What Is Generative AI
TLDRIn this informative video, the creator discusses the emerging field of generative AI, a subset of deep learning that leverages vast datasets to generate new content such as text, music, and images. The video emphasizes the growing importance of generative AI in job markets and introduces the concept of prompt engineering, which is crucial for customizing AI models using APIs like OpenAI. The creator also touches on the differences between generative and discriminative models, highlighting the potential of generative AI in creating innovative applications.
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 its growing applications in various industries.
- 🤖 Large Language Models (LLMs) like ChatGPT and Google Bard are examples of models that fall under the category of generative AI.
- 📈 Generative AI involves training models on large, unstructured datasets to learn patterns and distributions, rather than relying on labeled data.
- 🎨 Generative models can output various forms of content, such as text, audio, images, and videos, making them versatile tools for different applications.
- 🔍 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 existing data.
- 🌐 Generative AI is becoming popular due to its ability to generate content that can be used in applications like chatbots, image generation, and video creation.
- 🛠️ Prompt engineering is a key skill in working with generative AI, as it involves crafting the input to the model to get the desired output.
- 🔄 The training process of generative AI involves learning from unstructured content and using reinforcement learning to improve accuracy.
- 🎓 Understanding the basics of generative AI, deep learning, and machine learning is crucial for anyone looking to enter the field or develop a deeper knowledge of these technologies.
- 🔗 The script emphasizes the importance of learning about generative AI and its potential impact on various industries and job markets.
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 on Generative AI, its basics, applications, and future job prospects related to this field.
Why does Krishnaik believe there will be job opportunities specifically related to Generative AI in the upcoming years?
-Krishnaik believes there will be job opportunities related to Generative AI because many startups are being opened that focus on creating chatbots, image generation tools, video generation tools, and more, all of which utilize generative AI techniques.
What is the relationship between Generative AI and Large Language Models (LLMs)?
-Generative AI is a subset of deep learning, and Large Language Models (LLMs) like ChatGPT and Google Bard are examples of models that fall under the category 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 plan to structure the content of his playlist on Generative AI?
-Krishnaik plans to start with basic topics to establish a strong foundation, then move on to discuss models like Lang Chain and LLMs, practical implementation using open AI APIs, and prompt engineering.
What is the difference between discriminative techniques in deep learning and generative techniques?
-Discriminative techniques in deep learning involve classification and prediction tasks using labeled datasets, whereas generative techniques focus on generating new data or completing tasks like sentence completion based on the distribution of unstructured, unlabeled data.
How does Generative AI differ from traditional machine learning models in terms of data requirements?
-Generative AI does not require labeled datasets like traditional machine learning models. Instead, it works with large amounts of unstructured data to learn the distribution and generate new content based on that understanding.
What are some applications of Generative AI mentioned in the script?
-Some applications of Generative AI mentioned include chatbots, image generation tools, video generation tools, text translation, text summarization, and acting as a chatbot.
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, which helps improve the accuracy of the generated content. Human supervision is often required in this process.
How can one distinguish between a generative AI application and a non-generative AI application?
-An application can be distinguished as generative AI if its output is in the form of text, audio, images, or video frames, whereas non-generative AI applications typically output numerical values or class probabilities.
What is prompt engineering, and how is it relevant to the use of Generative AI?
-Prompt engineering is the process of formatting input text or prompts to elicit specific responses from a generative AI model. It is relevant because the quality and relevance of the output from an LLM depend on the effectiveness of the prompts used.
What are some of the future advancements expected in models like ChatGPT?
-Future advancements expected in models like ChatGPT include capabilities for image generation, video generation, and text-to-speech, which are targeted to be included in versions like ChatGPT 5.
Outlines
🤖 Introduction to Generative AI and its Future Scope
The speaker, Krishnaik, introduces himself and his YouTube channel, setting the stage for a new playlist focused on Generative AI. He predicts a rise in jobs related to Generative AI over the next two years due to the increasing number of startups working in this field, creating chatbots, image and video generation tools, and more. Krishnaik emphasizes the importance of understanding prompt engineering and plans to cover various topics including practical implementations and the creation of custom models using open AI APIs. The video aims to explain what Generative AI is and where it fits within the broader context of AI and deep learning, starting from the basics.
📊 Understanding Discriminative and Generative Techniques in AI
Krishnaik delves into the distinction between discriminative and generative techniques in AI. He explains that discriminative techniques, such as classification and prediction, are used when the dataset is labeled, while generative techniques operate on unstructured, large datasets to learn the distribution of data. Generative AI can create new data, such as text, music, or images, based on patterns and distributions it has learned. The speaker also touches on the training process of generative models and the importance of reinforcement learning for refining their output. He provides examples of generative language models like ChatGPT and generative image models like DALL-E, highlighting their potential applications.
🌐 Applications and Training of Generative AI Models
The speaker discusses the practical applications of Generative AI, emphasizing its ability to generate new content such as text, audio, images, and videos. He explains how Generative AI differs from traditional machine learning and deep learning models, which typically focus on prediction and classification based on labeled data. Krishnaik illustrates how Generative AI models are trained on large, unstructured datasets to understand data distribution and generate new outputs. He also mentions the use of open AI APIs and prompt engineering in creating custom chatbots and models, indicating a promising future for these technologies.
🚀 Conclusion and Future of Generative AI
Krishnaik concludes the video by reiterating the importance of understanding Generative AI and its potential impact on various industries. He encourages viewers to stay tuned for upcoming videos in the playlist, which will delve deeper into topics such as language models and prompt engineering. The speaker also urges viewers to subscribe to his channel for more informative content, promising a series of tutorials on prompt engineering and practical implementations using open AI APIs. He signs off, wishing viewers a great day and expressing his anticipation for the next video.
Mindmap
Keywords
💡Generative AI
💡LLM (Large Language Models)
💡Prompt Engineering
💡Deep Learning
💡Supervised Learning
💡Unsupervised Learning
💡Discriminative Models
💡Generative Models
💡Reinforcement Learning
💡OpenAI API
💡Generative Image Models
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 techniques are distinct from CNN and RNN models in deep learning.
The difference between supervised and unsupervised learning in the context of generative AI.
Large Language Models (LLMs) like ChatGPT and Google Bard are examples of generative AI.
Generative AI models are trained on vast amounts of unstructured data from the internet.
Generative models can be used for tasks such as text completion, music generation, and image creation.
The training process of generative AI involves learning patterns and distributions in unstructured content.
Generative AI can output various forms of content, including text, audio, images, and videos.
The distinction between generative AI and discriminative models based on the type of output they produce.
How generative AI can generate new data by learning from the distribution of a large dataset.
The role of reinforcement learning and human supervision in improving the accuracy of generative AI models.
The potential of generative AI in creating custom chatbots and models using APIs like OpenAI and Google API.
The future of generative AI and its applications in various industries, including the creation of custom models.
The importance of understanding the basics of generative AI for future learning and career prospects.
The upcoming playlist will cover topics like models, Lang chain, and practical implementations of generative AI.
The significance of generative language models and generative image models in the current AI landscape.