"Not Enough Stuff" arguments in AI

AI has a history of people saying that the systems and algorithms simply need more of some property and there will be a breakthrough.

To a large extent this has been true.

The main "Not Enough Stuff" arguments would be:

1. Not enough speed.
   "If we just get faster machines we will have full AI".
   • This has been happening for the whole history of AI. Faster machines make older algorithms suddenly more useful.
   • The recent use of GPUs as simple parallel computers has massively helped the speed of certain algorithms.

2. Not enough neurons/memory. Not big enough networks.
   "Neural networks need to be much bigger, and with a much higher number of layers."
   • Theoretical breakthroughs have made this happen.

3. Not enough data.
   "The machine needs to read half the Internet."
   • The rise of massive AI companies spending hundreds of millions on training has made this happen.

4. Not enough time.
   "We get 20 years to grow our intelligence. Maybe a machine needs a 20 year childhood too."
   • Many people have noted the gap between a human childhood, switched on and interacting with the physical world 15 hours a day, 365 days a year, for 20 years, and robot "learning", where the robot is typically switched on and learning only for very short time periods.
   • While software-only experiments can run for months, bringing a 20 year human childhood to a robot with a physical body is quite a challenge.
   • Cloud robotics is an idea that multiple robots could share their learning and have a collective brain, something that does not exist in nature.

5. Not enough authors. Not enough diversity in the mind.
   "Real brains will consist of a huge, diverse society of sub-brains of different types of intelligence and different forms of learning and memory."
   • This is the AI model behind Ancient Brain.
   • Ensemble learning is the field of constructing systems of multiple learners of different types, and trying to combine their strengths in a hybrid system. For example in classification taking the average over their outputs.
   • See also Mixture of experts.

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Some of my thoughts on this:

• "Not enough stuff" in AI. In Constructing complex minds through multiple authors, Proceedings of the 7th International Conference on the Simulation of Adaptive Behavior (SAB-02), 2002.

• AI is too big a problem. In "The World-Wide-Mind: Draft Proposal", Dublin City University, School of Computing, Technical Report no. CA-0301, 2001.

ancientbrain.com      w2mind.org      humphrysfamilytree.com

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