Background
The following framework is excerpted from my paper presented at the 11th Annual Conference on Computational Creativity in September, 2020, Coimbra, Portugal. The title of the paper is Artificial Creative Intelligence: Breaking the Imitation Barrier. This framework and its explanation given below are prerequisite reading for concepts and more frameworks that will appear in this newsletter at a later date. Some of you might have already read this article as it appeared as a post on my LinkedIn page a while ago. For some this will be a new and novel framework.
My colleagues (Roger Dannenberg, Bikshah Raj, and Rita Singh) and I, in the paper, introduced a new branch of artificial intelligence called Artificial Creative Intelligence (ACI) that proposes to stretch the current limitations of artificial intelligence (as of late 2020). ACI is not a solution to artificial general intelligence, but is directed at specific use cases requiring creativity and creative problem solving, such as language, the “trolley problem” in autonomous vehicles, and ethical decision-making in business and medicine. A possible next step is to develop a holistic understanding of natural creative intelligence integrating work from several domains (neuroscience, psychology, physiology, philosophy, mathematics, computer science, etc.).
Insight-Knowledge Object Model
A proposed Artificial Creative Intelligence framework, the Insight-Knowledge Object (IKO) model (Figure 1), builds on my prior work from 2009. Proposed in 2020 is a way of modeling the human thought process that can be used to shape the development of ACI machines. It is important that each level in the IKO hierarchy be present in the process of human thinking as well as in artificial attempts at reproducing that process. Knowledge objects are on the left-hand side, and insight processes appear on the right. In the IKO model, as insight processes and knowledge objects ladder up through the hierarchy, increasing levels of cognitive sophistication are reached. The IKO model has eleven levels of knowledge objects and ten levels of insight processes acting upon the knowledge objects. The IKO model begins with a state of knowledge object known as void. In this state, even the acknowledgement of nothing does not exist. At the very top of the IKO model is the knowledge object creation generated by an inspirational insight process.
Description of Each Insight Process
Instinctual insight acts on the void to generate null, the first true layer of knowledge. Null emerges from void as primal instincts create an awareness of one’s environment. This level of knowledge is deemed null because at this level, there is at least consciousness of existence or non-existence. In the void, even consciousness does not exist.
Definitional insight acts on the null to elevate knowledge into data. At this level, the insight process gives definition to objects and actions in the null. Definitional insight labels a collection of unnamed and unidentified things so that distinctions are drawn between them. Each object is now defined and becomes a datum.
Contextual insight acts on data to generate facts in the next layer of the hierarchy. Facts represent a richer and fuller set of knowledge than pure data. For example, if one takes the word coffee as a datum there is no context for reference. Given some context such as the commodities trading market, coffee takes on the meaning of a traded good. If food service is the context, then coffee takes on the meaning of a beverage. Contextual insight allows distinctions to be made between data to create different facts.
Utilitarian insight acts on facts to generate know-how, how an object is used and for what purpose. In our coffee example, utilitarian insight emerges to provide the know-how for what to do with coffee. In the commodities market context, know-how would be how to trade coffee on the spot or futures markets. In the food service context, know-how would be how to prepare coffee for consumption. Without utilitarian insight, coffee has no real value. Simply speaking, utilitarian insight provides knowledge of use.
Experiential insight acts on know-how to generate memories. The execution of know-how generates experiences that can be remembered and used in the future. Following the coffee example, experience in making coffee enables a barista to remember how much foam to put on top of a latte.
Reflective insight acts on memories to generate wisdom. Reflection works on a meta-plane of thinking and takes on a new layer of abstraction in the knowledge hierarchy. Insights are not simply generated on single points of execution but a set of memories. For example, remembering how to make a latte is a memory but digging deep to understand why people order lattes takes reflection. Wisdom emerges as a person can take a step back to reflect and learn from prior thoughts, decisions, and actions.
Recognitional insights act on wisdom to generate patterns. This insight function is in the realm of data science and data analytics. Recognizing patterns links related or unrelated pieces of wisdom to generate knowledge that would not emerge otherwise. For example, connecting the preparation of a perfect latte to the film “Seven Samurai” (Kurosawa 1954) in which one of the samurai has dedicated his whole life to perfect his skills as a swordsman represents connecting two topics that on the surface are completely unrelated. A pattern emerges with the recognition that these humans continually strive for mastery in their respective fields. Recognitional insights create connections that drive thinking further and produce patterns.
Extrapolative insights act on patterns to generate predictions. This insight function is in the realm of statistics and probability. Making predictions links related or unrelated patterns to generate knowledge that would not emerge otherwise. Extrapolative insights produce predictions used for weather forecasting, social media user preference, and the serving up of relevant advertising. Again, in our coffee example, extrapolative insight is required to predict the demand for lattes in a coffee shop during the course of a day.
Comparative insights act on predictions to generate imitations. This insight function is in the realm of machine learning and implementations such as generative adversarial networks (GANs). Predictions are compared against a reference with the goal of achieving an imitation that most closely matches the reference. Much work and many examples of painter style matching, voice impersonation, and literature already exist. This level of the knowledge hierarchy is the present boundary of today’s state-of-the-art deep learning techniques. One could surmise researchers and scientists have hit an imitation barrier. For lattes, skilled baristas can copy fanciful milk designs on the surface of lattes based on prior examples of a master barista’s work. This is latte style transfer.
Inspirational insights act on imitations to generate creations. This insight function has not yet been designed and requires fundamental research across the disciplines of neuroscience, physiology, psychology, and computer science. The proposed ACI framework surpasses the imitation level by using unique, to-be-developed computing machines and computational algorithms, simulating human inspirational insights, to output creations. A master barista thinks up and executes unique and novel designs for lattes (Figure 2). She is not simply mimicking prior art. The inspiration process of the proposed IKO model breaks through the imitation barrier. At this level of the IKO hierarchy, the challenge of Artificial Creative Intelligence could be solved.
“Where is the life the authors have lost in living?
Where is the wisdom the authors have lost in knowledge?
Where is the knowledge the authors have lost in information?”
The Rock, T.S. Eliot, 1934.