Thank you that works as desired!
rain8dome9
Karma: 27
I have see lots of advice on reddit, here and anki’s own forums about to formulate anki cards and use anki in general. Too much in fact. Sometimes its even contradictory. In the end I use some of it but ignored most. Alot of this has to do with the fact that the advice depends on the type of card and on you. If the card is just one word to another to automatize foreign language vocabulary you will want brevity speed fluency and more of these cards per day. If its cloze deletion to memorize a poem / notes card can not be brief and speed is not so important. If its a test like understanding a sentence of a foreign language then it wont go back into review and I do many of these cards irregardless of the count of new cards. I also suspect it depends on the user though I have not really had enough data to give examples. To further all of this including finding the right advice for each person, I have written some part of an analysis of anki data. Unfortunately not many people seem to care so I stopped. Please run this R notebook on your data.
Please give an example of JavaScript code that would make a minor wording tweak. I often find I need to not memorize the cue so precisely.
Why Mark Ruffalo? Will there be an audiobook? Edit: Yes; it can be preordered now.
“Random variable” is never defined. I though stochastic variable is just a synonym for random variable. I have seen posts where random variable is always written as r.v. and that helps a bit.
From Wikipedia: “In probability theory, the sample space (also called sample description space,[1] possibility space,[2] or outcome space[3]) of an experiment or random trial is the set of all possible outcomes or results of that experiment.
what is a measurable space?
“he function is constant,” you mean its just one outcome like a die that always lands on one side?
what makes a function measurable?
random variables
This term always sounds like it means a variable selected at random not a variable with randomness in it. Please use the term ‘stochastic variable’. Edit: or does it mean a variable composed entirely at random without any relation to any other variable?
Edit: I think this post would be much easier to learn from if it was a jupyter notebook with python code intermixed or R markdown. Sometimes the terminology gets away from me and seeing in code what is being said would really help understand what is going on as well as give some training on how to use this knowledge. Edit: there should be a plot illustrating ” which are jointly sampled according to a density .” including rugs for the marginal distributions. I could do that if anyone wants. Here is an example describing a different concept.
The best use of prediction markets is for decision makers to prove their competence. Or as Lizka suggests to advise voting public.
Shannon entropy
Some examples of what the formula produces:
Fair coin is 2 (sides) x .5 (probability) x log(2) = 1 x .3010 = .3
Fair hundred sided die 100 x .01 x log(100) = 1 x 2 = 2
just like a prion twisting nearby proteins
Literally sounds disgusting to me. And its used to describe
marginalized communities, it has this sort of natural prior of “people often turn out to be abusers,
The low-trust attractor starts to bend other people into reciprocal low-trust shapes, just like a prion twisting nearby proteins.
Convincing people using your actions sounds disgusting!
Started to work on this puzzle but gave up. Still want to show what I made. This picture is supposed to shows how tax increases as one item type increases. Each color line is an initial set of other items enumerated under ‘key’ (“Cockatrice” “Dragon” “Lich” “Zombie”). There is a picture like this for every taxable item type.
Please give an example of the output that the LLM produces that is so useful.
For years, my self-education was stupid and wasteful. … Teaching Company courses,
I wonder if this is still true. I really liked Language Families of the World lectures by a professor from Colombia University. It is the best audiobook by the Great Courses I have found so far. Listening felt more like intelligent entertainment than study but that could be remedied with secondary materials.
Could you describe the experiment you ran on all theses models? Like ‘if there are three boxes side by side in a line and each can hold one item and the red triangle is not in the middle and the blue circle is not in the box next to the box with a red triangle in it where is the green circle? ’. Chatgpt was not able to solve logic puzzles a year ago and can do it now.
That said, the dimensions of quality that the FDA concerns itself with (including physical functioning, self-reported pain, and other easily- and not-easily-measured things) is likely close enough to “improves quality of life” that it’s not necessary to have a new direction.
Athletic performance. Cognitive performance. Work performance. Also ability to accomplish the things needed in every day life to have uh fun..
Then wolfram alpha?
I thinks its worth mentioning that there are two levels of black box models too. ML can memorize the expected value at each set of variables (at 1 rmp crank wheel rotates at 2 rpm) or it can ‘generalize’ and, for this example, tell us that the wheel rotates at 2x speed of crank. To some extent ‘ML generalization’ provides good ‘out of distribution’ predictions.
There is no “Wikipedia for predictive models” that I know of. No big repository to easily share and find predictive scientific models other than the relevant domain’s scientific literature, which is not optimized for these tasks: it is not organized by the variables being predicted, it is not generally available as reusable and modular software components, it is usually not focused on predictive work, some of it is paywalled, etc.
Have you tried www.openml.org?
Prototypical example: imagine a scientific field in which the large majority of practitioners have a very poor understanding of statistics, p-hacking, etc. Then lots of work in that field will be highly memetic despite trash statistics, blatant p-hacking, etc. Sure, the most competent people in the field may recognize the problems, but the median researchers don’t, and in aggregate it’s mostly the median researchers who spread the memes.
Complicated analysis (like going far beyond p-values) is easy for anyone to see and it is evidence of effort. Complex analysis usually coocurs with thoroughness so fewer mistakes. Complicated analysis coocurs with many concurrent tests so less need to produce positive results so less p-hacking. Consequently, there is a fairly simple solution to researchers with mediocre statistical skills gaining too much trust: more plots! Anyway, I find correlation graphs and multiple comparison impressive. Also I am usually more skilled in data analysis than the subject of a paper so can more easily verify that.
The specific word ‘greyspaces’ makes me think of something like bad architecture a perspective on morality. Could you call this concept something else like ‘intermediary spaces’?