Outline
authorThomas Pietrzak <thomas.pietrzak@gmail.com>
Fri, 12 Apr 2019 16:59:13 +0000 (18:59 +0200)
committerThomas Pietrzak <thomas.pietrzak@gmail.com>
Fri, 12 Apr 2019 16:59:13 +0000 (18:59 +0200)
18 files changed:
figures/delegationevolution.pdf [new file with mode: 0644]
figures/delegationevolution.svg [new file with mode: 0644]
figures/instrumentedinteraction.pdf [new file with mode: 0644]
figures/instrumentedinteraction.svg [new file with mode: 0644]
figures/logo_cristal.pdf [new file with mode: 0644]
figures/logo_inria.pdf [new file with mode: 0644]
figures/logo_univ_lille.pdf [new file with mode: 0644]
figures/partner.pdf [new file with mode: 0644]
figures/partner.svg [new file with mode: 0644]
hdrmain.tex
tex/conclusion.tex
tex/controlautomation.tex [new file with mode: 0644]
tex/directmanipulation.tex [new file with mode: 0644]
tex/gestural.tex [deleted file]
tex/haptics.tex [deleted file]
tex/introduction.tex
tex/perceptionaction.tex [new file with mode: 0644]
tex/physical.tex [deleted file]

diff --git a/figures/delegationevolution.pdf b/figures/delegationevolution.pdf
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diff --git a/figures/delegationevolution.svg b/figures/delegationevolution.svg
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+    <path
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+       sodipodi:nodetypes="cc"
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index 47d487e965010b88b8393585ae2c5c9102e2859c..64445c91fd8c4a14976e87ce665e53ffbef5df59 100644 (file)
 \input{tex/introduction}\r
   \cleardoublepage\r
 \r
-\input{tex/relatedwork}\r
-  \cleardoublepage\r
+%\input{tex/relatedwork}\r
+%  \cleardoublepage\r
 \r
-\input{tex/physical}\r
+\input{tex/perceptionaction}\r
   \cleardoublepage\r
 \r
-\input{tex/gestural}\r
+\input{tex/directmanipulation}\r
   \cleardoublepage\r
 \r
-\input{tex/haptics}\r
+\input{tex/controlautomation}\r
   \cleardoublepage\r
 \r
-\input{tex/something}\r
-  \cleardoublepage\r
+%\input{tex/something}\r
+%  \cleardoublepage\r
 \r
 \input{tex/conclusion}\r
 \r
index 390061ae5e8986b9b1c68fd685356f7f10ef7448..01a3b79a5b37a6aeceda2819abfe36ec812658a8 100644 (file)
@@ -8,6 +8,8 @@
 Something about this incompletude phenomenon: ideas on how to tackle the problem
 
