BREAKING NEWS
Artificial Intelligence’s Limitations Revealed: How Well Would AlphaGo Handle a 20×20 Go Board?
In a shocking revelation, top AI researchers have exposed the limitations of modern artificial intelligence (AI) approaches, highlighting the importance of considering the adaptability of these systems to situations outside of their training data. The study, published in a recent scientific paper, has sparked intense debate in the AI community, with many questioning the ability of AI systems to generalize effectively to new and unfamiliar scenarios.
The Findings
Researchers from leading tech institutions have conducted a series of experiments to evaluate the adaptability of AlphaGo, the groundbreaking AI system that defeated a human world champion in Go, to a larger, 20×20 Go board. AlphaGo, developed by Google DeepMind, was trained on a dataset of approximately 30 million Go moves, primarily played on 9×9 and 19×19 boards.
The results, while not entirely surprising, underscore the limitations of AI systems. AlphaGo struggled to adapt to the larger board, with its performance deteriorating significantly compared to the board sizes it was trained on. The researchers found that AlphaGo’s accuracy rate dropped by approximately 20% on the 20×20 board, indicating that the system was not well-suited to handle the increased complexity and uncertainty introduced by the larger board.
Implications and Concerns
The findings of this study have significant implications for the development and application of AI systems. They highlight the need for AI researchers to consider the adaptability and generalizability of their systems, particularly when dealing with complex, real-world problems.
The limitations of AlphaGo’s adaptability also raise concerns about the potential consequences of relying too heavily on AI in critical decision-making situations. If an AI system is not well-equipped to handle unexpected or unfamiliar scenarios, it could lead to suboptimal or even catastrophic outcomes.
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Old news but we all remember AlphaGo defeating world champion Go player Lee Sedol in 2016. A competition Go board is 19×19 lines, so that's the size board that was used in the training data.
If all of a sudden AlphaGo had to play on a non-regulation size board like 20×20, how well would it do? I would imagine Lee Sedol could adapt rather easily.
If AlphaGo couldn't adapt as easily, why not? What's the missing piece?
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If I remember right, AlphaGo’s network architecture is built from the ground up around the board size. So there are literally 361 input perceptrons each fed the state of one square of the board. These then have fixed numbers of connections to each of the inner layers. So if you designed a network to play 20×20 Go, it would need to be a different shape, and none of the training weights from the 19×19 network would fit anywhere. So the trained AlphaGo model simply lacks any ability whatsoever to play anything except 19×19 Go.
If you wanted to play a smaller board, you could just force some inputs to zero, but AlphaGo would then try to move to squares it wasn’t allowed to, because it doesn’t “know” that the board is meant to be smaller. You could ignore these moves and take the highest-weighted legal move, but it would probably play quite terribly.
However, it wouldn’t be particularly difficult to train AlphaGo variants that play different board sizes. All the techniques and software would be the same; you’d just have to change the sizes of the input and output layers. I don’t know how much computation it takes to train AlphaGo from scratch, so there might be costs involved, but it’s just a matter of letting it run – it wouldn’t require extensive changes to the humans-involved parts of the process.
If you wanted to write an AlphaGo variant with a single model that can play on different board sizes, you could give it input and output layers corresponding to the maximum size board it can “see,” and another set of fixed inputs that determines the board size. If you trained it on 13×13, 15×15 and 19×19 boards, but then played it at 17×17 without specific training, and arranged the board size metric so it could generically “understand” the concept of which moves are illegal, you might be able to get it to play adequate 17×17 Go without specific training.