What kind of knowledge qualifies you as intelligent? What frameworks does your cognition utilise to help you understand the world, process new experiences, and make wise decisions? A organised conversation on how to effectively materialise these constructs and plan a road to more intelligent machines will be facilitated by defining a framework that articulates the types of information that enable understanding and higher cognition for humans or artificial intelligence (AI).
Higher degrees of machine intelligence will most likely be centred on knowledge constructs that allow an AI system to organise its perspective of the world, interpret meaning, and exhibit understanding of events and activities. Machine cognition will go beyond data and become grounded in knowledge constructs such as descriptive knowledge, world dynamics models, and provenance, among other things.

We distinguish between form and meaning when studying language: form refers to the symbols — the surface expressions — that are employed to represent meaning. Each form has a specific meaning in a certain situation, and different forms have distinct meanings in different circumstances. “The majority of contemporary machine learning accomplishments come down to large scale pattern recognition on adequately gathered independent and identically distributed (i.i.d.) data,” writes Schölkopf, Bengio, and colleagues in an article. Ingesting visible elements like as text characters, speech impulses, and image pixels, systems create patterns and stochastic correlations, resulting in excellent recognition outcomes.

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To achieve a higher level of machine intelligence, algorithms must go beyond superficial correlations into meaning and understanding, according to a growing consensus. System 2, 3rdWave, or broad generalization/flexible AI, will be enabled by this category shift. This next level of machine intelligence, as I outlined in the core blog ‘The Rise of Cognitive AI,’ necessitates deep constructs of knowledge that can transform AI from surface correlation to world comprehension, representing abstractions, relations, learned experiences, insights, models, and other types of structured information.

The parts of AI that will experience a transformative improvement in the 3rd generation of AI, according to DARPA’s John Launchbury, are abstraction (creating new meaning) and reasoning (planning and deciding). Contextual adaptation characterises the third wave, in which systems build contextual explanatory models for classes of real-world phenomena. The approach offered here provides insight into how knowledge constructs will aid such a transition.

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The descriptive dimension, with its conceptual abstractions of what is in the world, and the dynamic models of the real world and its phenomena, both reflect a view of the world. Humans get the ability to interpret and express complex narratives based on shared beliefs and myths as a result of stories. Meta-knowledge elements such as context and source attribution, as well as value and priorities, give a condition-based overlay of validity and knowledge-about-knowledge. Finally, idea references serve as the structural foundation, tying dimensions, modalities, and references together. These six dimensions of knowledge, when taken together, could add depth beyond event correlation by assuming underlying concepts that are persistent and can explain and predict past and future events, allow for planning and intervention, and consider counterfactual realities — hence the term “deep knowledge.”

Identifying the appropriate technique to implement the types of knowledge constructs required for machine intelligence can be aided by articulating and describing the types of knowledge structures required. The purpose of this blog is to define the core types of knowledge structures that are considered important for the development of AI cognitive skills at the next level.

Knowledge Dimensions in Support of Higher Intelligence

Implementing knowledge components observable in human comprehension and communication can bring significant intelligence to AI systems. When all sorts of knowledge are supported and merged, that value skyrockets.

Knowledge Dimensions in Support of Higher Intelligence Gadi Singer/Intel Labs/Gadi Singer/Intel Labs/Intel Labs/Intel Labs/

1. Hierarchy, taxonomies, and property inheritance are examples of descriptive knowledge.

Descriptive knowledge (also known as conceptual, propositional, or declarative knowledge) is a type of knowledge that explains things, events, and their properties as well as their interrelationships. Deep descriptive knowledge expands on this definition by assuming the usage of hierarchical stacking of classes or concepts (where applicable). Facts and record-keeping systems are examples of this type of knowledge. As hierarchical knowledge, facts and information relevant to certain use cases and contexts can be structured, used, and updated.

Individual AI systems’ underlying ontologies can be seeded with task-relevant classes and entities from curated systems (e.g., the OpenCyc ontology or the AMR named entity types). It should be expandable using neural network/machine learning methods, as new entities, relations, and classes will be added when new knowledge is acquired.

Models of the world (n.d.)

AI systems can understand circumstances, analyse inputs/events, forecast potential future outcomes, and take action using models of phenomena in the real world. These models are abstractions/generalizations that can be divided into formal models and approximate (informal) real-world models; they allow variables to be used and applied to instances in specific contexts, as well as symbol manipulation of a specific instance or a more generalised class.

Logic, mathematics/algebra, and physics are examples of formal models. Real-world models, in contrast to formal models, are frequently empirical, experimental, and occasionally untidy. Physical, psychological, and sociological models are all included. This class includes procedural models (‘know-how’).

Causality models are an excellent illustration of the types of models that can aid AI systems in reaching the next level of machine intelligence. In circumstances when the context has changed, past statistics can only be used to forecast futures if they are combined with a knowledge model such as causality, an awareness of the context that affects the causes at hand, and the ability to assess counterfactuals. These models aid in the comprehension of occurrences or events in terms of the circumstances and possible elements that led to them. Causal reasoning is an essential component of human mind that may be formalised to achieve machine intelligence on par with humans.

3. Narratives

According to historian Yuval Harari, stories play an important role in the culture and worldview of individuals and nations. To completely comprehend and analyse human behaviour and communication, the concept of stories is required. Within a linked narrative, stories can contain several events and a range of information. They’re more than merely lists of facts and happenings. Instead, they include vital information that aids in the development of understanding and generalisations beyond the data presented. Stories can be considered as historical, referential, or spiritual, in contrast to models of the world, which are meant to provide an operational picture of the world and how one can interact with it. People’s ideas and actions are influenced by their values and experiences, which can be represented via stories. Religious or national stories, mythology, and shared stories at any level of a group of people are all examples.

4. Source attribution and context

Context is a frame that surrounds an event or other piece of information and provides resources for proper interpretation. It’s like an overlay knowledge structure that modulates the knowledge it contains. Context might be permanent or temporary.

Persistent context can persist a long time (as in knowledge gleaned from a western vs. eastern philosophy standpoint) or it might shift over time as new information becomes available. It does not change depending on the work.

When a specific local context is significant, transient context is crucial. Words are interpreted in the context of the sentence or paragraph in which they appear. In most cases, regions of interest in a picture are evaluated in the context of the entire image or video.

The combination of a persistent and transitory context can create the ideal environment for understanding and operationalizing knowledge.

Data provenance (also known as its lineage) is another component of knowledge that comprises data origin, what happens to it, and where it moves over time. In the post-truth age, an AI system cannot presume that all information consumed is generally truthful or trustworthy. It may be important to associate information with its sources in order to create credibility, certifiability, and traceability.