I Turned Myself Into An AI Tutor...

Key Concepts

• Digital Twin/AI Replica: A virtual representation of the speaker created using AI.
• AI Tutor: An AI persona designed to explain complex subjects.
• Tavos: A platform for creating AI replicas, personas, and embedding them into applications.
• Flask: A Python web framework used to build the application.
• Live Calls: Real-time conversations with the AI replica.
• Video Answers: Asynchronous video responses generated by the AI replica.
• Persona: A role or identity assigned to the AI replica (e.g., tutor, narrator).
• API Key: Authentication credentials for accessing Tavos and OpenAI services.
• GPT-40: A language model used for generating text-based answers.
• Support Vector Machines (SVMs): A machine learning algorithm discussed as an example topic.
• Linear Regression: A statistical method discussed as an example topic.
• Back Propagation: A key part of how neural networks learn.

Creating an AI Replica with Tavos

• The speaker uses Tavos (tavos.io) to create a digital twin of himself. Tavos is sponsoring the video.
• The process involves creating a replica, which takes 3-4 hours to train. The speaker pre-trained a replica.
• The replica creation process requires providing consent to Tavos to use audio and video samples.
• The speaker acknowledges a slight "uncanny valley" effect with the replica, noting that the animations are a bit clunky.

Defining an AI Tutor Persona

• A persona is created on top of the AI replica to define its role as an AI tutor.
• The persona is named "AI tutor" and described as an AI and machine learning tutor.
• The persona's job is to explain complex subjects in a simple and understandable way without sacrificing details.
• GPT-40 is selected as the language model for the persona.
• Default perception, speech-to-text, and text-to-speech models are used.

Live Conversations with the AI Tutor

• The speaker demonstrates a live conversation with the AI tutor.
• The AI tutor is asked to explain the concept of back propagation.
• The speaker notes that the replica's facial movements are very accurate when listening, but less optimal when talking.

Generating Video Answers

• The speaker demonstrates generating a video answer using the AI replica.
• The speaker provides a prompt: "What is going on guys? Welcome back. Today, we will dive deep into support vector machines. So, let us get right into it."
• The generated video is expected to take some time to process.

Building a Flask Application for Live Calls

• The speaker builds a Flask application to embed the live call functionality.
• The application allows users to provide context before starting a live call.
• The following Python packages are used: flask, requests, openai, and python-dotenv.
• An .env file is created to store the Tavos API key and OpenAI API key.
• The Tavos API key is obtained from the Tavos website.
• The replica ID and persona ID are obtained from the Tavos replica and persona libraries, respectively.
• The API URL is defined as https://coltavosapi.com/v2.
• The HTML code includes a form for providing context and an iframe for displaying the live call.
• The Flask application defines an endpoint that handles both GET and POST requests.
• If a POST request is received, the application extracts the context from the form, sends it to the Tavos API, and renders the HTML with the conversation URL.
• The application is run in debug mode.

Building a Flask Application for Video Answers

• The speaker builds a Flask application to provide video answers to user questions.
• The application uses OpenAI's GPT-40 to generate a text-based answer to the question.
• The text-based answer is then sent to the Tavos API to generate a video.
• The HTML code includes a form for asking a question and an iframe for displaying the video answer.
• The Flask application defines an endpoint that handles POST requests.
• If a POST request is received, the application extracts the question from the form, sends it to the OpenAI API, and then sends the response to the Tavos API.
• The application polls the Tavos API to check if the video is ready.
• Once the video is ready, the application renders the HTML with the video URL.
• The speaker fixes a problem where the API returns a link to a website with the video embedded, rather than the video URL itself, by changing the video tag to an iframe.

Conclusion

• The video demonstrates how to clone oneself using AI and turn the replica into an AI tutor.
• The speaker uses Tavos to create the AI replica and persona.
• The speaker builds two Flask applications: one for live calls and one for video answers.
• The video provides a practical example of how to embed AI replicas into real-world applications.
• The speaker suggests that this technology could be used to build SaaS applications for tutors or other use cases.