Generative AI for Beginners: From Fundamentals to Applications

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Posted Nov 7, 2024

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An artist’s illustration of artificial intelligence (AI). This illustration depicts language models which generate text. It was created by Wes Cockx as part of the Visualising AI project l...
Credit: pexels.com, An artist’s illustration of artificial intelligence (AI). This illustration depicts language models which generate text. It was created by Wes Cockx as part of the Visualising AI project l...

Generative AI is a type of artificial intelligence that can create new content, such as images, music, or text, based on patterns it has learned from existing data.

This technology has the potential to revolutionize various industries, from art and design to marketing and entertainment. Generative AI can help create new and unique content, making it a valuable tool for businesses and individuals alike.

One key aspect of generative AI is its ability to learn from data, which allows it to generate new content that is similar in style and tone to the original material. This is achieved through complex algorithms and machine learning techniques.

By understanding the fundamentals of generative AI, you can unlock its full potential and explore its many applications.

Foundational Concepts

Generative AI is a general-purpose technology that can be applied to various fields. It's a broad concept, but understanding its basics is essential for beginners.

To get started, you'll need to understand the different components of generative AI. There are videos available to introduce you to the topic, such as the "Welcome" video and the "How Generative AI works" video.

Here's a brief overview of the foundational concepts:

  • Generative AI can be learned through interactive quizzes, like the "What is Generative AI?" quiz.
  • Understanding how generative AI works is crucial for applying it in real-world scenarios.

Generative AI works by following a step-by-step process, which is outlined in the article.

Lesson 1: What Is

Credit: youtube.com, Lesson 1: Foundational Concepts

Generative AI is a type of artificial intelligence that can create new and original content, such as text, images, or audio, based on a prompt or input.

Generative AI works by using various AI algorithms to process and transform the input data into new content. This can include essays, solutions to problems, or even realistic fakes created from pictures or audio of a person.

Generative AI is different from other types of AI, such as supervised and unsupervised learning, which focus on learning patterns and making predictions based on existing data. Generative AI, on the other hand, focuses on creating new and original content that doesn't exist in the training data.

Generative AI models use unsupervised learning techniques to learn patterns and relationships in data, but the focus is on creating new content rather than making predictions or classifications. These models are trained to learn the underlying structure and characteristics of the data, allowing them to generate new content similar to the training data.

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Credit: youtube.com, Summary of Lesson 1: Fundamental Concepts of Planning

Generative AI has been around for a while, but it's only recently that we've seen significant advancements in the field. The development of neural networks, which can learn from data and improve over time, has been a key factor in the progress of generative AI.

Here are some of the key types of generative AI models:

  • Generative Adversarial Networks (GANs): These models consist of two neural networks, a generator and a discriminator, that work together to create new content.
  • Transformers: These models use deep learning architecture to predict new text based on sequential data and can learn context and generate human-like text.
  • Foundation models: These are pre-trained models that can be fine-tuned for specific tasks, such as text generation or image creation.

Generative AI has many practical applications, including content creation, such as automatically generating articles, posts, and captions, and even generating realistic human faces or synthetic data for AI training.

Autoencoders

Autoencoders are neural networks that compress and reconstruct input data, creating a lower-dimensional representation known as a latent space or embedding.

This process involves two main parts: an encoder and a decoder. The encoder compresses the input data, while the decoder reconstructs the original input from the compressed representation.

Autoencoders are trained using a loss function that minimizes the difference between the original and reconstructed inputs, allowing them to learn the most important features of the input data.

Autoencoders have numerous applications in fields like computer vision, natural language processing, and anomaly detection, making them a powerful tool for unsupervised learning.

They're an essential component in generative AI, but traditional autoencoders have limitations, such as generating blurry images.

Model Customization

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Model customization is a powerful tool that allows you to tailor the default behavior of Google's foundation models to your specific needs. This process, called model tuning, helps reduce the cost and latency of your requests by simplifying your prompts.

You can customize the default behavior of Google's foundation models to consistently generate the desired results without using complex prompts. Model tuning is a key feature of Vertex AI, which also offers model evaluation tools to help you evaluate the performance of your tuned model.

By tuning your model, you can deploy it to an endpoint and monitor performance like in standard MLOps workflows. This means you can put your customized model to work right away, without having to worry about complex setup or maintenance.

Here are some benefits of model customization:

  • Reduced cost: By simplifying your prompts, you can reduce the cost of your requests.
  • Improved performance: Model tuning helps ensure that your model consistently generates the desired results.
  • Increased efficiency: With a customized model, you can automate processes and improve support agent skills.

