Mercurial > cortex
diff thesis/org/first-chapter.html @ 401:7ee735a836da
incorporate thesis.
| author | Robert McIntyre <rlm@mit.edu> |
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| date | Sun, 16 Mar 2014 23:31:16 -0400 |
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1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 1.2 +++ b/thesis/org/first-chapter.html Sun Mar 16 23:31:16 2014 -0400 1.3 @@ -0,0 +1,455 @@ 1.4 +<?xml version="1.0" encoding="utf-8"?> 1.5 +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" 1.6 + "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> 1.7 +<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"> 1.8 +<head> 1.9 +<title><code>CORTEX</code></title> 1.10 +<meta http-equiv="Content-Type" content="text/html;charset=utf-8"/> 1.11 +<meta name="title" content="<code>CORTEX</code>"/> 1.12 +<meta name="generator" content="Org-mode"/> 1.13 +<meta name="generated" content="2013-11-07 04:21:29 EST"/> 1.14 +<meta name="author" content="Robert McIntyre"/> 1.15 +<meta name="description" content="Using embodied AI to facilitate Artificial Imagination."/> 1.16 +<meta name="keywords" content="AI, clojure, embodiment"/> 1.17 +<style type="text/css"> 1.18 + <!--/*--><![CDATA[/*><!--*/ 1.19 + html { font-family: Times, serif; font-size: 12pt; } 1.20 + .title { text-align: center; } 1.21 + .todo { color: red; } 1.22 + .done { color: green; } 1.23 + .tag { background-color: #add8e6; font-weight:normal } 1.24 + .target { } 1.25 + .timestamp { color: #bebebe; } 1.26 + .timestamp-kwd { color: #5f9ea0; } 1.27 + .right {margin-left:auto; margin-right:0px; text-align:right;} 1.28 + .left {margin-left:0px; margin-right:auto; text-align:left;} 1.29 + .center {margin-left:auto; margin-right:auto; text-align:center;} 1.30 + p.verse { margin-left: 3% } 1.31 + pre { 1.32 + border: 1pt solid #AEBDCC; 1.33 + background-color: #F3F5F7; 1.34 + padding: 5pt; 1.35 + font-family: courier, monospace; 1.36 + font-size: 90%; 1.37 + overflow:auto; 1.38 + } 1.39 + table { border-collapse: collapse; } 1.40 + td, th { vertical-align: top; } 1.41 + th.right { text-align:center; } 1.42 + th.left { text-align:center; } 1.43 + th.center { text-align:center; } 1.44 + td.right { text-align:right; } 1.45 + td.left { text-align:left; } 1.46 + td.center { text-align:center; } 1.47 + dt { font-weight: bold; } 1.48 + div.figure { padding: 0.5em; } 1.49 + div.figure p { text-align: center; } 1.50 + div.inlinetask { 1.51 + padding:10px; 1.52 + border:2px solid gray; 1.53 + margin:10px; 1.54 + background: #ffffcc; 1.55 + } 1.56 + textarea { overflow-x: auto; } 1.57 + .linenr { font-size:smaller } 1.58 + .code-highlighted {background-color:#ffff00;} 1.59 + .org-info-js_info-navigation { border-style:none; } 1.60 + #org-info-js_console-label { font-size:10px; font-weight:bold; 1.61 + white-space:nowrap; } 1.62 + .org-info-js_search-highlight {background-color:#ffff00; color:#000000; 1.63 + font-weight:bold; } 1.64 + /*]]>*/--> 1.65 +</style> 1.66 +<script type="text/javascript">var _gaq = _gaq || [];_gaq.push(['_setAccount', 'UA-31261312-1']);_gaq.push(['_trackPageview']);(function() {var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;ga.src = ('https:' == document.location.protocol ? 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When you watch 1.129 + the video, you can imagine yourself skateboarding, and your 1.130 + knowledge of the human body and its dynamics guides your 1.131 + interpretation of the scene. For example, even if the skateboarder 1.132 + is partially occluded, you can infer the positions of his arms and 1.133 + body from your own knowledge of how your body would be positioned if 1.134 + you were skateboarding. If the skateboarder suffers an accident, you 1.135 + wince in sympathy, imagining the pain your own body would experience 1.136 + if it were in the same situation. This empathy with other people 1.137 + guides our understanding of whatever they are doing because it is a 1.138 + powerful constraint on what is probable and possible. In order to 1.139 + make use of this powerful empathy constraint, I need a system that 1.140 + can generate and make sense of sensory data from the many different 1.141 + senses that humans possess. The two key proprieties of such a system 1.142 + are <i>embodiment</i> and <i>imagination</i>. 1.143 +</p> 1.144 + 1.145 +</div> 1.146 + 1.147 +<div id="outline-container-1-1" class="outline-3"> 1.148 +<h3 id="sec-1-1">What is imagination?