- Limited Experience Problem - Many AI learning methods require huge quantities of data, but humans require few (some times as little as one) example(s) to learn a thing.
- Control Problem - How to prevent AGI from doing something?
- Consciousness Problem - What is the nature of consciousness, and what algorithm will allow us to reproduce it?
- Knowledge Transfer Problem - How do we transfer learning from one task to another.
- Ethics Problem - What ethics ought we give our AGI?
- Symbol Grounding Problem - How abstract symbols can be grounded in empirical experience.
- Catastrophic Forgetting Problem - Computer programs that learn to perform tasks also typically forget them very quickly.
- Black Box Problem - An understanding of what a neural network is doing can be difficult and often only guessed at.
- Bias Problem -
- Cost of Errors -
- Entanglement Problem -
- Brittle Dependencies -
- Freshness -
- Hidden Feedback Loops -
https://ai4life.github.io/problems/
A documentary based on Ray Kurzweil's book, The Singularity Is Near.
<iframe width="560" height="315" src="https://www.youtube.com/embed/y5jiGeQBLTk" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>Unsolved Problems in AI Rules of Machine Learning: Best Practices for ML Engineering