

Imagine you have two points, A and B. You're at A, and you can see B in the distance. How long will it take you to get to B?

Well, you're a pretty smart fellow. You measure the distance, you calculate your rate of progress, maybe you're extra clever and throw in a factor of safety to account for any irregularities during the trip. And you figure that you'll get to point B in a year or so.

Then you start walking.

And you run into a wall. 

Turns out, there's a maze in between you and point B. Huh, you think. Well that's ok, I put a factor of safety into my ... (read more)

7orthonormal
On the other hand, sometimes people end up walking right through what the established experts thought to be a wall. The rise of deep learning from a stagnant backwater in 2010 to a dominant paradigm today (crushing the old benchmarks in basically all of the most well-studied fields) is one such case. In any particular case, it's best to expect progress to take much, much longer than the Inside View indicates. But at the same time, there's some part of the research world where a major rapid shift is about to happen.

One implication of the Efficient Market Hypothesis (EMH) is that is it difficult to make money on the stock market. Generously, maybe only the top 1% of traders will be profitable. 

Nitpick: it's incredibly easy to make money on the stock market: just put your money in it, ideally in an index fund. It goes up by an average of 8% a year. Almost all traders will be profitable, although many won't beat that 8% average. 

The entire FIRE movement is predicated on it being incredibly simple to make money on the stock market. It takes absolutely zero skil... (read more)

Right, but I'm not sure how you'd "test" for success in that scenario. Usefulness to humanity, as demonstrated by effective product use, seems to me like the only way to get a rigorous result. If you can't measure the success or failure of an idea objectively, then the idea probably isn't going to matter much. 

On fuzzy tasks: I think the appropriate frame of comparison is neither an average subset (Mechanical Turk) or the ideal human (Go), but instead the median resource that someone would be reasonably likely to seek out. To use healthcare as an example, you'd want your AI to beat the average family doctor that most people would reach out to, as opposed to either a layman's opinion or the preeminent doctor in the field. 

4Charlie Steiner
Hello fellow Charlie! For half a second I thought I'd written a comment in a fugue state and forgotten it :P
2Raemon
I think that makes sense for "building a useful product", but less so for "test the hypothesis that you can get aligned superhuman performance out of an unaligned-by-default intelligence, for purposes of later being more informed when you go to build an aligned, godlike intelligence."

https://www.youtube.com/watch?v=vRBsaJPkt2Q

If you’re interested in this topic more and have an hour and a half to burn, there’s worse ways to spend it.

The world would undoubtedly be better if more Data Scientists became monks.