</h3> 1.149 +<div class="outline-text-3" id="text-1-1"> 1.150 + 1.151 + 1.152 +<p> 1.153 + One kind of imagination is <i>sympathetic</i> imagination: you imagine 1.154 + yourself in the position of something/someone you are 1.155 + observing. This type of imagination comes into play when you follow 1.156 + along visually when watching someone perform actions, or when you 1.157 + sympathetically grimace when someone hurts themselves. This type of 1.158 + imagination uses the constraints you have learned about your own 1.159 + body to highly constrain the possibilities in whatever you are 1.160 + seeing. It uses all your senses to including your senses of touch, 1.161 + proprioception, etc. Humans are flexible when it comes to "putting 1.162 + themselves in another's shoes," and can sympathetically understand 1.163 + not only other humans, but entities ranging animals to cartoon 1.164 + characters to <a href="http://www.youtube.com/watch?v=0jz4HcwTQmU">single dots</a> on a screen! 1.165 +</p> 1.166 +<p> 1.167 + Another kind of imagination is <i>predictive</i> imagination: you 1.168 + construct scenes in your mind that are not entirely related to 1.169 + whatever you are observing, but instead are predictions of the 1.170 + future or simply flights of fancy. You use this type of imagination 1.171 + to plan out multi-step actions, or play out dangerous situations in 1.172 + your mind so as to avoid messing them up in reality. 1.173 +</p> 1.174 +<p> 1.175 + Of course, sympathetic and predictive imagination blend into each 1.176 + other and are not completely separate concepts. One dimension along 1.177 + which you can distinguish types of imagination is dependence on raw 1.178 + sense data. Sympathetic imagination is highly constrained by your 1.179 + senses, while predictive imagination can be more or less dependent 1.180 + on your senses depending on how far ahead you imagine. Daydreaming 1.181 + is an extreme form of predictive imagination that wanders through 1.182 + different possibilities without concern for whether they are 1.183 + related to whatever is happening in reality. 1.184 +</p> 1.185 +<p> 1.186 + For this thesis, I will mostly focus on sympathetic imagination and 1.187 + the constraint it provides for understanding sensory data. 1.188 +</p> 1.189 +</div> 1.190 + 1.191 +</div> 1.192 + 1.193 +<div id="outline-container-1-2" class="outline-3"> 1.194 +<h3 id="sec-1-2">What problems can imagination solve?</h3> 1.195 +<div class="outline-text-3" id="text-1-2"> 1.196 + 1.197 + 1.198 +<p> 1.199 + Consider a video of a cat drinking some water. 1.200 +</p> 1.201 + 1.202 +<div class="figure"> 1.203 +<p><img src="../images/cat-drinking.jpg" alt="../images/cat-drinking.jpg" /></p> 1.204 +<p>A cat drinking some water. Identifying this action is beyond the state of the art for computers.</p> 1.205 +</div> 1.206 + 1.207 +<p> 1.208 + It is currently impossible for any computer program to reliably 1.209 + label such an video as "drinking". I think humans are able to label 1.210 + such video as "drinking" because they imagine <i>themselves</i> as the 1.211 + cat, and imagine putting their face up against a stream of water 1.212 + and sticking out their tongue. In that imagined world, they can 1.213 + feel the cool water hitting their tongue, and feel the water 1.214 + entering their body, and are able to recognize that <i>feeling</i> as 1.215 + drinking. So, the label of the action is not really in the pixels 1.216 + of the image, but is found clearly in a simulation inspired by 1.217 + those pixels. An imaginative system, having been trained on 1.218 + drinking and non-drinking examples and learning that the most 1.219 + important component of drinking is the feeling of water sliding 1.220 + down one's throat, would analyze a video of a cat drinking in the 1.221 + following manner: 1.222 +</p> 1.223 +<ul> 1.224 +<li>Create a physical model of the video by putting a "fuzzy" model 1.225 + of its own body in place of the cat. Also, create a simulation of 1.226 + the stream of water. 1.227 + 1.228 +</li> 1.229 +<li>Play out this simulated scene and generate imagined sensory 1.230 + experience. This will include relevant muscle contractions, a 1.231 + close up view of the stream from the cat's perspective, and most 1.232 + importantly, the imagined feeling of water entering the mouth. 