Model customization is a key part of making generative AI work for you, and it's a great way to improve the customer experience. By providing personalized support and automating processes, you can create a better experience for your customers and improve their satisfaction.

Get Started

Credit: youtube.com, Introduction to Generative AI

To get started with generative AI, try one of these quickstarts. You can generate text using the Gemini API in Vertex AI, which allows you to send requests to the Gemini API using the SDK.

The Gemini API in Vertex AI is a great place to start, but you can also send prompts to Gemini using the Vertex AI Studio Prompt Gallery, which requires no setup. This is a convenient way to test prompts without any hassle.

If you're interested in generating images, you can use Imagen on Vertex AI to create a watermarked image. This is a useful feature for verifying the authenticity of an image.

Here are some options to consider:

  • Generate text using the Gemini API in Vertex AI.
  • Send prompts to Gemini using the Vertex AI Studio Prompt Gallery.
  • Generate an image and verify its watermark using Imagen on Vertex AI.

Applications and Projects

As a beginner in generative AI, you're probably eager to dive into its applications and projects. Generative AI can be used for a variety of tasks such as video writing, reading, chatting, and even image generation.

You can explore these applications through video lessons, such as the ones on writing, reading, and chatting. These videos will give you a hands-on understanding of what generative AI can do.

Credit: youtube.com, Gen AI Course | Gen AI Tutorial For Beginners

To get started, you can try out the quiz on generative AI applications, which will help you assess your understanding of the subject. Additionally, you can identify and build generative AI use cases and technology options through projects, such as the ones mentioned in Week 2: Generative AI Projects.

Here are some specific applications and projects to consider:

  • Video: Writing
  • Video: Reading
  • Video: Chatting
  • Video: What LLMs can and cannot do
  • Video: Tips for Prompting
  • Video: Image generation (optional)
  • Quiz: Generative AI Applications
  • Generative AI Projects: identify and build generative AI use cases and technology options

Week 2: Projects

In Week 2, we dive into Generative AI Projects. This is where we identify and build generative AI use cases and technology options.

Generative AI is a powerful tool that can create new content, such as images, music, or text, based on patterns and structures it's learned from existing data. By exploring generative AI use cases, we can unlock new possibilities for innovation and creativity.

Some examples of generative AI use cases include generating new artwork, composing music, or even creating realistic simulations of real-world scenarios. These use cases have the potential to revolutionize various industries and aspects of our lives.

For more insights, see: New Generative Ai

Credit: youtube.com, How to Manage Multiple Projects [TIPS FOR PROJECT MANAGERS]

To get started with generative AI projects, we need to consider the technology options available. This includes identifying the best algorithms, models, and tools for our specific use case.

Here are some key technology options to consider:

  • Generative Adversarial Networks (GANs): A type of deep learning algorithm that can generate new content by learning from existing data.
  • Recurrent Neural Networks (RNNs): A type of deep learning algorithm that can generate sequential data, such as text or music.
  • Transformers: A type of deep learning algorithm that can generate sequential data, such as text or speech.

By exploring these technology options and use cases, we can build generative AI projects that have a real impact and create new value.

Week 3: Work and Life

Generative AI is transforming the way we work and live, with its impact on business and society being a major area of focus.

Teams can take advantage of Generative AI by leveraging its capabilities to automate routine tasks and free up time for more strategic and creative work.

AI risks and responsible AI practices are also crucial to consider, as they can help mitigate potential downsides and ensure that Generative AI is used for the greater good.

By implementing responsible AI practices, businesses can minimize the risks associated with Generative AI and maximize its benefits.

Here are some key considerations for teams looking to adopt Generative AI:

  • Impact on business and society
  • How teams can take advantage of Generative AI
  • AI risks and responsible AI

Software Applications

Credit: youtube.com, 3 Key Steps to Building Software Applications

Software applications are a key area where generative AI can be applied. Generative AI can be used in software applications to automate tasks, improve user experience, and enhance overall efficiency.

One way to get started with generative AI in software applications is to watch the video "Using generative AI in software applications". This video provides a comprehensive overview of how generative AI can be integrated into software development.

You can also try your hand at writing generative AI code yourself with the help of the "Trying generative AI code yourself (optional)" video and app item. This is a great way to gain practical experience and see the code in action.

The lifecycle of a generative AI project is an important consideration when developing software applications. The video "Lifecycle of a generative AI project" explains the different stages involved in developing a generative AI project.

