As often stated in multiple sources, AI is a highly interdisciplinary field of research and development leaning on many other sciences (Neuroscience, Linguistics, Anthropology, Philosophy, Psychology, Cognitive Science, Systems Science, Computer Science, Mathematics, Biology, Physics and Social Science). When trying to get grasp of the big picture of AI (like we are) one can really feel this first hand by easily getting lost in a dense jungle of publications coming from variety of different fields, each flavored with different approaches and methodologies of research.
Having experienced this jungle during the past weeks, and finally coming up with some kind of mental map for navigating in it, I thought I could share it in this post. Maybe there are others out there navigating in this same jungle, who could be interested in it as well. It is by no means the only way to see the world around AI, but at least one attempt to have a bit of structure and pathways in the jungle. The map is presented in form of Venn -diagram in the figure below.
The map was created by approaching AI from a viewpoint that in it’s core is system science, computer science and mathematics, which are applied with a common goal of realizing machines mimicking natural intelligence, which interface and interact with other systems in their environment. The map presents numbered interfaces of different sciences overlapping with AI, each representing a pathway between the two sciences. Let’s explore and characterize those pathways a bit:
1. Cognitive science is also an interdisciplinary (Neuroscience, Linguistics, Anthropology, Philosophy, Psychology and AI) field of research studying the cognitive processes of a mind. Cognitive science produces knowledge about the cognitive processes related to intelligence and behavior and has over the past 40 years contributed many cognitive architectures describing and implementing these processes using computing systems of different kinds. This interface is mainly characterized by AI implementing cognitive architectures within intelligent agents, multi-agent systems and other intelligent systems for various applications.
1.1 Apart from Cognitive Science and complete cognitive architectures, Neuroscience has also influenced AI research directly by inspiring AI research to develop and apply different kinds of artificial neurons and artificial neural networks inspired by the structure and function of their biological counterparts in human/mammal brains.
1.2 Apart from Cognitive Science and complete cognitive architectures, Linguistics has also been applied in AI research directly especially in natural language processing, understanding and generation. As a field of research since the 1950’s, natural language processing has used many different approaches from largely hand crafted rather rigid and formal systems to softer statistical probability based systems applying machine learning. In recent years various word vectors (e.g. Word2vec) based deep learning approaches have shown state-of-the art performance in natural language processing tasks and challenges.
2. The interface of AI and biology is twofold. Firstly, intelligence is a natural (biological) phenomena, which AI aims to mimic by creating artificial constructs capable of intelligent behavior observed and judged by their biologically intelligent role models (e.g. humans). Secondly, the AI research and development is bio-inspired as a whole and new discoveries within biology may also trigger new discoveries within AI (e.g potential new findings related to cells, neurons and brain).
3. The interface of AI and physics is also twofold. Firstly, the capability of AI R&D to create artificially intelligent constructs is dependent on availability and applicability of physical materials and energy for building and operating the constructs. Secondly, the physical environment and physical laws are highly relevant for AI applications that operate in the physical word (e.g. AI robotics applications) and may also be relevant to be simulated in those applications that operate in the cyber world only.
4. The interface of AI and Social Science is mainly related to acceptance, impact, need and use of AI applications by human individuals and organisations. Also the value of the AI applications is defined by humans and human organisations in social context.
So what have we learned from our visit to the AI related sciences jungle and why is it relevant to identify the relations between AI and related sciences? Firstly, the progress and success of AI exploitation is highly dependent on ,and influenced by, the progress of R&D on the related fields, which all are still active fields of research with potential for new discoveries relevant for AI R&D.
Secondly, multiple parallel tracks of AI research and development are proceeding in parallel and are not necessarily mutually inter-operable from the viewpoint of building new AI systems and applications (we will continue on this issue later in a separate post). For now, let’s just highlight that the lately mainstream machine learning/deep learning R&D branch of AI has not (at least yet) presented a unified cognitive architecture, as stated in the recent review on cognitive architectures.
Finally, it seems that majority of AI R&D is very task specific and often carried out in isolation (e.g. in game worlds) from the “real world” use environment, problems and social context. The path of transforming a state of the art solution from a game world into an application in a real wold may turn out to be a quite long and complex one to take. Accordingly, we hope to see more data sets, challenges and leader boards with closer link to real world data, application potential, context and constraints.