1.233 + 1.234 +</li> 1.235 +<li>The action is now easily identified as drinking by the sense of 1.236 + taste alone. The other senses (such as the tongue moving in and 1.237 + out) help to give plausibility to the simulated action. Note that 1.238 + the sense of vision, while critical in creating the simulation, 1.239 + is not critical for identifying the action from the simulation. 1.240 +</li> 1.241 +</ul> 1.242 + 1.243 + 1.244 +<p> 1.245 + More generally, I expect imaginative systems to be particularly 1.246 + good at identifying embodied actions in videos. 1.247 +</p> 1.248 +</div> 1.249 +</div> 1.250 + 1.251 +</div> 1.252 + 1.253 +<div id="outline-container-2" class="outline-2"> 1.254 +<h2 id="sec-2">Cortex</h2> 1.255 +<div class="outline-text-2" id="text-2"> 1.256 + 1.257 + 1.258 +<p> 1.259 + The previous example involves liquids, the sense of taste, and 1.260 + imagining oneself as a cat. For this thesis I constrain myself to 1.261 + simpler, more easily digitizable senses and situations. 1.262 +</p> 1.263 +<p> 1.264 + My system, <code>Cortex</code> performs imagination in two different simplified 1.265 + worlds: <i>worm world</i> and <i>stick figure world</i>. In each of these 1.266 + worlds, entities capable of imagination recognize actions by 1.267 + simulating the experience from their own perspective, and then 1.268 + recognizing the action from a database of examples. 1.269 +</p> 1.270 +<p> 1.271 + In order to serve as a framework for experiments in imagination, 1.272 + <code>Cortex</code> requires simulated bodies, worlds, and senses like vision, 1.273 + hearing, touch, proprioception, etc. 1.274 +</p> 1.275 + 1.276 +</div> 1.277 + 1.278 +<div id="outline-container-2-1" class="outline-3"> 1.279 +<h3 id="sec-2-1">A Video Game Engine takes care of some of the groundwork</h3> 1.280 +<div class="outline-text-3" id="text-2-1"> 1.281 + 1.282 + 1.283 +<p> 1.284 + When it comes to simulation environments, the engines used to 1.285 + create the worlds in video games offer top-notch physics and 1.286 + graphics support. These engines also have limited support for 1.287 + creating cameras and rendering 3D sound, which can be repurposed 1.288 + for vision and hearing respectively. Physics collision detection 1.289 + can be expanded to create a sense of touch. 1.290 +</p> 1.291 +<p> 1.292 + jMonkeyEngine3 is one such engine for creating video games in 1.293 + Java. It uses OpenGL to render to the screen and uses screengraphs 1.294 + to avoid drawing things that do not appear on the screen. It has an 1.295 + active community and several games in the pipeline. The engine was 1.296 + not built to serve any particular game but is instead meant to be 1.297 + used for any 3D game. I chose jMonkeyEngine3 it because it had the 1.298 + most features out of all the open projects I looked at, and because 1.299 + I could then write my code in Clojure, an implementation of LISP 1.300 + that runs on the JVM. 1.301 +</p> 1.302 +</div> 1.303 + 1.304 +</div> 1.305 + 1.306 +<div id="outline-container-2-2" class="outline-3"> 1.307 +<h3 id="sec-2-2"><code>CORTEX</code> Extends jMonkeyEngine3 to implement rich senses</h3> 1.308 +<div class="outline-text-3" id="text-2-2"> 1.309 + 1.310 + 1.311 +<p> 1.312 + Using the game-making primitives provided by jMonkeyEngine3, I have 1.313 + constructed every major human sense except for smell and 1.314 + taste. <code>Cortex</code> also provides an interface for creating creatures 1.315 + in Blender, a 3D modeling environment, and then "rigging" the 1.316 + creatures with senses using 3D annotations in Blender. A creature 1.317 + can have any number of senses, and there can be any number of 1.318 + creatures in a simulation. 1.319 +</p> 1.320 +<p> 1.321 + The senses available in <code>Cortex</code> are: 1.322 +</p> 1.323 +<ul> 1.324 +<li><a href="../../cortex/html/vision.html">Vision</a> 1.325 +</li> 1.326 +<li><a href="../../cortex/html/hearing.html">Hearing</a> 1.327 +</li> 1.328 +<li><a href="../../cortex/html/touch.html">Touch</a> 1.329 +</li> 1.330 +<li><a href="../../cortex/html/proprioception.html">Proprioception</a> 1.331 +</li> 1.332 +<li><a href="../../cortex/html/movement.html">Muscle Tension</a> 1.333 +</li> 1.334 +</ul> 1.335 + 1.336 + 1.337 +</div> 1.338 +</div> 1.339 + 1.340 +</div> 1.341 + 1.342 +<div id="outline-container-3" class="outline-2"> 1.343 +<h2 id="sec-3">A roadmap for <code>Cortex</code> experiments</h2> 1.344 +<div class="outline-text-2" id="text-3"> 1.345 + 1.346 + 1.347 + 1.348 +</div> 1.349 + 1.350 +<div id="outline-container-3-1" class="outline-3"> 1.351 +<h3 id="sec-3-1">Worm World</h3> 1.352 +<div class="outline-text-3" id="text-3-1"> 1.353 + 1.354 + 1.355 +<p> 1.356 + Worms in <code>Cortex</code> are segmented creatures which vary in length and 1.357 + number of segments, and have the senses of vision, proprioception, 1.358 + touch, and muscle tension. 