The curious tale of how I mistook my dyslexia for stupidity - and talked, sang, and drew my way out of it. 

Sometimes I tell people I’m dyslexic and they don’t believe me. I love to read, I can mostly write without error, and I’m fluent in more than one language.

Also, I don’t actually technically know if I’m dyslectic cause I was never diagnosed. Instead I thought I was pretty dumb but if I worked really hard no one would notice. Later I felt inordinately angry about why anyone could possibly care about the exact order of letters when the gist is perfectly clear even if if if I right liike tis.

I mean, clear to me anyway.

I was 25 before it dawned on me that all the tricks...

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I am trying to gather a list of answers/quotes from public figures to the following questions:

• What are the chances that AI will cause human extinction?
• Will AI automate most human labour?
• Should advanced AI models be open source?
• Do humans have a moral duty to build artificial superintelligence?
• Should there be international regulation of advanced AI?
• Will AI be used to make weapons of mass destruction (WMDs)?

I am writing them down here if you want to look/help: https://docs.google.com/spreadsheets/d/1HH1cpD48BqNUA1TYB2KYamJwxluwiAEG24wGM2yoLJw/edit?usp=sharing 

[Crossposted from Eukaryote Writes Blog.]

So you’ve heard about how fish aren’t a monophyletic group? You’ve heard about carcinization, the process by which ocean arthropods convergently evolve into crabs? You say you get it now? Sit down. Sit down. Shut up. Listen. You don’t know nothing yet.

“Trees” are not a coherent phylogenetic category. On the evolutionary tree of plants, trees are regularly interspersed with things that are absolutely, 100% not trees. This means that, for instance, either:

• The common ancestor of a maple and a mulberry tree was not a tree.
• The common ancestor of a stinging nettle and a strawberry plant was a tree.
• And this is true for most trees or non-trees that you can think of.

I thought I had a pretty good guess at this, but the situation...

Suppose Alice and Bob are two Bayesian agents in the same environment. They both basically understand how their environment works, so they generally agree on predictions about any specific directly-observable thing in the world - e.g. whenever they try to operationalize a bet, they find that their odds are roughly the same. However, their two world models might have totally different internal structure, different “latent” structures which Alice and Bob model as generating the observable world around them. As a simple toy example: maybe Alice models a bunch of numbers as having been generated by independent rolls of the same biased die, and Bob models the same numbers using some big complicated neural net. 

Now suppose Alice goes poking around inside of her world model, and somewhere in there...