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The Evolution of Information in Philosophy and AI

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Claude Shannon, often called the "father of information theory," developed a groundbreaking way to understand communication. His theory, created in the 1940s, showed how information could be transmitted efficiently, whether through telegraphs, radios, or computers. Shannon introduced the idea of entropy, which measures uncertainty in a message. For example, a completely random message has high entropy, while a predictable one has low entropy. Shannon’s work also addressed how noise, or interference, can affect communication and how redundancy can help correct errors.

The formula for Shannon's Entropy illustrates how the probability of each symbol contributes to the overall uncertainty or "information" in a system. This foundational equation in information theory has broad implications in both technology and philosophy, raising questions about the nature of knowledge and reality.
(Najera, Jesus. “Intro To Information Theory.” Setzeus, 18 March 2020, https://www.setzeus.com/public-blog-post/intro-to-information-theory)

Though Shannon's theory revolutionized the way we think about communication and data, it was mainly concerned with the mechanics—how to transmit information from one point to another. It didn’t deal with the meaning of the information being sent. This separation between data transmission and the content of that data is a key limitation of his work.

Philosophical Expansion: What is Information?

Beyond Shannon’s technical view, philosophers have asked: What is information, really? Is it just something we send and receive, or is it a fundamental part of how the world works? Luciano Floridi, a leading philosopher in this field, suggests that information is not just something we use to communicate—it may be a core part of reality itself. He believes information is like the building blocks of the universe, shaping everything we know and experience.

In this broader view, information isn't just a string of data but something that describes the essence of objects, processes, and even thoughts. This philosophical approach invites us to see information not just as something practical but as something deeply woven into the fabric of existence.

DNA, as the fundamental carrier of genetic information, illustrates how information isn't just a human construct but a fundamental component of life itself—suggesting that information might be woven into the very fabric of reality.
(The Editors of Encyclopaedia Britannica. “DNA | Definition, Discovery, Function, Bases, Facts, & Structure.” Britannica, 30 April 2024, https://www.britannica.com/science/DNA.)

Information and Meaning

Shannon’s theory treated information as data, whether it was meaningful or not. It didn’t matter whether the data represented a shopping list or a random string of numbers—as long as it was transmitted efficiently, the theory worked. But in philosophy, meaning is important. How do we know if information carries meaning, and how do we assign that meaning?

This question becomes more complicated in the world of artificial intelligence. AI systems process enormous amounts of data, but can they truly understand what it means? For example, a chatbot might analyze words and respond correctly, but is it really grasping the meaning behind the words? Philosophers explore how AI can (or cannot) process not just data, but meaning, which ties into how we define intelligence and understanding in machines.

Information in the Age of AI and Big Data

In today’s world of big data and artificial intelligence, we are flooded with more information than ever before. Machines help us process this information, finding patterns and making decisions based on data that humans might never fully analyze. But as AI becomes better at sorting through data, philosophical questions arise: What does it mean for AI to “know” something, and how does that change our relationship to information?

When AI makes decisions based on data, it often uses patterns we might not understand. This can lead to biases if the data itself is flawed. For example, if an AI system is trained on biased data, it might make decisions that reinforce those biases. This raises ethical concerns about how we handle data in an AI-driven world and how much we can trust machines to process information accurately.

In the age of big data, the role of information has expanded from being something we transmit and receive to something that shapes how we understand the world. We rely on AI to process vast amounts of information, but this also changes our relationship to knowledge and meaning in ways we are still exploring.

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