AI’s ‘iPhone Moment’
When the original iPhone was released on June 29, 2007, it was instantly clear that Apple had completely disrupted the mobile market. All you had to do was try the new iPhone side-by-side with a Blackberry to realize the inevitability of the mobile future. Yet despite the obvious implications of that comparison, it has taken a decade for many organizations to catch-up with strategy and implementation for mobile engagement. Android has since eclipsed both Windows and Linux as the most widely-used operating system in the world. According to Google CEO Sundar Pichai, the ‘mobile-first’ decade is already behind us, and the decade before us is ‘AI-first’.
For artificial intelligence (AI) and machine learning (ML), the equivalent of that ‘iPhone moment’ occurred on November 9, 2015. The fanfare was far less than that of the iPhone announcement, but future historians may well look back and consider this a far more consequential moment in the history of the world. The aforementioned date was the day when Google open-sourced their TensorFlow machine learning framework.
TensorFlow provides the machine learning capabilities we enjoy in Google’s existing services, like Google Search and GMail. Prior to the release of TensorFlow, there were other AI / ML frameworks in the marketplace, but none that were associated with Google’s powerhouse brand for innovation. TensorFlow entered the public consciousness with enormous credibility as the instant leader of the AI / ML tooling landscape. It was relatively mature, comprehensive, accessible, and already proven and battle-hardened at Internet-scale through Google’s various services.
By open-sourcing TensorFlow, Google democratized the field of machine learning, driving the implementation costs down to that of skilled labor and cloud computing resources. No more expensive license fees. Google also forced other AI-oriented organizations to open source their own tools and framework in order to stay relevant. In one fell swoop, the entire world of AI / ML switched from being proprietary to being open source. This transformation obliterated the enormous barriers to entry that had prevented millions of organizations around the world from taking advantage of modern artificial intelligence and machine learning.
This democratization of AI / ML has sparked the imaginations of data scientists and software engineers around the world. Machine learning is now available to everybody — not just the big players like Google, Microsoft, Facebook, Apple, and Baidu. Startups now have the same access to the open source AI tools as the largest companies in the world, and this has ignited a firestorm of AI-oriented innovation in products and services in every industry, as well as business intelligence and analytics.
We are nearly 17 months into this global renaissance of artificial intelligence and machine learning. The commercial and industrial winners of tomorrow are building solutions that are incorporating AI / ML capabilities today. Forward-thinking leaders in every industry are developing their own AI strategies, recognizing the inevitability of what is to come. And just as so many failed to react to the productive disruption in mobile a decade ago, today many are failing to react to the productive disruption from AI’s own ‘iPhone moment’. Are you?