  We discuss how over the last 30 to 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-piece, while during the second wave, the data-driven
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.
