Third Wave of Artificial Intelligence (AI)

If your business is either not developing or not using AI, then you may already be at a competitive disadvantage.

By Ravi Chalaka, Business Executive and Consultant for the Digital World

Today, talk of artificial intelligence (AI) is everywhere – from Amazon’s Alexa and Apple’s Siri to how Uber dispatches drivers and the way Facebook finds you in a photo. However, it wasn’t long ago that AI was regarded as purely science fiction – several flicks from Hollywood, such as 2001: A Space Odyssey were just a dream.

The term “artificial intelligence” was coined in 1956 with the hope of creating machines that could emulate human intelligence, such as reasoning and judgement. Those early days drew scientists from academia to enterprise, to ignite a revolution of innovation that we’ve been riding ever since. Since then we can group the AI developments into three significant waves – while the first two generated hype and limited commercial use, the third time is the charm, which has already produced many real-world use cases, taking us closer to the fulfillment of that early vision. I also expect more waves of AI innovation in this century, which will deliver super human level intelligence that we are yet to fully comprehend. That will be a topic for another blog and for now, we need to understand the trends in the first three waves to figure out what this means to our lives and businesses today.

First Wave: Age of Search and Deductive Reasoning

The first wave of AI generated a lot of interesting ideas in the 1950s. Engineers devised deductive reasoning programs with logical rules, a method in which a conclusion is based on multiple premises that are assumed to be true. This approach led to simple games such as the first computer program capable of playing the game of Draughts. Although these first-wave AI systems could perform straightforward reasoning tasks, they were unable to learn anything on their own. While they couldn’t be applied to business at the time, some of today’s applications in smartphone maps can be traced back to this first-wave of AI technologies. But in general, this wave did not have any material impact on business or people’s lives.

 Second Wave: Age of Knowledge Acquisition

The second wave of AI began in the 1980s with big AI projects when researchers turned their focus to helping machines acquire knowledge. Instead of just programming precise rules for machines to follow, they provided machines with the knowledge of experts, and developed statistical models which machines could use to adapt this knowledge to different situations. While second-wave AI machines had some breakthroughs – Deep Blue became the first computer system to defeat a reigning world champion Gary Kasparov in 1997 in chess – many of these second-wave expert systems struggled with accuracy due to complexity issues of increasing the knowledge it needed to perform tasks, rendering them impractical for most business applications. Commercialization of AI was still elusive.

Third Wave: Age of Machine Learning

Since the turn of century, we’ve been riding the third wave of AI, where computers use techniques such as machine learning and deep learning to automatically learn from vast amounts of data and improve from experience without being explicitly programmed with knowledge. Massive amount of data from machines, social networks and business systems as well as immense processing power that enabled development of artificial neural networks, better natural language processing, and enhanced image processing have contributed to the current wave.

Third-wave AI is now able to consume data from statistical models, identify patterns in the data, create common sense rules, and incorporate information from multiple sources to reach a conclusion on their own. For example, AI – along with internet of things (IoT) connectivity – is extracting data from wearable devices and public sources to create personal health updates in real time. We’re seeing third-wave AI, driving numerous applications in personal assistants and operating self-driving vehicles used in industry. This wave is expected to last for the next twenty years while dramatically changing the way we live and how businesses gain competitive advantage by enhancing productivity.

While the journey from the simple games of the 50s to the self-learning machines of today has been quite a ride – we have just scratched the surface of the full potential of AI. One thing is for certain: AI is more than Hollywood storytelling. It has become essential for suggesting alternate driving routes in rush hour traffic, making online shopping recommendations – even helping airport security officials use AI with video cameras to perform live face matching to recognize criminals within a crowd at busy terminals.

The big game changer is applying machine learning in industrial and commercial applications, such as manufacturing, transportation, energy, marketing, sales and design. AI is already enabling machines to predict failures, recommend options and even augment human activity to build safer cars, sustain the planet, understand individual customer interests, achieve mass customization, and reduce waste. Small to large companies are making significant investments in AI’s machine learning and deep learning research and development to deliver solutions in their platforms. Any company that ignores this wave will be doing so at its own peril. It's like Kodak’s senior executives claiming that digital photography will never amount to real business potential.  

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