1. The rise of LLMs in the AI landscape
Welcome to this course, where we will learn about Large Language Models! In this video, we will take a bird's eye view of the fascinating world of LLMs and understand their emergence in the Artificial Intelligence (AI) landscape. Let's dive right in!
2. Rapid developments in AI
AI is everywhere - in facial recognition systems that analyze and identify our facial features to unlock smartphones or other devices and services;
or the self-driving car, another fascinating application of AI algorithms that has revolutionized the transportation industry.
3. AI-powered recommendations
It assists us with various tasks, from recommending movies to suggesting songs on streaming platforms.
AI algorithms analyze user preferences, history, and behavior data to generate personalized recommendations that help users discover the most relevant content.
4. AI and data-driven tasks
In addition to personalization, AI has performed well in data-driven tasks such as sentiment analysis, fraud detection, and more. However, it traditionally lacked the ability to understand context, respond to open-ended questions, and generate human-like responses in conversation.
Recent developments in language-based AI models have led to a disruptive breakthrough. These models, called large language models, can process language as humans do.
5. The AI landscape
Before we discuss large language models, let's break down AI and the fields it includes from largest to smallest.
6. The AI landscape
Let's start with Machine Learning, a subfield of Artificial Intelligence that enables models to learn patterns from data without explicit instructions.
7. The AI landscape
A subset of Machine Learning is Deep Learning, which can recognize complex patterns like those found in computer vision and self-driving cars.
8. The AI landscape
Natural Language Processing (NLP) utilizes machine learning techniques, among others, to understand and process human language by computers.
9. The AI landscape
Large Language Models, which we call LLMs, use Deep Learning techniques to perform a variety of Natural Language Processing (NLP) tasks such as text classification, summarization, generation, and more.
10. Definition of LLMs
LLMs are called "large" because they require a large amount of training data and resources to work.
11. Definition of LLMs
They are powerful in processing and analyzing human language data. They have set new benchmarks in various NLP tasks, outperforming their predecessors and opening up new possibilities in AI.
12. Definition of LLMs
"Models" corresponds to the capability of learning complex patterns using data. In the case of LLMs, the data is text from the internet.
13. The defining moment
The LLM is considered the defining moment, or the iPhone moment, in the history of AI.
It has been the talk of many conversations - technical and non-technical alike.
14. Popular language generators
The GPT series by OpenAI is one of the most popular language models among the family of LLMs, primarily because of its advanced ability to engage in rich human interactions.
There are other language generators in the market, and new ones are added every day.
15. Applications
LLMs can perform a wide range of tasks, such as sentiment analysis, identifying themes, translating text or speech, and even generating code!
They are also used to predict the next words in a text based on what the user has already typed.
Isn't that amazing?
16. What shall this course cover?
In this course, we will learn all about LLMs. We will cover the conceptual working of what happens underneath these mighty and powerful language models. Besides the training data considerations, we will also uncover some of the ethical, privacy, and environmental concerns surrounding the development of such large models.
We will wrap up the course by covering the enhancements and future work in the space of LLMs.
17. Let's practice!
Now that we understand how LLMs are revolutionizing the AI landscape and enabling cutting-edge applications, let's practice!