Machine Learning Guide

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction. Links * Notes and resources at ocdevel.com/mlg/mlg35 [https://ocdevel.com/mlg/35?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg35] * Build the future of multi-agent software with AGNTCY [https://agntcy.org/?__hstc=105720540.0a8b762106a9ca77d03425faf542e3bd.1742839154237.1745445081089.1745506887590.70&__hssc=105720540.11.1745506887590&__hsfp=3347877758] * Try a walking desk [https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg35] stay healthy & sharp while you learn & code In-Context Learning (ICL) * Definition: LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters. * Types: * Zero-shot: Direct query, no examples provided. * One-shot: Single example provided. * Few-shot: Multiple examples, balancing quantity with context window limitations. * Mechanism: ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations. * Emergent Properties: ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples. Retrieval Augmented Generation (RAG) and Grounding * Grounding: Connecting LLMs with external knowledge bases to supplement or update static training data. * Motivation: LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge. * Benefit: Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information. * RAG Workflow: 1. Embedding: Documents are converted into vector embeddings (using sentence transformers or representation models). 2. Storage: Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant). 3. Retrieval: When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing. 4. Augmentation: Retrieved chunks are added to the prompt to provide up-to-date context for generation. 5. Generation: The LLM generates responses informed by the augmented context. * Advanced RAG: Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge). LLM Agents * Overview: Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage. * Key Components: * Reasoning Engine (LLM Core): Interprets goals, states, and makes decisions. * Planning Module: Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment. * Memory: Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems. * Tools and APIs: Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models. * Capabilities: Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability. * Current Trends: Research and development are shifting toward these agentic paradigms as LLM core scaling saturates. Multimodal Large Language Models (MLLMs) * Definition: Models capable of ingesting and generating across different modalities (text, image, audio, video). * Architecture: * Modality-Specific Encoders: Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images). * Fusion/Alignment Layer: Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content. * Unified Transformer Backbone: Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format. * Recent Advances: Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models. * Functionality: Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation. Advanced LLM Architectures and Training Directions * Predictive Abstract Representation: Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders). * Patch-Level Training: Predicting larger “patches” of tokens to reduce sequence lengths and computation. * Concept-Centric Modeling: Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model). * Multi-Token Prediction: Training models to predict multiple future tokens for broader context capture. Evaluation Benchmarks (as of 2025) * Key Benchmarks Used for LLM Evaluation: * GPQA (Diamond): Graduate-level STEM reasoning. * SWE Bench Verified: Real-world software engineering, verifying agentic code abilities. * MMMU: Multimodal, college-level cross-disciplinary reasoning. * HumanEval: Python coding correctness. * HLE (Human’s Last Exam): Extremely challenging, multimodal knowledge assessment. * LiveCodeBench: Coding with contamination-free, up-to-date problems. * MLPerf Inference v5.0 Long Context: Throughput/latency for processing long contexts. * MultiChallenge Conversational AI: Multiturn dialogue, in-context reasoning. * TAUBench/PFCL: Tool utilization in agentic tasks. * TruthfulnessQA: Measures tendency toward factual accuracy/robustness against misinformation. Prompt Engineering: High-Impact Techniques * Foundational Approaches: * Few-Shot Prompting: Provide pairs of inputs and desired outputs to steer the LLM. * Chain of Thought: Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality. * Clarity and Structure: Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring. * Affirmative Directives: Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”). * Iterative Self-Refinement: Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality. * System Prompt/Role Assignment: Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”). * Guideline: Regularly consult official prompting guides from model developers as model capabilities evolve. Trends and Research Outlook * Inference-time compute is increasingly important for pushing the boundaries of LLM task performance. * Agentic LLMs and multimodal reasoning represent the primary frontiers for innovation. * Prompt engineering and benchmarking remain essential for extracting optimal performance and assessing progress. * Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance. Links * Notes and resources at ocdevel.com/mlg/mlg34 [https://ocdevel.com/mlg/34?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg34] * Build the future of multi-agent software with AGNTCY [https://agntcy.org/?__hstc=105720540.0a8b762106a9ca77d03425faf542e3bd.1742839154237.1745445081089.1745506887590.70&__hssc=105720540.11.1745506887590&__hsfp=3347877758] * Try a walking desk [https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg34] stay healthy & sharp while you learn & code Transformer Foundations and Scaling Laws * Transformers: Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs. * Scaling Laws: * Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately. * The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient. Emergent Abilities in LLMs * Emergence: When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: * In-Context Learning (ICL): Performing new tasks based solely on prompt examples at inference time. * Instruction Following: Executing natural language tasks not seen during training. * Multi-Step Reasoning & Chain of Thought (CoT): Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps. * Discontinuity & Debate: These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties. Architectural Evolutions: Mixture of Experts (MoE) * MoE Layers: Modern LLMs often replace standard feed-forward layers with MoE structures. * Composed of many independent "expert" networks specializing in different subdomains or latent structures. * A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation." * Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead. * Specialization & Efficiency: Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists. The Three-Phase Training Process * 1. Unsupervised Pre-Training: Next-token prediction on massive datasets—builds a foundation model capturing general language patterns. * 2. Supervised Fine Tuning (SFT): Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed. * 3. Reinforcement Learning from Human Feedback (RLHF): * Collects human preference data by generating multiple responses to prompts and then having annotators rank them. * Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness). * Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways. Advanced Reasoning Techniques * Prompt Engineering: The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality. * Chain of Thought (CoT) Prompting: Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. * Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel). * Automated Reasoning Optimization: Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency. Optimization for Training and Inference * Tradeoffs: The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs. * Current Trends: Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.

