I wrote this in response to one of the reviews, so I'll share it with you since you asked. :-)
I worked on an ARPA-funded speech understanding project in the 1970's at SDC--it was definitely driven by military interests. One time some of us techies were in our soundproof lab drinking wine and eating cheese and crackers when our manager brought to the big picture window an Iranian general bristling with medals--they both looked extremely unhappy.
I also worked on ARPANET development at UCLA from 1969-1971, and there was none of that. The driving motivation was ARPA-funded researchers at universities being able to readily share their work. Is ARPA funding researchers at universities an issue that can be written about? Of course, but it has nothing to do with the ARPANET per se and isn't part of the story that this book is about.
Oops--I left out a critical part of the SDC story--we were in the lab because it had an incredible sound system featuring a pair of high end AR-3 speakers. I don't recall what we were playing but I'm sure it sounded wonderful.
We also did real work in that lab of course ... mostly recording things like "What is the surfaced displacement of the Lafayette?", which our primitive system running on (pathetically slow by today's standards) Raytheon 704 and PDP-11 computers would attempt to parse and answer. The text of course was selected for the sake of obtaining a grant from the USN.
This early work, funded by the military, laid the basis for today's ubiquitous speech understanding systems. Are there issues with fundamental research being funded by the military or, say, big pharma, rather than as part of a direct planned effort by society to achieve social goals? Sure, and much can and should be written about that, but it's not the subject matter of this book.
If it could actually parse your speech and come up with an answer in 1970, it must have felt amazingly futuristic. Star Trek was only a few year old at that point. Thanks for sharing that.
The parsing broke speech into phonemes--actually a string of candidate phonemes, each candidate having an assigned probability. It made a lot of mistakes--it couldn't generally distinguish between "a" and "the" in rapid speech, and the semantic phase didn't help disambiguate those. It worked better for female voices because they have an extra formant. It didn't work well if the speaker was intoxicated--we learned this from some anomalous results that one researcher dug into and discovered that there was a "knee" in the data--it turned out that our late night speaker Bill (a giant bearded guy who wore overalls that he ordered specially from Iowa IIRC and was known as wabblezabble) had taken a break, during which he drank a considerable amount of beer, on the hypothesis/excuse that it would make his speech more, er, fluid. It had the opposite effect--the automated recognition was consistently better before the break than after.
Coming up with the answer required doing a nondeterministic parallel search of the candidate phonemes through a DAG of phrases--the problem was contained because the DAG was highly restricted to the subject matter, in this case facts about Navy ships. This was a pilot and the dream was to have a much more massive semantic net of the English language. We had linguists and a resident lexicographist (he distinguished this from a lexicographer, though the dictionary says they are synonyms--but lexicographists know better than dictionaries created by lexicographers, heh heh) working with us. The parsing code that dealt with the audio signal was written in FORTRAN and assembler, IIRC, but all the language stuff was written in a local version of LISP. Jeff Barnett, on our team, was the author of SDC's LISP2, but I'm not sure that's what we were using. He was working on developing a more performant algolish LISP called CRISP when I left. Jeff had written the parallel search algorithm, which had a "knob", as he called it, which was a floating point value that controlled the depth first/breadth first balance--any possible balance could be achieved by dialing the "knob". This was needed because it took too long to do an exhaustive search--it bailed with an answer as soon as it found one that passed some threshold. Anyway, it required recording onto tape, digitizing it and feeding it to the minicomputer, running many passes, feeding the results into the LISP program running on a mainframe, waiting an indeterminate time to make a match against a highly restricted vocabulary--more of a grind than futuristic. I remember when programs like Dragon Speech showed up ... way advanced over what we had, but still needing to be trained on a specific speaker. Now we have realtime language translation in our pockets. The other day I accidentally turned it on and my friend at the other end of the line asked who was speaking Spanish ... everything I said was being repeated in Spanish.
BTW, when I left SDC because I wanted a break from work, they offered me a spot with their new development called EFTS, but I was pretty set on leaving. EFTS--Electronic Funds Transfer System--is the backbone of all of today's digital money transfers ... ATMs, ACH, etc. I really missed the boat on that one.
P.S. In trying to remember why Bill (aka Billy) also had the nickname wabblezabble, I managed to remember his last name, which yielded his initials WAB (at UCLA initials were used as login names). I found this lovely obit which very much fits the guy I knew: https://www.legacy.com/us/obituaries/latimes/name/william-br...
