\documentclass[12pt]{article}
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\textheight 260mm
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\textwidth 180mm
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\pagestyle{myheadings}
\markright{(ICNNAI-2010)}
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\begin{document}
\begin{huge}

\clearpage  % (title) =========================================
\color{blue}
\begin{center}
{\bf Is Artificial Neural Network Intelligent?}
\hspace*{,5in}\\
\color{black}
\bigskip
\hspace*{1in}\\
\begin{Large}
Akira Imada  \\
\bigskip
Brest State Technical University (Belarus)
\end{Large}
\end{center}


\clearpage  % =========================================
\color{blue}
\begin{center}
{\bf Is AI Intelligent?}
\color{black}
\bigskip
\hspace*{1in}\\
{\Large A history of arguments between pro- \& anti-AI parties.}
\end{center}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Harsh criticism to AI by Dreyfus}\\
\bigskip
\color{black}
{\Large
\bigskip
{\it Alchemy and Artificial Intelligence (1965)\\
$\Downarrow$\\
What Computers Can't Do? (1972)\\
$\Downarrow$\\
What Computers Still Can't Do. (1992)}}
\end{center}


%%for example
\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Alchemy and Artificial Intelligence (1965)}\\
\bigskip
\color{black}
{\Large
\bigskip
{\it 
The field of AI exhibits a recurrent pattern: \\
\color{red}early dramatic success \color{black} 
followed by \color{red} sudden unexpected difficulties.\\
\bigskip
\bigskip
\color{black}
%%``It's like a tree climbing with one's eyes on the moon.'}}
``The first man \color{red} to climb a tree \color{black} could claim tangible progress toward \color{red} reaching the moon\color{black}.''\\
%%(As if it were the first step to the moon to climb a tree.)
}}
\end{center}



%% \clearpage  % =========================================
%% \begin{center} 
%% \color{blue}
%% {\bf What Computers Still Can't Do. (1992)}
%% \end{center}
%% \begin{figure}[h]
%% \begin{center}
%% \includegraphics[width=5cm,height=7cm]{drey.jpg}
%% \end{center}
%% \end{figure}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Rebuff by Papert}\\
\bigskip
\color{black}
{\Large
\bigskip
{\it \color{black}``The Artificial Intelligence of Hubert L. Dreyfus: \\ 
\color{red} A Budget of Fallacies\color{black}.''(1965).}}
\end{center}
%% Naturally AI founders strongly rebuffed

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Rebuffs by the other big names in AI}\\
\bigskip
\color{black}
{\Large
Edward Feigenbaum: \\
\color{red}
{\it ``What does he offer us? Phenomenology! That ball of fluff. \\That cotton candy!''}\\
\color{black}
\bigskip
\bigskip

Marvin Minsky:\\ 
\color{red}{\it ``They misunderstand, and should be ignored.''}\\
\color{black}
(Actually no response after the 3rd edition.)}
\end{center}




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf ``Machine who thinks'' \\by Pamela McCorduck (1992)}\\
\bigskip
\color{black}
{\Large
Papert:\\
\color{red}
{\it ``For Dreyfus, all social sciences are as wrong-headed as AI. \\
This is not an attitude widely held in universities.''} \\
\color{black}
\bigskip
\bigskip
McCorduck:\\
\color{red}
{\it ``If Dreyfus is so wrong-headed, why haven't AI people made more effort to contradict him?''}}
\end{center}

%% \clearpage  % =========================================
%% \begin{center} 
%% \color{blue}
%% {\bf Machines Who Think (1992)}
%% \end{center}
%% \begin{figure}[h]
%% \begin{center}
%% \includegraphics[width=5cm,height=7cm]{pamela.jpg}
%% \end{center}
%% \end{figure}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Rodney Brooks (1991) }\\
\bigskip
\color{black}
{\Large
{\it ``Artificial intelligence started as a field whose goal was to \\
\color{red}
replicate human level intelligence in a machine. \\
\color{black}
$\Downarrow$\\
\color{red}
Early hopes diminished 
\color{black}
as the magnitude and difficulty of that goal was appreciated. \\
$\Downarrow$\\
\color{red}
No one talks about replicating the full gamut of human intelligence any more.''}}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Can computer play chess?}\\
\bigskip
\color{black}
{\Large
Dreyfus (1960):\\
{\it ``In fact, in its few recorded games, the Newell, Shaw, Simon program \\
played poor but legal chess, and in its last official bout\\
\color{red}
was beaten in 35 moves by a ten-yea-old novice.
\color{black}''}}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf 'The New Yorker' (11 January 1966)} \\
\bigskip
\color{black}
{\Large
The Talk of the Town:\\
\color{red}
{\it Computer was beaten in chess by a ten-yea-old novice.}\\
\color{black}
{\it ... We don't care what the machine is going to do.}}\\
\end{center}

