Policy inefficiencies

It has been shown that good intentions are not sufficient to make good policy decisions. Humans have generally been incompetent in making optimum policy in the presence of uncertainty and interconnectivity. A recent article (1) gives a powerful example of this phenomenon. The article demonstrates the futility of subsidies and massive investments into improving irrigation efficiencies on the premise that freshwater is a valuable resource. The blue planet has abundant water - but the species that apparently dominates it has not figured out how to harvest it. More importantly, it also shows how policymakers and politicians, in general, do not have the capabilities to optimize the system.

As the tumbling blue planet skirts disaster in an active shooting gallery in space, we have a system that allows incompetence to rise to the top. Knowledge and meritocracy do not matter, money and the ability to tweet garbage, do. As the "free market capitalists," raise tariffs and the socialists stand ready to dole out subsidies without thinking, it is clear that we are entering a regime of governance that will not be attractive for the rising millennials. A generation seems to have wasted time and space, adorned with ego and irrational ideas such as religion and prestige. Such is the disastrous state of affairs for the 8 billion that even scientists, who claim to think rationally and religious leaders, who claim to perpetuate good, have become numb.

If humans are observed from above by an entity that understands the non-linear effects of arbitrary actions on a complex and virtually unpredictable system, she will be sad. As the space agency makes plans to perpetuate the human species to Mars and beyond, there is a more important question it has to answer, first. Why? Will the universe lose knowledge and compassion if humans were to vanish as the Earth gets scorched under the expanding Sun? Will the universe lose information if humans were to vanish in a catastrophic impact from outer space. Will the universe care about a species that shows no positive slope in conceptual knowledge?

Likely not.


(1) The paradox of irrigation efficiency.  http://science.sciencemag.org/content/361/6404/748

R. Q. Grafton1,2,

J. Williams1,

C. J. Perry3,

F. Molle4,

C. Ringler5,

P. Steduto6,

B. Udall7,

S. A. Wheeler8,

Y. Wang9,

D. Garrick10,

R. G. Allen11

See all authors and affiliations

The end of "Machine Learning."

Machine learning, an obnoxious term, that simply means statistical modeling, has the potential to lead many budding data scientists and universities clamoring to create programs that support it, down blind alleys. Machines do not learn and apparently, those immersed in this concept do not either. In the coming decade, "Machine learning," could create a significant drag on the economy as the hype is pumped up by "reputable," academic institutions, software companies and even politicians.

Regression was the "original," machine learning. The statistical modeling platforms have added all sorts of ancient mathematics in neat little packages, they sometimes even call, Artificial Intelligence. But calling Arithmetic better names, does not improve anything, let alone intelligence. What is most disappointing is the fact that universities have created entire programs around this "fake news," as they have seen favorable economics and the possibility of their graduates skating to the C suite on the back of degrees. Academic integrity used to be important and as the crop of professors who loved to advance knowledge, vanish behind time, we are approaching a regime that will devalue education. We have education rendering casinos, with all the adornments that surpass the real thing and the bricks in the wall they manufacture are going to be incompetent to face the future.

Hype has a negative value. Academic institutions should understand this. If they do not, the only competitive advantage we possess, graduate education, could be at risk. Perhaps it is time we shut down the .ai domain names and academic courses designed to appeal to pure hype.

Artificial Intelligence: Long way to go

A recent observation that seems to provide additional structure on how the bots may be learning (1) is interesting. The Artificial General Intelligence enthusiasts, who have been making a lot of noise by observing that they can train a machine from pixels on the screen, may want to take note. Training a machine to play games is a lot easier than getting Silicon to "think." The computer scientists appear to be getting a bit ahead of themselves (even those with a Neuroscience degree). God does have a  sense of humor and She will lead many astray in the coming decades.

Engineering and Computer Science are easy. Medicine and Economics are less so. It is difficult to reduce complex questions to deterministic equations with binary outcomes. And, AI is squarely in the latter, for intelligence emanates from understanding uncertainty and how it affects outcomes. Equations do not work in this realm and experiments that create, "big noise," do not either. We are now creating a crop of computer scientists, locked onto the keyboard, hunting Python and drinking Java, as if there is nothing beyond it. This is problematic. If they continue in that vein, they could reduce themselves to the guy, who tweets garbage every day.

Humans have an ego - and that is going to keep them constrained from progress. The ones who make noise, are endowed with ignorance and the ones in the know, may keep quiet.


(1) http://www.sciencemag.org/news/2018/08/why-does-ai-stink-certain-video-games-researchers-made-one-play-ms-pac-man-find-out

Never look back

The human brain, a compendium of false and true memory, formed by past interactions and events, feels comfortable creating heuristics from history to deal with the future. For millennia, this was a dominant strategy as the ability to predict the presence and behavior of predators from historical data helped them survive. But now, this has become a huge liability. Even basic ideas in finance, such as sunk costs, have been difficult for many to internalize. Even those in the know, seem to make bad decisions because it has been difficult not to look back. The software giants found out recently that using historical data to model the future has some drawbacks and this has implications for decision-making and policy design at many levels.

Looking back has been costly for humans in the modern context. They may be better off rolling the dice to pick from available future states than using faulty heuristics shaped by the past. If machines can only learn from the past, then, they will be simply perpetuating the status-quo with no insights. This is equally true in education, where history and experience have been given undue credit and research, where conformation bias has led many astray.  What is most problematic is a recent experiment (1) that shows that children have a tendency to conform to robots. In the current technology regime that appears to be accelerating toward fake humanoids, we may be dumbing ourselves down by using history and the prompts provided by robots.

Looking back is costly in many ways for humans. Looking back is value enhancing only if the cost of doing so provides future benefits. It is tough to find use cases where such an activity adds value. There is little practical value in history or how one lived last year. If the future generations can mend the ills caused by the "greatest," that went before them, they could inherit a world that is peaceful and forward-looking. In such a world, there will be no looking back and every day will start with fresh ideas. In such a world, there will not be any recordings, only future possibilities. In such a world, they will reject past theories in favor of uncertain future hypotheses. In such a world, thought experiments will dominate over attempts at proving what was observed. In such a world, experiments will triumph over institutions and legacy.

Only look forward, for anything else will be costly.


(1) http://robotics.sciencemag.org/content/3/21/eaat7111