When we say that "an algorithm is biased" we usually mean, "biased people made an algorithm." This explains why so much machine learning prediction turns into phrenology.

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Researchers with phrenological delusions ask machines to find statistical correlates of personalities or emotions, and machines dutifully provides them. It's high-stakes, machine-human collaborative apophenia, detecting patterns where none exist.

https://t.co/2b0IkQrI62

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Regrettably, this junk science gets published in respected journals. In 2017, @nature published a study by Stanford's Michal Kosinski claiming that machine learning could detect the facial correlates of homosexuality, creating an alleged AI gaydar.

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Unsurprisingly, Kosinski's study spectacularly failed to replicate. But as is so often the case, the blockbuster finding gets all the press, the careful replication work that calls it into doubt is roundly ignored.

https://t.co/J4rd6nLA4n

4/
Kosinski hasn't given up on AI phrenology. His lab's latest paper (published by Nature...again!) claims that he can detect political affiliation from social media photos.

https://t.co/kNPJ4vOwEr

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Spoiler: he can't.

What his system is most likely detecting is certain conventions in poses and expressions that are used in different political subcultures. Resting Karen face, basically.

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Unfortunately, this claim is being credulously reported in the tech press as true, even as the writer notes that this ML system barely outperforms random chance.

https://t.co/z1nkFFod3H

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Scientific racism has been with us for centuries. It's enjoying a renaissance today, driven in part by the neophrenologists of the ML world. They are the modern descendants of the caliper-wielding eugenicists of yore.

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To understand the genomic science that refutes all of this nonsense, you can read @AdamRutherford's brilliant, short, witty, vastly informative book HOW TO ARGUE WITH A RACIST. You'll be glad you did.

https://t.co/bPO3y5CzIX

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Image:
Cryteria (modified)
https://t.co/ICebVcdH1f

CC BY:
https://t.co/5YJhpDj3vT

eof/

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What an amazing presentation! Loved how @ravidharamshi77 brilliantly started off with global macros & capital markets, and then gradually migrated to Indian equities, summing up his thesis for a bull market case!

@MadhusudanKela @VQIndia @sameervq

My key learnings: ⬇️⬇️⬇️


First, the BEAR case:

1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.

2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.

3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.

4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).

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