
Untold History of AI: Algorithmic Bias Was Born in the 1980s - spooneybarger
https://spectrum.ieee.org/tech-talk/tech-history/dawn-of-electronics/untold-history-of-ai-the-birth-of-machine-bias
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rococode
Tangential comment: When I first learned how long ML has been around I was
really surprised. Markov chains were invented more than a hundred years ago.
The perceptron was invented in the late 50s - and it's clear now that just a
simple perceptron can do a _lot_ of fairly impressive things. Backprop was
first worked on in the late 60s. RNNs have been around since the 80s.

It feels like ML is such a recent thing, but it's really just that we've only
recently gotten to the levels of hardware and data needed to make these
algorithms work smoothly. It makes me wonder what other interesting theories
are still gated by hardware limitations. I think astronomy has had similar
situations where things have been figured out long before they were actually
observed.

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ThJ
Machine learning libraries have been around for a long time also. I remember
seeing them around and playing with them in the late 90s, without really
knowing what to do with them. For some reason, few people cared about them
then, much like few people took virtual reality very seriously at the time.
Interest in these things swings back and forth like a pendulum. It seems that
you can pick almost any uninteresting thing that no one cares about anymore
and work on it, and soon enough, it will be back in the spotlight again.

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ThJ
At one point, naive Bayesian classifiers were considered machine learning. Now
we use them for spam filters. Chess playing algorithms were also considered
machine learning at one point. We keep moving the goalposts. We won't ever
reach a specific point of concluding that we have intelligent machines. There
won't be a breakthrough, much like living organisms never had an intelligence
breakthrough; they just gradually became more and more intelligent through
incremental development.

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macleginn
In the 1980s, they had to explicate their biases in order to make the
algorithm's predictions correlate well with human judgment. Today we can just
use neural nets to learn our biases for us.

