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Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Winter in this case being kind of like a recession in econom

Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Winter in this case being kind of like a recession in economic terms - most research funding dries up, etc. We essentially had one of those winters from the late 80s until about a dozen years ago. A lot of laymen now think of AI as being "magic" that can do anything and that's not a good thing when the reality turns out to be different. At this point I don't think we'll see an AI winter as deep as some of the previous ones. But we could certainly see an AI Fall.

0xdeadbeefbabe 7y ago
The name is overhyped and pretentious by itself, and history bears this out. Who cares if it's an AI fall or winter if it's an AI stupid, because of all the credulous students. Edit: Russel and Norvig's book is good though http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
YeGoblynQueenne 7y ago
>> Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Note that past AI winters have not occurred because of overhyping machine learning. They occurred because of overhyping of symbolic AI that had nothing to do with machine learning. For example, the last AI winter at the end of the '80s happened because of the overhyping of expert systems- which of course are not machine learning systems. Machine learning is not all, not even most, of AI, historically. It's the dominant trend right now, but it was not the dominant trend in the past. The dominant trend until the 1980's was symbolic reasoning.
- UncleOxidant 7y ago
  There was a neural net popularity surge in the late 80s, early 90s. Of course, the hardware wasn't there yet to be able to deliver on the promises. I was in a Goodwill book section about a year ago and there were a couple of NN books from that era on sale for $3, one titled "Apprentices of Wonder: Inside the Neural Network Revolution" from 1990 and the other was for programmers and included C code for a NN to predict the stock market from 1989. Anyway, that all had died out by about '92 or '93 and NNs were a pretty dead academic topic until about 2005 or so when they figured out that GPUs could be used to accelerate them.
- radarsat1 7y ago
  But symbolic reasoning mostly worked, did it not? However, its Achilles heel was that for it to be useful, it's necessary to distill a lot of domain knowledge into a format that can be processed by an expert system. That means, writing 10s upon thousands of rows of "if then then that". Machine learning is different in that it is more amenable to distilling those rules from the data automatically. It is successful where symbolic reasoning failed because it can go from the raw data. A good portion of machine learning research is in new ways to preprocess and format data into a structure that can be further consumed by linear algebra, which turns out to be a lot easier and practical than figure out a huge database of sensible first order predicate logic statements. If ML techniques can be used to feed symbolic systems, the latter would show promise again, which is already happening in recent trends in causal inference and graph networks. The marriage of these two fields is inevitable, and has already started.
  - mistrial9 7y ago
    > Machine learning is different .. partly because, Machine Learning is inherently probabilistic. Lots of room for errors and hype with systems that never claim to give the right answer all of the time!
  - blackrock 7y ago
    > That means, writing 10s upon thousands of rows of "if then then that". LOL.. It's full of ifs.. [1] [1] https://qph.fs.quoracdn.net/main-qimg-89cfa17ca63ddac683e04f0852336e47.webp https://qph.fs.quoracdn.net/main-qimg-89cfa17ca63ddac683e04f...

0xdeadbeefbabe 7y ago

The name is overhyped and pretentious by itself, and history bears this out. Who cares if it's an AI fall or winter if it's an AI stupid, because of all the credulous students. Edit: Russel and Norvig's book is good though http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/

YeGoblynQueenne 7y ago

>> Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Note that past AI winters have not occurred because of overhyping machine learning. They occurred because of overhyping of symbolic AI that had nothing to do with machine learning. For example, the last AI winter at the end of the '80s happened because of the overhyping of expert systems- which of course are not machine learning systems. Machine learning is not all, not even most, of AI, historically. It's the dominant trend right now, but it was not the dominant trend in the past. The dominant trend until the 1980's was symbolic reasoning.

UncleOxidant 7y ago
There was a neural net popularity surge in the late 80s, early 90s. Of course, the hardware wasn't there yet to be able to deliver on the promises. I was in a Goodwill book section about a year ago and there were a couple of NN books from that era on sale for $3, one titled "Apprentices of Wonder: Inside the Neural Network Revolution" from 1990 and the other was for programmers and included C code for a NN to predict the stock market from 1989. Anyway, that all had died out by about '92 or '93 and NNs were a pretty dead academic topic until about 2005 or so when they figured out that GPUs could be used to accelerate them.
radarsat1 7y ago
But symbolic reasoning mostly worked, did it not? However, its Achilles heel was that for it to be useful, it's necessary to distill a lot of domain knowledge into a format that can be processed by an expert system. That means, writing 10s upon thousands of rows of "if then then that". Machine learning is different in that it is more amenable to distilling those rules from the data automatically. It is successful where symbolic reasoning failed because it can go from the raw data. A good portion of machine learning research is in new ways to preprocess and format data into a structure that can be further consumed by linear algebra, which turns out to be a lot easier and practical than figure out a huge database of sensible first order predicate logic statements. If ML techniques can be used to feed symbolic systems, the latter would show promise again, which is already happening in recent trends in causal inference and graph networks. The marriage of these two fields is inevitable, and has already started.
- mistrial9 7y ago
  > Machine learning is different .. partly because, Machine Learning is inherently probabilistic. Lots of room for errors and hype with systems that never claim to give the right answer all of the time!
- blackrock 7y ago
  > That means, writing 10s upon thousands of rows of "if then then that". LOL.. It's full of ifs.. [1] [1] https://qph.fs.quoracdn.net/main-qimg-89cfa17ca63ddac683e04f0852336e47.webp https://qph.fs.quoracdn.net/main-qimg-89cfa17ca63ddac683e04f...