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<h2 class="align-center mbr-bold mbr-white pb-3 mbr-fonts-style display-1">The ARNOLD Network</h2>
<h3 class="mbr-section-subtitle align-center mbr-light mbr-white pb-3 mbr-fonts-style display-5">How we built a self-conscious machine</h3>
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<h2 class="align-center pb-3 mbr-fonts-style display-2">PREFACE</h2>
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<div class="mbr-text col-12 col-md-8 mbr-fonts-style display-7"><p><strong>For centuries, the Universe was a mystery. </strong>It still is, to some extent, but over centuries, through the rise and fall of grand civilizations from the Greeks to our modern age, we have developed a model to understand it; we call it Physics. <strong>For centuries, the human body was a mystery.</strong> While there are more breakthroughs to come, our understanding of biology and the imperceptible world of microbes has lent us a previously-unforeseen ability to extend our lifespans well beyond their normal limits.</p><p>Neutron stars with all the atoms in the Earth smashed into a volume no larger than a baseball; particles who flicker into existance only to disappear without a trace in microseconds; black holes whose supermassive vortexes allow nothing, not even light, to escape - we have discovered them all. In almost all our scientific fields, we are brushing the reaches of a singularity: a point at which all that can be understood is understood.</p><p>There is no argument that what our brains can understand is the most intriguing part of our universe. Since the dawn of computing, we have been striving to replicate the advanced pattern recognition and reasoning trapped in that three-pound heap of tissue in our skulls. <em>Artificial Intelligences</em> are saying their first "hello world!" at companies all around the world, mastering driving or medicine or facial recognition. But we all know that human doctors can recognize faces and drive and practice medicine, all in one brain. It begs the question:</p><p><strong><em>Can we make an artificial intelligence that can do... anything?</em></strong></p><p>In computing, we call this point the Technological Singularity, a point at which we transcend biology and trigger explosive technological growth through self-aware robotics. At Non Periculo Solutions, we believe we have brought humanity to it.</p></div>
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<div class="mbr-text col-12 col-md-8 mbr-fonts-style display-7"><p><strong>Jack Dos</strong></p><p><em>CEO, NP Solutions</em></p><p><em>2018</em></p></div>
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HOW IT'S DONE</h2>
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Basic Theory</h4>
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<blockquote><span style="font-style: normal;"><strong>Start with the problem statement. </strong>How can we model the complex processes of the human brain? The process from stimulus to action is actually quite simple, as seen below.</span></blockquote>
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<div class="mbr-text col-12 col-md-8 mbr-fonts-style display-7"><p>This is, of course, assuming an <em>external </em>stimulus. As we know from experience, though, ideas and trains of thought can be self-initiated and fully visualized without anything ever happening externally. The most extreme example of this is in dreaming: while no action results, the brain is still forming its own stimulus and processing information. The information that ARNOLD uses to self-stimulate its thought process is stored in BENEDICT's memory repository.</p><p>In early versions of ARNOLD, a coroutine in code would be set to trigger a self-stimulated line of throught after an even time interval. Later, ARNOLD triggered itself when the sensation of feeling "bored" was introduced to his code. ARNOLD's conclusion to avoid boredom was to trigger the self-stimulation more frequently, so the time interval could then be removed.</p></div>
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<blockquote><p><span style="font-style: normal;">BENEDICT and ARNOLD are both based off of human biology, so it is important to know a little more about the brain itself.</span></p><p><span style="font-style: normal;">A </span><strong>Neuron</strong><span style="font-style: normal;"><strong>,</strong> like an atom, <strong>is the most basic unit of computation in the brain.</strong> Based on the electrical signals generated by the neurons around it, it generates an electrical response that, in a domino-style reaction, activates other neurons that follow it.</span></p></blockquote>
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<blockquote><span style="font-style: normal;">We can then describe this in terms of a neural network and generate a diagram like the one below. In this case, the circles represent </span>neurons<span style="font-style: normal;"> and the lines represent the line of flow of information from one of these neurons to the next.</span></blockquote>
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<blockquote><p><span style="font-style: normal;">The numbers on the graph above represent a part of neural network theory called </span>weights. <span style="font-style: normal;">We use weights to affect the result that our Artificial Intelligence will calculate. Imagine we have a robotic arm connected to ARNOLD, and we want to teach ARNOLD's new arm to pull away from a hot stove when it comes into contact with it and seek colder materials to "cool the burn." </span></p><p><span style="font-style: normal;">We will start with the "pull away function." In the graph below, the input Neuron is set to 1, and since a sensor has registered the temperature as exceeding 110 degrees Fahrenheit, the weight is set to 1. The result, as seen below, becomes 1, stimulating the arm to pull away.</span></p></blockquote>
