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Artificial Intelligence: An Holistic Perspective

We face five major challenges with Artificial Intelligence (AI). Two occupy most of our attention today, whether computers can ever be like humans, and how to make AI algorithms more accurate, and I will leave those till later while I discuss the others. The five challenges are:

And the two we already discuss most:

This page discusses these issues employing the rich philosophy of Herman Dooyeweerd. Dooyeweerdian philosophy offers a radically different way to address many topics in the areas above, because he enables us to understand human and non-human, good and harm, diversity, coherence, meaning, human functioning, history, and the activity of scientific research. I will give links to these things where necessary. Though addressed from a philosophical perspective, I try to make it readable by those of different perspectives, because I have found Dooyeweerd's philosophy can be intuitively grasped by many.

Why should I write about AI? I began practical and academic activity in AI in the early 1980s, and developed expert systems in the 'real world' if materials technology, agriculture, quantify surveying and contract law, and advised on others. I developed a methodology for building expert systems, and innovative software tools too, then broadened my interests to human factors and digital informations systems in general, from which the above main areas emerge. In this page I apply them to AI.

Using AI so that we Gain Benefit and Not Harm

This requires that we understand benefit and harm of AI when in use. We would us Dooyeweerd's aspects to separate out kinds of benefit and harm. Each aspect, from the biotic onwards, makes meaningful a different kind of good and harm.

For how this works, see Chapter 6 of Foundations of Information Systems: Research and Practice.

Methods for systematic analysis of good and harm in each aspect have been developed. See Chapter 11 of Foundations and Practice of Research : Adventures with Dooyeweerd's Philosophy

Development of Good AI Systems

There are two kinds of AI:

They are developed in different ways, but both aim to embed or embody requisite, appropriate knowledge into the AI system, so that, when run, it operates as it should, with "intelligence". KR AI is constructed by eliciting human knowledge, especially tacit knowledge. ML AI is constructed by training a general-purpose learning algorithm, usually a neural net. But the result should be the same.

The main difference in use is that in ML AI, the knowledge embodied is completely opaque, unable to be investigated, while in KR AI (expert systems), the knowledge is to some extent transparent, able to be investigated, so that answers may be given to the user's question, "Why did you recommend that?" Whether such transparency is important depends on the application; for example, picture analysis might not needed while AI used to make legal judgements certainly does need it.

Constructing application AI systems either way requires taking responsibility in four major activities that constitute the development of digital systems:

For how this works, see Chapter 9 of Foundations of Information Systems: Research and Practice.

Methods for systematic analysis to identify stakeholders and potential usefulness have been developed. See Chapter 11 of Foundations and Practice of Research : Adventures with Dooyeweerd's Philosophy

AI and Society (in Both Directions)

This involves addressing two major issues:

See Chapter 8 of Foundations of Information Systems: Research and Practice for discussion, and detailed development of those.

AI Algorithms

This is the development of basic AI algorithms and data structures. For example, AI system that analyse photographs require different kinds of basic algorithm from those that carry out a conversation. Each kind of AI algorithm and data structure must incorporate (embody) the fundamental laws of the relevant aspects. So, for example:

See Chapter 7 of Foundations of Information Systems: Research and Practice for discussion, and detailed development of those ideas.

The "AI question" of "computer = Human ?"

This involves philosophical understanding of the nature of computers and human beings. First, we see an AI system, or a computer program more generally, as a virtual law-side, an embodiment of laws of various aspects by which the information-based entities within its world will operate; these laws can try to match those of the real world as far as possible, or they can be modified (such as in computer games, death is not final, but players usually start again).

Given that, then there are two answers to the AI question, "Computer = Human?"

See Chapter 5 of Foundations of Information Systems: Research and Practice for discussion, and detailed development of those ideas.

See Also


This page, "http://dooy.info/using/ai.html", is part of a collection that discusses application of Herman Dooyeweerd's ideas, within The Dooyeweerd Pages, which explain, explore and discuss Dooyeweerd's interesting philosophy. Email questions or comments are welcome.

Written on the Amiga and Protext in the style of classic HTML.

You may use this material subject to conditions. Compiled by Andrew Basden.

Created: 7 June 2021 Last updated: 12 October 2022 completed first brief version. 20 November 2023 See also uai.html. 11 January 2024 uai.pdf.