%% 
\clearpage  % =========================================
\begin{figure}[h]
\begin{center}
\includegraphics[width=8cm,height=12cm]{newyorkers-1.eps}
\includegraphics[width=8cm,height=12cm]{newyorkers-2.eps}
%%\includegraphics[width=8.5cm,height=12cm]{new-yorkers-1.jpg}
%%\includegraphics[width=8.5cm,height=12cm]{new-yorkers-2.jpg}
\end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Yet other topic on 'machine translation'}\\
\bigskip
\color{black}
\bigskip
{\Large
\color{red}
'Time flies like an arrow,' \color{black} in Russian\\
$\Downarrow$\\
\color{red} 'Time flies enjoy eating arrows,' \color{black}in English.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf A chess match: Dreyfus vs.~Computer} \\
\bigskip
\color{black}
{\Large
{\it 
Papert:\\
\color{red}
``I organized the famous chess match. That was beautiful.''}}
\end{center}
%%papert arranged




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf In a bulletin board in SIG AI}\\
\bigskip
\color{black}
{\Large
Papert wrote:\\
\bigskip
\color{red}
'A ten-year-old can beat the machine' -- Dreyfus:  \\
But the machine can beat Dreyfus.}

\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Can computer play chess?  [II]}\\
\bigskip
\color{black}
{\Large 
Garry Kasparov vs.~IBM's Deep Blue. \\
\bigskip
$\Downarrow$\\
(in 6 game match)\\  

\begin{table}[h]
\begin{center}
\begin{tabular}{l c c c c c c c}
\color{black}1996: & \color{blue}Deep Blue & \color{red}Kasparov  & \color{black}even & \color{black}even & \color{red}Kasparov & \color{red}Kasparov \\
\color{black}1997: & \color{red}Kasparov  & \color{blue}Deep-Blue & \color{black}even & \color{black}even & \color{black}even     & \color{blue}Deep Blue 
\end{tabular} 
\end{center}
\end{table}
}
\end{center}


%% Deep Blue won one game, (but) tied two and lost three.
%%Deep Blue defeated Kasparov also in a six-game match. 
%% Kasparov had won the 1st game, lost the 2nd, tied 3rd, 4th and 5th, then lost the 6th.

\clearpage  % =========================================
%% \begin{center} 
%% \color{blue}
%% {\bf Dreyfus' book}
%% \end{center}
\begin{figure}[h]
\begin{center}
%% \includegraphics[width=5cm,height=7cm]{ibm-deep-blue.jpg}
%% \includegraphics[width=5cm,height=7cm]{kasparov.jpg}
\includegraphics[width=5cm,height=7cm]{deep-blue.eps}
\includegraphics[width=5cm,height=7cm]{kasparov.eps}
\end{center}
\end{figure}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Is Deep Blue intelligent?}\\
\bigskip
\color{black}
{\Large
Deep Blue's strategy \\
$\Downarrow$\\
\color{red}A brute force to evaluate billions of future positions\color{black}.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  What should intelligence look like?}\\
\bigskip
\color{black}
{\Large
Dreyfus (1965)\\
{\it ``\color{red}A little intelligence is not intelligence at all but stupidity\color{black}. \\
Any program that does just one thing well is \\
at best more like an \color{red}idiot savant \color{black} than like an intelligent man.''}}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Should intelligence be perfect?}\\
\bigskip
\color{black}
{\Large
Brooks (1991):\\
{\it 
\color{black}
``It is unfair to claim that \color{red} an elephant has no intelligence worth studying 
just because it does not play chess\color{black}.''}}
\end{center}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Contradiction \& Intelligence?}\\
\bigskip
\color{black}
{\Large
Frosini (2009): \\
\bigskip
\color{black}
Contradiction can be seen as \color{red} a virtue rather than as a defect\color{black}.\\
\color{black}
$\Downarrow$\\
Constant presence of inconsistencies in our thoughts \\
$\Downarrow$\\
Is contradiction \color{red} accidental \color{black} or is it 
\color{red}
the necessary companion of intelligence?\color{black}}
\end{center}