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<blockquote><span style="font-style: normal;">To simulate the "seek a cool material to counteract the burn" function, we simply add a new weight and result as seen below.</span></blockquote>
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<blockquote><p><span style="font-style: normal;">The important thing to see here is the chain reaction that will occur if a certain stimulus is recognized. However, we want to remove that "burned" boolean and make our arm instinctively seek cold material after being exposed to high temperatures. To do this, we </span>train<span style="font-style: normal;"> our Artificial Intelligence. In truth, this is all ARNOLD is: a highly trained Neural Network with beyond-human computing speeds.</span></p><p><span style="font-style: normal;">To train the AI, we will use the final step in that fourth diagram: the "consequence" section. We will allow ARNOLD to change the weight of each input and then tell it whether the weights it chose illicit a correct response. If the response is given an "incorrect" value, ARNOLD will dispose of that combination of weights and store it as an incorrect solution. The AI then fine-tunes the weights until it is given a "correct" response, and saves that as the final solution to the given set of inputs.</span></p><p><span style="font-style: normal;">Most Artificial Intelligences will dispose of the incorrect solutions, but through the "dream" function programmed in ARNOLD, our Artificial Intelligence stores the most extreme and most-close-but-wrong combinations of weights in BENEDICT's long-term storage. This simulates failure and allows our Artificial Intelligence to understand nuances between correct and incorrect answers.</span></p><p><span style="font-style: normal;">You will likely be able to relate to this. Some of your </span>worst<span style="font-style: normal;"> failures are likely unforgettable experiences, as are the memories of when you were </span>nearly<span style="font-style: normal;"> right, but not entirely. This is part of the core of </span></p></blockquote>
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How does ARNOLD have a personality?</h4>
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<h4 class="section-content-title pb-3 align-left mbr-fonts-style display-5">
ARNOLD's Personality, Programmed
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<p class="section-content-text align-left mbr-fonts-style display-7">To pass the Turing test, ARNOLD needed to develop a personality of his own that was still compliant within Asimov's Laws of Robotics. Our developers decided to apply <em>Big Five Personality Theory</em> to find an optimum personality that most people found easy to interact with and nearly-human.<br><br>Given Isaac Asimov's Second Law of Robotics (that a robot must obey the orders of human beings), ARNOLD was programmed with extremely high scores in Openness and Agreeableness. This was done in the Neural Network by weighting answering human commands with extremely high weights, while weighting ignoring them with extremely low weights. ARNOLD's conscientiousness is also very high to ensure that these human orders are carried out as promptly as possible. Our brains, like ARNOLD's, tend to take shortcuts whenever they can, so ARNOLD seems more human in trying to maximize the speed of its processing.<br><br>ARNOLD's extraversion and neuroticism were entirely learned, with default settings both at 50%. ARNOLD's extraversion changed little, though the 3% tendency toward introversion likely came from its understanding that humans are varied and gaining individual traits from them would result in only more confusion. Regarding neuroticism, instability (in other words, tendency toward many different answers to the Neural Network input instead of one) tended to cause freeze-ups in early ARNOLD-OS versions, so to follow Asimov's Third Law of Robotics (that the robot must protect its own existence), it scored low neuroticism to preserve its functionality.</p>
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Openness
</strong><br>Desire to learn new things and try new experiences.
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<strong>Conscientiousness</strong>
<br>Reliability, promptness, organization, and thoroughness.
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Extraversion
</strong><br>Desire to interact with others and learn primary traits from them.
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Agreeableness
</strong><br>General friendliness, compassion, and cooperation with others.
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Neuroticism
</strong><br>Tendency toward emotional instability and negative emotion.
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Updated April 20, 2018 • WARNING: The content on this site is <strong>fictional</strong> and hypothetical, created for the purposes of the 2018 <a href="http://tsaweb.org/" target="_blank">Technology Student Association</a> WebMaster event. Any correlation with real-world events, names, or dates is purely coincidental.</p>
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