Thanks for the interview and the video.
It's probably worth correcting one of your misconceptions about Sophy. While the vanilla AI observes specific rules about specific race conditions and situations, Sophy does not follow a more sophisticated or complicated set of machine-generated "if-then" rules. It is being trained using reinforcement learning, meaning that it races against copies of itself and starts with zero knowledge, other than the positions of itself and its opponents on the track. It is then rewarded for fast lap times (i.e. winning races), and penalized for track excursions, collisions, etc. The result of this training process is a neural network that can "solve" the following mathematical relation: giving this set of environment variables (position, orientation and velocity of cars and coordinates of the surrounding track boundaries), what are the optimal numbers for brake/throttle (from -1 to 1) and left/right steering (from -1 to 1).
Given this architecture, Sophy should be able to learn to deal with weather and race strategy - by adding more environment variables, like the amount of water on the track, weather forecast, tyre type and status, percentage of fuel in the tank and number of remaining laps. Given these inputs, Sophy would eventually learn to avoid sliding, pit at the right moment, fuel save and choose the best tyre compound. (The vanilla AI doesn't just lack intelligence in overtakes or defending positions, but is also pretty stupid in terms of strategy - like making a 40 second pit stop just before the end of an endurance race to switch to the optimal tyre compound that will save 10 seconds on the final remaining lap. Sophy should be able to handle such situations correctly.)
It's worth noting that while it's relatively easy to train a version of Sophy that drives more (or less) aggressively by lowering (or raising) the penalty for collisions during training, it's harder to create a slower version of Sophy, since you can't just easily de-emphasize the reward for fast lap times. The most obvious way would still be to give it a technical handicap (slower car, slower tyres, higher fuel consumption). And while Sophy's knowledge should generalize from one track to almost any other (it knows how to deal with straights, left turns, right turns, tight turns, wide turns, banked turns etc), it won't generalize from one car to others, since the result of throttle/brake and steering input can vary pretty substantially from car to car. And given that the car physics in GT7 are quite elaborate, there's not much hope that you could feed the geometry/weight distribution of the car, transmission/suspension/downforce settings, type of brakes and engine characteristics to Sophy and hope for it to figure it all out.
EDIT: Here's a link to Sony AI's paper - worth reading if you're interested in the tech behind Sophy.
https://www.researchgate.net/public...ismo_drivers_with_deep_reinforcement_learning