The hype of AI

A recent article (1) further reinforces what autonomous vehicle industry has been doing. Neural net systems with feedforward and feedback control architectures trained by historical data on specific surfaces and conditions. Remnants of 1960s technologies, ably assisted by zero cost computing, have been percolating across the autonomous landscape. This trajectory is problematic for many reasons.

First, a brain trained on historical data selected by a biased human is a disaster waiting to happen. The situation is no better with hand-coded heuristics as demonstrated by recent aircraft failures. What computer and data scientists have to understand first is that their own brains still remain to be vastly superior to code they write running even on a super-computer. Hence, blind attempts at removing the human from complex decision-making processes are likely to fail.

Second, hype and ignorance have propelled AI to the stratosphere without significant practical use cases. AI is a tool and it is not a panacea. AI still fails when it encounters the unexpected. This is important as it indicates conventional computing and Silicon based architectures, albeit great engineering innovations, have nothing to do with "intelligence." We have not advanced AI much from the 80s, when the "oldies," used to call it expert systems. Granted, simulated voices, believable human faces, and incredible jumping robots are great inventions, but unfortunately, these have nothing to do with AI.

And finally, high human resource intensity in model building often leads to costly failures. For practical AI, two important things need to come together - rapid and flexible prototyping with automation and considering AI to be augmenting the human, not replacing her.

(1) http://robotics.sciencemag.org/content/4/28/eaaw1975

Alzheimer’s - we fail again!

Recent news (1) that high profile experiments, targeting a solution for the famous disease, Alzheimer's, has failed again is sobering. It clearly indicates that life sciences companies are on the wrong track to improve a condition, most dread. It is also a constant reminder that systemic problems cannot be solved by treating symptoms or tactical observations. The engineering view of medicine has run its course and it has been very successful in fighting opponents that can be clearly identified. But now, a system failing because of overuse, cannot be mended by such crude methodologies.

Immortality is prohibited by contemporary Physics. So, the optimization problem narrows to maximizing utility within an afforded time horizon. Humans have been naturally optimistic, an evolutionary advantage. They have been attempting to extend life rather than optimizing within constrains. Therein lies the paradox for healthcare. As artificial intelligence progresses, it is conceivable that a human could have reasonable estimates of life span and disease incidences, at birth. For the first time in history, we may be in a position to focus on optimization of utility rather than extending a highly uncertain horizon.

It is clear that the human hardware deteriorates in predictable ways. Most of it appears to be related to plumbing, an inability to remove waste at an optimum rate. From the brain to the kidneys, waste removal efficiency seems to decline over time, just as in any physical system. We may need to accept this as an unbreakable law and find ways to slow down the deterioration. In this context, research in the direction of cure for auto-immune diseases may be misplaced. What could be more important is predicting the likelihood of disease early and slowing its progression.

Artificial Intelligence could have a significant impact on human utility and happiness. If one can get over the hype and confusion, it will become clear that AI could provide useful guidance for humans to best utilize their limited time in the universe.

(1) https://www.sciencemag.org/news/2019/03/another-major-drug-candidate-targeting-brain-plaques-alzheimer-s-disease-has-failed