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In the realms of biology and computer science, two seemingly disparate fields have emerged as powerhouses of information processing: epigenetics and machine learning. While one deals with the complexities of life at the molecular level, and the other with artificial intelligence and data analysis, a fascinating parallel can be drawn between these two domains. This…

FS There seems to be a bizarre similarity between Blockchain decentralized distribution of encrypted information and LLM ANN decentralized distribution of encrypted (embedded) information. Is there a synergy lurking somewhere? Comment on this with a 2000 word essay. Your observation about the similarity between blockchain’s decentralized distribution of encrypted information and the decentralized distribution of…

1. Introduction The field of artificial intelligence, particularly in the domain of natural language processing (NLP), has seen remarkable advancements with the development of large language models (LLMs). These models have demonstrated unprecedented capabilities in understanding and generating human-like text. However, as we push the boundaries of what’s possible with classical computing, researchers are increasingly…

Introduction In the realms of neuroscience and artificial intelligence, two concepts have emerged as powerful explanatory frameworks for understanding learning and adaptation: Karl Friston’s free energy principle in biology and backpropagation in machine learning. While originating from different fields, these concepts share intriguing parallels and offer complementary insights into the nature of intelligence, both biological…

Introduction The field of artificial intelligence, particularly in the domain of large language models (LLMs) based on artificial neural networks (ANNs), has seen remarkable advancements in recent years. These models, capable of generating human-like text and performing complex language tasks, have become a subject of intense study not only in computer science but also in…

The part of the LLM process that is most intriguing is the embedding of multidimensional vector values and direction as a distribution of weights and biases across an artificial neural network. Even more so as the process builds a composite “memory” across the ANN. A memory that appears to increment vector values across the distribution…