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Some may find it interesting that John McCarthy coined the term Artificial Intelligence and was called a/the father of AI before the deep learning folks took ov

Some may find it interesting that John McCarthy coined the term Artificial Intelligence and was called a/the father of AI before the deep learning folks took over. This AI referred to the symbolic flavor not the connectionist one. Nowadays (god-)fathers of AI are Geoff Hinton and Yann LeCun and others, but 20 years ago things were very different…

morelisp 3y ago
20 years ago symbolic reasoning was already called GOFAI and the hotness was Bayesian statistical reasoning. 40 years ago, maybe.
- agnosticmantis 3y ago
  I was a kid back then, so not really aware of the cutting edge research happening in those days, but here’s for example the description of Russel & Norvig’s “Artificial Intelligence: A Modern Approach (2nd edition, published 2003)”: “The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.”
  - morelisp 3y ago
    By necessity, textbooks lag the actual work in most fields. Nonetheless the titular “modern approach” is the statistical reasoning (plus a heavy focus on search over inference).
fsckboy 3y ago
> This AI referred to the symbolic flavor not the connectionist one that's not a fair characterization at all. The title contains the term "Artificial Intelligience", and soon after item 3 in the proposal was "Neuron Nets". The word symbolic is not used at all, although the term Abstractions is used. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html http://www-formal.stanford.edu/jmc/history/dartmouth/dartmou... A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE J. McCarthy, Dartmouth College M. L. Minsky, Harvard University N. Rochester, I.B.M. Corporation C.E. Shannon, Bell Telephone Laboratories August 31, 1955 We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. The following are some aspects of the artificial intelligence problem: 1 Automatic Computers If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have. 2. How Can a Computer be Programmed to Use a Language It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. 3. Neuron Nets How can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work. 4. Theory of the Size of a Calculation If we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy. 5. Self-lmprovement Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well. 6. Abstractions A number of types of ``abstraction'' can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile. 7. Randomness and Creativity A fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking. In addition to the above collectively formulated problems for study, we have asked the individuals taking part to describe what they will work on. Statements by the four originators of the project are attached. ... the paper continues ...
0xpgm 3y ago
> Nowadays (god-)fathers of AI are Geoff Hinton and Yann LeCun Schmidhuber part of the story should also considered more, I think
- hedgehog0 3y ago
  May I ask for more details? I don’t know much about the ML DL scenes, is he related to LSTM and European side of the story?
  - freilanzer 3y ago
    He and Sepp Hochreiter invented the LSTM model.
hedgehog0 3y ago
As a Lisper. I’ll always refer to McCarthy and Minsk’s as the fathers of AI.

morelisp 3y ago

20 years ago symbolic reasoning was already called GOFAI and the hotness was Bayesian statistical reasoning. 40 years ago, maybe.

agnosticmantis 3y ago
I was a kid back then, so not really aware of the cutting edge research happening in those days, but here’s for example the description of Russel & Norvig’s “Artificial Intelligence: A Modern Approach (2nd edition, published 2003)”: “The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.”
- morelisp 3y ago
  By necessity, textbooks lag the actual work in most fields. Nonetheless the titular “modern approach” is the statistical reasoning (plus a heavy focus on search over inference).

fsckboy 3y ago

> This AI referred to the symbolic flavor not the connectionist one that's not a fair characterization at all. The title contains the term "Artificial Intelligience", and soon after item 3 in the proposal was "Neuron Nets". The word symbolic is not used at all, although the term Abstractions is used. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html http://www-formal.stanford.edu/jmc/history/dartmouth/dartmou... A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECT ON ARTIFICIAL INTELLIGENCE J. McCarthy, Dartmouth College M. L. Minsky, Harvard University N. Rochester, I.B.M. Corporation C.E. Shannon, Bell Telephone Laboratories August 31, 1955 We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer. The following are some aspects of the artificial intelligence problem: 1 Automatic Computers If a machine can do a job, then an automatic calculator can be programmed to simulate the machine. The speeds and memory capacities of present computers may be insufficient to simulate many of the higher functions of the human brain, but the major obstacle is not lack of machine capacity, but our inability to write programs taking full advantage of what we have. 2. How Can a Computer be Programmed to Use a Language It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out. 3. Neuron Nets How can a set of (hypothetical) neurons be arranged so as to form concepts. Considerable theoretical and experimental work has been done on this problem by Uttley, Rashevsky and his group, Farley and Clark, Pitts and McCulloch, Minsky, Rochester and Holland, and others. Partial results have been obtained but the problem needs more theoretical work. 4. Theory of the Size of a Calculation If we are given a well-defined problem (one for which it is possible to test mechanically whether or not a proposed answer is a valid answer) one way of solving it is to try all possible answers in order. This method is inefficient, and to exclude it one must have some criterion for efficiency of calculation. Some consideration will show that to get a measure of the efficiency of a calculation it is necessary to have on hand a method of measuring the complexity of calculating devices which in turn can be done if one has a theory of the complexity of functions. Some partial results on this problem have been obtained by Shannon, and also by McCarthy. 5. Self-lmprovement Probably a truly intelligent machine will carry out activities which may best be described as self-improvement. Some schemes for doing this have been proposed and are worth further study. It seems likely that this question can be studied abstractly as well. 6. Abstractions A number of types of ``abstraction'' can be distinctly defined and several others less distinctly. A direct attempt to classify these and to describe machine methods of forming abstractions from sensory and other data would seem worthwhile. 7. Randomness and Creativity A fairly attractive and yet clearly incomplete conjecture is that the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient. In other words, the educated guess or the hunch include controlled randomness in otherwise orderly thinking. In addition to the above collectively formulated problems for study, we have asked the individuals taking part to describe what they will work on. Statements by the four originators of the project are attached. ... the paper continues ...

0xpgm 3y ago

> Nowadays (god-)fathers of AI are Geoff Hinton and Yann LeCun Schmidhuber part of the story should also considered more, I think

hedgehog0 3y ago
May I ask for more details? I don’t know much about the ML DL scenes, is he related to LSTM and European side of the story?
- freilanzer 3y ago
  He and Sepp Hochreiter invented the LSTM model.

hedgehog0 3y ago

As a Lisper. I’ll always refer to McCarthy and Minsk’s as the fathers of AI.