How an information-centric classification of AI architectures can facilitate the construction of task-optimized AI systems

Image attribution: Jae Ryeong Lee;

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 related to information in the AI system. This blog will outline the third class in this classification and its promising role in supporting machine understanding, context-based decision making, and other aspects of higher machine intelligence.

The proposed information-centric classification includes three key classes of AI systems based on the architectural…

Thoughts and Theory

How knowledge constructs can transform AI from surface correlation to comprehension of the world

Credit: Learning and Education — Brain Functions Development Concept by Imagens Livres License:

What knowledge makes you intelligent? What are the constructs used by your cognition to understand the world, interpret new experiences, and make thoughtful choices? Defining a framework that articulates the kinds of knowledge that enable understanding and higher cognition for humans or artificial intelligence (AI) will facilitate a structured discussion on ways to effectively materialize these constructs and chart a path to more intelligent machines.

Knowledge constructs that allow an AI system to organize its view of the world, comprehend meaning, and demonstrate understanding of events and tasks will likely be at the center of higher levels of machine intelligence…

Thoughts and Theory

Structured, explicit, and intelligible knowledge can provide a path toward higher machine intelligence

Photo credit: Ting Ling Goay

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 and expectations that will drive a new generation of technologies beyond the deployments we are seeing today. By 2025, we are likely to see a categorical jump in the competencies demonstrated by AI, with machines growing markedly wiser.

Many of the current DL applications address perception tasks related to object…

Gadi Singer

Passionate about driving AI towards the next level of intelligence via deep knowledge. VP at Intel Labs. Named one of AI 50 global thought leaders & influencers

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