Blog #160: Machine Learning Review October 2023

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[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 October 2023.

Generate images in one second on your Mac using a latent consistency model This article from Replicate describes how to run Latent Consistency Models (LCMs) on Macs with M1 or M2 chips, enabling the generation of 512x512 images in one second. LCMs, a faster version of Stable Diffusion models, require fewer steps to produce high-quality images. The guide includes prerequisites and detailed steps for setting up Python, cloning the repository, and running the model, along with tips for continuous image generation. #LatentConsistencyModel, #StableDiffusion, #MacImageGeneration, #AIImaging, #PythonCoding

Emerging Architectures for LLM Applications (a16z.com) The article from Andreessen Horowitz discusses emerging architectures for applications using large language models (LLMs). It highlights the LLM app stack’s common systems, tools, and design patterns used by AI startups and tech companies. The article emphasizes in-context learning, where LLMs are used off-the-shelf and controlled through prompts and contextual data. It also explores the evolving landscape of vector databases, prompting strategies, and operational tools for LLMs, suggesting a shift towards more complex, efficient, and differentiated LLM applications. #LLMArchitecture, #InContextLearning, #AIStartups, #VectorDatabases, #LLMApplications

How to Match LLM Patterns to Problems This article by Eugene Yan addresses how to effectively match patterns with problems when using large language models (LLMs). It distinguishes between external and internal LLMs, emphasising their respective limitations and advantages. The piece further explores various patterns like fine-tuning, caching, and defensive UX, and how they can be applied to solve specific LLM-related problems, ranging from performance metrics to customer experience issues. #LLMProblems, #MachineLearning, #AIApplication, #PatternMatching, #LLMStrategy

Patterns for Building LLM-based Systems & Products This article by Eugene Yan provides an in-depth exploration of patterns for integrating large language models (LLMs) into systems and products. It covers seven key patterns, including evaluations, retrieval-augmented generation, fine-tuning, caching, guardrails, defensive UX, and user feedback collection, each with a focus on either performance improvement or cost/risk reduction. These patterns provide a framework for understanding and applying LLMs in various contexts, ranging from data-driven approaches to user-centric strategies. #LLMPatterns, #AIIntegration, #MachineLearning, #AIProductDesign, #LanguageModelApplications

Animated AI The website “Animated AI” offers animations and instructional videos about neural networks, focusing on concepts like convolution, padding, stride, and pixel shuffle. It provides clear visual explanations of these key concepts, making them accessible for learners at various levels. The site also includes companion YouTube videos for each topic, enhancing the learning experience. #NeuralNetworks, #AIAnimation, #Convolution, #MachineLearningEducation, #PixelShuffle

Fine tune Mistral 7B with the RTX 4090 and serve it with Nx This post by Toran Billups details the process of fine-tuning the Mistral 7B language model using an RTX 4090 graphics card and serving it with Nx in Elixir. It focuses on overcoming VRAM limitations through the use of lit-gpt, an open-source Python project, and discusses the steps for data preparation, model fine-tuning, evaluation, and serving. The post emphasises the advantages of local fine-tuning, including speed and data privacy. #Mistral7B, #FineTuningAI, #RTX4090, #ElixirNx, #LanguageModeling

Open-Source Elixir Alternatives to ChatGPT This post from the folks at Dockyard explores open-source Elixir alternatives to ChatGPT, highlighting the benefits of data privacy, reduced latency, task-specific performance, and cost efficiency. It discusses several models like Flan-T5, Llama, and OpenAssistant, offering insights on their usage, advantages, and how they integrate with Elixir’s Nx ecosystem for machine learning applications. The article positions Elixir as a promising language for the future of LLM-powered applications. #OpenSourceAI, #ChatGPTAlternatives, #ElixirProgramming, #MachineLearning, #LLM

The Killer Use Case for LLMs Is Summarization Sebastian Mellen’s blog post argues that the primary use case for large language models (LLMs) is summarisation. He discusses LLMs’ ability to condense large volumes of information and their potential to revolutionize information processing in various fields, including corporate settings and data management. Mellen predicts a future where executives rely on LLMs for efficient summary and organization of data, enhancing decision-making and operational efficiency. #LLMSummarization, #InformationProcessing, #CorporateEfficiency, #DataManagement, #AIInsights

