The Strong and Weak Artificial Intelligence Debate. Among all the philosophical debates surrounding AI, the most important is the debate on strong and weak AI.
Recently, I had a debate with my favorite new thinker about high-performance and low-performance AI, which reminded me of something I wrote more than a year ago, so I decided to pick up these dusty ideas. There is too much technical hype for artificial intelligence, and sometimes it should return to its philosophical roots. Among all the philosophical debates surrounding AI, the most important is the debate on strong and weak AI.
From a technical point of view, I agree with the idea that we have achieved one or two breakthroughs in realizing some form of powerful or general AI. But from a philosophical point of view, there are still some challenges that need to be reconciled. Many challenges can be explained by obscure theories pioneered by Austro-Hungarian mathematicians in the last century and one of the leading fields of neuroscience research.
In AI theory, low-performance AI systems usually just look smart, while high-performance AI machines are truly capable of thinking. Thinking here refers to real thinking, not just simulated thinking. This dilemma is often referred to as the “high-performance AI hypothesis.”
In a world where digital assistants and algorithms are used to defeat the Go champion and World of Warcraft 2 teams, the question of whether the machine can act intelligently seems foolish. In some limited environments, such as medical research, Go, travel, etc., we have been able to build a large number of AI systems that can exert their intelligence. Most experts agree that low-performance AI is definitely possible, but there are still great doubts about high-performance AI.
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With outdated dystopian movies like Terminator making headlines for possibly predicting the future and with companies like Google already releasing Artificial Intelligence (AI) tools and bots, is it time to rethink the Terminator narrative. As we move towards a world that is increasingly digitized, we must work to truly understand what it means to have and use AI.