I was thinking about what happens when you run independent Bernoulli trials forever, with probability pi of success, but you lose the first time you fail. If all pi are the same, then with probability 1 we will eventually fail[1]. Similarly, if the supremum of the sequence of probabilities p...
I've always had a fascination with decisions that rely on accepting/declining some continuous value. For instance, you might receive an unexpected email offering to purchase your domain name e.g. eeegnu.com, and depending on some huge number of factors, (i.e. how attached am I to the domain name, is the offer...
They probably do not know where the real difficulties are, they probably do not understand what needs to be done, they cannot tell the difference between good and bad work, and the funders also can't tell without me standing over their shoulders evaluating everything, which I do not have the physical stamina to do.
This was the sentiment I got after applying to the LTFF with an idea. Admittedly, I couldn't really say whether my idea had been tried before, or wasn't obviously bad, but my conversation basically boiled down to whether I wanted to use this project as a way to grow myself in the field, rather than any particular merits/faults of the... (read more)
For competitive programming questions, codeforces.com. It has a large audience of highly skilled competitive programmers, and someone will normally help you if it's an interesting question.
I'd rate these highly, there are many forms of anomalocarids (https://en.m.wikipedia.org/wiki/Radiodonta#/media/File%3A20191201_Radiodonta_Amplectobelua_Anomalocaris_Aegirocassis_Lyrarapax_Peytoia_Laggania_Hurdia.png) and it looks to have picked a wide variety aside from just candensis, but I'm thoroughly impressed that it got the form right in nearly all 10.
A prompt i'd love to see: "Anomalocaris Canadensis flying through space." I'm really curious how well it does with an extinct species which has very little existing artistic depictions. No text->image model i've played with so far has managed to create a convincing anomalocaris, but one interestingly did know it was an aquatic creature and kept outputting lobsters.
Never delete code or results!!!
There's a tool I've been interested in using (if it exists), basically a jupyter notebook, but which saves all outputs to tmp files (possibly truncating at the 1MB mark or something), and which maintains a tree history of the state of cells (i.e. if you hit ctrl-z a few times and start editing, it creates a branch.) Neither of these are particularly memory heavy, but both would have saved me in the past if they were hidden options to restore old state.
I'd also add that if you had a bug, and it took real effort/digging to find an online solution, archive the link to that solution in a list (preferably with date tags.) This has been preferable for me over painstakingly re-searching / trying to search through browser history when needed.
I was thinking about what happens when you run independent Bernoulli trials forever, with probability of success, but you lose the first time you fail. If all are the same, then with probability 1 we will eventually fail[1]. Similarly, if the supremum of the sequence of probabilities is not 1, then we're also guaranteed to fail, since . The condition that is necessary but not sufficient to guarantee that failure isn't certain[2]. That seems to say that not only do we need to improve, but we also can't improve too slowly, or else we're sure to fail. So just how quickly do we need to improve? Well we have that [3], which is much easier to... (read 333 more words →)
I'll probably end up thinking about this in the background for a while, and jotting down any interesting cases in case they can accumulate into a nice generalizable thing (or maybe I'll stumble upon someone else who's made such an analysis before.)
Where I see your example sharing a common idea is that one party makes what appears to be a suboptimal decision (e.g. if they just wanted to attract top talent, a salary at the top of the salary spectrum would suffice), and it leaves the other party to infer what might be the true reasoning behind the decision that would lead to it being an optimal decision (i.e. it assumes the... (read more)
I've always had a fascination with decisions that rely on accepting/declining some continuous value. For instance, you might receive an unexpected email offering to purchase your domain name e.g. eeegnu.com, and depending on some huge number of factors, (i.e. how attached am I to the domain name, is the offer enticing enough, do I want to go through the hassle, etc.) you'll arrive at a decision. Intuitively, we'd think that there's some hypothetical $ value X where every offer X yields No, and every offer X yields Yes, or framing this probabilistically, we'd expect a plot of $ from 0 to infinity against the probability of choosing yes to be non-decreasing. This might look... (read 702 more words →)
These are great points, and ones which I did actually think about when I was brainstorming this idea (if I understand them correctly.) I intend to write out a more thorough post on this tomorrow with clear examples (I originally imagined this as extracting deeper insights into chess), but to answer these:
- I did think about these as translators for the actions of models into natural language, though I don't get the point about extracting things beyond what's in the original model.
- I mostly glossed over this part in the brief summary, and the motivation I had for it comes from how (unexpectedly?) it works for GAN's to just start with random noise, and
... (read more)