Understanding the cost implications of generative AI is also crucial. The video "Cost Intuition" provides insights into the costs associated with developing generative AI projects.

For more insights, see: Generative Ai Model Lifecycle

Credit: youtube.com, 6 BEST Free Project Management Software | (2024)

Here are some key takeaways from the software applications section:

  • Generative AI can be used in software applications to automate tasks, improve user experience, and enhance overall efficiency.
  • Watching the "Using generative AI in software applications" video is a great way to get started with generative AI in software development.
  • Trying your hand at writing generative AI code yourself can help you gain practical experience and see the code in action.
  • The lifecycle of a generative AI project involves different stages, including development, deployment, and maintenance.
  • Understanding the cost implications of generative AI is crucial when developing software applications.

Improving Customer Experience

Using generative AI for customer service makes it easy for your support team to create an exceptional customer experience with more human-like interactions.

You can leverage generative AI to provide personalized support and create and update engaging self-service content in your knowledge base.

Generative AI can also gather insights from large datasets to improve your products and services.

Automating processes that enhance support agent skills allows them to provide exceptional customer service with every interaction.

Generative AI can help your support team resolve customer requests faster by structuring, summarizing, and auto-populating tickets.

This frees up human agents to focus on the rewarding tasks that require their empathy and strategic thinking.

Here are some ways generative AI can improve customer experience:

By using generative AI in these ways, you can create a more personalized and efficient customer experience that meets the needs of your customers.

Generative AI Techniques

Credit: youtube.com, What are Generative AI models?

Generative AI techniques are the backbone of this technology, and understanding them is essential for beginners.

Generative Adversarial Networks (GANs) work by training two different learning computers, called the "generator" and the "discriminator", to create increasingly more realistic content over time.

Flow-based models take complex data distributions and transform them into simple distributions, typically used for image generation. This enables the creation of new patterns or refinement of existing ones while maintaining a balance in the data.

Variational autoencoders (VAEs) are generative models that encode input data, simplify and optimize the data points, and store them in a hidden storage area called a latent space. They are often used to create generative AI images and text.

Curious to learn more? Check out: What Is Google's Generative Ai Called

Model Architecture

Generative AI models can be complex, but let's break down their architecture.

Generative AI models have various architectures, but one common type is a Generative Adversarial Network (GAN), which consists of two neural networks: a generator and a discriminator.

Credit: youtube.com, How ChatGPT Works Technically | ChatGPT Architecture

The generator creates new content based on the input it receives, while the discriminator evaluates the content and provides feedback to the generator. This process is crucial for generating realistic human faces, synthetic data for AI training, or even facsimiles of particular humans.

Techniques such as GANs and variational autoencoders (VAEs) are suitable for generating realistic human faces, synthetic data for AI training, or even facsimiles of particular humans. These models can also be used for music composition, enabling the creation of original compositions or accompaniments.

Musicians and producers can use generative AI as a tool for inspiration, generating musical ideas that they can further develop into complete compositions. By analyzing existing musical patterns and structures, these models can generate new melodies, harmonies, and rhythms.

DALL-E

DALL-E is an application developed by OpenAI that generates images from text prompts. Users can enter text descriptions of a potential image and DALL-E will create a visual that matches the text.

Credit: youtube.com, How to Use DALL.E 3 - Top Tips for Best Results

DALL-E was built using OpenAI's GPT implementation in 2021, and a second, more capable version, DALL-E 2, was released in 2022. It enables users to generate imagery in multiple styles driven by user prompts.

DALL-E can be used to produce visual representations of complex ideas or theories, making them easier to understand. It’s even used by healthcare professionals for high-quality medical imaging and radiology.

DALL-E is an example of a multimodal AI application that identifies connections across multiple media, such as vision, text and audio.

Take a look at this: Generative Ai in Cyber Security

ChatGPT

ChatGPT is a popular generative AI interface built on OpenAI's GPT-3.5 implementation. It's an AI-powered chatbot that took the world by storm in November 2022, incorporating the history of its conversation with a user into its results, simulating a real conversation.

ChatGPT is best for creating written content, like articles, social media posts, emails, and software code. It uses natural language processing (NLP) to create conversational interactions.

Credit: youtube.com, Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)!

One of ChatGPT's key features is its ability to generate text based on input data, which has practical applications in content creation. Websites, blogs, and social media platforms can benefit from automatically generated articles, posts, and captions.

ChatGPT is also capable of producing brand assets like logos and marketing images. It can be used to generate new content based on its input, such as a text-based generative AI model fed a sentence or paragraph and then generating a new paragraph or story based on that input.

