What Is Generative AI

Krish Naik
10 Jun 202315:50

TLDRThe video script introduces the concept of generative AI, emphasizing its growing importance in the job market and its applications in various industries. It explains that generative AI is a subset of deep learning, focusing on creating new data through techniques like large language models (LLMs) and generative image models. The speaker, Krishnaik, plans to cover topics like prompt engineering and practical implementations涉及 using open AI APIs, highlighting the potential for job creation and innovation in the generative AI field.

Takeaways

  • 📚 Generative AI is a subset of deep learning and is based on generative techniques, which involve creating new data.
  • 🚀 The demand for jobs related to generative AI is expected to increase in the next two years due to the rise of startups focusing on AI applications like chatbots and image generation tools.
  • 🌐 Large Language Models (LLMs) like ChatGPT and Google Bard are examples of models that fall under the category of generative AI and are trained with vast amounts of data.
  • 🔍 Generative AI models are trained on unstructured, large datasets from various sources like the internet, aiming to learn the distribution of data rather than the relationship between input and output.
  • 🎵 Applications of generative AI include text generation, music creation, image and video generation, and more, moving beyond traditional classification and prediction tasks.
  • 📈 The training process of generative AI involves learning patterns and distributions from unstructured content, and it often requires human supervision and reinforcement learning for improved accuracy.
  • 💡 Distinguishing generative AI applications can be done by checking if the output is in the form of text, audio, images, or videos, as opposed to numerical class probabilities.
  • 🌟 Generative AI is becoming increasingly popular and significant, with platforms like OpenAI API and Google's API providing tools to create custom models and applications.
  • 🛠️ Prompt engineering is a key skill in working with generative AI, involving the crafting of inputs to generate desired outputs from LLMs.
  • 🔥 The future of generative AI looks promising with potential advancements in areas like image and video generation, text to speech, and more interactive and dynamic applications.

Q & A

  • What is the main focus of the new playlist by Krishnaik on his YouTube channel?

    -The main focus of the new playlist is to discuss and cover topics related to Generative AI, its basics, models like ChatGPT, and the role of prompt engineering in the upcoming years.

  • Why does Krishnaik believe there will be job opportunities related to Generative AI in the next two years?

    -Krishnaik believes there will be job opportunities related to Generative AI because many startups are being opened that focus on this technology, creating chatbots, image generation tools, video generation tools, and more.

  • What is the relationship between Generative AI and Large Language Models (LLMs)?

    -Generative AI is a subset of deep learning, and Large Language Models like ChatGPT are a part of Generative AI. These models are trained with vast amounts of data and can perform various NLP tasks such as text translation, acting as chatbots, and text summarization.

  • How does Krishnaik define generative AI in simple terms?

    -Generative AI can be defined as a subset of deep learning that focuses on creating or generating new data, such as text, audio, images, and videos, based on the distribution and patterns it learns from unstructured, large datasets.

  • What are the differences between discriminative techniques and generative techniques in deep learning?

    -Discriminative techniques in deep learning focus on classification and prediction using labeled datasets, whereas generative techniques do not require labeled data and instead aim to learn the distribution of data to generate new, unobserved data samples.

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

    -Reinforcement learning plays a role in the training process of generative AI models by providing feedback to improve the accuracy of the generated content. It helps the model to fine-tune its learning based on the rewards and penalties associated with the generated outputs.

  • How does Krishnaik describe the process of training a generative AI model?

    -Krishnaik describes the process of training a generative AI model as involving unstructured, large datasets from various sources like the internet. The model learns patterns and distributions within this content to generate new, similar content based on the learned data distribution.

  • What are the types of data that generative AI models can generate?

    -Generative AI models can generate various types of data, including text, audio, images, and videos. They create new content based on the patterns and distribution they learn from the input datasets.

  • How does Krishnaik relate the concept of generative AI to the development of chatbots and custom models?

