Machine Learning Concepts
- Architects need to understand AI and machine learning concepts to have intelligent conversations with their co-workers. 2m30s
- Most people are referring to machine learning, specifically deep learning or neural networks, when they talk about AI. 3m21s
Machine Learning Models
- Software developers can think of machine learning models as functions that take complex inputs, such as images or audio, and produce complex outputs, such as transcripts or summaries. 4m20s
- Tensors are multi-dimensional arrays used in machine learning models. 5m57s
- Machine learning models are trained using a process called supervised learning, which involves providing the model with inputs and expected outputs, similar to unit tests in software development. 6m15s
Language Models
- Language models, such as ChatGPT, are trained on vast amounts of text data to predict the probability of a word occurring next in a sequence. 8m39s
- Large language models (LLMs) are characterized by having tens or hundreds of billions of parameters. 12m42s
- Hugging Face is a platform similar to GitHub, hosting and providing access to LLMs, including smaller models that can run on personal laptops. 13m39s
- LLMs utilize "tokens," which are units of text smaller than words, allowing them to generate novel words and phrases not found in a standard vocabulary. 17m2s
- Tokenization is a process that breaks down text into smaller units, typically larger than a character but smaller than a word. 17m54s
- Large language models (LLMs) like ChatGPT and OpenAI's API use tokens to measure usage and billing. 18m9s
- The "T" in GPT stands for Transformer, a neural network architecture that utilizes an "attention" mechanism to process and generate text. 20m59s
Large Language Model Access
- There are publicly available, commercial large language models (LLMs) such as GPT-4, ChatGPT, Claude, Google's Gemini, and offerings from AWS. These can be accessed through web-based APIs and integrated using SDKs. 23m56s
- While using commercial LLMs can be cost-effective in the short term due to a pay-per-token model, long-term cost and privacy concerns may arise. 24m34s
- Open-source LLMs offer an alternative to commercial options, allowing for in-house implementation and greater control over data privacy. 25m19s
Large Language Model Characteristics
- Large language models (LLMs) are non-deterministic, meaning the output for a given input is not always predictable. 29m5s
Retrieval Augmented Generation (RAG)
- Retrieval augmented generation (RAG) is a technique that can improve the quality of LLM results by providing the model with relevant context from a knowledge base. 30m38s
- RAG works by converting documents and user queries into vectors, then finding the closest matching vectors to provide context to the LLM. 32m0s
Transfer Learning and Fine-tuning
- Transfer learning is a machine learning technique used to pre-train a model for general purposes and then fine-tune it for specific tasks. 34m37s
- Fine-tuning involves restarting the training process with a smaller dataset specific to the desired outcome, allowing for adjustments to the model's responses. 35m1s
Vector Databases
- Vector databases, often used in semantic or neural search, employ nearest neighbor search algorithms to efficiently find vectors similar to a given input vector, enabling the retrieval of related content based on meaning. 38m9s
Large Language Model Applications
- LLMs can be understood as tools for addressing natural language processing tasks, such as named entity recognition and parts of speech recognition. 40m6s
- LLMs are versatile and can be fine-tuned for specific use cases or chosen based on their proximity to a desired application, offering advantages in cost, quality, and speed. 40m50s
AI Co-pilots and AI Agents
- A distinction is made between AI co-pilots, which require user initiation, and AI agents, which possess a degree of autonomy. 43m8s
