GenAI Architecture Shifting from RAG Toward Interpretive Retrieval-Centric Generation (RCG) Models — To transition from consumer to business deployment for GenAI, solutions should be built primarily around information external to the model using retrieval-centric generation (RCG). As generative AI (GenAI) begins deployment throughout industries for a wide range of business usages, companies need models that provide efficiency, accuracy, security, and traceability. The…

Assessing where we are on the journey to deep, meaningful communication between people and their AI — As people interact with conversational artificial intelligence (AI) systems, clear communication is a key factor in getting the intended outcome that will best serve and augment our lives. In the broader sense, what language should be used for the control system and conversations with machines? In this blog post, we…

Avoiding combinatorial explosions in what-if scenarios involving multiple people or intelligent machines — We are about to see a significant change in the role of simulation to evaluate real-time what-if scenarios in materializing machine intelligence. I believe that it can play an even more purposeful role if expanded to include agent-based simulation at inference time. This type of computation seeks to iteratively resolve…

The necessity of World Model, Theory of Mind, Continual Learning, and Late-Binding Context — AI research can be a humbling experience — some even claim that it remains at a relative standstill when it comes to replicating the most fundamental aspects of human intelligence. It can correct spelling, make financial investments, and even compose music; but it cannot alert someone to the fact that…

Most of us have heard the phrase, “Image is everything.” But when it comes to taking AI to the next level, it’s context that is everything. Contextual awareness embodies all the subtle nuances of human learning. It is the ‘who’, ‘where’, ‘when’, and ‘why’ that inform human decisions and behavior…

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? …

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…