As one of the main tech trends in 2018, AI has now definitely entered businesses as well as private zones. Yet, most intelligent systems around us use what’s called “narrow intelligence” or “narrow AI”. AI’s next phase is about approximating human intelligence.
At the core, advanced narrow intelligence (ANI) in systems such as Alexa, Siri, Google Home and Netflix, are powered by a finite set of possibility-outcome combinations that can retrieve seemingly unlimited information. AGI, on the other hand, requires machines to be at least as intelligent as humans with traits such as metacognition, deep comprehension, intuition, abstract reasoning, real-time learning, etc. Moreover, it requires the machine to take a human-like understanding of the implications of actions.
Autonomous driving cars are a great example of ANI – trillions of outcomes to be calculated in nanoseconds, with human life in the balance. But imagine if your car had a better understanding of you. Say she understood that you were in a financial crunch and “thought” about how she could help by offering ridesharing during the times when you weren’t using her. That’s an example of AGI: a comprehensive, intuitive solution that wasn’t coded into her at “birth.”
Across enterprises, there’s strong interest in adopting “intelligent,” customer-centric solutions to reduce cost pressure while enhancing customer service and the ever-looming threat posed by disruptors. The industry is flooded with intelligent point solutions. But how do we make the jump from narrow, niche intelligence to AGI awesomeness? How do we develop algorithms that encompass pragmatism and compassion, and avoid unconscious biases?
The possibilities of AGI
To make the business case for AGI R&D, let’s look at what AGI could potentially bring to a business or our lives as a whole. Take the case of inventory management. A simple use case would be a program to reorder items if the amount in stock drops below “X.”
To go from interconnected point solutions to AGI requires an extremely high level of computational creativity. And here’s the kicker: How do we define goals clearly enough so that strategic solutions devised by a super-intelligent neural network will balance both capitalistic gain and humanity? So far, there’s no good answer to those questions.
The wide gap between ANI and AGI
One popular suggestion for moving from ANI to AGI, is to imagine these point solutions as individual neurons, like in our brains, and then connecting them to form a neural network, similar to a biological nervous system. The result would be disparate point systems sharing data on customer behavior and industry trends, with an overarching algorithm that makes decisions on how to interpret the data, plan strategy and set goals – in other words, individual ANI systems working together for a greater goal. It’s a great idea, but there are very few (if any) proofs of concept.
A huge AGI stumbling block is that it isn’t as commercially viable as ANI. Institutions across domains are hard-pressed to deliver great customer service, lower cost, faster turnaround – all of which can be met through point ANI solutions. Strategists can define a dollar value to these advances: X seconds saved here translates to Y cents saved per transaction, for example, or knowledge of this trend allows prediction of a product choice, which increases click-through by Z%.
These are tangible, immediate gains. It could take decades, on the other hand, to build an AGI system that could replace the strategists and consultants that an enterprise currently employs. How many capitalists are willing to invest in a “tomorrow” that’s so far away?
Innovation, magnanimity, creativity – these are AGI dreams. For now, the way forward is continued reliance on humans to make the required connections – and encompass the requisite intelligence, empathy and reasoning – to bridge the divide that will take businesses from ANI to AGI.