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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.
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by expla
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving
AI systems have seen significant adoption in various domains. At the same time, further adoption in some domains is hindered by inability to fully trust an AI system that it will not harm a human. Besides the concerns for fairness, privacy, transpare
Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledg
AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method - specifically hypothesis testing - in AI is typically conducted in service of engineering objectiv