What Is Generative AI

Krish Naik
10 Jun 202315:50

TLDRIn this informative video, the creator discusses the emerging field of generative AI, a subset of deep learning that leverages large datasets to generate new content such as text, images, and music. The video emphasizes the growing importance of generative AI in creating chatbots, image generation tools, and more. It also touches on the concepts of large language models like ChatGPT and the role of prompt engineering in customizing AI responses. The creator plans to cover practical implementations and further topics in upcoming videos.

Takeaways

  • 📚 Generative AI is a subset of deep learning and is based on generative techniques.
  • 🚀 The demand for jobs related to generative AI is expected to increase in the next two years due to the rise of startups in the field.
  • 🤖 Large Language Models (LLMs) like ChatGPT and Google Bard are examples of models trained with vast amounts of data and are used for various NLP tasks.
  • 📈 Generative AI can be used to create new data such as text, music, images, and videos based on the distribution and patterns learned from unstructured data.
  • 🔍 The difference between generative and discriminative models lies in the type of tasks they perform; generative models create new data, while discriminative models classify or predict based on labeled data.
  • 🎯 Generative AI does not require labeled data for training but instead learns from the distribution of a large, unstructured dataset.
  • 🌐 The training process of generative AI involves learning patterns and distributions from unstructured content, which may require human supervision and reinforcement learning for accuracy.
  • 🛠️ Prompt engineering is a technique used to shape the output of LLMs and is an in-demand skill for creating custom models and chatbots using APIs like OpenAI.
  • 🔄 The script emphasizes the importance of understanding the basics of AI and deep learning to build a strong foundation for learning about generative AI.
  • 🎓 The playlist aims to cover a range of topics related to generative AI, including practical implementations and discussions on models like ChatGPT and LangChain.

Q & A

  • What is the main focus of the YouTube playlist discussed in the video?

    -The main focus of the YouTube playlist is to discuss generative AI, its basics, applications, and related technologies such as large language models (LLMs) and prompt engineering.

  • Why is generative AI considered important for future job prospects?

    -Generative AI is considered important for future job prospects because many startups are working in this field, creating chatbots, image and video generation tools, and other applications, leading to an increased demand for expertise in generative AI.

  • What is the relationship between generative AI and deep learning?

    -Generative AI is a subset of deep learning. It utilizes deep learning techniques to generate new data based on patterns and distributions learned from large datasets.

  • How does generative AI differ from discriminative models in deep learning?

    -Discriminative models in deep learning are used for classification and prediction tasks on labeled datasets, whereas generative AI focuses on creating new data by learning the distribution of unstructured content in large datasets.

  • What are some examples of applications that utilize generative AI?

    -Some examples of applications that utilize generative AI include chatbots, image generation tools like DALL-E, and music generation models.

  • What is a large language model (LLM) and how does it relate to generative AI?

    -A large language model (LLM) is a type of AI model trained on vast amounts of text data to perform various NLP tasks such as text translation, conversation, text summarization, and more. LLMs are a subset of generative AI as they generate new text data based on learned patterns.

  • What is the role of reinforcement learning in the training process of generative AI?

    -Reinforcement learning plays a role in the training process of generative AI by providing feedback to improve the model's accuracy. Human supervision and reinforcement help the model learn patterns and distributions more effectively.

  • How can generative AI be distinguished from other types of AI?

    -Generative AI can be distinguished by its output. If the output is in the form of numbers, class probabilities, or categories, it is not generative AI. However, if the output is in the form of text, audio, images, or video frames, then it is considered generative AI.

  • What is prompt engineering and how is it relevant to generative AI?

    -Prompt engineering is the process of formulating input prompts in a way that guides the generative AI model to produce a desired response. It is relevant to generative AI as it is a technique used to train custom models and extract specific outputs from LLMs.

  • What are some potential future developments for generative language models like GPT?

    -Potential future developments for generative language models like GPT include the ability to perform image and video generation, text to speech conversion, and enhanced capabilities for tasks like translation, summarization, and Q&A.

  • How does the speaker suggest one should approach learning about generative AI?

    -The speaker suggests that one should approach learning about generative AI from the basics, starting with an understanding of AI and machine learning, and then progressively learning about deep learning, discriminative models, and finally, generative AI techniques.

Outlines

00:00

🤖 Introduction to Generative AI and its Future Scope

The speaker, Krishnaik, introduces himself and his YouTube channel, emphasizing the importance of the new playlist focused on Generative AI. He predicts a rise in jobs related to Generative AI in the next two years due to the increasing number of startups working with AI technologies such as chatbots, image and video generation tools. Krishnaik highlights the significance of prompt engineering and its role in job openings. The video aims to explain Generative AI from the basics, discussing its place within deep learning and the differences between deep learning models like CNN and RNN, and Generative AI. Krishnaik encourages new viewers to subscribe for more informative content.

05:01

📊 Understanding Discriminative and Generative Techniques in AI

Krishnaik delves into the distinction between discriminative and generative techniques in AI. He explains that discriminative techniques involve classification and prediction tasks using labeled datasets, whereas generative techniques deal with creating new data based on patterns learned from unstructured datasets. Generative AI models, such as GPT and Google Bard, are trained on vast amounts of data to perform NLP tasks like text translation, acting as chatbots, and text summarization. Krishnaik also discusses the training process of generative models, emphasizing the use of reinforcement learning for better accuracy.