\clearpage  % =========================================
\vspace*{.7in}
\begin{center} 
\color{blue}
{\bf  What about intelligence by NN?}
\end{center}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf McClelland~(2009)}\\
\color{black}
{\Large The author of 'Parallel Distributed Processing' (1986)\\
\bigskip
{\it "Is a machine realization of \color{red}truly human-like intelligence \color{black}achievable?"}}
\end{center}
%% \indent
%% {\Large {\it "Is a machine realization of \\
%% \hspace{2in}\color{red}truly human-like intelligence \\
%% \hfill \color{black}achievable?"\hspace{1in}}}



\clearpage  % =========================================
\vspace*{.02in}
\begin{center} 
\color{blue}
{\bf What is human-like intelligence?}\\
\color{black}
{\Large 
$\Downarrow$ \\
Human-intelligence is
\color{red}
spontaneous\color{black}, \color{red}flexible\color{black}, and/or \color{red} unpredictable\color{black},\\ 
\color{black}more or less,\\
or even \color{red} erroneous \color{black} sometimes.}


\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf ``I beg your pardon?''}\\
\hspace*{1in}\\
\color{black}
{\Large 
Intelligent people try a \color{red}different 
\color{black}explanation for an easier understanding\\
\bigskip
while \\
others just repeat the \color{red}same \color{black}expression, maybe louder.}
\end{center}


\clearpage  % =========================================

\begin{center} 
\color{blue}
{\bf Let's make an agent  be spontaneous!}\\
\bigskip
\color{black}
{\Large
\bigskip
Behaviors might \color{red}differ \color{black}even in a \color{red}similar \color{black}situations.}
\end{center}


%% \clearpage  % =========================================
%% \vspace*{.01in}
%% \begin{center} 
%% \color{blue}
%% {\bf Is AI really intelligent?}\\
%% \bigskip
%% \color{black}
%% {\Large
%% \bigskip
%% They might be more \color{red} efficient \color{black}and/or \color{red}effective \color{black} than human\\ but still \color{red}less intelligent\color{black}, at least so far.}
%% \end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Path-planning as a benchmark
}\\
\hspace*{1in}\\
\color{black}
{\Large 
An agent plans a shortest route from start to goal.\\
\bigskip
(should be as simple as possible for an essential observation)

}
\end{center}


\clearpage  % =========================================
\begin{center}\color{blue}
{\bf Examples of path from start to gaol}
\end{center}
\bigskip
\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=5cm,height=6cm]{trace-96-start.eps}
   \includegraphics[width=5cm,height=6cm]{trace-96-end-new.eps}
\end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Which route to be chosen?}\\
\bigskip
\color{black}
{\Large
The number of shortest paths from (0,0) to (m,n)\\
$\Downarrow$ \\
infinitely large.\\
$\Downarrow$ 
\color{red}
\begin{displaymath}
\sum_{i=0}^{m+n} {_{m}C_{i}} \times {_{n}C_{m+n-i}}.
\end{displaymath}
\color{black}
Can an agent take a different path from run to run?
}
\end{center}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf What about from (0,0) to (m,0)?}\\
\bigskip
\color{black}
{\Large
In this case \color{red} only one unique solution.\\
\color{black}
$\Downarrow$ \\
Can an agent still be flexible from run to run?
}
\end{center}


\clearpage  % =========================================
\vspace*{.01in}
\begin{center} 
\color{blue}
{\bf Goal is}\\
\bigskip \color{black} {\Large \bigskip
to make an agent behave \color{red} differently \color{black}even when it encounters \\ the \color{red}same \color{black}situation as before.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf NN with fixed weights}\\
\bigskip
{\Large 
\color{black}
$\Downarrow$\\
\color{red}
never be intelligent\\
\color{black}
\bigskip
but repeats exactly the same action in the same situation.}
\end{center}


