Understanding AlphaZero: Artificial Intelligence (AI) or Machine Learning (ML)?

DeepMind, Google's artificial intelligence subsidiary, has made a significant splash in the tech world with its creation, AlphaZero. This advanced software has revolutionized the way we perceive machine learning and artificial intelligence, especially as it excels in complex games like chess, shogi, and Go. This article explores whether AlphaZero is best categorized as a form of artificial intelligence or machine learning.

The Evolution of AlphaZero

AlphaZero, launched in 2017, was designed to be more efficient and effective than its predecessor, AlphaGo, which first made headlines in 2016 by defeating a professional Go player. Unlike AlphaGo, which relied on a vast database of human expert plays, AlphaZero was given only basic rules to start and had to learn everything from scratch. It utilized an impressive combination of deep learning and reinforcement learning to refine its gameplay.

Deep Learning and Reinforcement Learning

Deep learning is a subset of machine learning that uses neural networks to replicate the human brain's ability to identify patterns and make decisions. AlphaZero employs deep learning in its neural networks to perform detailed analysis and predictions based on input data.

Reinforcement learning, on the other hand, is a type of machine learning where an algorithm learns to make decisions in an environment, achieving a reward through trial and error. AlphaZero used this method to continually improve its strategy, refining moves and tactics through gameplay.

The Nature of Narrow AI

AlphaZero is often referred to as narrow AI (or weak AI), meaning it is designed to perform a specific task, in this case, playing complex board games. Narrow AI is distinct from general AI (or strong AI), which would have the ability to perform any intellectual task that a human can. AlphaZero, while incredibly powerful in its domain, does not demonstrate these broader capabilities.

While playing, AlphaZero's approach was remarkably human-like in some ways. It made moves based on intuitive strategies, even when these moves were unconventional and had never been seen before. This intuitive approach is a characteristic of true AI rather than machine learning algorithms, further cementing its status as narrow AI.

Impact and Future Implications

The success of AlphaZero in surpassing human experts has profound implications for the future of AI. It demonstrates the potential of AI and machine learning to outperform humans in specific domains, potentially leading to significant advancements in fields such as medicine, engineering, and logistics.

However, the narrow scope of its current applications also highlights the challenges in developing true general AI. While AlphaZero showcases the power of machine learning, it also emphasizes the need for further research in creating more versatile and adaptable AI systems.

Conclusion

AlphaZero, while employing sophisticated machine learning and deep learning techniques, is ultimately considered a form of narrow artificial intelligence. Its ability to learn and adapt through reinforcement learning, combined with its impressive performance surpassing human experts, makes it a remarkable achievement in AI and machine learning. As research continues, the line between narrow and general AI may blur, paving the way for a new era of intelligent machines.