 
+\section{Future work}
+
 \begin{idee}
 Interactive programs have limited number of features and capabilities.
 They are bounded by their inputs.
diff --git a/tex/controlautomation.tex b/tex/controlautomation.tex
new file mode 100644 (file)
index 0000000..7fec9ce
--- /dev/null
@@ -0,0 +1,17 @@
+%!TEX root = ../hdrmain.tex
+
+\chapter{Control with automation}
+  \epigraph{\lorem}{Auteur}
+
+\begin{Abstract}
+\loremipsum
+\end{Abstract}
+
+\begin{itemize}
+  \item Métamorphe \cite{bailly13}
+  \item Living Desktop \cite{bailly16}
+  \item Ctrl Mouse \cite{pietrzak14}
+  \item RayCursor \cite{baloup18,baloup19}
+\end{itemize}
+
+Ongoing work: facial animation
\ No newline at end of file
diff --git a/tex/directmanipulation.tex b/tex/directmanipulation.tex
new file mode 100644 (file)
index 0000000..bd48b6c
--- /dev/null
@@ -0,0 +1,15 @@
+%!TEX root = ../hdrmain.tex
+
+\chapter{Direct Manipulation}
+  \epigraph{\lorem}{Auteur}
+
+\begin{Abstract}
+\loremipsum
+\end{Abstract}
+
+\begin{itemize}
+  \item Fingercuts \cite{goguey14,goguey14a,goguey17}
+  \item FlexStylus \cite{fellion17}
+  \item Haptic Direct Manipulation \cite{pietrzak15,gupta16}
+  \item Summon \& Select \cite{gupta17}
+\end{itemize}
diff --git a/tex/gestural.tex b/tex/gestural.tex
deleted file mode 100644 (file)
index 1caa9b2..0000000
+++ /dev/null
@@ -1,17 +0,0 @@
-%!TEX root = ../hdrmain.tex
-
-\chapter{Gestural interaction}
-  \epigraph{\lorem}{Auteur}
-
-\begin{Abstract}
-\loremipsum
-\end{Abstract}
-
-\section{Finger identification}
-\cite{goguey14,goguey14a,goguey17}
-
-\section{Summon\&Select}
-\cite{gupta17}
-
-\section{RayCursor}
-\cite{baloup18,baloup19}
diff --git a/tex/haptics.tex b/tex/haptics.tex
deleted file mode 100644 (file)
index a89b562..0000000
+++ /dev/null
@@ -1,19 +0,0 @@
-%!TEX root = ../hdrmain.tex
-
-\chapter{Haptics}
-  \epigraph{\lorem}{Auteur}
-
-\begin{Abstract}
-\loremipsum
-\end{Abstract}
-
-\section{Activibe}
-\cite{cauchard16}
-
-Previous: \cite{pietrzak05,pietrzak06,pietrzak09}
-
-\section{Direct Manipulation}
-\cite{gupta16}
-
-\section{Happiness}
-\cite{frisson17}
index 0917b8b9b5ec01c983a98d34c47b87d6ff9be4bd..66069e3a04fefc56dcb4dec78d16b6df89085c9f 100644 (file)
@@ -3,6 +3,7 @@
 \chapter*{Introduction}
 \label{chap:intro}
 \addcontentsline{toc}{chapter}{\nameref{chap:intro}}
+\addcontentsline{lof}{chapter}{\nameref{chap:intro}}
 
 When I was a teenager, I was fascinated by computers, much more than I am today. I started to program early, and at this time I thought it was possible to create a program for everything. I believed the only limit of computer capabilities was the programmer's ability to code it. My limit was… pointers at this time. I wanted to create a program that was able to have a relevant conversation with me. I learned later that many people were working on this for decades. I tried the ELIZA system~\cite{weizenbaum66}, and I was not satisfied with it. It was just repeating what I was saying, and used a small set of expressions to ask me to make the conversation. Most of the time it spotted the right word in my sentence, but sometimes not and what it was saying just did not make any sense. It clearly failed the famous Turing test~\cite{turing50}, I knew about many years later. I think I wanted the program to understand what I write, without having hard coded key sentences or all grammar rules. Nobody speaks like a grammar book. Naively, I wanted to write a better program. However I did not want to code associations between concepts, words, metaphors and so on. I knew this was a huge work, that would take me several lives to achieve, and that when I will be finished, half of it will be obsolete. In fact I had no idea how to start.
 
@@ -15,7 +16,7 @@ Later, when I was a computer science student, I followed \defword{Artificial Int
 The first time a chess-playing computer won a game against a world champion was Deep Blue against Garry Kasparov in 1996\footnote{\url{https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)}}.
 However Kasparov won the match 4-2.
 The computer won the next year $3\frac{1}{2}$ - $2\frac{1}{2}$.
-It is however only recently that computers clearly beat world-class chess or go players, with AlphaZero~\cite{silver18}, thanks to the recent advances in Deep learning [], which resurrected neural networks techniques.
+It is however only recently that computers clearly beat world-class chess or go players, with AlphaZero~\cite{silver18}, thanks to the recent advances in Deep learning~\cite{lecun15}, which resurrected neural networks techniques.
 
