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Notes -
Critical Social Justice in the Era of Large Language Models
https://parrhesia.substack.com/p/critical-social-justice-in-the-era
Many anticipate that AI will have the ability to engage in novel and complex philosophical reasoning or contribute to scientific progress. While AI has yet to achieve this level of sophistication, models like ChatGPT demonstrates an impressive ability to generate meaningful text. I am skeptical about the usefulness and meaningfulness of articles from certain disciplines falling under the banner of Critical Social Justice. Finding connections between abstractions or interpreting text through a postmodern critical lense isn't particularly difficult. Nor does it lend itself to error or falsification. I think traits that allow CSJ scholarship to be hoaxed will put it at risk of domination by AI-generated articles. Scholars in the future will be highly prolific, but all their work will be generated trough computers. This will become a sort of open secret. All this scholarship produced will not advance humankind because CSJ scholarship is, at best, rather useless, and, at worst, socially harmful. In other disciplines more tethered to reality, AI generating acceptable papers would mean genuine progress.
Referencing my comment from last month my interaction with chatgpt has convinced me that we are actually very far from general AI. I think the current approaches are deadends, we need to find a different way and this new breakthrough could happen tomorrow or it could happen in 500 years, with equal probability.
That's unfair, it really struggles comprehending that words are comprised of individual letters. It's opaque to the alphabet by the nature of what it can see and learn from. I asked it to generate anagrams and it was absolutely hopeless at it. It gave me nonsense like 'overwrite is an anagram of obverse'. When I really coaxed it for an anagram of obverse and observe, it gave me rubbish like 'oversbe' and 'beovers' but recognized they weren't words. It couldn't get verbose, which was really ironic seeing as it was incredibly verbose in its descriptions.
It also could not answer a question about perfect numbers, it could not find the pattern between 8128, 496 and 28 no matter how I coaxed it. I doubt a human would've made that error after being prodded and poked toward seeing the answer.
But I don't talk about the other 50 university challenge questions it got right. I don't talk about the fairly creative and reasonable ideas it came up with for how to redesign vehicles. Teething problems like the perfect number issue and your time zone issue could surely be solved by increasing the power a thousandfold - that's what GPT-4 will be doing. At least it's likely that's the case. The anagram problem or 'word ending in i' problem requires a different kind of data processing but it's not really that important. You don't need to be able to identify anagrams to be functionally intelligent and achieve things in the real world.
If we have an intelligence that's 95-98% human-level, with superhuman speed and knowledge, we're not that far from AGI.
There's a structural and idiosyncratic reason for this, which has to do with how text is processed before it goes in and out of the model. Basically it processes "tokens," which are chunks of words. Training text is "tokenized" before being fed in, and the model itself outputs tokens which are converted back to text before being printed. The specific tokenization scheme that it uses (as far as we know, based on prior iterations of OpenAI's large language models) is "byte-pair encoding," which has itself been optimized so that common words are a single token, and then common chunks of words are tokens, and then (way down the list) individual letters are tokens. BPE is designed to minimize the size of the tokenized training data within a vocabulary of (I think) ~65k possible tokens. So the trained model has no way of knowing that the word "overwrite" starts with the letter 'o', since most likely it sees one token for "over" and another for "write" and the encoding doesn't need to resort to tokens for the individual letters, and it does not know that the "over" token has any special relationship to the "o," "v" etc. tokens. Gwern hypothesizes that BPE encoding is why it also cannot rhyme, and I assume he's right although the specific mechanism for BPE creating that deficiency isn't as clear to me.
Can't rhyme? A poet friend of mine was lamenting that it rhymes too much!
A whisper on air,
A silent thought
That lingers there.
Though none can hear
It still exists,
A hushed connection
Of souls that share.
Glorpth yoozled down the blizbevy tree, His muttazels aflutter with glee. Beside him stood the zibbablock, Gloating and ready with a shock. But the brave Glorpth didn't falter, He battled the beast with a clatter. He thrashed and he slashed 'til the night was done, And the Jabberwock was won.
I specifically asked for three nonsense words for this one...
Through the dark woods I heard a howl,
Flittering through like a silken owl.
The slonky creatures stirred below,
As they rose and began to flow.
Crossing through the gnelpy grove
I saw what I could not move.
It was covered in gloopy scales
And had a thousand hissy tails.
It was the jabberwocky, fierce and wild
Ready to attack with a baleful smile.
I ran and ran and never looked back
The terror of its presence slack.
I guess it can! Maybe it finally has enough training to overcome whatever impediment hobbled previous incarnations' attempts.
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That's a shoddy non-rhyme, but if we accept Blake's "eye / symmetry"...
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