Artificial intelligence is riddled with paradoxes. As if the subject is not ethically and technically complex enough, AI also proves challenging in terms of its many self-contradictions.
For a technology ruled by logic, AI presents a series of illogical conflicts. Indeed, we’re still grappling to define what AI is and what it means even as we develop and deploy it at a never-before-seen level.
Here’s a closer look at four of the most hotly discussed AI paradoxes, and what they mean for today’s artificial intelligence.
Paradox 1: Moravec’s paradox
Moravec’s paradox revolves around the ability of AI tools. It observes that ‘high-level reasoning’ takes less computation than ‘low-level sensorimotor skills’.
In other words, the tricky things like advanced mathematics and logic take less for AI to pick up. We have put effort into learning these tasks and so know how to teach them to AI.
But when it comes to ‘simple’ skills — those we learn naturally as babies and toddlers — it’s a different story. These are skills such as sight, speech, comprehension and movement. And having an AI do these things is much harder, requiring more computation and effort.
This is why we already have AI that can handle complex mathematics, yet we’re only now ...
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