Found this paper on insecticide costs: https://sci-hub.st/http://dx.doi.org/10.1046/j.1365-2915.2000.00262.x
It's from 2000, so anything listed here would be out of patent today.
hardening voltage transformers against ionising radiation
Is ionization really the mechanism by which transformers fail in a solar storm? I thought it was that changes in the Earth's magnetic field induced large currents in long transmission lines, overloading the transformers.
Sorry for the self promotion, but some folks may find this post relevant: https://www.lesswrong.com/posts/uDXRxF9tGqGX5bGT4/logical-share-splitting (ctl-F for "Application: Conditional prediction markets")
tldr: Gives a general framework that would allow people to make this kind of trade with only $N in capital, just as a natural consequence of the trading rules of the market.
Anyway, I definitely agree that Manifold should add the feature you describe! (As for general logical share splitting, well, it would be nice, but probably far too much work to convert the existing codebase over.)
IMO, a very good response, which Eliezer doesn't seem to be interested in making as far as I can tell, is that we should not be making the analogy natural selection <--> gradient descent, but rather, human brain learning algorithm <--> gradient descent ; natural selection <--> us trying to build AI.
So here, the striking thing is that evolution failed to solve the alignment problem for humans. I.e. we have a prior example of strongish general intelligence being created, but no prior examples of strongish general intelligence being aligned. Evolution was strong enough to do one but not the other. It's not hopeless, because we should generally consider ourselves smarter than evolution, but on the other hand, evolution has had a very long time to work and it does frequently manage things that we humans have not been able to replicate. And also, it provides a small amount of evidence against "the problem will be solved with minor tweaks to existing algorithms" since generally minor tweaks are easier for evolution to find than ideas that require many changes at once.
People here might find this post interesting: https://yellow-apartment-148.notion.site/AI-Search-The-Bitter-er-Lesson-44c11acd27294f4495c3de778cd09c8d
The author argues that search algorithms will play a much larger role in AI in the future than they do today.
I remember reading the EJT post and left some comments there. The basic conclusions I arrived at are:
In my current thinking about non-coherent agents, the main toy example I like to think about is the agent that maximizes some combination of the entropy of its actions, and their expected utility. i.e. the probability of taking an action is proportional to up to a normalization factor. By tuning we can affect whether the agent cares more about entropy or utility. This has a great resemblance to RLHF-finetuned language models. They're trained to both achieve a high rating and to not have too great an entropy with respect to the prior implied by pretraining.
If you're working with multidimensional tensors (eg. in numpy or pytorch), a helpful pattern is often to use pattern matching to get the sizes of various dimensions. Like this: batch, chan, w, h = x.shape. And sometimes you already know some of these dimensions, and want to assert that they have the correct values. Here is a convenient way to do that. Define the following class and single instance of it:
class _MustBe:
""" class for asserting that a dimension must have a certain value.
the class itself is private, one should import a particular object,
"must_be" in order to use the functionality. example code:
`batch, chan, must_be[32], must_be[32] = image.shape` """
def __setitem__(self, key, value):
assert key == value, "must_be[%d] does not match dimension %d" % (key, value)
must_be = _MustBe()
This hack overrides index assignment and replaces it with an assertion. To use, import must_be from the file where you defined it. Now you can do stuff like this:
batch, must_be[3] = v.shape
must_be[batch], l, n = A.shape
must_be[batch], must_be[n], m = B.shape
...
Linkpost for: https://pbement.com/posts/must_be.html
Oh, very cool, thanks! Spoiler tag in markdown is:
:::spoiler
text here
:::
Heh, sure.
Promote from a function to a linear operator on the space of functions, . The action of this operator is just "multiply by ". We'll similarly define meaning to multiply by the first, second integral of , etc.
Observe:
Now we can calculate what we get when applying times. The calculation simplifies when we note that all terms are of the form . Result:
Now we apply the above operator to :
The sum terminates because a polynomial can only have finitely many derivatives.
Speaking of which, I wonder if multi-modal transformers have started being used by blind people yet. Since we have models that can describe images, I wonder if it would be useful for blind people to have a device with a camera and a microphone and a little button one can press to get it to describe what the camera is seeing. Surely there are startups working on this?