    -Krishnaik relates the concept of generative AI to the development of chatbots and custom models by using techniques like prompt engineering, which involves structuring inputs to get desired outputs from models like ChatGPT. This process can be used to create custom chatbots and models using APIs from OpenAI and other platforms.

  • What are the key takeaways from Krishnaik's introduction to generative AI?

    -The key takeaways from Krishnaik's introduction to generative AI include understanding that it is a subset of deep learning, its ability to generate new data types, the importance of unstructured, large datasets in training, and the potential job opportunities and applications in fields like chatbot creation and prompt engineering.

  • What can we expect in the upcoming videos of Krishnaik's playlist on Generative AI?

    -In the upcoming videos of Krishnaik's playlist, we can expect discussions on Large Language Models, practical implementations, creating custom models using OpenAI API, and in-depth tutorials on prompt engineering.

Outlines

00:00

🚀 Introduction to Generative AI and its Future Prospects

The speaker, Krishnaik, introduces himself and his YouTube channel, setting the stage for a new playlist focused on Generative AI. He predicts a surge in job opportunities related to Generative AI in the next two years due to the rise of startups creating chatbots, image and video generation tools, and more. Krishnaik emphasizes the importance of understanding prompt engineering, a field with numerous job openings. The video aims to explain Generative AI from the basics, including its relation to deep learning and differences from traditional CNN and RNN models. Krishnaik encourages newcomers to subscribe for more content and shares his screen to display materials related to Generative AI.

05:01

📚 Understanding Discriminative and Generative Techniques in AI

Krishnaik delves into the concepts of discriminative and generative techniques within AI. He explains that discriminative techniques, such as classification and prediction, are used when the dataset is labeled. In contrast, generative techniques do not require labeled data and focus on learning the distribution of data to generate new content. Generative AI, a subset of deep learning, is becoming increasingly popular with the advent of large language models (LLMs) like ChatGPT and generative image models like DALL-E. Krishnaik provides examples of how generative models can create new data, such as music or stories, and highlights the potential of generative AI in various industries.

10:02

🌟 Applications and Training of Generative AI Models

In this section, Krishnaik discusses the practical applications of Generative AI, including generative language models and image models, and how they are trained. He explains that generative AI models are trained on unstructured, large datasets from the internet to learn patterns and distributions. The output of generative AI can be in the form of text, audio, images, or videos, which sets it apart from traditional AI applications. Krishnaik also touches on the role of reinforcement learning and human supervision in refining the accuracy of generative models. He mentions the potential of using APIs like OpenAI and Google's API to create custom chatbots and models through prompt engineering.

15:04

🎓 The Role of Prompt Engineering in Utilizing Generative AI

Krishnaik concludes the video by highlighting the importance of prompt engineering in the effective use of Generative AI. He explains that the quality of responses from LLMs depends on prompt engineering, which involves crafting the input to elicit the desired output. He plans to create a series of tutorials on prompt engineering using OpenAI's API. Krishnaik reiterates the significance of understanding generative AI and encourages viewers to stay tuned for more videos in the playlist. He signs off by reminding viewers to subscribe to his channel and wishing them a great day ahead.

Mindmap

Keywords

💡Generative AI

Generative AI refers to a subset of artificial intelligence that focuses on creating new, previously unseen data based on patterns it has learned from existing data. In the context of the video, it is a key technology enabling the creation of chatbots, image generation tools, and other applications that can produce novel content. The video emphasizes the growing importance of generative AI and its potential to revolutionize various industries by automating content creation and offering new ways of interaction.

💡LLM (Large Language Models)

Large Language Models (LLMs) are a type of AI model specifically designed to process and generate human-like text based on the vast amount of data they are trained on. These models are capable of performing a variety of language-related tasks, such as translation, text summarization, and acting as chatbots. In the video, the presenter discusses the role of LLMs as a subset of generative AI, highlighting their ability to generate new text content and their importance in the field of natural language processing.

