> Then, in the late 1980s--spurred by the early work of Judea Pearl, a professor of computer science at UCLA, and breakthrough mathematical equations by Danish researchers--AI researchers discovered that Bayesian networks offered an efficient way to deal with the lack or ambiguity of information that has hampered previous systems.<p>The "mathematical equations by Danish researchers", for those interested, are most likely this paper:<p>Lauritzen, S.L. and Spiegelhalter, D.J. (1988), Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems. Journal of the Royal Statistical Society: Series B (Methodological), 50: 157-194. <a href="https://doi.org/10.1111/j.2517-6161.1988.tb01721.x" rel="nofollow">https://doi.org/10.1111/j.2517-6161.1988.tb01721.x</a><p>Direct PDF Link: <a href="https://www.eecis.udel.edu/~shatkay/Course/papers/Lauritzen1988.pdf" rel="nofollow">https://www.eecis.udel.edu/~shatkay/Course/papers/Lauritzen1...</a>
I like how in the mid 90s neural networks were almost a write-off. "But the neural nets won't help predict the unforeseen. You can't train a neural net to identify an incoming missile or plane because you could never get sufficient data to train the system."