1.359 +</p> 1.360 + 1.361 +<div class="figure"> 1.362 +<p><img src="../images/finger-UV.png" width=755 alt="../images/finger-UV.png" /></p> 1.363 +<p>This is the tactile-sensor-profile for the upper segment of a worm. It defines regions of high touch sensitivity (where there are many white pixels) and regions of low sensitivity (where white pixels are sparse).</p> 1.364 +</div> 1.365 + 1.366 + 1.367 + 1.368 + 1.369 +<div class="figure"> 1.370 + <center> 1.371 + <video controls="controls" width="550"> 1.372 + <source src="../video/worm-touch.ogg" type="video/ogg" 1.373 + preload="none" /> 1.374 + </video> 1.375 + <br> <a href="http://youtu.be/RHx2wqzNVcU"> YouTube </a> 1.376 + </center> 1.377 + <p>The worm responds to touch.</p> 1.378 +</div> 1.379 + 1.380 +<div class="figure"> 1.381 + <center> 1.382 + <video controls="controls" width="550"> 1.383 + <source src="../video/test-proprioception.ogg" type="video/ogg" 1.384 + preload="none" /> 1.385 + </video> 1.386 + <br> <a href="http://youtu.be/JjdDmyM8b0w"> YouTube </a> 1.387 + </center> 1.388 + <p>Proprioception in a worm. The proprioceptive readout is 1.389 + in the upper left corner of the screen.</p> 1.390 +</div> 1.391 + 1.392 +<p> 1.393 + A worm is trained in various actions such as sinusoidal movement, 1.394 + curling, flailing, and spinning by directly playing motor 1.395 + contractions while the worm "feels" the experience. These actions 1.396 + are recorded both as vectors of muscle tension, touch, and 1.397 + proprioceptive data, but also in higher level forms such as 1.398 + frequencies of the various contractions and a symbolic name for the 1.399 + action. 1.400 +</p> 1.401 +<p> 1.402 + Then, the worm watches a video of another worm performing one of 1.403 + the actions, and must judge which action was performed. Normally 1.404 + this would be an extremely difficult problem, but the worm is able 1.405 + to greatly diminish the search space through sympathetic 1.406 + imagination. First, it creates an imagined copy of its body which 1.407 + it observes from a third person point of view. Then for each frame 1.408 + of the video, it maneuvers its simulated body to be in registration 1.409 + with the worm depicted in the video. The physical constraints 1.410 + imposed by the physics simulation greatly decrease the number of 1.411 + poses that have to be tried, making the search feasible. As the 1.412 + imaginary worm moves, it generates imaginary muscle tension and 1.413 + proprioceptive sensations. The worm determines the action not by 1.414 + vision, but by matching the imagined proprioceptive data with 1.415 + previous examples. 1.416 +</p> 1.417 +<p> 1.418 + By using non-visual sensory data such as touch, the worms can also 1.419 + answer body related questions such as "did your head touch your 1.420 + tail?" and "did worm A touch worm B?" 1.421 +</p> 1.422 +<p> 1.423 + The proprioceptive information used for action identification is 1.424 + body-centric, so only the registration step is dependent on point 1.425 + of view, not the identification step. Registration is not specific 1.426 + to any particular action. Thus, action identification can be 1.427 + divided into a point-of-view dependent generic registration step, 1.428 + and a action-specific step that is body-centered and invariant to 1.429 + point of view. 1.430 +</p> 1.431 +</div> 1.432 + 1.433 +</div> 1.434 + 1.435 +<div id="outline-container-3-2" class="outline-3"> 1.436 +<h3 id="sec-3-2">Stick Figure World</h3> 1.437 +<div class="outline-text-3" id="text-3-2"> 1.438 + 1.439 + 1.440 +<p> 1.441 + This environment is similar to Worm World, except the creatures are 1.442 + more complicated and the actions and questions more varied. It is 1.443 + an experiment to see how far imagination can go in interpreting 1.444 + actions. 1.445 +</p></div> 1.446 +</div> 1.447 +</div> 1.448 +</div> 1.449 + 1.450 +<div id="postamble"> 1.451 +<p class="date">Date: 2013-11-07 04:21:29 EST</p> 1.452 +<p class="author">Author: Robert McIntyre</p> 1.453 +<p class="creator">Org version 7.7 with Emacs version 24</p> 1.454 +<a href="http://validator.w3.org/check?uri=referer">Validate XHTML 1.0</a> 1.455 + 1.456 +</div> 1.457 +</body> 1.458 +</html>