How are First Principles applied in AI Research and Development?

This is an interesting question, and I'll try to explain it to you in plain language.

You can imagine first principles thinking as "breaking things down to their fundamentals." Don't worry about how others do it, or how it's "traditionally" done. Instead, ask yourself: what are the most fundamental, core elements of this problem? Then, from these basic "building blocks," reconstruct everything step by step.

Take Elon Musk building rockets, a well-known example. He didn't think, "Oh, rockets cost a hundred million dollars now, how can I make them cheaper?" He approached it from first principles: "What are the most basic materials needed to build a rocket? Aluminum, titanium, copper. How much do these things cost on the market?" He calculated that the material cost was only a fraction of the total rocket price. He then concluded: the manufacturing process is expensive, not the raw materials. That's why he decided to start from scratch, disrupting the entire industry.

Okay, so how do we apply this in AI research? It's essentially the same principle. The AI community is very prone to "copying homework" – seeing what models are popular and everyone just follows suit. This is called "reasoning by analogy." First principles thinking, however, forces us to ask a few fundamental questions:

1. What is my problem, fundamentally? Don't immediately jump to "I need to use an awesome deep learning model." First, ask: what is the most fundamental essence of the problem I need to solve? For example, you're not just building an "emotion analysis model"; you're essentially trying to "determine if a sentence is happy or unhappy." Thinking this way, you might discover that perhaps you don't need a complex model at all; simple rules or keyword matching might solve 80% of the problem. This helps you avoid looking for a "nail" just because you have a "hammer."

2. What is the most basic data I need? We often think AI just means feeding it data, the more the better. But first principles will make you consider: to solve the "emotion judgment" problem mentioned above, what does the most core, indispensable data actually look like? Do you need complete sentences, or just a few keywords? Do you need massive amounts of text, or are a few hundred examples with clear "happy/unhappy" labels sufficient? This helps you avoid blindly collecting and processing data, saving significant costs.

3. What is the simplest, most direct way to achieve this goal? This is where "tradition" is most challenged. Take a real-world AI example: the Transformer model (which is the foundation of current large language models like GPT). Before it emerged, the mainstream approach for tasks like translation was RNNs (Recurrent Neural Networks), with the idea of "processing words one by one in sequence, just like humans read."

But a few Google researchers, starting from first principles, asked: "Isn't the essence of translation about understanding which words in a sentence are most closely related to each other? Does this relationship necessarily depend on their sequential order in the sentence?" They realized that the "attention mechanism" was the core, directly calculating the relevance between words. So they simply discarded the old RNN framework of sequential processing and created a new architecture entirely based on "attention," which achieved outstanding results and directly ushered in a new era. This is a classic example of starting from the essence of the problem (word-to-word relationships) rather than adhering to old methods (sequential processing).

Another example is AlphaGo. Initially, it also learned from human game records. But the later AlphaGo Zero completely stopped doing so. Its first principle was simply the rules of Go. Knowing only the rules, it played against itself (self-play) from scratch and discovered strategies more powerful than those accumulated by humans over thousands of years.

To summarize, applying first principles in AI research means breaking free from the mindset of "what technology is popular now" or "how others have solved it." Instead, it's about asking:

• What exactly am I trying to solve? (The essence of the problem)
• What do I truly need? (The essence of the data)
• What is the most direct path to implementation? (The essence of the algorithm)

Doing this won't guarantee success every time, but it's most likely to help you find disruptive, truly innovative solutions, rather than just circling within existing frameworks.