Here are some of the key capabilities of generative AI models like ChatGPT:

  • Learn how to perform new tasks
  • Access external information
  • Block harmful content

These capabilities are essential for generating content that's useful in real-world applications. ChatGPT and other generative AI models can be customized through model tuning and can access information outside of their training data to create more accurate and relevant content.

By combining these capabilities, generative AI models like ChatGPT can generate new content, such as text, images, and videos, that closely resemble real-world examples. This ability has revolutionized industries like entertainment, fashion, and design, allowing for the creation of new, unique patterns and styles.

Generative AI Models

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Generative AI models combine various AI algorithms to represent and process content. This can include text, images, and even proteins. Techniques such as GANs and variational autoencoders (VAEs) are suitable for generating realistic human faces and synthetic data for AI training.

Generative Adversarial Networks (GANs) consist of two neural networks: a generator and a discriminator. The generator creates new content based on the input it receives, while the discriminator evaluates the content and provides feedback to the generator. This process allows the generator to create realistic outputs that fool the discriminator.

There are several types of generative AI models, each with its architecture. The most common type is a Generative Adversarial Network (GAN), which consists of a generator and a discriminator. The generator creates new content based on the input it receives, while the discriminator evaluates the content and provides feedback to the generator.

  1. GANs are suitable for generating realistic human faces and synthetic data for AI training.
  2. Flow-based models take complex data distributions and transform them into simple distributions.
  3. Generative AI models can be used for image and video generation, text generation, and music composition.

Types of Models

Generative AI models come in various forms, each with its unique strengths and applications.

Credit: youtube.com, Generative AI Models Types | How Does Generative AI Work?

Generative Adversarial Networks (GANs) are a type of generative AI model that consists of two neural networks: a generator and a discriminator. The generator creates realistic outputs, while the discriminator identifies whether the output is real or fake.

GANs have been used for applications such as image and video generation, text generation, and music composition. With their ability to create realistic and diverse outputs, GANs have quickly become one of the most exciting AI research areas.

Flow-based models take complex data distributions and transform them into simple distributions. They're often used for image generation, such as creating new patterns or refining existing ones.

Here are some of the most common types of generative AI models:

These models differ in size, modality, and cost, and can be explored through platforms like Model Garden, which offers a variety of generative AI foundation models accessible through a managed API.

Bard

Bard is a conversational generative AI chatbot created by Google as a competitor to ChatGPT. It uses context-aware translation between languages, making it globally accessible.

Credit: youtube.com, Google Bard and Generative AI Search Revealed

Bard is best for predictive analytics and identifying business trends. Google offers two generative AI models, PaLM, a multimodal model, and Google Bard.

Users employ Bard for reporting and analytics, content generation, information on current events, language translation, and image surfacing. This makes it a valuable tool for news agencies, e-commerce platforms, and websites, social media platforms, and blogs.

Bard can help streamline content production and free up human resources for more strategic tasks, such as creating high-quality, engaging content.

Explore further: Generative Ai Analytics

Benefits and Applications

Generative AI is a powerful tool that can automate tasks, improve workflows, and enhance customer experiences. It can make it easier to interpret and understand existing content and automatically create new content.

One of the key benefits of generative AI is automating the manual process of writing content, reducing the effort of responding to emails, and improving the response to specific technical queries. This can be especially helpful for customer service teams.

Here's an interesting read: Generative Ai for Customer Support

Credit: youtube.com, Generative AI explained in 2 minutes

Generative AI can also create realistic representations of people, summarize complex information into a coherent narrative, and simplify the process of creating content in a particular style. This can be a game-changer for businesses looking to streamline their content creation process.

Some of the potential benefits of implementing generative AI include improved agent productivity and efficiency, as well as enhanced customer experiences. With generative AI, agents can focus on more complex issues and provide personalized support.

Here are some ways generative AI can boost agent productivity and efficiency:

  • Ticket summaries: Generate a quick summary of ticket content so agents can understand the issue and respond faster.
  • Advanced bots: Deflect tickets with bots that provide data-driven suggestions for instant, conversational support.
  • Content creation: Automate and streamline the process of creating content so content owners don’t have to.

You can leverage generative AI to provide personalized support, create and update engaging self-service content in your knowledge base, gather insights from large datasets to improve your products and services, and automate processes that enhance support agent skills.

Is Reliable?

Generative AI's reliability is a crucial aspect to consider.

The reliability of generative AI depends on the data source used to train the model.

