Re: The Forgotten Fifth Generation
> First of all, there was a computer from the Japanese ICOT "Fifth Generation Computer Systems (FGCS)" project that was commercially sold...
Thanks for that info.
That particular footnote [4] was supposed to say "as far as I'm aware, updates most welcome" but you may have noticed I was having trouble keeping the footnotes in line.
> , but if you gave the expert systems more than a few hundred rules to follow in their decision trees they became painfully slow at returning answers.
Can you provide any citations for this? Serious question, not snark: in about 1985 we had an Expert System Shell applying simple Bayesian stats and I don't recall ever having to worry about it becoming painfully slow at calculating as you increased the rule set - it might start to ask more questions to help it traverse the data, but generally IIRC it organised the rules along the lines of a decision graph, walked from one side to the next, choosing the next node in the path based upon the data & weightings so far: a "wide but thin" set of k nodes would be solved faster (as in, it asked fewer questions before finding a sufficiently likely node that couldn't be disproved, aka "the answer") than a "narrow but thick" arrangement of the same number of nodes that arranged themselves in a different fashion.
But there are other ways of working an XPS (e.g. it need not be interactive with the User - ours was and so any calculation time was peanuts compared to waiting for them to type) and it is never too late to learn about them.
What helped kill off XPS (in the public eye) from our p.o.v. was mainly not showing a good enough cost/benefit: creating and validating rule sets is expensive and takes time away from your human expert. Simple XPS that could be created cheaply, such as an initial triage scheme, could be done as fast by the human operator once they'd run through the system a few times (i.e. the humans were trainable! Who knew?). Large rulesets (from our set of clients at least) were used too infrequently compared to the creation costs (and we didn't get into any of the big funded research studies). And some fields just learnt embarrassing lessons: like Stock Market trading. They can afford the set up costs, given the potential returns, right? But remember all those trials that showed that a monkey, an octopus or just throwing a die tended to give better returns on the market than the high-paid, so-called "expert traders"? And you *tried* to get an octopus to work alongside a Knowledge Engineer? Or vice versa, for that matter (the suckers! The horrible, horrible suckers!).
And back in the day there was something called "ethics" that controlled the use of computer models released into the wild: the XPS could show how it worked out a path to its answers, but if there wasn't a human around who could validate that when someone disagreed, the ethical liability was too high - and your human experts were not wild about being tied forever to that project, especially if they were planning to retire...
Luckily, all the current crop of LLMs and GANs etc are totally incapable of being queried for how they reached a result, so there are no ethical restrictions on just dumping them into the wild.
> more standardized, general purpose, and better-funded platforms to steamroll over them and crush them in the market.
True.