I’ve been diving into The Almagest, translated by G.J. Toomer, and the first chapter gave me deep insights into philosophy, mathematics, and the nature of knowledge. Here are my key takeaways:
1️⃣ The Divide Between Practical and Theoretical Philosophy
Philosophy is divided into two parts: practical and theoretical. Practical philosophy can often be learned through experience—people can possess moral virtues without formal teaching. Theoretical philosophy, on the other hand, requires instruction and study. Practical wisdom improves through action, while theoretical knowledge grows through deep inquiry.
2️⃣ Mathematics as the Most Certain Knowledge
The discussion highlights three divisions of theoretical philosophy: physics, mathematics, and theology. Physics studies the material and changing world, making it unstable and uncertain. Theology deals with what is beyond perception, making it speculative. Mathematics, however, remains unchanging, eternal, and the most reliable form of knowledge. It serves as a bridge between the material and the divine, capable of supporting both physics and theology.
3️⃣ Mathematics as Our Language for the Physical World
One passage that truly stood out to me was:
"As for physics, mathematics can make a significant contribution. For almost every peculiar attribute of matter becomes apparent from peculiarities of its motion from place to place. Thus, one can distinguish the corruptible from the incorruptible—whether it undergoes motion in a straight line or in a circle, heavy from light, or passive from active, whether it moves towards the center or away from the center."
This is a beautiful description of how mathematics allows us to describe the nature of the physical world—how we categorize motion, weight, and forces. Mathematics has been our human language for encoding the attributes and qualities of reality. But now, with the rise of AI and machine learning, we are seeing a new way to represent the physical world—through neural network weights.
What if these AI systems, by learning from vast amounts of data, could encode the laws of physics in ways we can’t yet fully comprehend? Could these neural networks serve as a new kind of mathematical language, capturing not just the mechanics of the universe, but perhaps even the deeper truths that theology seeks to understand?
This makes me wonder—are we at the beginning of a paradigm shift in how we define and discover truth? Would love to hear your thoughts!
#Philosophy #Mathematics #AI #MachineLearning #Physics #TheAlmagest #Knowledge #IntellectualGrowth
Style
Training
Issues with Socrates
Issues with the Church
Way to deal with Nihilism
Causation might not be there
To conclude:
What I learned from reading Parmenides' (540 BC) fragments, which were preserved in Simplicius's commentary:
The old argument was whether the world is composed of small parts that make up the whole or whether the whole is just one big, unified whole. One of the thinkers who believed that the universe and the world are one big unchanging whole was Parmenides. This belief had implications for justice and how we perceive meaning in our life experience.
According to Parmenides, truth ("what it is") is one, continuous, and has no beginning or end. He argued that the whole has no beginning, reasoning that "if it came from nothing, what need could have made it arise later rather than sooner?" Therefore, he encouraged us to seek knowledge by focusing on the whole rather than on fragments, appearances, and human-named objects.
First, he was wrong in claiming that we cannot learn from negations ('what it is not'). Actually, we have learned a great deal by modeling our knowledge probabilistically, using information we gathered from what does not exist.
Secondly, He argued that the big whole "remains constant in its place; for hard Necessity keeps it in the bonds of the limit that holds it fast on every side." Here he might have been wrong here partially because, in physics, we have identified that the universe is expanding. This is shown by the light we receive on Earth, which is redshifted (its wavelength is longer than it would be if the planets and stars were stationary).
He is partially correct because however, he is saying, I think, that there is an underlying universal physical law. In the end, I think we can remember that the law—the divine, as Parmenides calls it—is constant. Quantum mechanics, which is probabilistic in nature, helps us model the known "what is." In the end, atomic theory and quantum mechanics are tools for describing and understanding the universe, but the fact remains that life is deterministic (even the knowledge from modeling the indeterministic microscopic pieces in quantum mechanics). Whether the universe is expanding or staying the same is a fact. It is binary. The whole and truth is constant.
Parmenides might have been wrong in suggesting that we should seek knowledge only by thinking about what is real, rather than considering negation and what does not exist. But he might have been right in his final that there is a law that unites everything and that we are part of the whole. The most life-affirming aspect of his philosophy was his belief that we can learn about "what is" and the whole from anything we encounter. In the end, he suggested, learning one thing leads to learning them all.
Hannah Arendt wrote about what she thought of a top-ranking Nazi, Eichmann's trial in Jerusalem after World War II. She argued that Eichmann's evilness resulted from the banality of evil—or at least, I think that's what she meant. I never understood what she meant by "Banality of Evil." But does my LLM understand what Hannah Arendt meant by "Banality of Evil"?
Without the context of the trial, you and I might interpret "Banality of Evil" in various ways. Large Language Models run into the same problem too if they don't know the context. But when I say context, I mean the real context of the outside world—not just words and written history, but also images, videos, and audio. In other words, LLMs and we need to see what Hannah Arendt saw in the Eichmann Trial to be grounded in the real world to truly understand her.
In natural language, which is not transparent but very context-heavy, there are many ways "Banality of Evil" can be interpreted. Since we cannot prove whether the various ways a text can be interpreted are finite or infinite, we cannot prove that it is computable in finite time to check if its meaning exactly equals something. Therefore, we cannot check if the LLM's meaning of "Banality of Evil" matches Arendt's. The paper attached shows the limitation of not grounding LLMs with something other than text in order to check if they truly understand meaning. In other words, we cannot prove whether the various ways a language with variables like "Banality of Evil" could be interpreted are finite or infinite, nor can we prove or assert that the LLM's understanding of "Banality of Evil" would align with Hannah Arendt's using a computer.
What do we mean by a language with variables? You should agree with me that English is a variable language because you must have used the Oxford Dictionary now and then to understand, perhaps, what Hamlet was saying in his soliloquies. Whereas if you form a language using only integers, 1 + 1 is a transparent language, and you can assert, for example, 1 = 0 + 1 or 1 + 1 = 2 and check that the meanings match. On the other hand, (X + 1) is a form of a language with a variable. Since X might have an infinite number of possible options, it becomes a non-transparent language and not computable to check the meaning's correctness because we don't know whether there would be finite options for X or infinite options for X. Therefore, we could not test if the meanings match. It is like the idea of constantly uncovering new layers of meaning. There is always more to explore and understand, potentially leading to Kant's "infinite regress," where you can always delve deeper into the analysis.
If you have taken some CS, you might remember the concept of compilers. Compilers translate human-readable computer language into computer-readable CPU instructions. From the paper, you can learn how to check if meaning is correct through assertions. For example, you can check or assert if 1 = 1, and it will return true. Similarly, you can learn how our brains are like compilers that are reading the written code, whether in Python, SQL, Java, or C. We compile the code in our heads to understand the order of execution that would happen in the computer once the code is compiled. The person who wrote the code is imagining and trying to assert what the actual Python compiler would do, agreeing on the meaning with the computer at each line while writing the code.
Unfortunately, checking if LLMs understand the "Banality of Evil" as a variable in our English language is not computable in finite time with computers—just as you and I might differ in our understanding of "Banality of Evil" due to the definition of the word "justice." By grounding LLMs in the outside world through images, audio, and video, we could check/assert if an LLM's understanding of "Banality of Evil" matches Hannah Arendt's.
For further reading, check out: Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand? at https://arxiv.org/abs/2104.10809