 \myquote{Machines will be capable, within twenty years, of doing any work a man can do.}{Herbert Simon, 1965}
 
@@ -24,15 +25,14 @@ This is still far from being true today. Machines still have to be programmed fo
 \myquote{Within a generation, the problem of creating ‘artificial intelligence’ will substantially be solved.}{Marvin Minsky, 1967}
 
 The definition of “intelligence” or “artificial intelligence” is far from being consensual.
-I would argue that computers are still far from being intelligent as we would measure intelligence of a human being.
-Dessalles argues that despite their capabilities, computers do not even know they are intelligent~\cite{dessalles19}.
+Despite the tremendous progress of Machine learning in the past years, I would argue that computers are still far from being intelligent as we would measure intelligence of a human being.
+Dessalles says that despite their capabilities, computers do not even know they are intelligent~\cite{dessalles19}.
+Neural networks are still tied to a particular task, and are unable to evolve to different problems.
+They still require humans to design them and train them.
 
-\myquote{In from 3 to 8 years, we will have a machine with the general intelligence of an average human being.}{Marvin Minsky, 1970}
+%\myquote{In from 3 to 8 years, we will have a machine with the general intelligence of an average human being.}{Marvin Minsky, 1970}
 
-Not only it did not happen in the worst case scenario predicted by Minsky. But it is still not the case today.\\
-
-At some point I realized that this idea of a computer able to have a conversation is futile in the sense that this is something humans do very well. The point of playing chess or any other game is to have fun, or challenging other individuals. There is no point playing chess against a computer (or anything else) you cannot beat.
-+ about machine learning
+%Not only it did not happen in the worst case scenario predicted by Minsky. But it is still not the case today.\\
 
 %http://biology.kenyon.edu/slonc/bio3/AI/TIMELINE/timeline.html
 %1943: McCulloch and Pitt propose neural-network architectures for intelligence.
@@ -48,7 +48,7 @@ I learned $\lambda$-calculus~\cite{church32} and Turing machines~\cite{turing38}
 These concepts define the notions of computation, calculability and programming language.
 In the late 30', Turing defined a \defword{computable function} as follows:
 
-\begin{definition}
+\begin{definition}{Computable function}
 We shall use the expression \defword{computable function} to mean a function calculable by a machine, and let “effectively calculable” refer to the intuitive idea without particular identification with any one of these definitions.
 \end{definition}
 
@@ -126,6 +126,8 @@ Instead of finite set of inputs, interaction machines are connected to single (S
 Their expressiveness in not defined by induction, but respectively by non-well founded sets and co-induction.
 Induction (hence algorithms, TMs, $\lambda$-c, \dots) outputs are recursively enumerable while non-well founded sets and co-induction outputs are non-enumerable~\cite{gordon94}.
 This proves that interaction has a greater expressiveness than algorithms, allowing Wegner to extend the Church-Turing thesis for interaction machines.
+The trade-off is that interaction machines are not made for finding optimal solutions to well specified problems, but satisfying solutions to evolving problems.
+This is precisely the difference between rationalism and empiricism.
 
 %Now, Interaction as described by Wegner includes every paradigm that can handle infinite streams, including Object Oriented programming or Neural Networks for example.
 % is more powerful than algorithms\cite{wegner95,wegner97}.
@@ -142,59 +144,171 @@ At the opposite, the interaction machine plays the whole game from start to fini
 By using this mechanism, interaction machines sort of include external behavior.
 It is obvious that Wegner's echo interaction machine passes the Turing Test, twice at the same time.
 Therefore, more than just observing the environment with sensors or probes, it can absorb any external computing or thinking power.
-It is only limited by what it observes, and the encoding of this observation.
+It is only limited by what it observes, and how this observation is encoded.
 
 %Wegner presents interaction as a way to extend algorithms with external computation of some sort, with his interactive machines.
 
