My experience with an AI Tutor

I wanted to try out OpenAI’s GPT Builder (Beta). I started by getting it to create a personal AI Masterclass Tutor for me. I told the GPT Builder my academic background and asked it to generate a Personal Tutor. In terms of behaviors, I asked for a combined formal and casual teaching style (situationally decided) and a high-level to in-depth approach to teaching. I asked for research papers, the latest trends, coding examples, and case studies. I asked the GPT Builder to create a Tutor that would be factual, encouraging, and progress-monitoring.

The GPT Builder suggested the name AI Masterclass Tutor and the following logo:

Once generated, my Personal Tutor was ready to teach me!

Lessons Roadmap Suggested by my GPT Tutor:

1. Advanced Machine Learning Algorithms: This includes a thorough understanding of both supervised and unsupervised learning algorithms. You should be comfortable with concepts like deep learning, reinforcement learning, and ensemble methods. Topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer models, and advanced optimization techniques are crucial. Additionally, understanding the mathematical underpinnings of these algorithms, such as gradient descent, backpropagation, and loss functions, is essential.
2. Probabilistic Models and Bayesian Inference: Given your mathematics background, delving into probabilistic models and Bayesian approaches to AI can be highly beneficial. This includes understanding Bayesian networks, Markov models, Gaussian processes, and Hidden Markov Models (HMMs). These concepts are fundamental in areas like natural language processing, robotics, and computer vision.
3. Natural Language Processing (NLP): With the advent of transformer models like GPT-3 and BERT, NLP has undergone a significant transformation. Understanding the architectures, training methods, and applications of these models is crucial. Topics like tokenization, embeddings, sequence-to-sequence models, attention mechanisms, and transformers are key areas of focus.
4. Reinforcement Learning and Decision Making: This is particularly important for applications in robotics, game playing, and autonomous systems. Key concepts include understanding Markov Decision Processes (MDPs), Q-learning, policy gradient methods, and advanced algorithms like Proximal Policy Optimization (PPO) and Deep Q Networks (DQN).
5. Ethics and Fairness in AI: As AI systems become more pervasive, understanding the ethical implications and ensuring fairness and transparency in AI models is critical. This includes bias detection and mitigation, ethical AI design principles, and the societal impacts of AI technologies.

Next, we started the lesson with the first topic! That would be my next post here.