The Three Buckets of Knowledge: Connecting Physics, Math, History, and Deep Learning Insights

The Three Buckets of Knowledge: Connecting Physics, Math, History, and Deep Learning Insights

When we embark on a journey to understand the world, we are often faced with the question of where to start. Which sources of knowledge are the most fundamental and useful? Peter Kaufman proposes that we can gain a deeper understanding of the world by mastering the three oldest and most invariant forms of knowledge: physics, math, and human history. These three buckets of knowledge offer us endless learning opportunities and mental models. Let's explore how these buckets of knowledge connect and provide valuable insights.

Physics and math form the first bucket of knowledge. From these disciplines, we derive the rules that govern the universe. By studying physics, we learn about the fundamental laws that shape our reality. Math provides us with a language to describe and quantify these laws. Together, physics and math offer us a framework to understand the workings of the physical world. They provide us with a set of rules, like the laws of gravity and thermodynamics, that govern how objects interact and how energy flows.

Moving on to the second bucket of knowledge, we find human history. History is subject to geology, and studying geologic time teaches us important lessons. The concept of gradualism, or slow incremental change over time, is a fundamental principle we can draw from studying geology. Similarly, human nature changes in geologic time, not human time. This explains why the fundamental problems of history tend to recur. We are essentially the same as we have always been, and history repeats itself on a large scale.

Resilience is another lesson we learn from studying the long history of Mother Nature. Just as fragile species eventually break under the stresses of competition, fragile relationships also break. On the other hand, strong win-win relationships have a resilience that keeps parties together. Understanding this resilience can guide us in building and maintaining strong relationships in our own lives.

Human biology provides us with the rules of the chessboard on which the human condition plays out. It is the third bucket of knowledge. Knowing the rules of biology is crucial because it determines the arena in which we operate. As populations grow and resources become limited, the concept of Darwinian natural selection arises. It is a way to determine who gets the spoils. We see similar interesting results in human culture, where selective survival of creative minds becomes a form of immortality.

The concept of passed-down ideas in human culture overlaps with major mental models derived from physics/math and biology. Ideas and knowledge compound in the same mathematical way that money or population does. Additionally, human ideas go through natural selection, just like biological life does. Intellect is a vital force in history, but it can also be a dissolvent and destructive power. No individual can fully understand or dismiss the customs and institutions of society in one lifetime. The wisdom of generations, accumulated through centuries of historical experiments, shapes our present.

Now, let's connect these insights to the field of deep learning. In recent years, deep learning has made significant progress, driven by a few key factors. One of these factors is the constant drive to create bigger neural networks. Scaling up neural networks has been a consistent theme in deep learning, allowing for more complex and accurate models.

Unsupervised learning has also played a crucial role in deep learning advancements. Large language models (LLMs) trained on raw data gathered from the internet have demonstrated the power of unsupervised learning. These models, such as OpenAI's DALL-E 2 and Google's Imagen, utilize loosely captioned images from the internet to find intricate patterns between textual and visual information. This approach eliminates the need for manual labeling and enables the models to learn from vast amounts of data.

Multimodality is another area where deep learning has made strides. Text-to-image generators, for example, combine multiple data types in a single model. The ability to process multiple modalities allows deep learning models to tackle more complex tasks. DeepMind's Gato, trained on various data types including images, text, and proprioception data, showcased decent performance in tasks like image captioning, interactive dialogues, robotic arm control, and gaming.

While deep learning has achieved impressive feats, there are still challenges to overcome. Causality, compositionality, common sense, reasoning, planning, intuitive physics, and abstraction and analogy-making are among the problems that remain unsolved in the field. Deep learning models, especially text-to-image generators, can struggle with tasks that require meticulous step-by-step reasoning and planning. Coherence and consistency over long stretches of text are achievable, but complex descriptions and compositionality pose difficulties.

To conclude, mastering the three buckets of knowledge – physics, math, and human history – provides us with a deeper understanding of the world. These fundamental sources of knowledge offer us mental models and lessons that guide our understanding of the physical world, human nature, and the resilience of relationships. Connecting these buckets of knowledge to the insights from the field of deep learning highlights the importance of scale, unsupervised learning, and multimodality in pushing the boundaries of artificial intelligence. By combining these diverse sources of knowledge, we gain a holistic perspective and the ability to navigate the complexities of our ever-changing world.

Actionable advice:

1. Embrace the three buckets of knowledge: Make a conscious effort to study physics, math, and human history to gain a deeper understanding of the world's fundamental principles and human nature.
2. Explore the field of deep learning: Stay updated on the latest advancements and research in deep learning, particularly in areas like scaling neural networks, unsupervised learning, and multimodality. This knowledge can be valuable in various domains.
3. Foster interdisciplinary thinking: Encourage cross-pollination of ideas between different fields of knowledge. Seek connections and insights that can be gained by combining diverse perspectives, such as integrating concepts from physics, math, human history, and deep learning. This interdisciplinary approach can lead to innovative solutions and a broader understanding of complex problems.