Successful agent-human partnerships require that any agent generated information is understandable to the human, and that the human can easily steer the agent towards a goal. Such effective communication requires the agent to develop a finer-level notion of what is understandable to the human. State-of-the-art agents, including LLMs, lack this detailed notion of understandability because they only capture average human sensibilities from the training data, and therefore afford limited steerability (e.g., requiring non-trivial prompt engineering). In this paper, instead of only relying on data, we argue for developing generalizable, domain-agnostic measures of understandability that can be used as directives for these agents. Existing research on understandability measures is fragmented, we survey various such efforts across domains, and lay a cognitive-science-rooted groundwork for more coherent and domain-agnostic research investigations in future.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Human and machine powers are most productively harnessed by designing hybrid human- machine superintelligence (HMSI) cyber-physical networks in which each party complements each other's strengths and counterbalances each other's weaknesses. Software universe, as the web applications, application software and system software, source or machine codes, as AI/ML codes, programs, languages, libraries; V. Hardware universe, as the Internet, the IoT, CPUs, GPUs, AI/ML chips, digital platforms, supercomputers, quantum computers, cyber-physical networks, intelligent machinery and humans; How it is all represented, mapped, coded and processed in cyberspace/digital reality by computing machinery of any complexity, from smartphones to the internet of everything and beyond. AI is the science and engineering of reality-mentality-virtuality [continuum] cyberspace, its nature, intelligent information entities, models, theories, algorithms, codes, architectures and applications. Its subject is to develop the AI Cyberspace of physical, mental and digital worlds, the totality of any environments, physical, mental, digital or virtual, and application domains. AI as a symbiotic hybrid human-machine superintelligence is to overrule the extant statistical narrow AI with its branches, as machine learning, deep learning, machine vision, NLP, cognitive computing, etc. #machinelearning #deeplearning #artificialintelligence #nlp #algorithms #dataengineering

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner With a virtually infinite superintelligence, as a Trans-AI, which is both transdisciplinary, transcendental and transcendent, our world would look different, as a trans-world, or I-World. It is powerful to project an image of the world into its digital mind making it to comprehend the immensity of the whole universe, its reality, causality, space-time. In pure life forms, there are material beings, energy beings and information entities. Material beings are natural objects of different scales, from elementary particles to the infinite universe, including our universe with its planets, stars, galaxies, clusters, etc. Energy beings are mostly composed of energy, they are imagined as a translucent glowing fluid or a collection of flames or electrical sparks or bolts or fire balls or ghosts.

The Information Artifact Ontology is an ontology in the domain of information entities. Core to the definition of what it is to be an information entity is the claim that an information entity must be `about' something, which is encoded in an axiom expressing that all information entities are about some entity. This axiom comes into conflict with ontological realism, since many information entities seem to be about non-existing entities, such as hypothetical molecules. We discuss this problem in the context of diagrams of molecules, a kind of information entity pervasively used throughout computational chemistry. We then propose a solution that recognizes that information entities such as diagrams are expressions of diagrammatic languages. In so doing, we not only address the problem of classifying diagrams that seem to be about non-existing entities but also allow a more sophisticated categorisation of information entities.