Freeloading? Bad word. There are plenty of free/open source alternatives like Discourse, Zulip, Matrix, so why would anybody pay for something which should be free to use and respect user privacy?
Uh there's no way for me to allow it to be played on mobile because 1. I don't own the website, 2. It's designed for another set of hardware with very little controls, the game works with WASD and K
Id have bought a lot of lunches for myself if I had a dollar for every time I’ve pushed my team for documentation and had it turn into a discussion of “Well, how does that stack up against other priorities?”
It’s a pretty foolproof way for smart political operators to get out of a relatively dreary - but high leverage - task.
AI doesn’t complain. It just writes it. Makes the whole task a lot faster when a human is a reviewer for correctness instead of an author and reviewer.
Not the same reason at all. In genetics the reason is that you're losing gene variety and eventually recessive genes aren't suppressed anymore. In case of LLM it's just error accumulation.
It's a few days late but "losing gene variety" isn't the cause. What happens is genetic errors compound and are more likely to be expressed. I.E. "error accumulation".
How about a number of grad level genetics courses? Does that beat your google search? Because that is what I have. And what I am telling you is what happens.
This is really easily searched (as you said).
You might read up on it if interested. Check out why inbreeding can lead to expression of genetic defects. What is the mechanism? (hint: it's not "losing gene diversity" or "suppression").
`Homozygous, as related to genetics, refers to having inherited the same versions (alleles) of a genomic marker from each biological parent. Thus, an individual who is homozygous for a genomic marker has two identical versions of that marker. By contrast, an individual who is heterozygous for a marker has two different versions of that marker.`
In other words, errors can accumulate and are more likely to be expressed. Not "gene diversity" (this is a topic relating to evolutionary fitness, selection potential etc.), not "suppression". Error accumulation.
I had this conversation before. I point out how your interpretation is insane and doesn't follow logical reasoning, and you accuse me of gaslighting. I don't want to waste anyone else's time. We could just paste to an AI our both initial statements and ask who is more correct, but I'm sure you would either say AIs (all of them or 99% of them) are wrong, or you would interpret them saying I'm more correct, as you being right.
I have no problems being wrong on the Internet. Unfortunately, for some magical reason, in the overwhelming majority of my conversations, I either recognize it within a minute (or one reply when in writing), or never.
Let me give you a simple example maybe you will understand better.
Let's say a person has a recessive faulty gene. The gene doesn't get expressed because there is only one copy (recessive). We can notate this Aa (small "a" being the faulty gene, large "A" being the good copy). The person has two copies because they get one from each parent.
So "Aa" has a partner we can notate as "AA" (two good copies of the gene). AA and Aa have a child. What is the chance the child has the recessive gene? 25% because we have 4 possibilities with 1 bad outcome. Can the child have two bad copies (i.e. "aa" where the gene gets expressed)? No, they cannot because there are not two copies available from the parents, only one. At most they get "Aa". 75% chance they get "AA".
Let's say AA and Aa have a bunch of kids, the kids intermarry. Then their kids intermarry. Now what is the chance of an individual having two bad copies (i.e "aa"). What is the chance they have 1 bad copy (Aa)?
It's just probability calculations, and the expression becomes more probable as there are more copies of the bad gene in the gene pool. I.E within a population, the errors accumulate, they build up, there is a larger chance of getting expression of the defect (aa) with continued inbreeding.
This works with desirable genes too which is why we have so many kinds of dogs for instance. We select for it and build up copies of gene expressions we want to see to the point there is a 100% (or close to) chance of expression.
Hopefully you get this now. If not, read up on Mendelian genetics and table calculations maybe that will help you see.
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So let me take this back to the original example of LLMs.
Suppose there is 1% chance an LLM confidently claims Python library "Foo" exists and does XX when it's not true. This is analogous to a bad copy of the gene. If you train on that output (i.e. "inbreeding"), then use that as a reference (more inbreeding), soon many sources will say "Foo" exists and you'll have a larger chance of getting "Foobarred" information from the LLM.
