I'm composing this tweetstorm from a tweetstorm of a dream I had. It begins with the idea that Ptolemy's model of the movement of the planets was extremely accurate.
Ptolemy's model was accurate enough to be very useful for navigators of their time. But it worked well because it was finely tuned to fit with observed experimental data.
But was wrong with Ptolemy's model is that it did not correctly capture cause and effect. The earth and the planets revolve around the sun due to gravity and not everything revolves around the earth. This was the Copernicus model which he paid gravely for proposing.
200 years later Newton invented calculus and formulated the laws of gravity that could be used to mathematically derive the motion of planets around the sun. What was inside Newton's calculus that exposed otherwise unknown patterns?
In calculus, there are sums of infinite series that converge to a fixed number. In these infinite series there are patterns such that you can deduce what numbers they converge to. Calculus works because the sums of these repeating patterns converge to fixed numbers
Curve fitting also works in an analogous way in that a function is approximated by sums of functions each with different coefficients. The difference is that an algorithm does not need patterns to arrive at a convergent number.
Deep Learning has shown that given enough good data, a universal approximator can conjure up the parameters required to fit any function. This is just what Ptolemy's model was able to accomplish.
The problem with Ptolemy's model is that it does not reveal the actual causal mechanism. That's also the problem with neural networks. It does not matter if they can explain how they arrive at a conclusion. This is because the abstraction they might is likely to be wrong.
This conclusion of course is damn obvious and it's a surprise that too many deep learning practitioners don't get understand this! Ptolemy was wrong just as deep learning researchers are wrong with their models.
But how do you get the right model? Oh, that's when I woke up and realized it was all a dream. ;-)
Machine learning algorithms are very good at curve-fitting models. But what kind of technology is available for explaining models that are intuitive to humans? medium.com/intuitionmachi…
When AlphaGo executed move #37, what was its explanation? The only explanation was that a human would not make the same move. Is this a good explanation?
It is curious to note that AlphaGo was aware of what was a human move. This is perhaps not the case for AlphaZero that trained from scratch. How does AlphaZero explain itself in terms of human abstractions that it does not know about?
It occurred to me the @BretWeinstein is crafting his concern about the covid19 vaccines in a way to appeal to a specific crowd and avoiding the more likely dangerous scenario. This is why his message is biased and self-serving. Let me explain.
Weinstein argues that there is a concern that vaccines being imperfect (as they always are) will give the virus an opportunity for a breakthrough that can lead to an arms race. Oddly enough, he doesn't apply the same logic for cures like the unsubstantiated one he promotes.
However, the obvious danger is that the mRNA virus mutates faster proportionally to the size of the unvaccinated and infected group. We already saw this in India where the most dangerous delta variant emerged from a huge population of unvaccinated.
It occurs to me that modern society has led to the perspective that we have immense control of our lives. This was not always true in the past where people could die for many reasons out of their control.
The modern understanding of the word 'tragedy' is that it when someone suffers for something that they could have avoided entirely. That there exists this means of control one's destiny and that it was ignored.
We see this play out on a mass scale with our actions in the pandemic (facemasks and vaccinations) and our absence of risk mitigation against climate change. But we remain utterly perplexed as to why people can't see the tragedy that is happening in slow motion?
It is a surprise to many that the math used in physics is a weird kind of handwavy math.
"Quantum field theory is mathematics that has not yet been invented by mathematicians." quantamagazine.org/the-mystery-at…
The math in physics is not as rigorous as found in math but it works with extreme accuracy! Maps (i.e. models) are not the territory, but you want your maps to accurately represent the territory.
As Feynman has said "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."