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There's this trend of Expert Systems being commented on as a failure and starting some sort of AI winter, yet to my knowledge they were among the first commerci

There's this trend of Expert Systems being commented on as a failure and starting some sort of AI winter, yet to my knowledge they were among the first commercially viable digital animation tools, the first high speed trading platforms, and are still being used successfully in military planning. Where's the evidence that they were a failure or that they were oversold?

sgt101 9y ago
Well, there was this place called "The Turing Institute" which was in Glasgow. It was run by a very nice man called Don Michie. I met Don a handful of times, and he was extremely kind and generous to me. He worked at Bletchley Park and died aged 91 having hit a bridge at 120mph going the wrong way up the M11 at 2am sitting next to his wife who had defected to Russia thirty years before. Don was driving, at the time he had been blind for six months. 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 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.
- taneq 9y ago
  > I met Don a handful of times, and he was extremely kind and generous to me. He worked at Bletchley Park and died aged 91 having hit a bridge at 120mph going the wrong way up the M11 at 2am sitting next to his wife who had defected to Russia thirty years before. Don was driving, at the time he had been blind for six months. Wow, what a rollercoaster of a paragraph! Thanks for the story. :)
  - yvhhjbb 9y ago
    Clearly that's his way of saying something dodgy happened. Note the mention of Russia.
    - mlvljr 9y ago
      What's wrong with Russia, green characters? :)
    - nl 9y ago
      No, I think the poster is mis-remembering things which were based on facts. 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-Donald-Michie.html http://www.telegraph.co.uk/news/obituaries/1556846/Professor... [2] https://en.wikipedia.org/wiki/Anne_McLaren https://en.wikipedia.org/wiki/Anne_McLaren
    - deleted 9y ago
      [deleted]
  - deleted 9y ago
    [deleted]
  - icc97 9y ago
    That's eerily similar to how John Nash died [0] [0]: https://www.washingtonpost.com/national/john-f-nash-jr-dies-nobel-laureate-was-subject-of-a-beautiful-mind/2015/05/24/61463418-0219-11e5-bc72-f3e16bf50bb6_story.html?utm_term=.ebbf273931ce 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.
ethbro 9y ago
Don't think you deserve to be down voted for asking a reasonable question. 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."
- kobeya 9y ago
  And also the wealth of venture capital that existed in the 1980's to fund AI dried up and from ~90 to ten years ago it was nearly impossible to get significant startup funding for an AI play, despite the epic influx of capital in the 90's. AI missed out entirely on the dotcom years.
- ghaff 9y ago
  One of the big things that eventually changed was data--the availability of lots of data and the capability to transmit, store, and process it relatively economically. As I remember a lot of the late-80s, early-90s AI efforts, there was a lot of focus on encoding the world and the rules associated with it manually. Effectively, scaling out the knowledge of experts by embedding it in computers. 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.
  - yourapostasy 9y ago
    > 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. [1] http://www.rifters.com/real/Blindsight.htm#Notes http://www.rifters.com/real/Blindsight.htm#Notes

sgt101 9y ago

Well, there was this place called "The Turing Institute" which was in Glasgow. It was run by a very nice man called Don Michie. I met Don a handful of times, and he was extremely kind and generous to me. He worked at Bletchley Park and died aged 91 having hit a bridge at 120mph going the wrong way up the M11 at 2am sitting next to his wife who had defected to Russia thirty years before. Don was driving, at the time he had been blind for six months. 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 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.

taneq 9y ago
> I met Don a handful of times, and he was extremely kind and generous to me. He worked at Bletchley Park and died aged 91 having hit a bridge at 120mph going the wrong way up the M11 at 2am sitting next to his wife who had defected to Russia thirty years before. Don was driving, at the time he had been blind for six months. Wow, what a rollercoaster of a paragraph! Thanks for the story. :)
- yvhhjbb 9y ago
  Clearly that's his way of saying something dodgy happened. Note the mention of Russia.
  - mlvljr 9y ago
    What's wrong with Russia, green characters? :)
  - nl 9y ago
    No, I think the poster is mis-remembering things which were based on facts. 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-Donald-Michie.html http://www.telegraph.co.uk/news/obituaries/1556846/Professor... [2] https://en.wikipedia.org/wiki/Anne_McLaren https://en.wikipedia.org/wiki/Anne_McLaren
  - deleted 9y ago
    [deleted]
- deleted 9y ago
  [deleted]
- icc97 9y ago
  That's eerily similar to how John Nash died [0] [0]: https://www.washingtonpost.com/national/john-f-nash-jr-dies-nobel-laureate-was-subject-of-a-beautiful-mind/2015/05/24/61463418-0219-11e5-bc72-f3e16bf50bb6_story.html?utm_term=.ebbf273931ce 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.

ethbro 9y ago

Don't think you deserve to be down voted for asking a reasonable question. 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."

kobeya 9y ago
And also the wealth of venture capital that existed in the 1980's to fund AI dried up and from ~90 to ten years ago it was nearly impossible to get significant startup funding for an AI play, despite the epic influx of capital in the 90's. AI missed out entirely on the dotcom years.
ghaff 9y ago
One of the big things that eventually changed was data--the availability of lots of data and the capability to transmit, store, and process it relatively economically. As I remember a lot of the late-80s, early-90s AI efforts, there was a lot of focus on encoding the world and the rules associated with it manually. Effectively, scaling out the knowledge of experts by embedding it in computers. 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.
- yourapostasy 9y ago
  > 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. [1] http://www.rifters.com/real/Blindsight.htm#Notes http://www.rifters.com/real/Blindsight.htm#Notes