 History and Future of LLMs Jul 23rd 2024, 21:00, by Kevin Vu Inception of LLMs: NLP and Neural Networks The creation of Large Language Models didn't happen overnight. Remarkably, the first concept of language models started with rule-based systems dubbed Natural Language Processing. These systems follow predefined rules that make decisions and infer conclusions based on text input. These systems rely on if-else statements processing keyword information and generating predetermined outputs. Think of a decision tree where output is a predetermined response if the input contains X, Y, Z, or none. For example: If the input includes keywords "mother," output "How is your mother?" Else, output, "Can you elaborate on that?" The biggest early advancement was neural networks, which were considered when first introduced in 1943 inspired by neurons in human brain function, by mathematician Warren McCulloch. Neural networks even pre-date the term "artificial intelligence" by roughly 12 years. The network of neurons in each layer is organized in a specific manner, where each node holds a weight that determines its importance in the network. Ultimately, neural networks opened closed doors creating the foundation on which AI will forever be built. |  From Observability To Architectural Observability — Shifting Left for Resiliency Jul 23rd 2024, 20:00, by John Vester In my previous article, Managing Architectural Tech Debt, I talked about understanding and managing architectural technical debt. Architectural technical debt is the often ignored, but ironically one of the most damaging, categories of technical debt. In this article, I want to dive deeper into one way to manage architectural technical debt (and technical debt as a whole) — architectural observability (AO). AO is a new category of observability that I believe is just as important, if not more so, as application performance management (APM). I believe we need to shift left observability — to the architectural stage — where we can not just see symptoms, but fix core problems. |  Understanding Concurrency Patterns in Go Jul 23rd 2024, 19:00, by Suleiman Dibirov Go, also known as Golang, has become a popular language for developing concurrent systems due to its simple yet powerful concurrency model. Concurrency is a first-class citizen in Go, making it easier to write programs that efficiently use multicore processors. This article explores essential concurrency patterns in Go, demonstrating how to leverage goroutines and channels to build efficient and maintainable concurrent applications. The Basics of Concurrency in Go Goroutines A goroutine is a lightweight thread managed by the Go runtime. Goroutines are cheap to create and have a small memory footprint, allowing you to run thousands of them concurrently. |  Step-By-Step Guide To Setting up and Training GANs for Image Generation Jul 23rd 2024, 18:00, by Neha Dhaliwal Here is a basic tutorial on setting up and training image generation models using Generative Adversarial Networks (GANs) with TensorFlow and PyTorch. This guide assumes a fundamental understanding of Python and basic machine learning concepts. 1. Setting up Your Environment Install Necessary Libraries Ensure you have Python installed. You will also need to install TensorFlow or PyTorch along with some other essential libraries. |  Cross-Platform Integration: Enabling Seamless Workflow Between AI, Microservices, and Azure Cloud Jul 23rd 2024, 17:00, by Nilesh Charankar, Venkata Nedunoori Importance of Cross-Platform Integration in Modern IT Architectures As digital transformation continues to accelerate, the demand for integrating various technological platforms is also increasing at a rapid pace. In today's digital world organizations execute their operations in complex environments where multiple technologies need to work together seamlessly. The complexity of the environments can be dependent on the nature of the business and the services that the organizations are offering. Specific to IT architectures, cross-platform integration is crucial in enabling different systems to communicate and collaborate effectively, enhancing overall efficiency and productivity. Moreover, this integration is particularly critical and has to be effectively designed when combining AI capabilities, microservices architecture, and cloud platforms like Azure. These components, when integrated, offer powerful solutions that drive innovation and efficiency across various domains. Benefits of Integrating AI, Microservices, and Azure Cloud Applications of AI and integrating AI in IT architectures span multiple industries, including healthcare, where it aids in diagnostics and personalized medicine; finance, where it enhances fraud detection and algorithmic trading; and retail, where it drives personalized marketing and customer service automation. AI's ability to analyze vast amounts of data and derive meaningful insights makes it a transformative technology in modern enterprises. |  How To Migrate From Godaddy DNS to AWS Route 53 Jul 23rd 2024, 16:00, by Alfonso Valdes In this post, we are going to learn about how to migrate from Godaddy to AWS Route53. First, it is necessary to buy a domain in Godaddy and later on migrate it to Amazon's DNS system, which is Amazon Route 53. Transfer a Godaddy Domain to AWS Route53. 1. Godaddy DNS Management Okay, so once we already have the Godaddy domain, we will log into our account and access it to manage our domain. On GoDaddy products, we will be able to see the domains we own, additional products, and recommendations. | |
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