10:02

🌐 Applications and Significance of Generative AI

The speaker discusses the increasing popularity of Generative AI and its applications, such as generative language models and image models. He mentions Dali 2 as an example of an image model that converts text into images. Krishnaik explains that Generative AI is a subset of deep learning and works with large amounts of data to understand data distribution and generate new content like text, music, images, and videos. He also talks about the potential of using APIs like OpenAI and Google Vertex for creating custom chatbots and models, highlighting the demand for professionals skilled in prompt engineering.

15:04

🚀 Conclusion and Future of Generative AI

Krishnaik concludes the video by reiterating the potential and future scope of Generative AI. He explains how generative AI models function like a human who has learned about a topic and can generate various responses when asked questions. He also mentions the upcoming functionalities in GPT-5, such as image and video generation, and text to speech. Krishnaik encourages viewers to stay tuned for future videos in the playlist, focusing on LM models and prompt engineering, and reminds viewers to subscribe to his channel for more informative content.

Mindmap

Keywords

💡Generative AI

Generative AI refers to a subset of artificial intelligence that focuses on creating or generating new data that is similar to the data it was trained on. In the context of the video, it is a key technology behind various applications like chatbots, image generation tools, and language models. The video emphasizes its growing importance and application in the job market and tech startups.

💡LLM (Large Language Models)

Large Language Models, or LLMs, are a type of AI model specifically designed to process and generate human-like text. They are trained on vast amounts of data and can perform tasks such as translation, text summarization, and acting as a chatbot. In the video, the presenter discusses the role of LLMs in generative AI and mentions examples like ChatGPT and Google Bard.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to learn and make decisions. It is the foundation of generative AI, enabling the creation of models that can understand and generate complex patterns in data. The video positions generative AI as a subset of deep learning and discusses its relationship with other deep learning techniques like CNN and RNN.

💡Prompt Engineering

Prompt engineering is the process of crafting input text or 'prompts' for AI models to elicit desired responses. It is a critical skill in working with large language models, as the way a question or request is phrased can significantly affect the output. The video mentions prompt engineering as a job skill in demand and explains its relevance in customizing AI responses.

💡ChatGPT

ChatGPT is a specific example of a large language model that is part of the generative AI family. It is trained to generate human-like text based on the input it receives. In the video, the presenter uses ChatGPT as an illustration of how generative AI can be applied in creating conversational AI systems.

💡Discriminative Models

Discriminative models in deep learning are designed to classify or predict outcomes based on input data. They learn from labeled datasets to recognize patterns and make decisions. The video contrasts discriminative models with generative models, emphasizing that the latter does not require labeled data and instead focuses on understanding the distribution of data.

💡Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data, with the goal of discovering hidden patterns or structures in the data. It is different from supervised learning, which requires labeled data. The video mentions unsupervised learning in the context of generative AI, where models learn from vast, unstructured datasets to generate new content.

💡Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions and receiving rewards or penalties. It is used in generative AI to improve the accuracy of models by adjusting their behavior based on feedback. The video mentions that human supervision and reinforcement learning are important for training generative AI models to generate more accurate and human-like content.

💡Generative Image Models

Generative image models are AI models that are capable of creating new images from existing data. They learn from a dataset of images to generate new visual content. The video mentions DALL-E 2 as an example of a generative image model that can convert text descriptions into images, demonstrating the versatility of generative AI in different types of media.

💡Open AI API

The Open AI API is a set of tools and interfaces provided by Open AI that allows developers to integrate AI models, like GPT, into their applications. It enables the creation of custom AI solutions using pre-trained models. The video highlights the practical use of the Open AI API in creating chatbots and custom models through prompt engineering.

💡Deep Learning Models

Deep learning models are neural network-based models that can process complex data like images, audio, and text. They are capable of learning from large datasets and performing tasks such as classification, prediction, and generation of new data. The video places deep learning models as a category that includes both discriminative and generative models.

Highlights

Introduction to the new playlist focusing on Generative AI and its job prospects in the upcoming years.

Generative AI is a subset of deep learning and is the basis for many startups creating chatbots, image and video generation tools.

The importance of understanding Prompt Engineering as it is related to many job openings in the field of Generative AI.

Overview of the differences between supervised and unsupervised machine learning in the context of deep learning.

Explanation of Generative AI as a part of generative techniques in deep learning, contrasting it with discriminative models like CNN and RNN.

The role of large language models (LLMs) like ChatGPT and Google Bard as subsets of Generative AI, trained with vast amounts of data.

The process of training generative models on large datasets to generate new content such as text, music, or images.

The distinction between generative AI and discriminative techniques based on the type of output they produce.

The significance of unstructured content in the training process of generative AI and the role of reinforcement learning.

The potential of generative AI in creating custom chatbots and models using OpenAI API and prompt engineering.

The evolution of machine learning and deep learning models from independent features to complex data like images and video frames.

How generative AI mimics human learning to generate responses and its application in creating chatbots and other AI-driven tools.

The future of generative language models, including the anticipated capabilities of ChatGPT 5 such as image and video generation, and text to speech.

The practical applications of generative AI through the use of APIs like OpenAI and Google's generative AI Studio.

The role of prompt engineering in defining the format and quality of responses from LLMs and its importance in the job market.

The upcoming tutorials on prompt engineering and practical implementations using OpenAI API to create custom models.

Casual Browsing

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