ElixirConf 2023 - Toran Billups - Fine-tuning language models with Axon This video discusses fine-tuning language models using Axon, an Elixir-based machine learning library. It shares insights and methodologies on integrating language models with Elixir’s ecosystem, demonstrating practical applications and technical details of this process. #MachineLearning #Axon #NX #ElixirConf #EducationalVideo #TechLearning

Retrieval Augmented Generation at scale — Building a distributed system for synchronizing and ingesting billions of text embeddings #RetrievalAugmentedGeneration, #DistributedSystems, #AIInnovation, #LargeScaleAI, #MachineLearning

Why AutoGPT engineers ditched vector databases The post by Dariusz Semba discusses why AutoGPT engineers decided to move away from using vector databases. Initially believed to be crucial for managing AI agents’ long-term memory, AutoGPT now relies on simpler memory management techniques, like using JSON files. This shift is part of a broader trend towards specialised, task-oriented AI agents with in-context learning rather than a single, general-purpose agent. The post highlights the importance of practicality and simplicity over complex, over-engineered solutions in AI development. #AutoGPT, #VectorDatabases, #AIDevelopment, #InContextLearning, #TechInnovation

AI hype is built on high test scores. Those tests are flawed. The MIT Technology Review article critically examines the practice of evaluating large language models (LLMs) using human intelligence tests. It discusses the limitations and misleading implications of this approach, suggesting the need for a revised, more appropriate evaluation method. The article highlights the disparity between LLMs’ performance on such tests and their actual understanding, calling for a more nuanced understanding of AI capabilities beyond anthropomorphised interpretations. #AIAssessment, #HumanIntelligenceTests, #LargeLanguageModels, #TechnologyReview, #AIUnderstanding

Unbundling AI The article “Unbundling AI” by Benedict Evans discusses the evolution of AI, particularly large language models (LLMs), and their impact on product design and functionality. It compares the paradigm shift in AI to historical advancements like GUIs, emphasising the transition from specific, hand-built responses to general, automated solutions. The article also explores the challenges of error rates and product design in AI, proposing a future where AI integrates into specialised tools, moving beyond a one-size-fits-all approach. #AIevolution #LanguageModels #TechInnovation #ProductDesign #FutureOfAI

End-to-End Machine Learning in Elixir The article “End-to-End Machine Learning in Elixir” from DockYard discusses how to build scalable machine learning applications in Elixir. It covers creating a newsfeed application that processes real-time headlines with machine learning enrichments like named-entity recognition and sentiment analysis. The author emphasises Elixir’s efficiency in handling large-scale applications and its potential to simplify application stacks, despite its smaller ecosystem compared to languages like Python. #MachineLearning #ElixirProgramming #RealTimeProcessing #DataScience #TechInnovation

Elixir and Machine Learning: Q3 2023 roundup This article provides an overview of the latest developments in machine learning within the Elixir community. It highlights advancements in projects like Nx, Explorer, and Bumblebee, which enhance Elixir’s capabilities in numerical operations, data processing, and model training. The article emphasizes the community’s focus on scalability, integration, and productivity, showcasing improvements in tools guided by production feedback and application in real-world scenarios. #ElixirML #TechAdvancements #DataScience #MachineLearning #CommunityDevelopment

Announcing LangChain for Elixir This article introduces the Elixir LangChain framework, designed to simplify integrating and utilising large language models (LLMs) like ChatGPT in Elixir applications. It provides tools for automating tasks, handling different LLMs, and removing repetitive code, making it easier to build conversational AI applications in Elixir. This initiative represents an important step in making advanced AI technologies more accessible to Elixir developers. #LangChain #ElixirAI #ConversationalAI #TechInnovation #LLMIntegration

From Python to Elixir Machine Learning The article discusses the process of transitioning machine learning projects from Python and PyTorch to Elixir using Nx. It focuses on how Elixir’s growing ML ecosystem offers new opportunities for developers familiar with Python. The author shares insights on the porting process, comparing Python’s and Elixir’s handling of machine learning tasks, and highlights the strengths of Elixir’s Nx library in creating efficient, scalable ML solutions. #PythonToElixir #MachineLearning #ElixirNx #TechTransition #DataScience

Originally published by M@ on Medium.