rowntreerob/transformers-complex-neural-learning.html

## transformers-complex-neural-learning.html
<!DOCTYPE html><html class="light"><head>  <meta charset="utf-8"/>  <title>Chat GPT: Transformers: Complex Neural Learning</title>  <meta name="generator" content="chatGPT Saving Bookmark"/><style>html.dark {  background-color: rgb(32,33,35);  color: rgb(236,236,241);}html.light {  background-color: white;  color: rgb(52,53,65);}body {  font-size: 16px;  font-family: sans-serif;  line-height: 28px;  margin: 0;}body > .w-full {  padding: 30px;}.flex {  display: flex;  max-width: 100%;}.m-auto {  margin: auto;}.text-base {  max-width: 50rem;}.gap-1 {    gap: 0.25rem;}/* model name */.justify-center {  justify-content: center;}.items-center {  align-items: center;}.w-4 {  width: 1rem;}.h-4 {  height: 1rem;}/* prompt */.dark body > .w-full:nth-of-type(2n+1) {  background: rgb(52,53,65);}/* response */.dark body > .w-full:nth-of-type(2n+2) {  background: rgb(68,70,84);}.light body > .w-full:nth-of-type(2n+2) {  background: rgb(247,247,248);}.light body > .w-full {  border-bottom: 1px solid rgba(0,0,0,.1);}a, a:visited {  color: #7792cd;}pre {  margin: 0 0 1em 0; 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display: inline-block; overflow: hidden; width: initial; height: initial; background: none; opacity: 1; border: 0px; margin: 0px; padding: 0px; position: relative; max-width: 100%;"><img alt="user avatar" decoding="async" data-nimg="intrinsic" class="rounded-sm"></span></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex flex-col items-start gap-4 whitespace-pre-wrap break-words">my friends misunderstand neural networks based on transformers and make erroneous statements like there is just another software algorythm under the hood and to shed light on a decision made by AI all you have to do is examine or rerun the algorythm that is one and the same with the AI. I try to explain the model training process with transformers in neural net arrangement but it is complicated and difficult to describe the structure and the operation of a transformer based Neural net. Please help me describe why transformers are unique and why they are not at all like algorythms and should not be considered analogs to simple software algorythms producing deterministic output.</div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 bg-gray-50 dark:bg-[#444654]"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-xl xl:max-w-3xl md:py-6 lg:px-0 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(171, 104, 255);"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" stroke-width="1.5" class="h-6 w-6" role="img"><title>ChatGPT</title><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 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10.2712C26.906 10.1554 26.6803 10.0943 26.4504 10.0943C26.2206 10.0943 25.9948 10.1554 25.7963 10.2712L16.0726 15.8858V11.9982C16.0715 11.9783 16.0753 11.9585 16.0837 11.9405C16.0921 11.9225 16.1048 11.9068 16.1207 11.8949L24.1719 7.25025C25.4053 6.53903 26.8158 6.19376 28.2383 6.25482C29.6608 6.31589 31.0364 6.78077 32.2044 7.59508C33.3723 8.40939 34.2842 9.53945 34.8334 10.8531C35.3826 12.1667 35.5464 13.6095 35.3055 15.0128ZM14.2424 21.9419L10.8752 19.9981C10.8576 19.9893 10.8423 19.9763 10.8309 19.9602C10.8195 19.9441 10.8122 19.9254 10.8098 19.9058V10.6071C10.8107 9.18295 11.2173 7.78848 11.9819 6.58696C12.7466 5.38544 13.8377 4.42659 15.1275 3.82264C16.4173 3.21869 17.8524 2.99464 19.2649 3.1767C20.6775 3.35876 22.0089 3.93941 23.1034 4.85067C23.0427 4.88379 22.937 4.94215 22.8668 4.98473L14.9024 9.58517C14.7025 9.69878 14.5366 9.86356 14.4215 10.0626C14.3065 10.2616 14.2466 10.4877 14.2479 10.7175L14.2424 21.9419ZM16.071 17.9991L20.4018 15.4978L24.7325 17.9975V22.9985L20.4018 25.4983L16.071 22.9985V17.9991Z" fill="currentColor"></path></svg></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex flex-col items-start gap-4 whitespace-pre-wrap break-words"><div class="markdown prose w-full break-words dark:prose-invert light"><p>Certainly, I can help you explain this.</p><p>Firstly, it's important to understand that a neural network, such as the transformer models used in natural language processing, isn't just another software algorithm. An algorithm is a deterministic set of rules or steps to solve a problem. In contrast, a neural network is a model inspired by the human brain, and it's trained to learn patterns in data rather than following predefined rules.</p><p>In the case of transformers, they're designed to handle sequential data in tasks like translation, summarization, etc. Unlike other sequential models such as RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory), transformers don't process data in order. Instead, they use a mechanism called "attention" to focus on different parts of the input sequence when generating the output sequence.</p><p>To understand why transformers aren't deterministic like an algorithm, consider the training process. Transformers, like all neural networks, are trained on vast amounts of data and adjust their internal parameters based on the patterns they find. These parameters, which can number in the millions or even billions, are initially set randomly. Thus, even with the same training data, two separate instances of the same model might end up with slightly different parameters.</p><p>Moreover, the learning process is probabilistic, meaning that the model calculates probabilities for its predictions, rather than deterministic outputs. For example, when generating text, a transformer model will calculate the probabilities of different words following a given input sequence, and then select a word based on those probabilities.</p><p>As for understanding the decisions made by a transformer model, it's not as simple as examining or rerunning the algorithm. Because the model's behavior is determined by its learned parameters, and these parameters are learned from data in a complex, nonlinear way, understanding why the model made a particular decision often requires advanced techniques like attention visualization or feature attribution, and even then, the interpretation can be challenging.</p><p>Moreover, the fact that these models are often trained on massive amounts of data, and the fact that their internal workings are high-dimensional and nonlinear, means that they can exhibit complex, emergent behaviors that aren't easily predictable from their structure or the data they were trained on.