artificial intelligence

The relationship between traditional sciences and modern artificial intelligence represents two distinct approaches to understanding our world. In traditional sciences, researchers have long attempted to apply the laws of celestial physics to other fields, attracted by their comprehensible, predictable, and verifiable nature. However, the world’s complexity and our inability to measure all influencing factors means these mathematical models rarely achieve perfect accuracy.

This limitation led to the development of statistics, which attempts to derive meaningful insights by aggregating multiple events. While statistical approaches remain predictable and verifiable, they can often seem counterintuitive. Statistics emerged from our recognition that analyzing many real-world problems at an atomic level simply isn’t feasible.

Neural computation takes a fundamentally different approach. Scientists observed that the brain excels at making decisions at an atomic level, even in highly complex environments. Rather than trying to understand the world through high-level mathematical models, neural networks learn by aggregating patterns from countless individual observations, continuously optimizing their internal models based on the data they encounter. Instead of acting as architects trying to decode the universe, they simply aim to replicate the observable consequences of the world at an atomic level.

This bottom-up approach has proven remarkably successful, often outperforming traditional scientific methods in certain domains by combining numerous “atomic simulations” to approximate real-world phenomena. However, this paradigm shift has created challenges for those deeply rooted in traditional scientific thinking, as it requires embracing a fundamentally different philosophical approach.

Looking forward, we’re likely to see these two approaches converge. Patterns and insights discovered through traditional scientific methods will increasingly inform and enhance artificial intelligence’s atomic-level understanding, while AI’s bottom-up discoveries will influence traditional scientific thinking. This synthesis of approaches promises to advance our understanding of the world in exciting new ways.

I just had the pleasure to watch this video on YouTube.

Basically, it described some fundamental rules that were not as crisp to me as it was presented in the video. The rules were the following:

1. Any public information will become a commodity. Therefore, any private information will become relatively more valuable for the information owner. So from a perspective of today, that means that all public information will decrease in value generally, and just some small of your unique information will increase in value. For example, a consultant will not be able to make money off any information that is public right now, but needs to sell private information because the AI will be as good as the consultant with the public information, and its only assets are the private information in the future.

2. AI will solve all tasks that are easy to verify or can be made easy to verify. That also means that AI will always have trouble competing with humans on tasks that are not easy to verify and we have trouble to make them verifiable.

3. Intelligence will not penetrate all areas of life similarly fast. The capability and the rate of improvement are fundamentally dependent on the nature of the task. Questions that give us an intuition on the learnability of a tasks are:

• Is it difficult for humans?
• Is it digital?
• Is it easy to create/access data about it?
• Is there a simple/single heuristic with which we can measure the task success

I think these three rules are quite easy to grasp, but make for quite good tools to project the AI influence in your domain of interest in the future.