💡Prompt Engineering

Prompt engineering is the process of crafting input text or 'prompts' to guide LLMs and other generative AI models to produce desired outputs. It involves carefully structuring the input to elicit specific responses from the AI. The video emphasizes the significance of prompt engineering in training custom models and getting the most out of AI APIs, as the quality and relevance of the output are heavily dependent on the way the prompt is formulated.

💡Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to learn and make decisions. It is particularly effective in handling complex tasks such as image and speech recognition. In the video, deep learning is presented as the foundation of generative AI, with the presenter explaining that generative AI models, including LLMs, are built upon deep learning techniques to generate new data.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on a labeled dataset, meaning that each training example has an associated output label. The goal is to learn a mapping from input features to output labels, which can be used for prediction or classification tasks. In the video, the presenter contrasts supervised learning with the generative techniques used in AI, highlighting that supervised learning relies on labeled data to establish relationships between inputs and outputs.

💡Unsupervised Learning

Unsupervised learning is a type of machine learning that deals with analyzing data without any prior labels or target outputs. The goal is to find patterns or structures in the data by looking at the intrinsic properties of the dataset. The video briefly mentions unsupervised learning as part of the broader machine learning landscape, contrasting it with the generative AI approach, which does not require labeled data to discover the distribution of data and generate new content.

💡Discriminative Models

Discriminative models in machine learning and deep learning are designed to learn the boundary or decision rule that separates different classes in the data. They are typically used for tasks like classification and prediction. In the video, the presenter explains that discriminative models, such as CNNs and RNNs, are different from generative models because they focus on identifying categories or making predictions based on labeled data, rather than generating new data.

💡Generative Models

Generative models, as opposed to discriminative models, are designed to learn the joint probability distribution of all the input and output data and then generate new data samples that fit this distribution. In the context of the video, generative models are central to generative AI, as they are responsible for creating new content, such as text, images, and music, by learning from large datasets and mimicking the underlying patterns.

💡Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. It involves learning from the consequences of actions, rather than from a fixed dataset. The video mentions reinforcement learning as a technique that can be used in the training of generative AI models, suggesting that the models can improve over time by receiving feedback on the quality of their generated content.

💡Open AI API

The Open AI API is a set of tools and interfaces provided by Open AI that allow developers to access and utilize AI models, such as GPT-3, for various applications. In the video, the presenter discusses the Open AI API as a resource that can be used for creating custom chatbots and other generative AI applications, emphasizing the practical applications and commercial potential of prompt engineering with these APIs.

💡Job Market

The job market, as discussed in the video, refers to the current and future demand for professionals with expertise in generative AI and related technologies. The presenter anticipates a significant increase in job opportunities specifically related to generative AI, LLMs, and prompt engineering, as more startups and companies are entering the field and seeking to develop innovative applications using these technologies.

Highlights

Introduction to generative AI and its growing importance in the job market.

Generative AI is a subset of deep learning and is used in creating chatbots, image generation tools, and more.

The significance of prompt engineering in generative AI and its impact on job opportunities.

Generative AI's ability to work with unstructured, large datasets from the internet.

The difference between generative AI and discriminative models in deep learning.

The role of reinforcement learning in training generative AI models.

Generative AI's capability to generate new data such as text, music, images, and videos.

The distinction between generative AI and other AI applications based on the type of output produced.

The potential of generative language models like ChatGPT and Google Bard in performing NLP tasks.

How generative AI models can be used to create custom chatbots and other applications through OpenAI API and prompt engineering.

The future of generative AI, including the anticipated features in ChatGPT 5 such as image and video generation.

The educational approach of starting with basic topics to build a strong foundation in understanding generative AI.

The upcoming video series on LM models and their practical implementations.

The importance of learning generative AI from basics to excel in interviews and practical applications.

The tutorial's aim to cover a wide range of topics related to generative AI, from basics to advanced concepts.

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