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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 man \color{red} climbed a tree \color{black} claims a 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 few responded 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 human intelligence any more.''}}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf  Can computer play chess?}\\
\bigskip
\color{black}
{\Large
Dreyfus (1960):\\
\bigskip
%% {\it ``In fact, in its few recorded games, the Newell, Shaw, Simon program \\
%% played poor but legal chess, and its last official bout\\
{\it ``Program written by Newell, Shaw, and Simon played legal chess, \\
but \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 another 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.
%%
%% Kasparov had won the 1st game, lost the 2nd, tied 3rd, 4th and 5th, then lost the 6th.
%%
%% Deep Blue defeated Kasparov 

\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}. \\
\bigskip
\bigskip
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)}\\
\bigskip
\color{black}
{\Large The author of the book '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 
%%$\Downarrow$ \\
\bigskip
\bigskip
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  % =========================================
%% \vspace*{.01in}
\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  % =========================================
\bigskip
\begin{center} 
\color{blue}
{\bf To see if it's possible or not}\\
\hspace*{1in}\\
\color{black}
{\Large 
let's experiment\\
in a situation as simple as possible for an essential observation.}
\end{center}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Path-planning as a benchmark}\\
\color{black}
\bigskip
\bigskip
{\Large 
An agent plans a shortest route from start to goal\\
in a \color{red}grid-world\color{black}.}
\end{center}


\clearpage  % =========================================
\bigskip
\begin{center}\color{blue}
{\bf Examples of path from start to goal}
\end{center}
\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}
\hspace*{.85in} {\Large \color{red}No obstacle like walls, corridors, or dangerous pond.}



\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
\bigskip
\color{black}
{\Large
In this case \color{red} only one unique solution.\\
\color{black}
$\Downarrow$ \\
Can an agent still be \color{red}flexible \color{black}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 only 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}of agent\\
\bigskip
\color{black}with either one of the four 
\color{red} Hebbian \color{black}and \color{red}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 counterpart of the 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} $\Delta t = t_{post} - t_{pre}$}
\begin{figure}[bhtp]
\begin{center}
%%   \includegraphics[width=12cm,height=3.5cm]{stdp.eps}
     \includegraphics[width=8cm,height=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 firing\\
\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) neurons.
   \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}
\hspace*{.5in}\color{black}and evolves in continuous time $t$.
}



\clearpage  % =========================================
\begin{center} 
\color{blue}
{\bf Di Paolo's implicit STDP}\\
\bigskip
\color{black}
{\Large 
Using two functions $P^{+}(t)$ and $P^{-}(t)$} \\
\end{center}
{\Large 
$\bullet$ When no firing occurs, they decay exponentially: 
\hspace{.2in}$\tau \frac{d}{dt}$}{\small $P(t) =  -P(t)$}\\
%% \hspace{.2in}$\tau \frac{d}{dt} P(t) =  -P(t)$}\\
\hspace*{1in}\\
{\Large 
$\bullet$ Every time a spike arrives at the synapse $P^{+}(t)$ is incremented by $A^{+}$
\begin{center}
\color{red}$w_{ij}(t) \rightarrow w_{ij}(t) - w_{max}P^{+}(t)$\color{black}
\end{center}
\bigskip
$\bullet$  Every time the post-synaptic neuron fires $P^{-}(t)$ is decremented by $A^{-}$
\begin{center}
\color{red} $w_{ij}(t) \rightarrow w_{ij}(t) - w_{max}P^{-}(t)$\\
\bigskip
\color{black}(Note: Weight is not a fixed value but  a function of time.)
\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}
\indent
\color{black} where $\delta t$ is discrete time step.
}


\clearpage  % =========================================
\begin{center} \color{blue}
{\bf Reward-modulated STDP Learning}\\
{\Large
(Florian 2007)
\color{black}
\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}


%% allow me to skip the detail but

\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
\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 spontaneous, flexible or unpredictable 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}.\\
\bigskip
\bigskip
\color{black}
$\Downarrow$\\
Let's try to incorporate a \color{red}memory \color{black}of previous navigations}
\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 'Science of the conscious mind'} \\
{\Large
\color{black}
Ascoli et.~al (2008)\\
\bigskip
\bigskip
\color{red}Cognitive maps \color{black} in \color{red} hippocampus \color{black}are made up of contexts,\\
such as \color{red}spatial locations \color{black}mammals employ for their \color{red}path-finding \color{black}}

\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 hippocampus.
   \end{itemize}
\item Mapping is one-to-one but points in 2-D are randomly assigned to neurons.
\item \color{red}Distance relation in 2-D space is stored as a 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?}\\
{\bf Animal's navigation by hippocampus}\\
\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...}\\
{\bf Are they intelligent behaviors?}\\
\bigskip
\bigskip
\color{black}
{\Large
Brooks (1990) again: \\
\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}\\
{\bf Still it's far from intelligence?}\\
\bigskip
\color{black}
{\Large
Bratman (1967)\\
The \color{red}belief-desire-intention \color{black}(BDI) model to construct a 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 NN 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}}
\includegraphics[width=2cm,height=4cm]{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.''}\\
\bigskip
$\Downarrow$\\
Let's try to be optimistic.}
\end{center}


\clearpage  % ========================================= positive
\begin{center} 
\color{blue}
{\bf Mars Land-rover 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*{0.7in}
\begin{center} 
\color{blue}
{\bf Thanks!}
\end{center}




\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{blue} Assume you like Chopin then you also like Chopin from CD?
\end{itemize}
}


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

\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also like Chopin from CD?
   \begin{itemize}
   \item [$\cdot$] Different action even in a similar situation. \color{black}
   \end{itemize}
\item Is elephant intelligent or idiot?
   \begin{itemize}
   \item [$\cdot$] Should intelligence be such perfect?
   \end{itemize}
\item Expert of just one thing is not intelligent but an idiot savant?
\item \color{blue} Can NN with fixed weights never be intelligent?
\end{itemize}
}

\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also like Chopin from CD?
   \begin{itemize}
   \item [$\cdot$] Different action even in a similar situation. \color{black}
   \end{itemize}
\item Is elephant intelligent or idiot?
   \begin{itemize}
   \item [$\cdot$] Should intelligence be such perfect?
   \end{itemize}
\item Expert of just one thing is not intelligent but an idiot savant?
\item Can NN with fixed weights never be intelligent?
\item \color{blue} Is spiking neuron more biologically plausible?
\end{itemize}
}

\clearpage  % ========================================= positive
{\Large
\begin{itemize}
\item \color{black} Assume you like Chopin then you also like Chopin from CD?
   \begin{itemize}
   \item [$\cdot$] Different action even in a similar situation. \color{black}
   \end{itemize}
\item Is elephant intelligent or idiot?
   \begin{itemize}
   \item [$\cdot$] Should intelligence be such perfect?
   \end{itemize}
\item Expert of just one thing is not intelligent but an idiot savant?
\item Can NN with fixed weights never be intelligent?
\item Is spiking neuron more biologically plausible?
\item \color{blue}Is STDP still unclear about how to be applied for learning?
\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 
	