machine learning revolution
Take agriculture, for example. In agriculture you plant seeds, you fertilize them, you spray them, and then you wait six months and hope for the best. Once upon a time, people really understood the land, they understood the plants, and they had this intuitive feel for what would happen. Then farms got bigger and there were fewer people on them. We didn't have the ability to walk the land, and feel it, and close the loop. In the absence of information, we had to resort to monoculture. We closed the loop on an industrial scale. We went to predictability: If I use this Monsanto seed, spray it with this chemical, in this very well understood soil, I'm likely to get a pretty good outcome. In the absence of information, we went with predictability, simplicity, and a lot of chemicals.
- Closing the Loop (Chris Anderson)
We literally rationalised the shit out of the environment after we successfully formalised the inner workings of the consciousness. (We basically got super excited once we found out how to exert scalable control over the nature.)
Now we are living through another massive revolution. This time we have discovered the inner workings of the unconsciousness and it is a lot better at dealing with the sort of complexities and non-linearities exhibited by ecosystems. Intuitive control has finally become scalable.
The worst is behind us. Our relationship with the environment will slowly recover. Machine learning will allow control structures to stay beautiful and organic even as they scale.
Artificial intelligence (AI) is a misnomer. Intelligence is a notion we associate with consciousness (which works rationally / analytically), not unconsciousness (which works intuitively / statistically.)
This is not surprising, since the actual historical evolution of the idea of AI started off as an analytical project and ended up as a statistical one. (Analytical approach to automate human-level intelligence failed.)
This too is not surprising, since nature itself too invented unconsciousness first. (In other words, the straight-forward looking consciousness has to be deeper and more complex than unconsciousness.)
Side Note: Here, by unconsciousness, I am referring to the intuitional part of the reasoning mind, not to the lower level older brain parts that are responsible for things like the automation of bodily functions. Hence the remarks are not in contradiction with Moravec’s Paradox.
Notice that there is a strange alteration going on here. Human reason was built on top of human intuition. Now machine intuition is being built on top of (advanced) human reason. In the future, machine reason will be built on top of machine intuition.
The reason why our unconscious mind copes better with complexity has to do with its greater capacity to simultaneously deal with many variables. Rational mind on the other hand extracts heuristics and stories out of patterns. This process results in a drastic reduction of number of variables which in turn results in a loss of accuracy.
Take, for example, the problem of predicting the distribution of trees in a forest using only map data, such as elevation, slope, sunlight, and shade. Jock Blackard and Denis Dean did the original calculations in 1999, and left behind an enormous public database for other mathematicians to use. According to Vapnik, when computers were trained using 15,000 examples, their predictions were 85 to 87 percent accurate. Pretty good. But when they were fed more than 500,000 examples, they developed more complex rules for tree distribution and boosted the accuracy of their predictions to more than 98 percent.
“This means that a good decision rule is not a simple one, it cannot be described by a very few parameters,” Vapnik said. In fact, he argues that using many weak predictors will always be more accurate than using a few strong ones.
- Teaching Me Softly (Alan S. Brown)
What distinguishes excellent service (and maintenance) from very good service (and maintenance) is anticipatory power. Machine learning techniques are increasing our predictive capacity. We will soon be able to fix problems before they even occur. All problems, including those related to customer unhappiness and mechanical breakdowns, exhibit early signs which can be picked up by machine learning frameworks.
Life is about to become significantly happier and safer for our children.
