The Future of AI: From Passive Models to Agentic AI

🚀AI systems have an exponential growth curve, with increased computer power and data resulting in more powerful systems.

🌍The relationship between computer power and AI capabilities is predictable and follows scaling laws.

😮Human beings have difficulty comprehending exponential growth, as exemplified by the COVID-19 pandemic.

💡The power of AI systems is expected to continue increasing exponentially with the input of more data and computing power.

👨‍💻The development of agentic AI, where models can perform tasks and interact with the real world, requires further technological advancements and scale.

What is the scaling law hypothesis in AI?

—The scaling law hypothesis suggests that as more computer power and data are fed into AI systems, their capabilities will increase exponentially.

Why do human beings struggle to comprehend exponential growth?

—Human beings have difficulty thinking in exponentials, which is evident in the challenges faced during the COVID-19 pandemic.

What is agentic AI?

—Agentic AI refers to AI models that can complete tasks and interact with the real world, going beyond passive information retrieval.

What are the challenges in transitioning to agentic AI?

—Achieving agentic AI requires advancements in technology, particularly in scaling models to handle complex tasks and ensuring accuracy in each step of decision-making.

How fast is the development of AI models progressing?

—The industry is seeing new generations of AI models every four to eight months, but it may require one to four more generations to reach the level of agentic AI.

00:00AI developers believe that as long as more data and computing power are fed into AI systems, their capabilities will continue to increase exponentially.

02:55The relationship between computer power and AI capabilities is predictable and follows scaling laws.

08:00Human beings struggle to comprehend exponential growth, as exemplified by the challenges during the COVID-19 pandemic.