We can’t program our AI “models” now because they are based on chains of probabilities. Therefore they can run in real time. They don’t have any real logic that we specifically programmed, only what the model was able to infer and then encode.
Perhaps there should be a marriage between these two attempts of AI.
Hum... The numerical route for AI necessarily has the same complexity in computation time. Those two have equivalent semantics. If you can get a result with one, you can get the same result with the other, it can just differ on how easy it is to program.
By the way, symbolic AI can learn too, you don't need to model things in code.
We as humans have the intelligence to simply see for fractions of a second and then just know — we can know the answers to math problems and how a person is feeling. The symbolic AI programming was an attempt to codify what symbols we are seeing but they are so diverse and nuanced it became apparent this was an ineffective scientific route.
Besides, keep in mind that the currently useful NNs are stuff that requires dozens of GB of memory to run.
Anyway, training a NN is also an attempt to codify what symbols we see on the world.
My entire premise was about an _if_ between hand-coded logic and probabilistic training. Hand coding the logical internals of a neural network is one marriage, but there could be others.
How do lane keeping assists work?
IMHO, that makes no sense. Assuming we have a really large state machine with all many possible situations in a state of driving on a straight highway with a few vehicles with more or less constant relative velocity, determining if we need to make a turn is definitely doable in a few computation steps.