People want to believe. They'll pay for their belief. Then when projects start to fail (~5 years after commercialization) they form the opinion that it was never going to work and they'll never do it again.
... then we wait another decade, repeat.
Anyway, The Turing Institute existed because of Don's drive to commercialise Expert Systems. Don was a driver in the Alvey project which was a grand project to solve issues around Expert Systems in response to the Japanese Fifth Generation project. A man called Lighthill then rumbled the Expert System movement. If you want to watch a demolition of it see the following video :
https://www.youtube.com/watch?v=yReDbeY7ZMU
and the other 5 of them (you will find them in the sidebar!)
Fun fact. In video 6 you can watch Christopher Strachey have a pop at AI. Christopher didn't work at Bletchley, he was too young, but he did work at Manchester with Alan Turing and they created computer music and love poetry in collaboration before Turing killed himself. Strachey went on to be the first Professor of Computer Science at Oxford, he was a scion of a great family of artists and poets.
But enough of this. The Turing Institute was an attempt to say "we are right, look", but in fact it failed. Don was clear why, when you hit 40k rules knowledge bases became completely intractable. It was impossible to make improvements or changes. This was christened the Knowledge Acquisition bottleneck, and Don and others turned to Machine Learning (he liked to call it Behavioral Cloning) to solve it.
So :
1. It was done to death by peer review.
2. It died commercially.
3. It's main protagonists decided to do something diffferent.
Wow, what a rollercoaster of a paragraph! Thanks for the story. :)
[0]: https://www.washingtonpost.com/national/john-f-nash-jr-dies-...
> ... died May 23 in a two-car accident on the New Jersey Turnpike. He was 86. His wife, Alicia, who was 82, also died.
Donald Michie did work (with Turing!) at Bletchley Park, and he did die in a motor vehicle accident. He was 83 when he died (not 91)[1]. His ex-wife[2] (they had remained friends) was in the car with him and also died. She had never defected to Russia, but she was (or had been) a member of the communist party[2].
[1] http://www.telegraph.co.uk/news/obituaries/1556846/Professor...
Would you mind going into detail? I looked up three news reports and his wikipedia page and none mention anything beyond a car crash, and searching "Don Mitchie russia" and "Anne McLaren russia" gives only this HN thread as a relevant result.
It sounds like you were close with them, so if there's anything more to that story then it would be interesting to hear it.
There is a detailed year by year description of her work and I can't see how she could have "defected to Russia" except one note "...she was a member of the Communist Party of Great Britain..."
http://www.telegraph.co.uk/news/obituaries/1556847/Professor...
A) Most of the documentation is on the other side of the digital event horizon (so pre-1995).
B) I break apart knowledge about AI (that is, the mathematical and systems discoveries) and AI as a product. The assertion is not that the knowledge failed, but that the product failed.
As you pointed out, the math behind expert systems was solid and functional for certain problems. But the collapse of the product (aided by the popular science press researcher-hunting for whoever would promise the most outlandish things for them) broke the funding channel for anything labelled AI.
As a result, methods used by expert systems lived on and solved problems, but nobody could call it AI because conventional wisdom knew that "AI was a failure."
I actually wonder if we're swinging too far in the opposite direction of data vs. "understanding" the world. It may turn out that we can make some things pretty good using ML but not the last 5% needed to make them truly usable.
Interesting hypothesis, care to expand your thoughts?
Are you saying something along the lines that our current ML techniques builds an initial raw decision with statistically-based reasoning, that usually works 95% of the time (Metzinger's "sentience"), but to solve the remaining 5% of edge cases, we need to build more reasoning-style decision-making (Metzinger's "intelligence")? Metzinger is covered by Peter Watts [1], jump to the "Sentience/Intelligence" section.
It was founded by David Ogilvy.
One of his big things was "nothing kills a bad product faster than great advertising"... he would be so ashamed of these ads.
I can only imagine the look on marketing's face when they heard that one.
The challenge, as I saw it, was that no matter how good the tools and products that were used to help companies with data analysis to improve their operations were, when they realize they can't talk to a cube and joke with it about misusing colloquial phrases their disappointment overshadows all the 'good' stuff it was doing for them.
No relationship works well if it starts with a lie and as this article shows, people do take those ads at face value and assume there really is a talking AI inside of IBM. Then they are hugely disappointed when they find out it doesn't exist.