 \subsection{Beaudouin-Lafon}
 
+
+Beaudouin-Lafon defined the Instrumented interaction paradigm~\cite{mbl00}, which extends Schneiderman's direct manipulation©\cite{schneiderman83}. Instruments are tools that enables users to interact with domain objects. To do so, the user performs actions on the instrument, which provide the user an immediate feedback, and perform a command on the domain object. As a consequence, the domain object returns a response to the user. The immediate feedback provides high level of control thanks to a fast perception/action cycle. The response to the command lets the user know the result of his actions, and allows him to continue or correct errors where necessary.
+
+Beyond instrumented interaction, Beaudouin-Lafon describes three interaction paradigms: \emph{computer-as-a-tool}, \emph{computer-as-a-partner} and \emph{computer-as-a-medium}~\cite{mbl04}.
+
+
+Reification polymorphism reuse \cite{mbl00a}
+
 \section{My stuff}
 
+The idea of computers replacing humans for every task is futile.
+The result is not always the only important thing, sometimes it is not even important at all.
+The process, whether it is methodological, artistic, technical or athletic is often the reason why we perform these tasks.
+We not only run for going somewhere, most of the time it is for keeping a healthy physical condition.
+We do not play music or games in the quest of an eventual optimal performance, but for entertainment.
+
+Chess and Go playing computers are often cited as examples of the superiority of machines over humans.
+In my opinion, Chess or Go are not convincing and fair example for this, because these games were designed to be challenging for humans, not for machines.
+Rather, the point is about showing what is achievable thanks to computers.
+Following Engelbart's vision, my objective is to use technology as a way to augment humans' intellect and capabilities.
+My rationale is to study honestly the strengths and weaknesses of both humans and computers, and use the best of both to resolve problems.
+
+It is up to us (researchers, designers, developers, \dots) to decide what we delegate to machines that follow our instructions, and what we keep doing by ourselves with the help of interactive tools.
+It is first of all a matter of purpose.
+There is no point playing a game against a computer you cannot beat. This is not entertaining, and it is unlikely you can reuse its strategy for learning how to play better. However, Garry Kasparov proposed the concept of \defword{advanced chess}\footnote{\url{https://en.wikipedia.org/wiki/Advanced_Chess}}, a variation of chess in which players can use computers to get an overview of possible moves and their implication. Kasparov's idea was to increase chess level, eliminate bad moves, and give the public more understanding on players and computers strategies. He claims that such an advanced chess player is better than humans or computers alone.
+
+%\myquote{An advanced chess player is stronger than a human or computer alone}{G. Kasparov}
+
+Delegating tasks to computers is not a matter of computing power and methods, but also a matter of trust in the process and the outcome.
+Whatever the automatic system capabilities, we need to know its status in real time, to be able to influence its behavior, and to take the control if necessary.
+
+
+Continuum between Beaudouin-Lafon's tool and partner.
+
+instrumented interaction for partners? 
+
+\begin{figure}[htb]
+\centering
+\begin{subfigure}[b]{.45\textwidth}
+\includegraphics[width=\textwidth]{instrumentedinteraction}
+\caption{Instrumented interaction}
+\label{fig:instrumentedinteraction}
+\end{subfigure}
+\hfill
+\begin{subfigure}[b]{.45\textwidth}
+\includegraphics[width=\textwidth]{partner}
+\caption{Delegated interaction}
+\label{fig:delegatedinteraction}
+\end{subfigure}
+\label{fig:directmanipulationextensions}
+\caption{Extensions to direct manipulations, for tools and partners.}
+\end{figure}
+
+Evolution in time on this scale (ex. with machine learning).
+
+\begin{definition}{Fluidity}
+\defword{Fluidity} of interactive systems. 
+\end{definition}
 
-\begin{idee}
+Rigid systems perform long calculations with rare notifications, therefore rare occasions for the user to see the result, and change the partner's behavior. Fluid systems are direct manipulation/tools: calculations are fast (at least real time), the user has a continuous view on the system's state, and can interact at any time.
 
