\makeatother
%	\author{Mitesh M. Khapra}
\title{Module 2}
\subtitle{From Spring to Winter of AI}
\author{}
\institute{}
\date{}
%\institute{Department of Computer Science and Engineering\\ Indian Institute of Technology Madras}
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	\myheading{Chapter 2: From Spring to Winter of AI}
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				\only<1>{\myheading{McCulloch Pitts Neuron} McCulloch (neuroscientist) and Pitts (logician) proposed a highly simplified model of the neuron (1943)\cite{pitts}}
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				\only<2>{\myheading{Perceptron} ``the perceptron may eventually be able to learn, make decisions, and translate languages'' -Frank Rosenblatt}
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				\only<3>{\myheading{Perceptron} ``the embryo of an electronic computer that the Navy expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.'' -New York Times}
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				\only<4>{\myheading{First generation Multilayer Perceptrons} Ivakhnenko et. al.\cite{mlp}}
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				\only<5>{\myheading{Perceptron Limitations} In their now famous book ``Perceptrons'', Minsky and Papert outlined the limits of what perceptrons could do \cite{perceptron}}
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				\only<6>{\myheading{AI Winter of connectionism} Almost lead to the abandonment of connectionist AI}
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				\only<7>{\myheading{Backpropagation} \begin{itemize} \item Discovered and rediscovered several times throughout 1960's and 1970's \item Werbos(1982) \cite{Werbos:81sensitivity} first used it in the context of artificial neural networks \item Eventually popularized by the work of Rumelhart et. al. in 1986\cite{Rumelhart:86}\end{itemize}}
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				\only<8>{\myheading{Gradient Descent} Cauchy discovered Gradient Descent motivated by the need to compute the orbit of heavenly bodies}
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				\only<9>{\myheading{Universal Approximation Theorem} A multilayered network of neurons with a single hidden layer can be used to approximate any continuous function to any desired precision\cite{hornik1989}}
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