Blog #156: Machine Learning Review June 2023

Photo by Glen Carrie on Unsplash

[ED: There’s a bit of a mix of content here. On balance, it’s 3/5 propeller hats.]

Here’s my review of all of the interesting things that happened in machine intelligence in June 2023.

Bridging the gap between neural networks and functions This article explores the fundamental concepts of neural networks, focusing on their structure, interpretability, and training processes like backpropagation. It aims to simplify these complex ideas for software engineers, providing a comprehensive understanding of how neural networks work and their implications for AI development. #NeuralNetworks #AIUnderstanding #Backpropagation #SoftwareEngineering #TechEducation

What Is a Transformer Mode. ? This post explains the concept and significance of transformer models in AI. It covers their basic functioning, including the attention mechanism and their applications in various fields such as language translation, healthcare, and more. The article also discusses the evolution of transformer models from earlier neural networks and their growing importance in advancing AI technologies. #TransformerModels #AIExplained #MachineLearning #NVIDIA #DeepLearning

LLM Bootcamp This is a comprehensive course for those interested in developing applications with large language models (LLMs). It covers topics such as prompt engineering, LLMOps, UX for language user interfaces, augmented LLMs, and practical project walkthroughs. Designed for a range of experience levels, this bootcamp aims to equip participants with the skills needed to build and deploy LLM applications effectively. #LLMBootcamp #AIProgramming #MachineLearning #AIApplicationDevelopment #TechEducation

AI Canon (A16Z) This is a comprehensive resource guide on artificial intelligence featuring a curated list of influential papers, blog posts, courses, and guides. It covers foundational concepts in AI, practical guides for building with large language models, deep dives into AI technologies, market analysis, and landmark research results, providing a thorough understanding of the current AI landscape. #AICanon #AIResources #ArtificialIntelligence #MachineLearning #TechEducation

Here are some of the most interesting articles and posts from the A16Z AI Canon:

• Build a GitHub support bot with GPT3, LangChain, and Python: One of the earliest public explanations of the modern LLM app stack. Some of the advice in here is dated, but in many ways, it kicked off widespread adoption and experimentation of new AI apps.
• Building LLM applications for production: Chip Huyen discusses many of the key challenges in building LLM apps, how to address them, and what types of use cases make the most sense.
• Prompt Engineering Guide: For anyone writing LLM prompts — including app devs — this is the most comprehensive guide, with specific examples for a handful of popular models. For a lighter, more conversational treatment, try Brex’s prompt engineering guide.
• Prompt injection: What’s the worst that can happen? Prompt injection is a potentially serious security vulnerability lurking for LLM apps, with no perfect solution yet. Simon Willison gives a definitive description of the problem in this post. Nearly everything Simon writes on AI is outstanding.
• OpenAI cookbook: For developers, this is the definitive collection of guides and code examples for working with the OpenAI API. It’s updated continually with new code examples.
• Pinecone Learning Center: Many LLM apps are based around a vector search paradigm. Pinecone’s learning centre — despite being branded vendor content — offers some of the most useful instructions on how to build in this pattern.
• LangChain docs: As the default orchestration layer for LLM apps, LangChain connects to just about all other pieces of the stack. So their docs are a real reference for the full stack and how the pieces fit together.

I Discovered The Perfect ChatGPT Prompt Formula This video offers insights into crafting effective prompts for ChatGPT. It guides viewers through techniques for improving the quality of ChatGPT’s responses, providing practical examples and tips to enhance user experience with the AI model. #ChatGPT #PromptCrafting #AIUserExperience #ChatGPTTips #MachineLearning

Understanding GPT tokenizers This post delves into the intricacies of how tokenisers work in GPT models, including GPT-3 and GPT-4. It explains the tokenisation process, discusses language biases, and explores interesting quirks like glitch tokens. The post also introduces tools for experimenting with tokenisation and discusses practical applications like token counting in API interactions. #GPTTokenization #AIInsights #MachineLearning #NLP #TechExploration

Generative AI Learning Path (Google) Google Cloud Skills Boost offers an “Introduction to Generative AI Learning Path,” which includes five activities focusing on the basics of generative AI, large language models, and responsible AI practices. This course path is designed for beginners and provides insights into Google’s tools and principles in AI development. #GenerativeAI #GoogleCloud #MachineLearningBasics #ResponsibleAI #AIeducation

Building Systems with the ChatGPT API This course (offered by DeepLearning.AI in collaboration with OpenAI) teaches efficient techniques for developing complex workflows using large language models. It’s suitable for beginners to advanced learners, focusing on prompt engineering, safe and accurate outputs, and creating practical systems like customer service chatbots. #ChatGPT #DeepLearningAI #MachineLearningCourse #AIProgramming #PromptEngineering

Originally published by M@ on Medium.