- Link: Introduction-to-Generative-AI
- Author: Simranjeet Singh
- Publication date: July 31, 2023
Introduction
Hi Guys!, Welcome to the series “Learn Generative AI from Scratch using Python”. This series covers everything about generative AI that a Data Scientist and Software Engineer could know and start their journey in this fantastic GenAI domain. I will tell you everything from Python to Machine Learning then Deep Learning and then Natural Language Processing and Generative Models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (ChatGPT, Google Bard, Claude 2 or many more). So let’s start this introductory blog on Generative AI that gives you a brief about Generative AI and all the latest AI tools in the market that we could use to build GenAI Applications using Python from scratch.
Pre-Requisite to Learn Generative AI
- Python —To Learn Click Here
- Machine Learning — To Learn Click Here
Generative AI Landscape
The Generative AI Landscape refers to the diverse and rapidly evolving field of artificial intelligence that focuses on creating models and systems capable of generating new data instances that resemble a given dataset. These models are known as generative models. Generative AI is a subset of artificial intelligence that is distinct from discriminative models, which are primarily used for classification and regression tasks.
The Generative AI Landscape encompasses a wide range of techniques and models, each with its strengths and applications. Some of the key components of the Generative AI Landscape include:
1. Generative Adversarial Networks (GANs): GANs are a popular class of generative models. They consist of two neural networks: the generator and the discriminator. The generator attempts to create realistic data samples, while the discriminator tries to distinguish between real and generated samples. Through adversarial training, GANs can produce highly realistic and novel data, making them widely used in image synthesis, video generation, and more.
2. Variational Autoencoders (VAEs): VAEs are another prominent class of generative models. They are probabilistic models that learn to encode data into a latent space and decode it back to the original data. VAEs are used for tasks such as image generation, data compression, and generating new data instances from the learned latent space.
3. Autoregressive Models: These models generate data one element at a time, conditioned on the previously generated elements. PixelRNN and PixelCNN are examples of autoregressive models that have been successful in generating images.
4. Language Models: Language models, especially Transformers, have revolutionized natural language processing (NLP) and text generation tasks. Models like GPT (Generative Pre-trained Transformers) have been instrumental in generating coherent and contextually relevant text.
5. Deep Reinforcement Learning (RL) and Generative Adversarial Imitation Learning (GAIL): These techniques use reinforcement learning to learn generative policies, enabling AI agents to generate new sequences of actions in dynamic environments.
6. Hybrid Models: Some approaches combine elements from multiple generative models to create novel architectures with enhanced capabilities.
The Powerhouse of AI in 2023
1. Google Bard: It is built on the Pathways Language Model 2 (PaLM 2), a language model released in late 2022. A powerful language model, offering customizable responses in more than 40 languages. It is a hypothetical artificial intelligence system designed to create poetic and artistic content and can customize the responses based on tone, quality, and length. It can also analyze images and understand the context and is also integrated with all Google apps like Drive, Docs, excel, and many more.
2. Anthropic’s Claude 2: Mastering Deep Analysis Anthropic’s Claude 2 has taken the AI chatbot scene by storm! With an ability to analyze in-depth documents containing up to 100,000 tokens, Claude 2 showcases unprecedented proficiency in comprehension and context. Moreover, its prowess in Python coding tests makes it a versatile assistant for developers and data scientists alike.
3. OpenAI ChatGPT: The Constant Companion Amidst these new AI marvels, OpenAI’s ChatGPT remains a beacon of excellence. Since its introduction in November 2022, ChatGPT has charmed users with its human-like responses and vast knowledge base. It can generate anything with greater accuracy than Google Bard, and Claude 2 and now running on the GPT4 model.
All these Large Language models offer API support so that users can use their models and build customized AI applications using different programming languages like javascript, python, java, Go, and many more. So in this series, I will use Python to build the programs and applications using these LLM APIs.
Conclusion
Join us on this captivating voyage, where Generative AI converges with Python and Machine Learning to unlock the boundless potential of AI-generated creativity. Whether you’re a novice or an enthusiast, there’s a treasure trove of knowledge awaiting you. So let’s set sail into the future of AI! Stay tuned for more engaging content and thrilling discoveries. Let’s embark on this adventure together!