Anything on twitter post-login-wall for one. A million only-semi-paywalled news articles for others. But mainly an unfathomably long tail.

It was extremely distressing when the admin started(?) behaving badly for this reason. That others are starting to react this way to it is understandable. What a stupid tragedy.

This https://cdna.pcpartpicker.com/static/forever/images/trends/2... will happen to every class of thing (once it hits energy, everything is downstream of energy).

Holy shit what a perspective. Put it in a museum. If this is representative, put it on our grave.

Instant classic destined for the engineering-disasters-drilled-into-1st-year-engineers canon (or are the other swiss cheese holes too confounding)

Where do you think it would fit on the list?

https://www.youtube.com/watch?v=3BxiYkCPZwI

I think greater pasta thickness is underexplored, and the teflon-vs-bronze die thing as the highest determinant of pasta quality, while not nothing, is slightly-overstated r*dditry.

Might want to write this out in full lol I thought this in particular was going to be a much more entertaining point.

1. was designed to statistically select for members of favored identity groups and against members of disfavored identity groups, and not in any way to measure ATC job aptitude, resulting in highly-scored questions like "The high school subject in which I received my lowest grades was" where the only correct answer was "Science", and failing the test disqualified you permanently

2. then-current FAA employees distributed the exact answer key to outside racial identity organizations to give to their members

https://www.courtlistener.com/docket/4542755/139/24/brigida-...

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Example questions with the score given for each answer:

https://storage.courtlistener.com/recap/gov.uscourts.dcd.182...

    15. The high school subject in which I received my lowest grades was:
    A. SCIENCE (+15)
    B. MATH (0)
    C. ENGLISH (0)
    D. HISTORY/SOCIAL SCIENCES (0)
    E. PHYSICAL EDUCATION (0)

    16. Of the following, the college subject in which I received my lowest grades was:
    A. SCIENCE (0)
    B. MATH (0)
    C. ENGLISH (0)
    D. HISTORY/POLITICAL SCIENCE (+15)
    E. DID NOT ATTEND COLLEGE (0)

    29. My peers would probably say that having someone criticize my performance (i.e. point out a mistake) bothers me:
    A. MUCH LESS THAN MOST (+8)
    B. SOMEWHAT LESS THAN MOST (+4)
    C. ABOUT THE SAME AS MOST (+8)
    D. SOMEWHAT MORE THAN MOST (0)
    E. MUCH MORE THAN MOST (+10)

Prevost was hovering around 1% on Polymarket, and was <0.5% between white smoke and announcement.

We will use models that make us feel good over models that don't make us feel good.

This one was a little too ham-fisted (at least, for the sensibilities of people in our media bubble; though I suspect there is also an enormous mass of people for whom it was not), so they turned it down a bit. Later iterations will be subtler, and better at picking up the exact level and type of sycophancy that makes whoever it's talking to unsuspiciously feel good (feel right, feel smart, feel understood, etc).

It'll eventually disappear, to you, as it's dialed in, to you.

This may be the medium-term fate of both LLMs and humans, only resolved when the humans wither away.

https://techcrunch.com/2017/11/15/twitter-removes-verified-c...

Not pictured: innumerable other accounts which were never granted a blue check in the first place, despite being the easily verifiable real accounts of journalists and public figures.

It was de facto a caste system of political favor and connections.

University 1 gets $100. $10 of it goes to admin, $90 to researcher. Researcher spends $60 on supplies and equipment. This is accounted for as 10% administrative overhead.

University 2 gets $100. $20 of it goes to admin, $40 to researcher, $40 to supplies and equipment. This is accounted for as 60% administrative overhead.

Is this an accurate characterization of your claim?

Instead, they're almost always trained with (what we see as, but they literally do not) multi-character tokens as the atomic unit, so 'strawberry' is spelled 'Ⰹ⧏⏃'. Processing that is only 3 sequential steps, only 3 complete runs through the entire LLM. But it needs to encounter enough relevant text in training to be able to figure out that 'Ⰹ' somehow has 1 'r' in it, '⧏' has 0 'r's, and '⏃' has 2 'r's, which really not a lot of text demonstrates, to be able to count the 'r's in 'Ⰹ⧏⏃ correctly.

The tradeoff in this is everything being 3-5x slower and more expensive (but you can count the 'r's in 'strawberry'), vs, basically only, being bad at character-level tasks like counting letters in words.

Easy choice, but leads to this stupid misundertanding being absolutely everywhere and just by itself doing an enormous amount of damage to peoples' ability to understand what is happening and about to happen.