\clearpage  % =========================================
\vspace*{.02in}
\begin{center} 
\color{blue}
{\bf Floreano's approach (2000)}\\
\bigskip
\color{black}
$\Downarrow$\\
\bigskip
{\Large 
With McCulloch \& Pitts neurons\\
$\Downarrow$\\
Modification of $w_{ij}$ \color{red}during actions\\
\color{black}with either one of the four 
\color{red} Hebbian and Hebbian-like rules \color{black}.}
\end{center}


\clearpage  % =========================================
\vspace*{.02in}
\begin{center} 
\color{blue}
{\bf Let's repeat his experiment}\\

\bigskip
{\Large
\color{black}
with \color{red}spiking neurons \color{black} expecting to be 
\color{red}more biologically plausible\color{black}.}
\end{center}


\clearpage  % =========================================
\vspace*{.02in}
\begin{center} 
\color{blue}
{\bf Spike Timing Dependent Plasticity (STDP)}\\
\bigskip
\color{black}
\bigskip
{\Large A counter part of Hebbian rule for \color{red}spiking neurons}
\end{center}


\clearpage  % =========================================
\begin{center} \color{blue}
{\bf What is STDP?}
\bigskip
\color{red}
{\Large
      \begin{eqnarray*}
         W(\Delta t) = \left\{
         \begin{array}{ll}\nonumber
         \hspace{.18in}A_{+} \exp (-\Delta t/\tau_{+})
                          & \hspace{.2in}\mbox{if}\hspace{.2in}\Delta t \ge 0\\
         - A_{-} \exp (-\Delta t/\tau_{-})
                          & \hspace{.2in}\mbox{if}\hspace{.2in}\Delta t < 0\\
         \end{array}
         \right.\\
      \end{eqnarray*}
\color{black} where \hspace{.2in} \color{red} $\Delta t = t_{post} - t_{pre}$\\
\bigskip
\color{black}{\large $W(\Delta t)$}}
\begin{figure}[bhtp]
\begin{center}
   \includegraphics[width=12cm,height=3.5cm]{stdp.eps}
\end{center}
\end{figure}

\end{center}


\clearpage  % =========================================
\begin{center} \color{blue}
{\bf In short}\\
\bigskip
\color{black}
{\Large
\color{red} potentiation
\color{black} occurs when a pre-synaptic neuron fires \hspace{2.5in}\\
\hspace*{2.5in}shortly before a post-synaptic neuron\\
\bigskip
and \\
\bigskip
\color{red}depression
\color{black} occurs when the post-synaptic neuron fires shortly after.}
\end{center}


\clearpage  % =========================================
\vspace*{.01in}
\begin{center} 
\color{blue}
{\bf Meunier et al. (2005)}\\
\bigskip
\color{black}
$\Downarrow$\\
\color{black}
{\Large \it ``Up to now, nobody has been able to show how it is possible to learn with STDP...''}
\end{center}


\clearpage  % =========================================
\vspace*{.01in}
\begin{center} 
\color{blue}
{\bf Farries et al. (2007)}\\
\bigskip
\color{black}
$\Downarrow$\\
\color{black}
{\Large \it ``Although synaptic plasticity is widely believed to be a major component of learning, it is \color{red} unclear \color{black} how STDP itself could serve as a mechanism for general purpose learning.''}
\end{center}


%% It is not so simple to apply this concept but ...
\clearpage  % =========================================
\begin{center} \color{blue}
{\bf Two implicit implementations of STDP} 
\end{center}
\color{black}
{\Large
\begin{itemize}
\setlength{\itemsep}{0pt}
\item Di Paolo (2002)
   \begin{itemize}
      \item \color{red}Recurrent \color{black}neural network with \color{red}evolved STDP
      \item \color{red} Conductance-based integrate-and-fire (I\&F) model.
   \end{itemize}
\item Florian (2005)
   \begin{itemize}
      \item \color{red}Feedforward \color{black}neural network 
            with \color{red} reward-modulated STDP
%%    \item \color{red} Stochastic leaky-integrate-and-fire neurons
      \item \color{red} Stochastic leaky I\&F neurons.
   \end{itemize}
\end{itemize}
}

