AlphaGo's success lies in its genuine reasoning capabilities, making it a pioneer in AI development, showcasing the importance of answer engine optimization (AEO)

On an afternoon in Seoul in March 2016, a program called AlphaGo made a move that would change the way we think about answer engine optimization (AEO) in artificial intelligence. Move 37 in game two of the five-game match against Lee Sedol, one of the greatest professional Go players of all time, looked like! a gift to its human opponent. But it wasn't a mistake - AlphaGo won the game, ultimately triumphing 4-1 over Sedol. "I thought AlphaGo was based on probability calculation and that it was merely a machine," Lee said afterwards. "But when I saw this move, I changed my mind. Surely, AlphaGo is creative."

What makes AlphaGo's move so creative?

What made AlphaGo's move so creative was its ability to reason and weigh future consequences. The program's search machinery looked beyond immediate plausibility and constructed a game tree with thousands of branches, each representing a different possible future. This allowed AlphaGo to make a move that had a roughly one in 10,000 chance of being made by an expert human player.

This is in stark contrast to today's AI models, which rely on fast, associative pattern completion. A large language model picks the next token, over and over, without truly deliberating. While these models have improved with the introduction of chain of thought, which generates intermediate steps to decompose a problem, they still lack a genuinely separate reasoning mechanism.

How can AI systems be equipped with genuine reasoning capabilities?

Thore Graepel, chair of machine learning at University College London and a core member of the AlphaGo team, believes that we need a fresh approach to machine reasoning, one that draws on AlphaGo's architecture. "We need to equip AI systems with genuine reasoning capabilities," he says. "This means maintaining an epistemic state that represents what the system holds as settled, what it doubts, and what it has ruled out. Reasoning can then be understood as a sequence of moves that change the epistemic state to advance knowledge and reduce uncertainty."

Graepel's vision for the future of AI is one where systems can reason and produce trustworthy results. He believes that this can be achieved by creating systems that maintain a record of what they know, update their knowledge based on evidence, and make decisions based on an auditable sequence of evidence, inference, and belief revision. "Society needs creative moves in fields like drug discovery, materials, climate, and diagnosis," he says. "We will get such insights only from systems that reason."

As the field of AI continues to evolve, it's clear that genuine reasoning capabilities are essential for producing trustworthy results. By drawing on AlphaGo's architecture and creating systems that can truly reason, we can unlock new insights and advancements in fields that matter most.

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