Model Context Protocol (MCP) standardizes tool communication, enabling AI coding agents to perform complex tasks like executing commands, interacting with web browsers, and integrating local or cloud resources. MCP servers broaden AI applications beyond coding. In machine learning, use AI tools to help optimizing data engineering, model deployment, and augmenting typical machine learning tasks. Links * Notes and resources at ocdevel.com/mlg/mla-24 [https://ocdevel.com/mlg/mla-24?utm_source=podcast&utm_medium=mlg&utm_campaign=mla24] * Try a walking desk [https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mla24] stay healthy & sharp while you learn & code * Try Descript [https://ocdevel.com/creator] audio/video editing with AI power-tools Tool Use in AI Code Agents * File Operations: Agents can read, edit, and search files using sophisticated regular expressions. * Executable Commands: They can recommend and perform installations like pip or npm installs, with user approval. * Browser Integration: Allows agents to perform actions and verify outcomes through browser interactions. Model Context Protocol (MCP) * Standardization: MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools. * Implementation: * MCP Client: Converts AI agent requests into structured commands. * MCP Server: Executes commands and sends structured responses back to the client. * Local and Cloud Frameworks: * Local (S-T-D-I-O MCP): Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres. * Cloud (SSE MCP): SaaS providers offer cloud-hosted MCPs to enhance external integrations. Expanding AI Capabilities with MCP Servers * Directories: Various directories exist listing MCP servers for diverse functions beyond programming. modelcontextprotocol/servers [https://github.com/modelcontextprotocol/servers] * Use Cases: * Automation Beyond Coding: Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management. * Creative Solutions: Encourages innovation in automating routine tasks by integrating diverse MCP functionalities. AI Tools in Machine Learning * Automating ML Process: * Auto ML and Feature Engineering: AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions. * Pipeline Construction and Deployment: Facilitates the use of infrastructure as code for deploying ML models efficiently. * Active Experimentation: * Jupyter Integration Challenges: While integrations are possible, they often lag and may not support the latest models. * Practical Strategies: Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency. * Action Plan for ML Engineers: * Setup structured folders and documentation to leverage AI tools effectively. * Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows.

Links * Notes and resources at  ocdevel.com/mlg/mla-23 [https://ocdevel.com/mlg/mla-23?utm_source=podcast&utm_medium=mlg&utm_campaign=mla23] * Try a walking desk [https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mla23] stay healthy & sharp while you learn & code * Try Descript [https://ocdevel.com/creator] audio/video editing with AI power-tools Model Current Leaders According to the Aider Leaderboard [https://aider.chat/docs/leaderboards/] (as of April 12, 2025), leading models include for vibe-coding: * Gemini 2.5 Pro Preview 03-25: most accurate and cost-effective option currently. * Claude 3.7 Sonnet: Performs well in both architect and code modes with enabled reasoning flags. * DeepSeek R1 with Claude 3.5 Sonnet: A popular combination for its balance of cost and performance between reasoning and non-reasoning tasks. Local Models * Tools for Local Models: Ollama [https://ollama.com/] is the standard tool to manage local models, enabling usage without internet connectivity. * Privacy and Security: Utilizing local models enhances data security, suitable for sensitive projects or corporate environments that require data to remain onsite. * Performance Trade-offs: Local models, due to distillation and size constraints, often perform slightly worse than cloud-hosted models but offer privacy benefits. Fine-Tuning Models * Customization: Developers can fine-tune pre-trained models to specialize them for their specific codebase, enhancing relevance and accuracy. * Advanced Usage: Suitable for long-term projects, fine-tuning helps models understand unique aspects of a project, resulting in consistent code quality improvements. Tips and Best Practices * Judicious Use of the @ Key: Improves model efficiency by specifying the context of commands, reducing the necessity for AI-initiated searches. * Examples include specifying file paths, URLs, or git commits to inform AI actions more precisely. * Concurrent Feature Implementation: Leverage tools like Boomerang mode [https://docs.roocode.com/features/boomerang-tasks] to manage multiple features simultaneously, acting more as a manager overseeing several tasks at once, enhancing productivity. * Continued Learning: Staying updated with documentation, particularly Roo Code [https://docs.roocode.com/]'s, due to its comprehensive feature set and versatility among AI coding tools.