</p><p>In summary, transformer-based models are not just simple algorithms with deterministic outputs. They are complex, probabilistic, learning machines that are trained to find patterns in data, and their decisions can be difficult to interpret due to the complexity and high dimensionality of their internal workings.</p></div></div></div><div class="flex justify-between lg:block"><div class="text-gray-400 flex self-end lg:self-center justify-center mt-2 gap-2 md:gap-3 lg:gap-1 lg:absolute lg:top-0 lg:translate-x-full lg:right-0 lg:mt-0 lg:pl-2 visible"></div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 dark:bg-gray-800"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-xl xl:max-w-3xl md:py-6 lg:px-0 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative flex"><span style="box-sizing: border-box; display: inline-block; overflow: hidden; width: initial; height: initial; background: none; opacity: 1; border: 0px; margin: 0px; padding: 0px; position: relative; max-width: 100%;"><img alt="user avatar" decoding="async" data-nimg="intrinsic" class="rounded-sm"></span></div></div></div><div class="relative flex w-[calc(100%-50px)] flex-col gap-1 md:gap-3 lg:w-[calc(100%-115px)]"><div class="flex flex-grow flex-col gap-3"><div class="min-h-[20px] flex flex-col items-start gap-4 whitespace-pre-wrap break-words">continue and explain the importance of math, statistics, and vector relationships as input data flows through the training process and how the layers and layers math and token interrelationships makes it difficult to explain exactly why and how an AI like an autonomous car took a decision to swerve into a parked car</div></div></div></div></div><div class="group w-full text-gray-800 dark:text-gray-100 border-b border-black/10 dark:border-gray-900/50 bg-gray-50 dark:bg-[#444654]"><div class="flex p-4 gap-4 text-base md:gap-6 md:max-w-2xl lg:max-w-xl xl:max-w-3xl md:py-6 lg:px-0 m-auto"><div class="flex-shrink-0 flex flex-col relative items-end"><div class="w-[30px]"><div class="relative p-1 rounded-sm h-[30px] w-[30px] text-white flex items-center justify-center" style="background-color: rgb(171, 104, 255);"><svg width="41" height="41" viewBox="0 0 41 41" fill="none" xmlns="http://www.w3.org/2000/svg" stroke-width="1.5" class="h-6 w-6" role="img"><title>ChatGPT</title><text x="-9999" y="-9999">ChatGPT</text><path d="M37.5324 16.8707C37.9808 15.5241 38.1363 14.0974 37.9886 12.6859C37.8409 11.2744 37.3934 9.91076 36.676 8.68622C35.6126 6.83404 33.9882 5.3676 32.0373 4.4985C30.0864 3.62941 27.9098 3.40259 25.8215 3.85078C24.8796 2.7893 23.7219 1.94125 22.4257 1.36341C21.1295 0.785575 19.7249 0.491269 18.3058 0.500197C16.1708 0.495044 14.0893 1.16803 12.3614 2.42214C10.6335 3.67624 9.34853 5.44666 8.6917 7.47815C7.30085 7.76286 5.98686 8.3414 4.8377 9.17505C3.68854 10.0087 2.73073 11.0782 2.02839 12.312C0.956464 14.1591 0.498905 16.2988 0.721698 18.4228C0.944492 20.5467 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mathematical principles. Every input to the network, every transformation that occurs within the network, and every output from the network, is expressed in terms of numerical data and mathematical operations. Here's a more detailed explanation:</p><ol><li><p><strong>Input Representation:</strong> Every piece of data that's fed into a neural network, whether it's an image, a sound, or a sentence, is first transformed into a numerical representation. For transformer models dealing with text, this involves converting words or phrases (tokens) into vectors using techniques such as word embeddings. Each dimension in these vectors can represent different characteristics of the word.</p></li><li><p><strong>Mathematical Operations:</strong> As the input data flows through the network, it undergoes a series of transformations defined by mathematical functions. For instance, transformer networks heavily use linear algebra (matrix multiplications and additions), calculus (for backpropagation during training), and probability theory (in the form of softmax functions for prediction).</p></li><li><p><strong>Layers and Parameters:</strong> The structure of a neural network is composed of layers, and each layer contains numerous parameters (or weights) that are adjusted during the training process. These parameters define the relationship between the input and output of each layer.</p></li><li><p><strong>Statistics and Probability:</strong> The training process involves adjusting the network's parameters based on statistical patterns in the training data. This is typically done using a variant of stochastic gradient descent, an optimization algorithm that incrementally adjusts the parameters to minimize the difference between the model's predictions and the actual data.</p></li></ol><p>Because of this complex, multi-layered process involving high-dimensional vector spaces and non-linear transformations, it's difficult to directly relate the input and output of a transformer model. The model's decision-making process is distributed across millions or billions of parameters, and it's generally not possible to point to a single parameter or even a group of parameters as being the reason for a specific decision.</p><p>For example, if an autonomous car equipped with a transformer model were to swerve into a parked car, the decision would be the result of complex interactions between the inputs (the sensory data from the car's cameras and other sensors), the model's internal parameters (which have been trained on large amounts of data), and the model's mathematical operations. To understand why the model made the decision it did, one would need to analyze the high-dimensional vector representations and the multitude of mathematical transformations the model performed on the input data.</p><p>This complexity, combined with the fact that the training process involves randomness and that the model's behavior emerges from high-dimensional, non-linear patterns in the data, makes it difficult to provide a simple, deterministic explanation for the model's decisions. This is a major area of ongoing research in the field of AI, known as explainability or interpretability.</p></div>
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