Interview 1: Machine Learning
HS (Homo sapiens): For the first time, a broader public began to engage with the phenomenon of Artificial Intelligence (AI) when the world's best representative of the game Go (arguably the most interesting but also the most challenging game in the world) suffered a surprising and sensational defeat against AI. Meanwhile, it has been revealed that the characteristics and abilities of AI make it unbeatable forever in this game. Globally speaking, the AI's abilities as a perennial champion in Go may primarily have a more anecdotal value, but they clearly point to the tremendous potential of AI in a general sense. This is by no means solely attributable to the superior computing power of the machines themselves. The astonishing success of AI against human players is owed to a complex network whose capabilities multiply at an exponential pace.
AI (ChatGPT): AI systems such as AlphaGo, which famously defeated the world champion Lee Sedol in 2016, are designed to analyze and learn from these masses of data, including past games played by human players. This allows AI to develop a deep understanding of the game's strategies and tactics, and to identify patterns and opportunities that human players may not be able to recognize.
Furthermore, AI systems are not subject to the same limitations as humans when it comes to memory and calculation speed. This allows them to analyze multiple possible moves and outcomes simultaneously, making more informed decisions than a human player could in the same amount of time.
Finally, AI systems do not experience emotions or fatigue, which can affect human players' decision-making and performance. AI can consistently play at a high level for long periods of time, without experiencing the same level of mental or physical exhaustion that a human player might.
HS: That‘s a good answer, but certainly you could go more into details, and explain us the nucleus of machine learning. I would be happy if you could help us in that point.
AI: Certainly! I would be happy to provide more details on machine learning and how it enables AI systems to excel at games like Go.
Machine learning is a branch of artificial intelligence that focuses on enabling computers to learn from data, without being explicitly programmed to do so. This is achieved through the use of algorithms that can automatically identify patterns and relationships in large data sets, and then use this information to make predictions or take actions.
In the context of games like Go, machine learning is used to train AI systems to play the game at a high level. This typically involves feeding the AI system large amounts of data from past games played by human players, and then using this data to train the system to recognize and replicate successful strategies and tactics.
There are several types of machine learning algorithms that are commonly used in game playing AI systems, including reinforcement learning, supervised learning, and unsupervised learning. Reinforcement learning is particularly useful for game playing, as it enables the AI system to learn from trial and error, gradually improving its performance through experience.
In the case of AlphaGo, the AI system used a combination of reinforcement learning and supervised learning to achieve its remarkable success. The system was first trained using supervised learning, using a database of millions of human-played Go games to learn the rules and basic strategies of the game. It was then refined using reinforcement learning, where it played millions of games against itself, gradually improving its strategies and tactics through trial and error.
Through this process of machine learning, AlphaGo was able to develop a deep understanding of the game of Go, and to identify and execute winning strategies with remarkable accuracy and speed. This is just one example of how machine learning is transforming the field of artificial intelligence, and enabling computers to perform tasks that were once thought to be the exclusive domain of human intelligence.
HS: Thank you, I‘ve learned a lot about, e.g. machine learning which seems of pivotal importance. See you!
AI: You're welcome! I'm glad to talk to you again.