There is no magic
- Failure cases:
- Shamans claimed to cure diseases.
- Witches casted love spells.
- Dowsers attempted to find water
- Countless other people claimed to be able to perform a certain task and failed.
- Success cases:
- Modern astronomers predict solar eclipse.
- Engineers design cars.
- Many others perform a task reliably.
- The difference:
- The former group can’t explain why they should succeed or doesn’t even attempt to.
- The later group can prove that their success must follow from an established body of knowledge.
- Proofs can take many different forms but they are all independent of experiments.
- To borrow the language of logics, they are “syntactic” instead of “semantic”.
- In other words, they fall into the realm of “reason” as opposed to “experience”.
- Showing that you succeed 99% (or even 100%) of the times doesn’t amount to a proof. Such activities are experiments and the results are sensitive to the settings and assumptions of that particular experiment.
- There is no magic in this world.
- If an ability can’t be proved (i.e. doesn’t follow from its description), it is likely not a real ability.
- The performer might succeed by luck but they will fail when more cases are tested and they will fail in a systematic way.
- The only way to perform a task reliably is to construct a procedure that can provably perform it.
- Modern neural networks are the equivalent of shamans, witches, and dowsers:
they succeed for the wrong reasons and fail systematically.
- Only (future) neural networks that we can prove to have a ability truly have that ability.
- Distributed representations cannot be proven and therefore will never be reliable.
- Current neural networks falls prey to adversarial examples and fooling examples.
- Current neural networks are strongly activated to random samples.
- Current neural networks don’t work for out of distribution examples.
- The only way to prove the ability of a neural network is to identify and prove the ability of its individual (or small group) of neurons.
- We need to create “grand-mother neurons” or small groups of neurons that can be proven to perform a functionality.
- We need to apply logics and math to proving the functionality of artificial neurons (repeat: calculating evaluation metrics is not proving).