Berlin Brown and Software Development

 Here are more questions on that example from the LLM book. https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/gpt_train.py   From Chat Gpt - Open AI Response Yes, AI and Large Language Models (LLMs) typically start with randomly initialized weights, though more sophisticated methods have evolved from this simple technique. A random starting point is a crucial part of the training process that allows the model to learn and differentiate between features.   Why random initialization is necessary Breaks symmetry: If all the weights in a network were initialized to the same value, every neuron in a layer would learn the exact same features. This prevents the network from learning anything useful. By starting with random, different weights, each neuron is forced to compute something different from its neighbors, allowing for a more diverse and robust learning process. Enables learning from data: With random starting weights, the model's initial pre...

 Flag Burning and Trump Recent news.

 Businesses In Georgia https://calendly.com/about Prompt Owl AI Platform Company https://theaiinsider.tech/2025/04/29/worlds-first-agency-for-custom-enterprise-ai-workers-introduced-by-promptowl/ Micron Biomedical, Inc. https://www.micronbiomedical.com/ https://www.micronbiomedical.com/news/mi

 Here is my chat with ChatGPT on what is AI LLM? Break down the weights vs training data ELI5 Analogy: Learning Piano Training data = all the sheet music + recordings you practiced with Imagine you’re learning piano. You practice thousands of songs, scales, and drills. That’s the training data. Weights = your brain’s memory of “how to play” after all that practice After months of practice, you don’t carry around every sheet of music you ever saw. Instead, your brain has adjusted so you can play in certain styles, recognize patterns, and even improvise. That “muscle memory” is like the weights file. It’s just billions of little adjustments stored as numbers. Inference (running llama.cpp) = you playing piano on demand Now someone asks you to play something new. You don’t need the training books—you just use what you’ve already learned (weights) to generate the performance. ... Each token (word piece) is chosen based on probabilities learned in the weights If randomness (temperature, ...

 More Thousand Brains Videos: " The Thousand Brains Project is dedicated to building a new type of AI that works on the same principles as the neocortex. These principles are described in the Thousand Brains Theory and focus on sensorimotor learning with a repeatable computational unit modeled after cortical columns. "

 Review of Projects: Java Version: https://github.com/berlinbrown/htm.java https://github.com/htm-community htm core: https://github.com/htm-community/htm.core Thousand Brains Project https://www.youtube.com/watch?v=VqDVUWgJQPI https://github.com/thousandbrainsproject/feat.dynamic_resizing Jobs https://thousandbrains.org/company/careers/open-source-software-engineer/

Posed question to Chat GPT: response from chatgpt openai. ...  That is really interesting — you’re describing something like a "human-like AI operator" that interacts with the world only through the same physical channels a person would. Instead of APIs, direct memory access, or digital hooks, it would: See through a camera (screen scraping instead of data feeds). Hear through microphones (parsing audio instead of reading text). Act by pressing keys, moving a mouse, or even flipping physical switches. This would create an AI that is forced to deal with the world in the same messy way humans do , where perception and action are mediated by physical hardware. Some implications: Advantages Robustness — It can interact with any system humans use, regardless of APIs or locked digital environments. Security sandboxing — Because it only uses physical I/O, it’s less likely to bypass boundaries in unsafe ways. Human-aligned learning — Its training match...

 Good Meeting Atlanta Java Group: https://www.meetup.com/atlantajug/events/310453214/ "Pratik Patel is a Java Champion, developer advocate at Azul Systems, and has written three books on programming" "AI-Powered Data Exploration: Interacting with Apache Iceberg via Spark and LLMs"

 Have to read the book: Jeff Hawkins – A Thousand Brains: A New Theory of Intelligence Explains the "Thousand Brains Theory" in an accessible but deep way. Great for grounding AGI ideas in cortical theory.  Architectures, AGI Projects & Platforms OpenCog Hyperon – An open-source AGI framework using symbolic + probabilistic reasoning. SingularityNET / Cogito – AGI-focused decentralized AI project. Nengo (by Applied Brain Research) – A Python framework for large-scale brain modeling. Often used with Eliasmith’s work.

 Running the Chapter 5 Training Example,  This is an interesting example and without fully understanding it, asking Github Copilot, Chat GPT abotu the code. ... Here it is running, and also updates on the code.  The model has approximately 124 million parameters (hence the name) primarily from:  Position Embeddings: context_length × emb_dim = 256 × 768 parameters  Transformer Layers: 12 layers, each with attention heads and feed-forward networks  Even though "The Verdict" is only 3,600 words:  The model is designed to learn general language patterns, not just memorize the text   The vocabulary size (50,257 tokens) matches GPT-2's full vocabulary  The architecture follows smaller GPT-2 specifications (768-dim embeddings, 12 layers)  This is the total number of unique tokens the model can recognize and generate ''' ''' The full vocabulary (50,257 tokens) gives it the capacity to represent  many words it never sees in training It's bas...

 Bela Thank you for attention to this matter.

 Looking at the code from Sebastian Raschka on LLMs, here is a small snippet.  Training a model from scratch and basic test.  The key is understanding the basics. Here is the text to train, "I HAD always thought Jack Gisburn rather a cheap genius--though a good fellow enough--so it was no great surprise to me to hear that, in the height of his glory, he had dropped his painting, married a rich widow, and established himself in a villa on the Riviera. (Though I rather thought it would have been Rome or Florence.)" See: https://pytorch.org/ https://github.com/openai/tiktoken import matplotlib.pyplot as plt import os import torch import urllib.request import tiktoken   class GPTDatasetV1 (Dataset):     def __init__(self, txt, tokenizer, max_length, stride):         self.input_ids = []         self.target_ids = []         # Tokenize the entire text         token_ids = tokenizer.enco...

 So just random searches, Who is still using blogger, blogspot. https://returntoatl.blogspot.com/ https://diego-pacheco.blogspot.com/

  Darks Souls 1 and 2 are great games

 Yes, absolutely — the idea of building and training AI models using distributed computing , especially with tiny or resource-constrained devices like Raspberry Pis , is possible in concept and increasingly viable with the right technology stack. There are a few real-world technologies and approaches that relate to what you're describing. 🔹 What You’re Describing: You're imagining a world where: A small, local device (like a Raspberry Pi) can train or fine-tune a small model. These devices are networked , forming a distributed system . They collaboratively train a model or share knowledge (like a federated system). This is an intersection of: Distributed Computing Federated Learning TinyML Edge AI 🔧 Technologies and Concepts to Look Into 1. Federated Learning A decentralized approach to training models where each device trains on its own data locally, and only updates (not raw data) are sent to a central server or aggregator. Used by : G...