How Did Philosophy Help Develop Artificial Intelligence?

What is the relationship between philosophy and AI? Philosophy, with its inquiries into the nature of knowledge, reasoning, and consciousness, has played a pivotal role in shaping the foundations of AI. One of the fundamental questions that philosophers have grappled with is the nature of human intelligence. Philosophical theories of logic, reasoning, and language have provided the groundwork for computational models and algorithms of AI systems. Moreover, philosophical debates around consciousness, perception, and moral reasoning have spurred discussions on whether AI can possess these qualities and what ethical implications arise from creating intelligent machines.

The influence and contribution that logic had to the development of AI is more than evident. That’s why, first and foremost, we’ll analyze its significance and explore which ideas in particular were the ones that contributed to AI’s development.

Aristotle (384-322 B.C.) was the first to formulate laws that governed rationality: he invented the first system of formal logic. While his specific contributions to the development of artificial intelligence were indirect due to the vast time gap between his era and the emergence of AI as a field, some of his philosophical concepts and methods have influenced AI research and development.

Aristotle invented a system of syllogisms that was supposed to guide proper and valid deductions. The syllogisms were the first step toward the basic mechanism that would allow humans to derive conclusions from premises in a mechanical way. This laid the foundation for contemporary formal logic and deductive reasoning. AI systems use algorithms to process information, make inferences, and draw conclusions. Aristotle’s logical framework provided a basis for building computational models of reasoning which these algorithms use.

Leonardo da Vinci (1452-1519) was one of the first engineers to design a mechanical calculator. No functioning prototype was built based on this design during da Vinci’s life. There have been recent—successful—attempts at building a calculator based on these designs.

Blaise Pascal (1623-1662) built one of the first working calculating machines when he was only 18 years old. It was a basic mechanical calculator that could perform additions and subtractions. The machine is now known as Pascal’s machine, or sometimes as the Pascaline.

A few decades later, Gottfried Wilhelm Leibniz (1646-1716) built a mechanical calculator that was somewhat more sophisticated than Pascal’s. Leibniz’s calculator, now known as the stepped reckoner, could not only add and subtract but also multiply and take the square root of a number. These inventions led to speculation that machines might go beyond being mere calculators and could actually think and act in similar ways to human beings. Thomas Hobbes suggested a similar idea in his Leviathan: “For what is the heart but a spring; and the nerves, but so many strings, and the joints, but so many wheels.”

While Leibniz did not specifically contribute to the development of artificial intelligence in the modern sense, his ideas and work laid the groundwork for certain aspects of AI. Leibniz developed calculus, a mathematical framework that revolutionized scientific and mathematical reasoning. Calculus provides the foundation for many AI techniques, such as optimization algorithms, pattern recognition, and machine learning algorithms that deal with continuous and dynamic systems.

Leibniz conceived the idea of a universal characteristic, a symbolic language or notation system that could represent all knowledge and facilitate precise communication. Although his vision for a universal language was not realized during his lifetime, the concept influenced subsequent work in formal languages and symbolic systems, which are fundamental to AI. The development of formal languages was a significant step toward the development of artificial intelligence.

These philosophers argued that the mind works according to logical rules. Given a set of logical rules, we can build systems in the material world, systems that mimic the application of those rules. Rene Descartes (1596-1650) even proposed a view stating that the mind is already such a system. Descartes’s work popularized the mind-body problem and developed the idea of dualism, which is the idea that there is a fundamental separation between the mind and the physical body.

This idea sparked discussions and debates about the nature of consciousness and cognition. These debates were invaluable to AI research, prompting many to hold that we needed a fully formed picture of the mind and its operations before we could develop AI fully. The mind-body problem and the ideas surrounding it is key to figuring out what we need to do in order to create something like a mind. Descartes emphasized the power of human reasoning and logical thinking. His method of systematic doubt influenced the development of formal logic and rationalist approaches to knowledge.

Descartes believed that the mind, or res cogitans, is a separate kind of substance that is immaterial and separate from the physical world. An alternative to dualism is materialism, which holds that the mind is actually material in some sense, and as such, it works in accordance with natural laws. Materialists have had to reckon with the problem of free will: how can humans make choices and think freely if the operations of the mind are fully determined by their physical causes?

Now, since we’ve established that the mind manipulates knowledge, the next problem is to examine that source of knowledge. Modern empiricism starts with Francis Bacon’s (1561-1626) Novum Organum. He pointed out the importance of experience in acquiring knowledge. At this point, we have to note that by “experience,” he meant experimenting and observing, which is sort of the job that the scientist does in order to confirm or reject a certain theory or statement.

Empiricism was further characterized by this dictum of John Locke (1632-1704): “Nothing is in the understanding, which was not first in the senses.” With this, empiricists developed a defense from the rationalists, who claimed that reason was the ultimate source of our knowledge. Later on, David Hume (1711-1776) examined the mind’s reliance upon the principle of induction: many general rules are acquired by exposure to repeated associations between their elements.

Building on the early work of Ludwig Wittgenstein (1889-1951), the famous Vienna Circle, led by Rudolf Carnap (1891-1970), developed the philosophy of logical positivism, a new empiricist philosophy. Logical positivism held that all knowledge can be characterized by logical theories connected to observation sentences which in turn correspond to sensory inputs, meaning the raw data we gather of the world. To a certain extent, logical positivism combined the doctrines of rationalism and empiricism. Carnap’s book The Logical Structure of the World (1928) posited a computational procedure for extracting knowledge from more elementary experiences; as such, it was a pioneering theory of mind as a computational process.

Now, let’s see why the contribution of logic was so important to the development of AI, especially Aristotle’s logic. Aristotle’s work on logic and reasoning is important because it later served as an inspiration to George Boole, a 19th-century mathematician and logician. Aristotle’s system of syllogistic logic provided a foundation for Boole’s development of mathematical logic, and Boole’s major contribution to AI lies in his development of Boolean algebra and Boolean logic.

Boole developed a symbolic algebraic system called Boolean algebra, which represented logical relationships and operations using algebraic equations and binary variables. Boolean algebra forms the basis for digital circuit design and Boolean logic gates, which are fundamental components of modern computing systems and AI technologies. Boole’s algebraic system allowed the representation of logical operations using simple logic ‘gates,’ such as AND (conjunction), OR (disjunction), and NOT (negation) gates. These gates can be combined to construct complex circuits that perform logical operations.

The concept of logic gates and circuit design provided a practical foundation for building digital computers and laying the groundwork for the computational processes involved in AI. Boole’s work in symbolic logic, which enabled the manipulation and analysis of logical propositions using symbols and formulas, laid the foundation for automated reasoning and deduction. Symbolic logic, which was invented by Aristotle, provides a framework for expressing and manipulating logical relationships, which is essential for tasks such as rule-based systems, logical inference, and automated theorem proving.

The foundational concepts that Aristotle and Boole came up with have had a profound impact on the development of AI, serving as the building blocks for computational logic, digital circuit design, automated reasoning, and information retrieval systems. Boole’s work provided the groundwork for logical reasoning and manipulation of symbols that is central to many AI algorithms and technologies today.