I can think of 4 modes of human cognition:

• Abstract Reasoning
  • Logic: how all humans universally know that  proof by contrapositive or induction is compelling.
• Causal Understanding
  • Cause and effect: an infant learns in only a handful of trials that something he drops will hit the floor. Without causal understanding, this would require far more trials to learn.
• Spatial Intuition
  • Bigger, smaller, inside, outside, etc. We visualize shapes and interactions in our minds without having to see from every angle or test every combination.
• Pattern Recognition
  • The classic “which of these doesn’t belong?”. Humans are so good at pattern recognition we often find patterns that don’t even exist!

I don’t claim this list is exclusive, but I can’t think of any others. Every form of cognition seems to fall into one of these categories. Yet all forms of AI currently in practical use – neural nets, decision trees, etc. rely on pattern recognition alone. We simply don’t understand the other modes of cognition well enough to formalize them into algorithms.

We don’t even know if they can be formalized into algorithms! It’s possible – though by no means proven – that they might lie outside what Turing Machines can do. The reverse also may be possible – these forms of cognition might boil down in their fundamental elements to a single form of cognition expressible formally, for example as Turing Machine instructions. If so, the fact that we perceive them as different would be a mental illusion.

Either way, the fact remains that we don’t understand these other forms of cognition well enough to formalize into algorithms. Thus, all forms of AI are essentially based on pattern recognition.

Combining these forms of cognition is extremely powerful. A toddler learns to identify cats with only a handful of examples, yet Googe’s best image recognition AI requires millions. By combining spatial intuition with causal understanding and pattern recognition, the toddler learns much more quickly. Relying on pattern recognition alone severely handicaps AI in two critical ways:

• It learns slower, requiring orders of magnitude more training examples.
• Properly trained AI can out-perform humans on specific tasks, yet still performs poorly on tasks that are open-ended or outside its training.

A human toddler is still light-years ahead of AI. Will we find a way to make AI better despite these limitations? Will we break through these limitations – find a way to formalize other modes of cognition into algorithms?

These questions and the topics of human cognition, AI, and Turing Machines intrigue me.