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Monday, July 30, 2018

Redefining Artificial Intelligence

Artificial Intelligence, the contemporary darling of technologists and investors, has been largely focused on trivial consumer-oriented applications and robotics/automation, thus far.  Constrained by conventional computing, AI has been bottled up in hype and confusing name calling. What the AI enthusiasts do not seem to understand is that AI was never meant to be a technology that fakes what a human being appears to do externally but rather it was supposed to replicate her thought processes internally. As the search giant demonstrates how its technology could fool a restaurant reservation system or play games, as the world's largest shipper of trinkets demonstrates how they could send you things faster and the purveyors of autonomous vehicles demonstrate how they could move people and goods without the need for humans at the driving wheel, they need to understand one important thing: these technologies are not using AI, they are using smarter automation. They do not replicate human thought processes. They either fake what a human appears to do or simply automate mundane tasks. We have been doing this for over half a century and as everybody knows, every technology gets better over time. So, before claiming victory in the AI land, these companies may need to think deeply about if their nascent technologies could actually do something good.

However, there is a silver lining on the horizon that could move AI to real applications (1) including predicting and controlling the environment, designing materials for novel applications and improving the health and happiness of humans and animals. AI has been tantalizingly "close" since the advent of computers. Imagination and media propelled it further than what it could ever deliver. As with previous technology waves, many companies attempt(ed) to reduce this problem to its apparently deterministic components. This engineering view of AI is likely misguided as real problems are driven fundamentally by dynamically connected uncertainties. These problems in domains such as the environment, materials, and healthcare require not only computing resources beyond what is currently available but also approaches further from statistical and mathematical "precision."

Less sexy areas of AI such as enhancing business decisions have attracted less interest, thus far. Feeble attempts at "transforming," a large healthcare clinic using a "pizza-sized," box of technology that apparently solved all the world's problems already, seem to have failed. Organizations chasing technology to solve problems using AI may need to spend time understanding what they are trying to tackle first, before diving head first into "data lakes" and "algorithms." Real solutions exist at the intersection of domain knowledge, technology, and mathematics. All of these are available in the public domain but the combination of this unique expertise does not.

Humans, always excitable by triviality and technology, may need better skills to succeed in the emerging regime, driven by free and fake information and the transformation of this noise into better decisions. Those who do this first may hold the keys to redefining AI and the future of humanity. It is unlikely to be the companies you know and love because they are focused on the status-quo and next quarter's earnings.

(1) http://science.sciencemag.org/content/361/6400/342


Sunday, July 8, 2018

Biological entanglement

Research from Northwestern University (1) that apparently demonstrates quantum entanglement in biological entities opens up new possibilities. A century-old but enigmatic theory has kept a few interested in thought experiments. The recent demonstration of a quantum superposition of a photon in a bacterium (2) is further proof that existing theories are inadequate to describe the universe around us. The status-quo foundational theories are not sufficiently robust to explain reality and that should provide excitement to the emerging generation as there is still much to be explained.

Engineering has kept Physics bottled up for many decades. In a regime of low knowledge, Occam's Razor has to rule, for proof can be manufactured by technology for any hypothesis. It is clear that we lost a century, chasing noise with no fundamental advancement in understanding. Entanglement has been intriguing in many aspects - it proves that the theories we take for granted are likely untrue. It is time to leave grand experiments behind and return to paper, pencil and thought experiments. Advancements can only come from such an avenue as it will require significant shifts away from established notions in Physics.

The struggle between determinism and uncertainty can be seen in many fields, Physics and Economics included. Humans are more comfortable with precision as their senses have been designed to fool them into such an idea. This should have had evolutionary advantages as pattern finding is more about reducing information into neatly organized classifications - predators, tribes, and poisons. And now, technologists have been getting ahead of themselves by machine and deep learning to reduce noise into recognizable patterns. Some have been even calling it "Artificial Intelligence," that includes facial recognition, synthetic speech, NLP, vision, and robotics. A less pretentious term could have been "expert systems," but then the millennials are never short of creative wordsmithing. All of these exciting technologies are simple applications of established mathematics with a deterministic end.

The fork on the road has been between determinism and uncertainty. Nearly 90 years ago, it was shown that the world does not work like we perceive it. That is ironic as perceptions have been the basis of most modern ideas, religion and politics included. They assert something to be true without doubt as the more precise one is, the better she is in the eyes of her followers. Scientists seem to have picked up some bad habits along the same lines, as they look for precision in experiments with the aid of massive computers and bigger particle smashers. Precision, however, is their Achilles' heel as attempts at reducing noise into pre-determined chunks will lead them down blind alleys with no exit.

The same struggle happens in economics, where researchers attempt equations and charts to explain outcomes in a clear and concise way. But not many have asked if the underlying assumptions are true and how uncertainty plays into decision-making. Without a clear understanding of the macro uncertainty that drives systems, some have been wasting time in "behavioral economics," as if explaining human irrationality has utility. If anybody has doubts about the fact that individuals are irrational, just study the zombies who trade back and forth looking at electronic terminals all day. But the behavior of the system could be distinctly different from those of the participants and it is something that engineering processes cannot tease out.

An evolutionary advantage, that bestowed humans with an ability to quickly classify predators, tribes, and poisons, will work against them in the future. As progress comes from diving into a pool of uncertainty and having the flexibility to challenge anything that has already been established. It does not take huge capital nor titles, just the ability to keep an open mind.


(1) https://www.sciencedaily.com/releases/2017/12/171205130106.htm
(2) https://www.tandfonline.com/doi/abs/10.1080/00107514.2016.1261860