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