Saying that bias in AI applications is "just because of the datasets" is like saying the 2008 crisis was "just because of subprime mortgages".
Technically, it's true. But it's singling out the last link in the causality chain while ignoring the entire system around it.
How do we understand things? By building models in our (computer-enhanced) minds. Obviously, it makes sense if the model is smaller than the actual thing; which is only possible if reality is compressible.
The Levels of Problem-solving:
Level 1 — You solve the problem.
Level 2 — You solve the problem that caused the problem.
Level 3 — You avoid the problem that caused the problem.
Level 3 is the most valuable but hardest to see.
Some folks still seem confused about what deep learning is. Here is a definition:
DL is constructing networks of parameterized functional modules & training them from examples using gradient-based optimization.... https://t.co/jmHpWZOMH8
2019, Personal Review
1-Managed to Yuugely upset monocultures: both anti-Iran & pro-Iran shills; both pro-Trump & anti-Trump; both the "left" & the Neo-nazis/Quillette/eugenists.
2-Busted IQ as a fraud,on stat/math grounds.
3-Finished Stat Cons Fat Tails, Tech Incerto vol 1
I saw a guy debugging his model today.
No idpb.
No unittest.
No visualization.
No disabling regularization.
He just sat there staring at every line of code and cursing TensorFlow.
Like every machine learning researcher I know.
With recent news of many all-stock deals—I share my favorite story on VALUE
A dad comes home from work—
Son: “Dad! I sold our dog 🐶 today for $1MM”
Dad: “Wait you did what! to whom!?”
Son: “Yup—to our neighbor”
Dad: “He gave you $1M?!”
Son: “Yeah! He gave me 2 $500k cats🙀 🙀”