Discussion 6

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Southern New Hampshire University *

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Computer Science

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Feb 20, 2024

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 What are the differences between AlphaGo Zero and its predecessors? How did these differences improve AlphaGo Zero's performance?  How do the neural networks and reinforcement learning algorithms interact in AlphaGo Zero? How does this affect performance?  How does the thinking of programs such as AlphaGo and AlphaGo Zero compare with how humans think? How does this affect gameplay? How does this affect our perception of AI?  What implications does AlphaGo or AlphaGo Zero's performance have for future AI developments?   How does the thinking of programs such as AlphaGo and AlphaGo Zero compare with how humans think? How does this affect gameplay? How does this affect our perception of AI?  What implications does AlphaGo or AlphaGo Zero's performance have for future AI developments? Humans learn by relying on memory to develop our skills and abilities. Humans learn by repeating patterns and based on the desired result, repeating the pattern if desirable or changing the habit to make it desirable. In playing the game of Go, humans learn the game by playing the game over and over, looking at different patterns and strategies to win (B- Cube.Ai, 2023). AlphaGo and AlphaGo Zero use deep learning techniques and reinforcement learning algorithms. AlphaGo was trained by supervised learning using databases of top human games for learning. AlphaGo Zero stepped it up and used reinforcement learning to learn the game from scratch and without any past data. AlphaGo Zero played itself,

teaching itself to play the game of Go leading to better outcomes after playing itself over and over (Granter et al. (2017). This affects gameplay as humans are limited compared to AI. Humans have linear strategies while playing the game of Go while AI programs like AlphaGo Zero can perform moves that completes multiple objectives and not constrained by inefficient strategies. AlphaGo Zero has changed our perception of AI as it showed AI programs can learn fully through reinforcement learning without human examples or human guidance (Silver et al, 2017). AlphaGo Zero has implications for the future of artificial intelligence and its potential to surpass human performance in various tasks. AlphaGo Zero has also inspired other researchers to use similar techniques to AlphaGo Zero as self- learning algorithms have the potential to accelerate AI development without human input. References B-Cube.Ai. (2023, June 28). How do humans learn and how does the machine? - Becoming Human: Artificial Intelligence Magazine. Medium . https://becominghuman.ai/how-do- humans-learn-and-how-does-the-machine-a923244b27f6 Granter, S. R., Beck, A. H., & Papke Jr, D. J. (2017). AlphaGo, Deep Learning, and the Future of the Human Microscopist. Archives of Pathology & Laboratory Medicine , 141 (5), 619–621. https://doi-org.ezproxy.snhu.edu/10.5858/arpa.2016-0471-ED Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550 (7676), 354-359,359A-359L. https://doi.org/10.1038/nature24270

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