Links * Notes and resources at  ocdevel.com/mlg/mla-22 [https://ocdevel.com/mlg/mla-22?utm_source=podcast&utm_medium=mlg&utm_campaign=mla22] * Try a walking desk [https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mla22] stay healthy & sharp while you learn & code * Try Descript [https://ocdevel.com/creator] audio/video editing with AI power-tools I currently favor Roo Code. Plus either gemini-2.5-pro-exp-03-25 for Architect, Boomerang, or Code with large contexts. And Claude 3.7 for code with small contexts, eg Boomerang subtasks. Many others favor Cursor, Aider, or Cline. Copilot and Windsurf are less vogue lately. I found Copilot to struggle more; and their pricing - previously their winning point - is less compelling now. Why I favor Roo. The default settings have it as stable and effective as Cline, Cursor. But you can tinker more with these settings - eg, for Gemini 2.5 I disable partial file reads (since it has a huge context window). Their modes are elegantly just custom system prompts (an oversimplification), making custom workflows very powerful. A potent example is their Boomerang Mode, which is an orchestrator that delegates planning and edit subtasks, to keep context windows tight. Boomerang mode specifically is a plugin-seller, it's incredibly powerful. Aider is still a darn decent exacto-knife, but as Roo has grown, I haven't found much need for Aider. Tools discussed: 1. Roo Code [https://github.com/RooVetGit/Roo-Code] 2. Aider [https://aider.chat/] 3. Cursor [https://www.cursor.com/] 4. Cline [https://github.com/cline/cline] 5. Copilot [https://github.com/features/copilot] 6. Windsurf [https://codeium.com/windsurf] Other: 1. Leaderboards [https://aider.chat/docs/leaderboards/] 2. Video of speed-demon [https://www.youtube.com/watch?v=QlUt06XLbJE&feature=youtu.be] 3. Reddit [https://www.reddit.com/r/chatgptcoding/] "Vibe coding" using AI agents in software development. It uses LLMs for code generation and project management. Developers are increasingly relying on agentic tools and IDE plugins to improve productivity. Use of AI in Code Generation * AI tools facilitate the generation and editing of code. * Integration typically occurs within IDEs or as plugins. * These tools offer features like inline editing, bug fixing, and project scaffolding. Evolution and Adoption * The concept is gaining popularity due to its efficiency and competitive edge in development. Popular AI Tools for Vibe Coding Cursor * Characteristics: Most popular, stable, with advanced agentic capabilities. * Pricing: $20 per month, additional charges for power-use. * Strengths: Reliable, focuses on integrating new models effectively. Windsurf * Characteristics: Cost-effective, a VS Code fork. * Pricing: Starts at $15, with higher usage at $60. * Strengths: Similar to Cursor, with a competitive pricing model. GitHub Copilot * Characteristics: Operates within GitHub code spaces, developed by Microsoft. * Pricing: $10 to $40 monthly. * Strengths: Deep integration with cloud-based development environments. Cline * Characteristics: Open-source, known for customizable features. * Pricing: BYOM (Bring Your Own Model), costs based on individual API usage. * Strengths: Community-driven, rapid development cycles. Roo Code * Characteristics: Fast-moving, offers the latest technological advancements. * Pricing: Uses BYOM model, similar to Cline. * Strengths: Frequent updates, for users wanting cutting-edge features. Aider * Characteristics: CLI-based, focuses on precision and minimal token usage. * Pricing: BYOM, efficient token usage strategies. * Strengths: High accuracy for small adjustments, good for backup use. Choosing the Right Tool * Beginner Recommendation: Start with Cursor for reliability. * Experimentation: Try Copilot and Windsurf for comparisons. * Advanced Configuration: Use Kline or Roo Code for sophisticated tasks and ER for precise adjustments. Cost Management * Open Router: Centralize API billing to manage interactions across multiple models, preventing fragmented payments.