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We discuss how, over the last 30–50 years, artificial intelligence (AI) systems that focused only on data have been handicapped and how knowledge has been critical in developing smarter, intelligent, and more effective systems. In fact, the vast progress in AI can be viewed in terms of the three waves of AI as identified by DARPA. During the first wave, handcrafted knowledge has been at the center, while during the second wave, the datadriven approaches supplanted knowledge. Now we see a strong role and resurgence of knowledge fueling major breakthroughs in the third wave of AI underpinning future intelligent systems as they attempt human-like decision making and seek to become trusted assistants and companions for humans. We find a wider availability of knowledge created from diverse sources, using manual to automated means both by repurposing as well as by extraction. Using knowledge with statistical learning is becoming increasingly indispensable to help make AI systems more transparent and auditable. We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.

The Duality of Data and Knowledge Across the Three Waves of AI