As Dr Hawke puts it, "the complexity of most real-world tasks is greater than is possible to solve with handcrafted rules, and it's well known that expert systems built with rules tend to struggle with complexity.This is true regardless of how well thought out or structured the formal logic is."Such a system might, for instance, craft a rule that a car should stop at a red light.But lights are designed differently in different countries, and some are intended for pedestrians rather than cars.There are also situations in which you might need to jump a red light, such as to make way for a fire engine."The beauty of machine learning", Dr Hawke says, "is that all these factors and concepts can be automatically uncovered and learned from data.And with more data, it continues to learn and become more intelligent."Nicholas Rhinehart, who studies robotics and AI at the University of California, Berkeley, also backs machine learning.He says Dr Bhatt's approach does indeed show you can combine the two approaches.But he is not sure it is necessary.In his work, and also that of others, machine-learning systems alone can already predict probabilities a few seconds into the future― such as whether another car is likely to give way or not―and make contingency plans based on those predictions.
