What Is Generative AI
TLDRIn this informative video, the creator introduces the concept of Generative AI, emphasizing its role as a subset of deep learning and its potential to revolutionize various industries. The video discusses the distinction between generative and discriminative models, the significance of large language models (LLMs) like ChatGPT, and the rise of prompt engineering in job markets. The creator also hints at future tutorials on practical implementations and creating custom models using open AI APIs.
Takeaways
- ๐ Generative AI is a subset of deep learning and is based on generative techniques.
- ๐ The demand for jobs related to generative AI is expected to rise in the next two years due to the increasing number of startups in the field.
- ๐ค Large Language Models (LLMs) like ChatGPT and Google Bard are examples of models that fall under the generative AI category.
- ๐ Generative AI models are trained on vast amounts of unstructured data, as opposed to labeled datasets used in supervised learning.
- ๐จ Both generative language and image models are becoming popular, with examples like DALL-E 2 that can convert text to images.
- ๐ The main goal of generative AI is to learn the distribution of data and generate new, similar data based on that understanding.
- ๐ Generative AI does not require supervised datasets; it can work with unstructured and semi-structured data from various sources like the internet.
- ๐ ๏ธ Prompt engineering is a key skill in working with LLMs, which involves crafting the input to get the desired output from the model.
- ๐ The speaker emphasizes the importance of learning the basics of any technology, including generative AI, to build a strong foundation and improve problem-solving skills.
- ๐ The use of open AI APIs, such as those from OpenAI and Google, allows for the creation of custom models and applications, such as chatbots and content generators.
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 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 LLMs that are trained using Generative AI techniques to perform various NLP tasks.
How does Krishnaik define Generative AI in simple terms?
-Generative AI is defined as a subset of deep learning that focuses on creating new data by learning patterns and distributions from unstructured, large datasets.
What are some of the generative tasks that can be performed using Generative AI?
-Generative tasks include text completion, music generation, image creation, and video generation, among others.
What is the difference between discriminative techniques in deep learning and generative techniques?
-Discriminative techniques in deep learning involve classification and prediction using labeled datasets, whereas generative techniques focus on generating new data by learning the distribution of unlabeled datasets.
How does Krishnaik plan to cover the topic of Generative AI in his playlist?
-Krishnaik plans to cover the topic from basics, discussing where Generative AI fits in the broader context of AI and deep learning, its differences from other models like CNN and RNN, and practical implementations using open AI APIs and prompt engineering.
What is Prompt Engineering and how is it related to Generative AI?
-Prompt Engineering refers to the process of formatting inputs or prompts to Generative AI models to elicit specific responses. It is an important skill for creating custom models and chatbots using AI APIs.
What are some of the applications of Generative AI that Krishnaik mentions in the transcript?
-Some applications of Generative AI mentioned include chatbots, image generation tools, video generation tools, text translation, text summarization, and acting as a chat deputy for specific topics.
What is the significance of unstructured data in training Generative AI models?
-Unstructured data is significant in training Generative AI models because it allows the model to learn from a wide variety of data sources without the need for specific labels, enabling the model to generate new data based on the learned patterns and distributions.
How does Krishnaik suggest new viewers can benefit from his content?
-Krishnaik suggests that new viewers can benefit from his content by gaining a solid understanding of Generative AI from basics, which will help them in cracking interviews and staying updated with the latest developments in the field.
Outlines
๐ 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 surge in job opportunities related to Generative AI over the next two years due to the rise of startups in the field. Generative AI, a subset of deep learning, is the foundation for creating chatbots, image and video generation tools, and more. Krishnaik emphasizes the importance of understanding prompt engineering, a skill in high demand for these new job openings. The video aims to explain the basics of Generative AI, its relation to deep learning, and how it differs from traditional machine learning models like CNN and RNN.
๐ค Understanding Discriminative and Generative Techniques in AI
Krishnaik delves into the distinction between discriminative and generative techniques within AI. Discriminative models, used for tasks like classification and prediction, are trained on labeled datasets. In contrast, generative models are trained on unstructured, large datasets to learn the distribution of data. The speaker explains that generative AI can create new data, such as text, music, or images, by understanding the underlying patterns in the data. He provides examples like training a generative model on a Wikipedia database for text generation or music to produce new compositions. The focus is on how generative AI fits into the broader landscape of AI and machine learning.
๐ Applications and Training of Generative AI Models
This paragraph discusses the applications and training processes of Generative AI models. Krishnaik explains that generative AI is a subset of deep learning and works with vast amounts of unlabeled data to identify data distribution and features. He mentions generative language models like ChatGPT and generative image models like DALL-E 2, which can convert text to images or create new images and videos. The speaker also touches on the importance of reinforcement learning in training generative models and the potential of using APIs like OpenAI and Google's to create custom applications, such as chatbots and image generation tools.
๐ The Role and Future of Generative AI in Industry
Krishnaik wraps up the video by discussing the significance and future prospects of Generative AI. He explains how machine learning has evolved from using hand-picked features to deep learning models that can process images and video frames for tasks like classification and detection. With Generative AI, the focus shifts to generating new content based on learned patterns. The speaker also mentions that generative language models can perform tasks like translation, summarization, and image generation, and hints at upcoming advancements in these models, such as video generation and text-to-speech capabilities. He encourages viewers to stay tuned for future videos in the playlist, which will delve deeper into topics like language models and prompt engineering, and he invites viewers to subscribe to his channel for more informative content.
Mindmap
Keywords
๐กGenerative AI
๐กLLM (Large Language Models)
๐กDeep Learning
๐กPrompt Engineering
๐กDiscriminative Models
๐กGenerative Models
๐กReinforcement Learning
๐กUnsupervised Learning
๐กChatGPT
๐กGenerative Image Models
๐กOpen AI API
Highlights
Introduction to generative AI and its growing importance in the upcoming years.
Generative AI is a subset of deep learning and is used in creating chatbots, image and video generation tools.
The significance of prompt engineering in job openings and its relation to generative AI.
Generative AI's ability to create new data using unstructured, large datasets.
The difference between generative AI and discriminative models in deep learning.
Generative AI techniques do not require labeled data, unlike supervised learning.
The role of reinforcement learning in training generative AI models.
Generative language models (LLMs) like ChatGPT and their applications in NLP tasks.
Generative image models and their capability to create new images and videos from text.
The potential of generative AI in various industries and the rise of startups in this field.
The distinction between generative AI and non-generative AI applications based on output type.
The evolution of machine learning and deep learning leading up to generative AI.
How generative AI models learn patterns and distributions from unstructured content.
The importance of human supervision and reinforcement learning in refining generative AI outputs.
The practical applications of generative AI through APIs like OpenAI and Google's APIs.
The future of generative AI with the potential inclusion of image generation and text-to-speech in models like GPT-5.
The role of prompt engineering in customizing responses from LLM models.
The increasing demand for professionals skilled in working with OpenAI API and prompt engineering.