Enabling smarter, adaptive AI with innovative multimodal systems — Written in collaboration with Vasudev Lal and the Cognitive AI team at Intel Labs. An exciting frontier in Cognitive AI involves building systems that can integrate multiple modalities and synthesize the meaning of language, images, video, audio and structured knowledge sources such as relation graphs. Adaptive applications like conversational AI…

Large language models (LMs) have demonstrated that they can serve as relatively good knowledge models (KMs). But do they excel at performing all the functions required for enabling truly intelligent, cognitive AI systems? The answer, I believe, is no. This post will discuss the five capabilities that make up an…

Cognitive AI, hierarchy of knowledge, and the answer to the unsustainable growth of all-inclusive deep-learning models — Can we drive effectiveness and efficiency of AI at the same time? If we want our systems to be more intelligent, do they have to become more expensive? Our goal should be to significantly increase the capabilities and improve the results of AI technologies while minimizing power and system cost…

A missing link to machine understanding and Cognitive AI — While most contemporary discussions and classifications of AI capabilities center around what a system can do, I believe the path to higher intelligence and machine cognition relies on what a system can know and understand. Using rich AI knowledge representation frameworks and comprehensive models of the world can increase an…

Rethinking knowledge layering and construction for long-term viable AI — Intelligence, be it human or machine, is based on knowledge. Finding the right balance between the effectiveness of intelligence (what it can achieve) and its efficiency (how much it costs in energy or other currency), is essential. In my WAIC21 keynote presentation I introduce Cognitive AI and propose that humans…

How an information-centric classification of AI architectures can facilitate the construction of task-optimized AI systems — In a series on the choices for capturing information and using knowledge in AI systems, I introduced the concept of an information-centric classification of AI systems as a complementary view to a processing-based classification such as Henry Kautz’s taxonomy for neural symbolic computing. The classification emphasizes the high-level architectural choice…

How knowledge constructs can transform AI from surface correlation to comprehension of the world — What knowledge makes you intelligent? What are the constructs used by your cognition to understand the world, interpret new experiences, and make thoughtful choices? …

Structured, explicit, and intelligible knowledge can provide a path toward higher machine intelligence — Deep learning (DL) is generating a great deal of progress and revolutionizing entire industries across all aspects of life, including healthcare, retail, manufacturing, autonomous vehicles, security and fraud prevention, and data analytics. However, to build the future of artificial intelligence (AI), it is necessary to define a set of goals…