Decoding Tech: AI Evolution, Generative AI Basics Mastery Guide

The field of Artificial Intelligence (AI) focuses on enabling machines to think and make decisions without human intervention, leveraging data analysis and pattern recognition. AI allows machines to adapt their knowledge based on new inputs, but the accuracy of predictions depends on the quantity and quality of the training data. For instance, in applications like self-driving cars, AI plays a crucial role.

Machine Learning (ML), a subset of AI, provides statistical tools to explore and analyze data, utilizing algorithms for learning and prediction. It encompasses Supervised ML (using labeled data), Unsupervised ML (clustering data), and Reinforcement ML (semi-supervised with labeled/unlabeled data).

Deep Learning (DL), a subset of ML, involves multi-layered neural architectures, particularly Artificial Neural Networks (ANN). Inspired by the human brain, ANN consists of interconnected nodes that learn tasks by processing data and making predictions. DL models, with multiple layers, can understand complex patterns. Semi-supervised learning in DL involves training on a small amount of labeled data and a large amount of unlabeled data for improved generalization.

Generative AI (Gen AI), a subset of DL, creates new content such as text, images, audio, and video by learning from existing data. It utilizes prompts to generate unique outputs. An example is OpenAI’s DALL-E model, which generates impressive images through generative technology.

Generative AI operates by inputting a substantial corpus of both structured and unstructured data into a training phase.

For instance, OpenAI’s model undergoes extensive training over months with vast amounts of data.

The unique capability of generative AI lies in its ability to perform not just one task but a multitude of tasks. With a single large language model, tasks such as question answering and sentiment analysis can be effortlessly accomplished, showcasing the versatility of generative AI across diverse applications.

Application and Use cases

Large Language Models (LLMs):

LLMs are neural networks trained on massive text data, utilizing deep learning algorithms for tasks like translation, sentiment analysis, and text generation. They are general-purpose, pre-trained on extensive text and focused on language understanding and generation. LLMs commonly use vector similarity search to retrieve information from external databases.

Langchain:

Langchain is an open-source framework designed for developers to create applications powered by LLMs. It acts like Lego blocks, allowing the use of multiple LLMs for various behaviors without starting from scratch. Langchain facilitates the creation of pipelines, speeding up application development. It also enables linking LLMs to different data sources such as file systems, databases, and APIs.

Why LangChain:
LangChain is essential because many existing LLMs may not be up-to-date, lack domain knowledge, and struggle with proprietary data. Working with different LLMs individually can be tedious, and LangChain simplifies this process.

Embedding Model:

The Embedding Model transforms unstructured data into numerical data, creating vectors that computers can understand. These vectors represent data in different ways. One application of vectors is finding similar items through vector similarity search, although this process can be slow.

Vector Database:

Vector databases store and index vector embedding for fast retrieval and similarity search. This infrastructure allows for quicker searching and retrieval of relevant information based on vector representations.