According to current estimates investment in AI in 2025 will exceed one trillion dollars and might possibly top two trillion dollars in 2026. Market capitalization of the largest 65 AI firms exceeds 20 trillion dollars. These are big numbers. AI is responsible for one of the largest build-up of capital in history. Investments in AI were motivated by the expectation that AI would generate huge profit. The expectation of profit, in turn, has produced a great increase in market

In this post I discuss how AI even the current generation of Generative AI, follows the framework of modern science. The framework of modern science developed over a period of several centuries. It is useful to resume the scientific framework in the following points. First , scientific explanation follows the Deductive Nomological – DN – principle of Carl Hempel and Paul Oppenheim. The DN principle (Hempel and Oppenheim, Studies in the logic of explanation, 1948) states that

It is fair to say that technology is greatly indebted to science. In fact, most technological achievements depend on basic scientific knowledge acquired without having in mind any specific application. For example, modern electronics is greatly indebted to the development of quantum mechanics. In a number of cases, technological needs originated important scientific discovery. In general, however, technology exploits some basic scientific knowledge. Most technological devices

Most people think they perfectly understand causality and, if asked, probably would say that scientific explanation is causal. Psychologically this is understandable, because we leave in a world full of instances of causality. From cars to planes, from home appliances to gardening equipment we are surrounded by artefacts that can be controlled. But reality is much more complicated; people with decision making responsibility should learn to distinguish sharply what can be obse