-Algorithms are not designed for knowing how their inputs were built.
-Humans take care of this information.
-When reading a sentence, one may interpret it differently depending on who wrote it, where, and who was supposed to read it.
-The context is a complex combination of background knowledge, alternative inputs, …
-Interpretation of input must evolve as the context changes.
-Algorithms must be modified.
-Humans just deal with it.
+\begin{figure}[htb]
+\centering
+\includegraphics[width=.5\textwidth]{delegationevolution}
+\label{fig:delegation}
+\caption{The evolution of delegation over time.}
+\end{figure}
 
-Algorithms are made to compute an exact (sometimes approached) solution to a well specified problem. Humans (hopefully) find a reasonable solution for non-formalized and evolving problems.
+Discussion on initiative?
 
 co-adaptation \cite{mackay90}
 
+\subsection{Technology incompleteness}
+
+Every time a new technology is out, people predict the end of older technology.
+Voice recognition was said to replace keyboards.
+Gesture recognition was supposed to replace the mouse.
+Some even claimed that tablets will replace desktop computers.
+In reality, even if these new technologies proved to be useful in some contexts, they were unable to replace them in all contexts.
+
+\myquote{Everything the best for something and the worst for something else}{W.A.S. Buxton}
+
+
+\subsection{Designer as part of problem solving}
+
+
 designeering\cite{huot13}
-\end{idee}
-
-Humans started building computers as an exercise of thought.
-Computers quickly could perform operations organized in \defword{algorithms}, such as calculus on large numbers, that humans could never achieve with their own skills alone.
-Nowadays, computers use advanced methods such as \defword{machine learning}, and clearly have capabilities beyond human competences.
-Computers can beat the best chess or go players in the world, with highly trained neural networks~\cite{silver18}.
-This example is often cited as a success of \defword{Artificial Intelligence} (AI).
-This is undoubtedly due to the fact they are considered as difficult games, and requiring high mental skills.
-In reality these games were designed to be challenging for humans, not to be challenging for machines.
-Their rules are fully specified, and the exploration of possibilities is easy for a computer.
-%Things get more complicated with variants such as Real-Time chess\footnote{http://rtchess.deapthoughts.com/}, in which players may move any piece at any time.
-%To balance the game, there is a delay between moving a piece and the ability to move this piece again.
 
+ever running systems ~\cite{black10}
 
+adjacent possibles~\cite{kauffman95}
 
-(examples). Go, Chess? Turn based games with fully specified rules are easy for computers. What about real time games with random events and multi-parameter environment changing in real time (SimCity example)? Tetris \url{https://arxiv.org/pdf/cs/0210020.pdf}
-However human still have expertise in plenty of areas long way off computers grasp, to begin with the design, implementation, maintenance and evolution of software.
-(other examples, creative activities).
+%At some point I realized that this idea of a computer able to have a conversation is futile in the sense that this is something humans do very well.
+%The point of playing chess or any other game is to have fun, or challenging other individuals.
 
-The field of \defword{Human-Computer Interaction} (HCI) was built on Engelbart's vision of computers that extend human capabilities rather than replacing them~\cite{engelbart68}.
 
-Advanced chess: human + computer better than computer or human alone according to G. Kasparov.
 
-\myquote{An advanced chess player is stronger than a human or computer alone}{G. Kasparov}
+% Algorithms are not designed for knowing how their inputs were built.
+% Humans take care of this information.
+% When reading a sentence, one may interpret it differently depending on who wrote it, where, and who was supposed to read it.
+% The context is a complex combination of background knowledge, alternative inputs, …
+% Interpretation of input must evolve as the context changes.
+% Algorithms must be modified.
+% Humans just deal with it.
+
+% Algorithms are made to compute an exact (sometimes approached) solution to a well specified problem. Humans (hopefully) find a reasonable solution for non-formalized and evolving problems.
+
+% Humans started building computers as an exercise of thought.
+% Computers quickly could perform operations organized in \defword{algorithms}, such as calculus on large numbers, that humans could never achieve with their own skills alone.
+% Nowadays, computers use advanced methods such as \defword{machine learning}, and clearly have capabilities beyond human competences.
+% Computers can beat the best chess or go players in the world, with highly trained neural networks~\cite{silver18}.
+% This example is often cited as a success of \defword{Artificial Intelligence} (AI).
+% This is undoubtedly due to the fact they are considered as difficult games, and requiring high mental skills.
+% In reality these games were designed to be challenging for humans, not to be challenging for machines.
+% Their rules are fully specified, and the exploration of possibilities is easy for a computer.
+%Things get more complicated with variants such as Real-Time chess\footnote{http://rtchess.deapthoughts.com/}, in which players may move any piece at any time.
+%To balance the game, there is a delay between moving a piece and the ability to move this piece again.
 