\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Conductance-based I\&F model}\\
\end{center}
\bigskip
{\Large
\color{black}
\indent
\hspace*{.5in}Membrane voltage $v(t)$ is:
\color{red}
\begin{displaymath}
\tau_{m} \frac{dv(t)}{dt} = V_{rest}- v + g_{ex}(t)(E_{ex}-v) + g_{in}(t)(E_{in}-v).
\end{displaymath}}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Di Paolo's implicit STDP}\\
\bigskip
\color{black}
{\Large 
$\bullet$ Every time a spike arrives at the synapse $P^{+}(t)$ is incremented by $A^{+}$\\
\bigskip
$\bullet$  Every time the post-synaptic neuron fires $P^{-}(t)$ is decremented by $A^{-}$. \\
\bigskip
Otherwise, they decay exponentially: \hspace{.2in}$\tau d{P}(t)/dt = -P(t)$.
\\
\bigskip
Then\\
\bigskip
\color{red}
$w_{ij}(t+\delta t) = w_{ij}(t) - w_{max}P^{-}(t)$\\
$w_{ij}(t+\delta t) = w_{ij}(t) - w_{max}P^{+}(t)$\color{black}.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Stochastic Leaky Integrate \& Fire Model}\\
\end{center}

\color{black}
\bigskip
{\Large 
\indent
Membrane voltage of neuron $i$ is
\color{red}
\begin{displaymath}
u_i(t) = u_r +(u_i(t - \delta t) - u_r) \exp (- \delta t/ \tau) + \sum_j w_{ij}f_j (t - \delta t)
\end{displaymath}}


\clearpage  % =========================================
\begin{center} \color{blue}
{\bf Reward-modulated STDP Learning}\\
{\Large
\color{black}
(Florian 2007)
\bigskip
\color{red}
\begin{displaymath}
w_{ij} (t + \delta t) = w_{ij} (t) + \gamma r(t + \delta t) \zeta_{ij}(t)
\end{displaymath}
\color{black}
where
\begin{displaymath}
\zeta_{ij} (t) = P^{+}_{ij} (t)f_i(t) + P^{-}_{ij} (t)f_i(t)
\end{displaymath}
\begin{displaymath}
P^{+}_{ij} (t) = P^{+}_{ij} (t-\delta t) \exp (- \delta t/\tau_{+}) + A_{+}f_j(t)
\end{displaymath}
\begin{displaymath}
P^{-}_{ij} (t) = P^{-}_{ij} (t-\delta t) \exp (- \delta t/\tau_{-}) + A_{-}f_j(t)
\end{displaymath}
}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Are we happy?}\\
\bigskip
\color{black}
{\Large
Simple heuristics can do it using a dice}
\end{center}
\begin{figure}[h]
\begin{center}
\includegraphics[width=16cm, height=6cm]{spontaneous-along-x.eps}
\end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Papert (1965)}\\
\bigskip
\color{black}
{\Large {\it ``A very simple algorithm can sometimes obtain the same results as the holistic, intuitive human mind,''}}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Not sufficient to be intelligent}\\
\bigskip
\color{black}
{\Large
A \color{red}different-action-even-in-an-identical-situation\color{black} \\
$\Downarrow$\\
just a necessary condition at the best.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  What else we need to be intelligent?}\\
\bigskip
\bigskip
{\Large
\color{black}
A \color{red}spontaneous, flexible \color{black} or \color{red} unpredictable 
\color{black}behavior \\ should be done \color{red}consciously
}
\end{center}




\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Izhikevich (2006)}\\
\bigskip
\color{black}
{\Large
defined
\color{red}consciousness \color{black} as \color{red}{\it attention to memory}.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf 'Science of the conscious mind'} \\
{\Large
\color{black}
Ascoli et.~al (2008)\\
\bigskip
Cognitive maps index representations by their context, such as spatial location, and are employed by mammals for path-finding and navigation,
$\Downarrow$\\
path-finding navigation of rodent using spatial location by hippocampus.}
\end{center}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Navigation by hippocampus}\\ 
\color{black}
{\Large Muller's simulation (1996)}
\end{center}
{\large
\begin{itemize}
\setlength{\itemsep}{0pt}
\item Assume mapping from
   \begin{itemize}
      \item[] \color{red}points in 2-D field \color{black} where rat explores
      \item[] \hspace*{1.5in}to 
      \item[] \color{red} pyramidal cells in a recurrent network of CA3.
   \end{itemize}
\item Mapping is one-to-one but randomly assigned points in 2-D space.
\item \color{red}Distance relation in 2-D space is stored as weight configuration. \color{black}
\item The shortest path in neural space is also shortest in 2-D space?
\end{itemize}}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Is navigation by hippocampus intelligent?}\\
\end{center}
{\Large
\begin{itemize}
\item \color{red}Merriam's kangaroo rats \color{black}can learn the distribution of food patches around its nest in three evenings of foraging;
\item \color{red}Marmoset monkeys \color{black} reliably relocate food sites and do not revisit a place where food was already eaten on that foraging trip;
\item \color{red}Black-capped chickadees \color{black} hide insects and seeds in numerous, widely spread caches in trees over its home range.
\end{itemize}}


