Is it a personal choice though? If in the morning I decide between wearing a blue shirt or a red shirt, that's arguably a matter of personal choice, but deciding to believe in God is as much a 'personal choice' as deciding to believe in gravity or deciding to believe in homeopathy.
To be fair, the Olympic weightlifting movements in their current form are pretty leg-dominant. In the past when there was a third event, the clean-and-press [1,2], athletes needed a lot more upper body strength.
> After decades of riding (sometimes wildly irresponsibly) I can only count on one hand motorists who were actively trying to run me over.
I don't see how this is a good thing? I've been riding my bike since I was a little kid (in the Netherlands) and I've had zero people actively try to kill me, I'm not sure why any nonzero number is not a big deal.
Does it do that correctly though? I've always wondered why the formula is not w / h^3, since I imagine your weight should scale cubicly with your height.
IMO the key takeaway from GTD is that you need to write things down in some sort of anything-goes inbox, so that they don't bounce around in your head. What kind of sorting/tracking mechanism you use after that is less important.
Another example is that in Dutch, the bigram 'ij' is considered a single letter, and so at the beginning of a word, both have to be uppercased. See for example the Dutch Wikipedia page for Iceland: https://nl.wikipedia.org/wiki/IJsland.
Surely you can do some basic filtering by checking if the ratio vowels :: consonants is not too large? Or if there are five consonants in a row or something.
So the standard argument against such a procedure is that if you generate N random bits, each of {0,1}^N bitstrings is equiprobable and therefore no mapping of {0,1}^N to {0..U-1} can map an equal number of bitstrings to each output.
A priori the method seems to work around this by conditionally sampling more bits, so that your domain is not one of fixed-length bitstrings. But then there is this paragraph:
> More intriguing still, this algorithm can be made unconditional by removing the early out, so that every value computed requires word size + 64 bits from the stream, which breaks the loop-carried dependency for fast generators, unlocking vectorization and parallelization where it was previously impossible. This is an especially powerful advantage when paired with bitstream generators that allow skip-ahead such as counter-based generators or PCG.
But now we are mapping {0, 1}^N into {0..U-1} right? So this mapping ought not be uniform? In fact I'm not sure why the early-termination method even works, because for a fixed U we know the maximum depth of the generated-bitstring-tree, so we can just pad it with imaginary random bits to arrive at the contradiction that U does not divide 2^N.
I imagine I missed something, what is it?
EDIT: thinking about it more, if you sample finitely many bits then each possible output has a probability that is a dyadic rational (fractions of the form a/2^k) which does not include all integer reciprocals.
People's political views are generally interwoven with their notion of morality and in the case of the religious, that morality is informed by their religion. The idea that you can separate religion and politics is misguided at best.
In the linked article they resolve collisions via chaining:
> A typical hash function distributes keys randomly across the slots in a hash table, causing some slots to be empty, while others have collisions, which require some form of chaining of items
I.e. each field in the table is a linked list of values that hash to this position, and the new value is inserted in the shortest of the two lists it hashes to.
I'm curious if this genetic diversity is just something that shows up as a number on a computer, or does it translate into something phenotypically* visible?
Also, how much of, say, our medical/biological knowledge actually only applies to the descendants of the bottleneck mentioned in the post? I understand the "very diverse" in the article have better things to do than be subjected to researchers, but reading the article I can't help but be curious what this genetic diversity means in practice?
Multiplicative weights, like mirror descent, is a special case of a more general algorithm called mirror descent. You can indeed view it as optimizing an \ell_\infty norm, though I think the mirror descent perspective is more insightful. I wrote about this in my thesis, see section 2.4 here: http://timonknigge.com/univ/msc-thesis.pdf
Not exactly -- multiplicative weights is a special case of mirror descent (as is gradient descent). It arises doing prox-steps on the simplex using the negative entropy as a regularizer (rather than the usual Euclidian distance, as is the case with gradient descent). In particular, if you do regular gradient descent then the runtime will be exponentially worse (the \log n in the guarantee will become a \sqrt{n}).
My understanding was that Waymo’s are autonomous and don’t have a remote driver?