-As we may think\cite{bush45}
 
-\begin{definition}
+%\begin{idee}
+
+%(examples). Go, Chess? Turn based games with fully specified rules are easy for computers. What about real time games with random events and multi-parameter environment changing in real time (SimCity example)? Tetris \url{https://arxiv.org/pdf/cs/0210020.pdf}
+%However human still have expertise in plenty of areas long way off computers grasp, to begin with the design, implementation, maintenance and evolution of software.
+%(other examples, creative activities).
+
+% The field of \defword{Human-Computer Interaction} (HCI) was built on Engelbart's vision of computers that extend human capabilities rather than replacing them~\cite{engelbart68}.
+
+%\end{idee}
+
+\begin{definition}{Interactive system}
 An \defword{interactive system} is…
 \end{definition}
 
-\cite{mbl00,mbl04,mbl00a}
+
+\section{Outline}
+
+Elements of perception/action
+
+\begin{itemize}
+  \item Lagmeter \cite{casiez17}
+  \item Friction textures \cite{potier12,potier16}
+  \item Activibe \cite{cauchard16}
+\end{itemize}
+
+Direct manipulation
+
+\begin{itemize}
+  \item Fingercuts \cite{goguey14,goguey14a,goguey17}
+  \item FlexStylus \cite{fellion17}
+  \item Haptic Direct Manipulation \cite{pietrzak15,gupta16}
+  \item Summon \& Select \cite{gupta17}
+\end{itemize}
+
+Control with automation
+
+\begin{itemize}
+  \item Métamorphe \cite{bailly13}
+  \item Living Desktop \cite{bailly16}
+  \item Ctrl Mouse \cite{pietrzak14}
+  \item RayCursor \cite{baloup18,baloup19}
+\end{itemize}
+
diff --git a/tex/perceptionaction.tex b/tex/perceptionaction.tex
new file mode 100644 (file)
index 0000000..c572ba7
--- /dev/null
@@ -0,0 +1,18 @@
+%!TEX root = ../hdrmain.tex
+
+\chapter{Elements of perception/action}
+  \epigraph{\lorem}{Auteur}
+
+\begin{Abstract}
+\loremipsum
+\end{Abstract}
+
+Previous work: \cite{pietrzak05,pietrzak06,pietrzak09}
+
+\begin{itemize}
+  \item Lagmeter \cite{casiez17}
+  \item Friction textures \cite{potier12,potier16}
+  \item Activibe \cite{cauchard16}
+\end{itemize}
+
+Ongoing work: haptic latency, haptics and embodiment
\ No newline at end of file
diff --git a/tex/physical.tex b/tex/physical.tex
deleted file mode 100644 (file)
index 4b2a8cf..0000000
+++ /dev/null
@@ -1,17 +0,0 @@
-%!TEX root = ../hdrmain.tex
-
-\chapter{Physical interaction}
-  \epigraph{\lorem}{Auteur}
-
-\begin{Abstract}
-\loremipsum
-\end{Abstract}
-
-\section{Métamorphe}
-\cite{bailly13}
-
-\section{LivingDesktop}
-\cite{bailly16}
-
-\section{Flexstylus}
-\cite{fellion17}