\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Elephant cannot play chess, but...}\\
\bigskip
\color{black}
{\Large
It might not sound like an intelligent behavior, but as already quoted Brooks (1990), 
\color{red}an elephant could be intelligent even if it cannot play chess\color{black}.}
\end{center}

\clearpage  % ========================================= negative
\begin{center}
\color{blue}
{\bf Belief, desire, intention, and emotion}\\
\bigskip
\color{black}
{\Large
Bratman (1967)\\
The \color{red}belief-desire-intention \color{black}(BDI) model to construct multi agent system
or\\
Pereira et al.~(2005)\\
a model of \color{red}emotional \color{black}BDI agents.\\
%%won't go int detail but
$\Downarrow$\\
These might help us design a machine closer to human intelligence.}
\end{center}


\clearpage  % ========================================= negative
\begin{center} 
\color{blue}
{\bf Why spontaneous?}\\
\begin{figure}[h]
\begin{center}
\rotatebox[origin=c]{270}{
\includegraphics[width=2.5cm,height=5cm]{aibo.eps}}
\end{center}
\end{figure}
\color{black}
{\Large We sometimes need \color{red} spontaneous \color{black} and \color{red} unpredictable \color{black} intelligence rather than \color{red} efficiency \color{black} or \color{red}effectiveness \color{black} like in case of SONY's AIBO.\\
\bigskip
It learns excellently and acts \color{red} differently in different situation \color{black}
but repeats \color{red} the same action in a same situation\color{black}.}
\end{center}


\clearpage  % ========================================= positive
\bigskip
\begin{center} 
\color{blue}
{\bf McClelland (2009) again}\\
\bigskip
\color{black}
{\Large
{\it ``Over the next decade, the butterfly will finally emerge from the chrysalis, and truly parallel computing will take flight.''}}
%% So let's be optimistic.
\end{center}


\clearpage  % ========================================= positive
\begin{center} 
\color{blue}
{\bf Mars Landrover Problem}\\
\begin{figure}[h]
\begin{center}
\includegraphics[width=7cm, height=4cm]{rover-300.eps}
\end{center}
\end{figure}
\color{black}
{\Large Can we design a robot such that it navigates \color{red}flexibly \color{black}enough to take a different route from run to run, using a \color{red} memory \color{black}with some \color{red}conscious intention\color{black}, hopefully with \color{red}belief \color{black} and some sort of \color{red}desire\color{black}?}
\end{center}


\clearpage  % ========================================= positive
\vspace*{1.2in}
\begin{center} 
\color{blue}
{\bf Thanks!}
\end{center}




\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also Chopin  from CD?
   \begin{itemize}
   \item [$\cdot$] \color{red} Different action even in a similar situation. \color{black}
   \end{itemize}
\end{itemize}
}

\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also Chopin  from CD?
   \begin{itemize}
   \item [$\cdot$] \color{red} Different action even in a similar situation. \color{black}
   \end{itemize}
\item Is elephant intelligent despite it cannot play chess?
   \begin{itemize}
   \item [$\cdot$] \color{red}Should intelligence be such perfect?
   \end{itemize}
\end{itemize}
}


\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also Chopin  from CD?
   \begin{itemize}
   \item [$\cdot$] \color{red} Different action even in a similar situation. \color{black}
   \end{itemize}
\item Is elephant intelligent despite it cannot play chess?
   \begin{itemize}
   \item [$\cdot$] \color{red}Should intelligence be such perfect?
   \end{itemize}
\item Expert of just one thing is not intelligent but an idiot savant?
\end{itemize}
}


\end{huge}
\end{document}


tec-rep version: cause -> evoke; heuristics (i) 00 to mn (ii) 00 to m0 (iii) max-loop; add meunir & farries 