Stacked Exponential Growth: AI Is Outpacing Moore’s Law And Evolutionary Biology

Bootstrapping itself on the back of both Moore’s Law and evolutionary biology, AI is using its own insights to fuel growth.

GPT Summary: AI is experiencing a rate of growth far exceeding Moore’s Law and evolutionary biology due to bootstrapping itself on the back of both concepts. Moore’s Law, which posits that the number of transistors on a microchip doubles every two years, and evolutionary biology, which explores how living organisms adapt to their environments over millions of years, provide valuable insights into how systems evolve over time. AI leverages the rapid advancements in computing power offered by Moore’s Law and the foundational understanding of biological processes gained from evolutionary biology, resulting in an unprecedented rate of growth. The concept of stacked exponential acceleration refers to the idea that AI development can not only benefit from the advancements brought about by Moore’s Law and evolutionary biology but can also contribute to its own acceleration, resulting in a feedback loop where AI development begets further AI development. Several factors contribute to this phenomenon, including transfer learning, collaborative AI, AI-driven optimization, and human-AI interaction.

Moore’s Law and evolution are both concepts that examine change in a system over time. Moore’s Law looks at the growth and development of computer technology, specifically the rate at which the number of transistors in a microchip doubles. Meanwhile, evolution explores the way in which living organisms change and adapt to their environments over millions of years. Both concepts are concerned with the rate and nature of change, whether it’s the development of technology or the evolution of species, and provide valuable insights into how systems evolve and adapt over time.

A recent tweet by Epic Games CEO, Tim Sweeney brought these two core concepts into a very contemporary perspective.

Artificial intelligence is doubling at a rate much faster than Moore’s Law’s 2 years, or evolutionary biology’s 2M years. Why? Because we’re bootstrapping it on the back of both laws. And if it can feed back into its own acceleration, that’s a stacked exponential.

Let’s take a closer look at this insightful perspective and its relevance today. The rapid progression of artificial intelligence has become a source of both awe and concern in modern society. While the development of AI technology has led to numerous advancements in various fields, the speed at which it evolves has raised questions about our ability to control and understand its implications. Let’s explore the acceleration of AI in comparison to Moore’s Law and evolutionary biology, discussing the factors that contribute to its rapid growth and the concept of stacked exponential acceleration.

Moore’s Law and Evolutionary Biology: A Foundation for AI Acceleration

Moore’s Law, proposed by Gordon Moore in 1965, posits that the number of transistors on a microchip will double approximately every two years, leading to an exponential increase in computing power. This observation has held true for decades, driving advancements in technology and the digital revolution.

In contrast, evolutionary biology, which deals with the process of evolution in living organisms, operates on a vastly slower timescale. Changes in species occur over millions of years, with natural selection and adaptation shaping their development.

AI leverages the rapid advancements in computing power offered by Moore’s Law, combined with the foundational understanding of biological processes gained from evolutionary biology. These two factors, in conjunction, have propelled AI into a state of accelerated growth, far exceeding the pace of either Moore’s Law or evolutionary biology alone.

Bootstrapping AI: Stacked Exponential Acceleration

The concept of stacked exponential acceleration refers to the idea that AI development can not only benefit from the advancements brought about by Moore’s Law and evolutionary biology but can also contribute to its own acceleration. This creates a feedback loop where AI development begets further AI development, resulting in an unprecedented rate of growth.

There are several factors that contribute to this phenomenon:

Transfer Learning: AI systems can leverage pre-existing knowledge and expertise, shortening the learning process and allowing for rapid adaptation to new tasks.

Collaborative AI: Multiple AI systems can work together, sharing knowledge and learning from one another, further accelerating development.

AI-Driven Optimization: AI can be used to optimize its own algorithms, hardware, and software, resulting in faster, more efficient, and increasingly powerful systems.

Human-AI Interaction: Human researchers and developers can learn from AI-generated insights, driving further innovations and discoveries.

By understanding the factors contributing to AI’s rapid development, we can better navigate the challenges and harness the potential of AI technology for the betterment of humanity. It is essential for stakeholders in AI research, development, and application to engage in open dialogue and collaboration, ensuring that the rapid growth of AI leads to a more informed, equitable, and innovative future.