Artificial Intelligence and Machine Learning Advancements

Moore's Law - which turned 55 this year - elucidates the truth that processing power doubles every two years. It is also the first of the four primary drivers of exponential growth in AI and Machine Learning. The second is the vast increase in the volume of data made available by the explosion of data streams from smartphones and connected sensors plus DVD's and photos. The remaining two propellants of this exponential growth - the rise of algorithms and network effects - are not as universally understood. When combined, the sum of these four parts doesn't equal four; indeed, the potential impact is beyond our ability to grasp with linear thinking.

Examining the rise of algorithms first, their intrinsic ability to evolve is based on the way they learn - namely through reinforced and supervised learning - so that they can evolve based on increasingly fewer examples. In addition, the open-source marketplaces in start-ups like Algorithmia generate an explosion of collaboration.

Moreover, the fact that Google's Deep Mind program AlphaGo defeated world Go champion Ke Lie is a case in point. Lie noted that he detected weaknesses in his first encounter with AlphaGo and attempted to leverage those to his advantage in subsequent matches to no avail. This is owing to the program's inherant ability to detect and correct its shortcomings. The game of Go is considered to be a highly daunting task for AI since, unlike chess, it is intuition rather than logic based.

Computer Chip

The final of the four drivers of AI and ML is network effects. There is perhaps no better example of this phenomenon than the Israeli start-up Waze, which was acquired for $1B by Google. In contrast, Nokia invested over $8B in Navteq placing its bet on the combination of the former's vast infrastructure of mobile phone transmission towers combined with Navteq's mapping capabilities. Waze took the approach that it would leverage the data provided by their now wildly popular app on its user's mobile phones to detect traffic congestion thereby shortening Subscriber's commutes. Waze didn't make a better sword. It invented gunpowder.

Furthermore, consider the example of a computer or robot equipped with deep learning software in one part of the world struggling with a problem. Once it solves the problem, it can upload what it has learned to the cloud and instantaneously transmit it to a multitude of similarly equipped machines with deep-learning platforms.

Abstract Movement

There are additional breakthroughs in other technologies such as quantum computing in lockstep with challenges that drive modern supercomputers to the limits of their capabilities. Mapping complex molecules is a case in point. In classical computing there are ones and zeros: In quantum computing ones and zero's can exist simultaneously. The research on this technology in 2008 reveal that top leaders in the space speculated that it would be actualized somewhere around 2050. Fast forward to 2018 and there are several firms - including Intel, Google and IBM now operating such machines with the first commercially available unit purchased by Lockheed Martin now installed at NASA. The little know startup D-Wave was the creator of this machine.

This example drives home the point that established businesses are challenged to drive innovation. Stanford professor and accomplished serial entrepreneur Steve Blank posits a couple of underlying causes, including that large companies are primarily focused on shareholder value and that their leaders' long suits tend toward finance, supply chain or production. So when blindsided by explosive technological change, it can spell serious trouble. This explains why Uber disrupted transportation, AirBnB in hospitality, Tesla in automobiles and Netflix for video rentals.

But innovation can come from inside corporations such as GE's FastWorks program. Google too is leaning into innovating as a core strategy evidenced by its acquisition of over 160 companies in the past decade.

To succeed, corporations in all industries must re-think and then re-invent their corporate innovation approach which entails replacing their static execution model with three horizons of continual innovation. This requires a corporate culture, organizational structure and employee incentives that reward innovation along with instituting acceptable risk level key perfomance indicators for each horizon. This requires understanding the difference between executing the existing business model, extending the business model and searching for and disrupting the business model. This is the formula for companies to disrupt themselves before a more savvy competitor beats them to the punch.

By so doing, businesses can move nimbly in response to the blistering pace of growth in ML and AI while generating ROI in the process.

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