Even though AI's core success is machine learning, machines possess only a little silver of humans' versatile learning abilities. AI already can analyse medical images with superhuman accuracy or call it Superintelligence, which is a piece of cake for a human doctor.
What if we wake up one morning to the news that a super-power AI has emerged with disastrous consequences? Nick Bostrom’s Superintelligent and Max Tegmark’s Life 3.0 books argue that malevolent superintelligence is an existential risk for humanity.
Rather than endless anticipation, it’s better to ask a more concrete, empirical question: What would alarm us that superintelligence is indeed at the doorstep?
If an AI program develops fundamental new capabilities, that’s the equivalent of a canary collapsing.
AI’s performance in games like Go, poker, or Quake 3, is not a canary. The bulk of AI in such games is social work to highlight the problem and design the solution. The credit for AlphaGo’s victory over human Go champions was the talented human team at DeepMind that merely ran the algorithm the people had created. It explains why it takes several years of hard work to translate AI success from one little challenge to the next. Techniques such as deep learning are general, but their impactful application to a particular task needs extensive human intervention.
Over the past decades, AI’s core success is machine learning, yet the term ‘machine learning’ is a misnomer. Machines own only a narrow silver of humans’ versatile learning abilities. If you say machine learning is like baby penguins, know how to fish. The reality is that adult penguins swim, catch fish, digest it. They regurgitate fish into their beaks and place morsels into their children’s mouths. Similarly, human scientists and engineers are spoon-feeding AI.
In contrast to machine learning, human learning plans personal motivation to a strategic learning plan. For example, I want to drive to be independent of my parents (Personal motivation) to take driver’s ed and practice on weekends (strategic learning). An individual formulates specific learning targets, collects, and labels data. Machines cannot even remotely replicate any of these human abilities. Machines can perform like superhuman; including statistical calculations, but that is merely the last mile of learning.
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