Relevant related work : NNs are surprisingly modular

https://arxiv.org/abs/2003.04881v2?ref=mlnews

On the topic of pruning neural networks, see the lottery ticket hypothesis

https://arxiv.org/abs/1803.03635

How might we quantify size in our definitions above?

Random K complexity inspired measure of size for a context / property / pattern.

Least number of squares you need to turn on, starting from an empty board, so that the grid eventually evolves into the context.

It doesn't work for infinite contexts though.

My user experience

When I first load the page, I am greeted by an empty space.

From here I didn't know what to look for, since I didn't remember what kind of things where in the database.

I tried clicking on table to see what content is there.

Ok, too much information, hard to navigate.

I remember that one of my manuscripts made it to the database, so I look up my surname

That was easy! (and it loaded very fast)

The interface is very neat too. I want to see more papers, so I click on one of the tags.

I get what I wanted.

Now I want to find a list of all the tags. Hmmm I cannot find this anywhere.

I give up and look at another paper:

Oh cool! The Alignmnet Newsletter summary is really great. Whenever I read something in Google Scholar it is really hard to find commentary on any particular piece.

I tried now to look for my current topic of research to find related work

Meh, not really anything interesting for my research.

Ok, now I want to see if Open AI's "AI and compute" post is in the dataset:

Huhhh it is not here. The bitter lesson is definitely relevant, but I am not sure about the other articles.

Can I search for work specific to open ai?

Hmm that didnt quite work. The top result is from OpenAI, but the rest are not.

Maybe I should spell it different?

Oh cool that worked! So apparently the blogpost is not in the dataset.

Anyway, enough browsing for today.

Alright, feedback: 

1. This is a very cool tool. The interface is neatly designed.
2. Discovering new content seems hard. Some things that could help include a) adding recommended content on load (perhaps things with most citations, or even ~10 random papers) and b) having a list of tags somewhere
3. The reviewer blurbs are very nice. However I do not expect to use this tool. Or rather I cannot think right now of what exactly I would use this tool for. It has made me consider reaching out to the database mantainers to suggest the inclusion of an article of mine. So maybe like that, to promote my work?

One more question: for the BigGAN which model do your calculations refer to?

Could it be the 256x256 deep version?

Do you mind sharing your guesstimate on number of parameters?

Also, do you have per chance guesstimates on number of parameters / compute of other systems?

Very tangential to the discussion so feel free to ignore, but given that you have put some though before on prize structures I am curious about the reasoning for why you would award a different prize for something done in the past versus something done in the future

Thank you! The shapes mean the same as the color (ie domain) - they were meant to make the graph more clear. Ideally both shape and color would be reflected in the legend. But whenever I tried adding shapes to the legend instead a new legend was created, which was more confusing.

If somebody reading this knows how to make the code produce a correct legend I'd be very keen on hearing it!

EDIT: Now fixed

re: impotance of oversight

I do not think we really disagree on this point. I also believe that looking at the state of the computer is not as important as having an understanding of how the program is going to operate and how to shape its incentives. 

Maybe this could be better emphasized, but the way I think about this article is showing that even the strongest case for looking at the intersection of quantum computing and AI alignment does not look very promising. 

re: How quantum computing will affect ML

I basically agree that the most plausible way QC can affect AI aligment is by providing computational speedups - but I think this mostly changes the timelines rather than violating any specific assumptions in usual AI alignment research.

Relatedly, I am bullish that we will see better than quadratic speedups (ie Grover) - to get better-than-quadratic speedups you need to surpass many challenges that right now it is not clear can be surpassed outside of very contrived problem setup [REF].

In fact I think that the speedups will not even be quadratic because you "lose" the quadratic speedup when parallelizing quantum computing (in the sense that the speedup does not scale quadratically with the number of cores).

I think this helped me a lot understand you a bit better - thank you

Let me try paraphrasing this:

> Humans are our best example of a sort-of-general intelligence. And humans have a lazy, satisfying, 'small-scale' kind of reasoning that is mostly only well suited for activities close to their 'training regime'. Hence AGIs may also be the same - and in particular if AGIs are trained with Reinforcement Learning and heavily rewarded for following human intentions this may be a likely outcome.

Is that pointing in the direction you intended?