Why was AI able to beat humans at Go, and what does AlphaGo’s success mean for AI research and the future?

In 2016, Google DeepMind’s AlphaGo defeated Lee Sedol 9, making it the first AI to beat a human in Go. AlphaGo’s success was due to its deep learning-based AI algorithm, which opened up new possibilities for AI research and brightened the future of AI.

 

In 2016, AlphaGo, a Go program developed by Google DeepMind, made headlines around the world. AlphaGo won 5 games, 4 wins, and 1 loss in a grandmaster match against the legendary Go player Lee Sedol. This is the first time in the history of Go that an artificial intelligence has ever beaten a professional player. Compared to games like chess, Go is considered a difficult game for AI to beat a professional. This is because chess involves a limited number of pieces on an 8×8 chessboard, while Go involves a large number of moves on a 19×19 board. Computing all the possible positions on these 361 squares would require an enormous amount of computation, which would be impossible for a simple algorithm.
However, AlphaGo was able to overcome this difficulty and beat the world’s strongest Go player thanks to a new artificial intelligence (AI) algorithm called deep learning. Deep learning is the culmination of nearly 50 years of AI advancements, and it uses innovative methods that are incomparable to traditional algorithms. The history of AI dates back to the mid-20th century, and it all started with Alan Turing’s concept of artificial intelligence. Turing’s theories, famously known as the Turing Test, became the cornerstone of early AI research, including programs for mathematical proofs and chess games. However, computers of the time lacked computational power, so algorithms that simply calculated the number of all cases were bound to run into limitations.
To overcome these limitations, researchers explored various methods, one of which was an algorithm called machine learning. Unlike traditional algorithms that simply deal with fixed relationships between inputs and outputs, machine learning allows programs to learn from a variety of data to discover rules and patterns on their own. In particular, various learning methods, including supervised learning, unsupervised learning, and reinforcement learning, have been developed to dramatically improve the performance of AI.
In the case of AlphaGo, it learned to play Go by analyzing about 30 million professional Go moves. In this process, it learned the basic rules of Go from the moves through supervised learning, and it learned the correct rules and responses to each situation through autonomous learning. Finally, through reinforcement learning, AlphaGo was able to evaluate the probability of winning each move and select the move with the highest probability of winning. Thanks to this learning process, AlphaGo was able to reach a level where it could beat the 9th dan of Go.
Deep learning is a more advanced form of artificial neural network algorithms based on machine learning. Artificial neural network algorithms mimic the way neurons in living things connect, and they use multiple processes to produce results. While early neural networks had limitations, deep learning has improved flexibility by layering these processes on top of each other. And as modern computers have become more capable of handling the massive computational demands of deep learning, the algorithm has become a key technology in AI research.
The rise of deep learning has brought back the glory days of AI. What was once a promising field that soon entered a dark age is now showing new promise. The success of AlphaGo has demonstrated AI’s ability to solve complex problems, which has opened up new frontiers in AI research. There are even suggestions that AI may soon be able to think like humans. AlphaGo’s emergence was a major milestone that showed that AI has advanced to the point where it can actually compete with humans, not just change the game of Go.
As such, AlphaGo is more than just a technological achievement; it is an iconic realization of the possibilities of AI that humans have dreamed of. Given that we’ve only just begun to scratch the surface, it’s hard to imagine the impact AI will have on our society in the future. There’s still a lot of excitement about how far human creativity and technology can go and how it will change our lives. The future of AI is still being shaped right now, and we are at the center of that change.