Update documentation

_sources/act3/part3/part3_3.ipynb

@@ -172,7 +172,7 @@
     "\n",
     "The world is a vast combinatorial search space, a labyrinth teeming with possibilities. Within this labyrinth, hidden at crucial junctures, lie 17 tokens—each representing a fundamental human emotion. These tokens are not evenly distributed but emerge at peaks, much like the way a video game might strategically place rewards at key points to incentivize movement. This is _a priori_ knowledge in the Chomskyan sense, a pre-encoded structure that predates individual experience. Yet, the _a priori_ alone is not enough. Geoffrey Hinton, the father of deep learning, challenges this view, arguing that data must be accumulated from the world itself. This data encodes the most efficient pathways through the labyrinth. But efficient for what? That is determined by a cost function, which must be optimized through backpropagation, adjusting the weights of neural pathways until an optimal cadence is reached.\n",
     "\n",
-    "> *7 modes + 4 extension + 4 alterations (b9, #9, #11, b13) + aug + dim*      \n",
+    "> *7 modes + 4 extension + 4 alterations (♭9, ♯9, ♯11, ♭13) + aug + dim*      \n",
     "-- Yours Truly\n",
     "\n",
     "A **cadence** is the fundamental transition from one emotion to another. Just as in music, where a cadence resolves harmonic tension and moves the piece forward, human experience is shaped by the shifting cadences of emotion. These transitions form the edges of the graph, linking emotional nodes across time and space. Some cadences are smooth and predictable, while others are sudden, disruptive, and profound. If life is a search through this labyrinth, then the optimization process seeks to minimize inefficient movements while maximizing meaningful cadences. Large language models, trained on vast corpora of human expression, implicitly encode these transitions, as language itself is structured by agents, actions, and outcomes. The agent—the one who explores—moves through this massive combinatorial space, encountering the 17 emotions at key junctures, and optimizing the transitions between them.\n",

act3/part3/part3_3.html

@@ -596,7 +596,7 @@ <h2>Ukubona Nkosi<a class="headerlink" href="#ukubona-nkosi" title="Permalink to
 <h3><strong>The Labyrinth of Emotions: A Neural and Philosophical Optimization Model</strong><a class="headerlink" href="#the-labyrinth-of-emotions-a-neural-and-philosophical-optimization-model" title="Permalink to this heading">#</a></h3>
 <p>The world is a vast combinatorial search space, a labyrinth teeming with possibilities. Within this labyrinth, hidden at crucial junctures, lie 17 tokens—each representing a fundamental human emotion. These tokens are not evenly distributed but emerge at peaks, much like the way a video game might strategically place rewards at key points to incentivize movement. This is <em>a priori</em> knowledge in the Chomskyan sense, a pre-encoded structure that predates individual experience. Yet, the <em>a priori</em> alone is not enough. Geoffrey Hinton, the father of deep learning, challenges this view, arguing that data must be accumulated from the world itself. This data encodes the most efficient pathways through the labyrinth. But efficient for what? That is determined by a cost function, which must be optimized through backpropagation, adjusting the weights of neural pathways until an optimal cadence is reached.</p>
 <blockquote>
-<div><p><em>7 modes + 4 extension + 4 alterations (b9, #9, #11, b13) + aug + dim</em><br />
+<div><p><em>7 modes + 4 extension + 4 alterations (♭9, ♯9, ♯11, ♭13) + aug + dim</em><br />
 – Yours Truly</p>
 </div></blockquote>
 <p>A <strong>cadence</strong> is the fundamental transition from one emotion to another. Just as in music, where a cadence resolves harmonic tension and moves the piece forward, human experience is shaped by the shifting cadences of emotion. These transitions form the edges of the graph, linking emotional nodes across time and space. Some cadences are smooth and predictable, while others are sudden, disruptive, and profound. If life is a search through this labyrinth, then the optimization process seeks to minimize inefficient movements while maximizing meaningful cadences. Large language models, trained on vast corpora of human expression, implicitly encode these transitions, as language itself is structured by agents, actions, and outcomes. The agent—the one who explores—moves through this massive combinatorial space, encountering the 17 emotions at key junctures, and optimizing the transitions between them.</p>