To ensure the output contains trusted and correct information, it's essential to train generative AI models on accurate, reputable sources with verifiable data.

Worth a look: Train Generative Ai

Generative AI Tools and Examples

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Generative AI tools exist for various modalities, such as text, imagery, music, code, and voices. Some popular AI content generators include GPT, Jasper, AI-Writer, and Lex for text generation, Dall-E 2, Midjourney, and Stable Diffusion for image generation, and Amper, Dadabots, and MuseNet for music generation.

Text generation tools can be used to create natural language responses in chatbots, while image generation tools can produce realistic images and videos that closely resemble real-world examples. These models can be used in various industries, including entertainment, fashion, and design.

Generative AI models, particularly those like Generative Adversarial Networks (GANs), are capable of producing images and videos that closely resemble real-world examples. This ability has revolutionized industries like entertainment, where CGI effects in movies and video games can now be created with astonishing realism.

Some popular generative AI tools include:

  • Text generation: GPT, Jasper, AI-Writer, Lex
  • Image generation: Dall-E 2, Midjourney, Stable Diffusion
  • Music generation: Amper, Dadabots, MuseNet
  • Code generation: CodeStarter, Codex, GitHub Copilot, Tabnine
  • Voice synthesis: Descript, Listnr, Podcast.ai

Generative AI History and Future

Generative AI has come a long way since the 1960s when the Eliza chatbot was created by Joseph Weizenbaum. It was one of the earliest examples of generative AI, but it had limitations due to a limited vocabulary, lack of context, and overreliance on patterns.

Credit: youtube.com, THE FUTURE OF HUMANITY: A.I Predicts 400 Years In 3 Minutes (4K)

The field saw a resurgence in 2010 with advances in neural networks and deep learning. This enabled generative AI to automatically learn to parse existing text, classify image elements, and transcribe audio.

Ian Goodfellow introduced GANs in 2014, which provided a novel approach for generating and rating content variations. This led to the creation of realistic people, voices, music, and text, and sparked interest and fear about the potential misuse of generative AI.

Generative AI has continued to evolve, making advancements in translation, drug discovery, anomaly detection, and the generation of new content, from text and video to fashion design and music.

Here's an interesting read: Ai Generative Music

History

The early days of generative AI were marked by the creation of the Eliza chatbot by Joseph Weizenbaum in the 1960s, which was one of the first examples of generative AI.

These early implementations used a rules-based approach that broke easily due to a limited vocabulary, lack of context, and overreliance on patterns.

Credit: youtube.com, The History of AI: From Beginnings to Breakthroughs

The field saw a resurgence in 2010 with advances in neural networks and deep learning, enabling the technology to automatically learn to parse existing text, classify image elements, and transcribe audio.

Ian Goodfellow introduced GANs in 2014, providing a novel approach for organizing competing neural networks to generate and then rate content variations.

Since then, progress in other neural network techniques and architectures has helped expand generative AI capabilities, including the use of VAEs, long short-term memory, transformers, diffusion models, and neural radiance fields.

See what others are reading: Neural Network vs Generative Ai

The Future of

Generative AI will continue to evolve and make advancements in various areas, including translation, drug discovery, anomaly detection, and the generation of new content, from text and video to fashion design and music.

These new tools will integrate capabilities directly into the tools we already use, making them more efficient and effective. Grammar checkers will get better, and design tools will seamlessly embed more useful recommendations into our workflows.

Curious to learn more? Check out: Top Generative Ai Tools

Credit: youtube.com, What's the future for generative AI? - The Turing Lectures with Mike Wooldridge

As we continue to harness these tools to automate and augment human tasks, we will find ourselves having to reevaluate the nature and value of human expertise. Industry and society will build better tools for tracking the provenance of information to create more trustworthy AI.

The popularity of generative AI tools has fueled an endless variety of training courses at all levels of expertise, helping developers create AI applications and business users apply the new technology across the enterprise.

Generative AI Techniques and Models

Generative AI techniques and models are the backbone of this powerful technology. Generative AI models combine various AI algorithms to represent and process content, such as text and images.

One common type of generative AI model is the Generative Adversarial Network (GAN), which consists of two neural networks: a generator and a discriminator. The generator creates new content based on the input it receives, while the discriminator evaluates the content and provides feedback to the generator.

Credit: youtube.com, Generative AI Full Course – Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More

GANs have been used for various applications, including image and video generation, text generation, and music composition. With their ability to create realistic and diverse outputs, GANs quickly became one of the most exciting AI research areas.

There are several other types of generative AI models, including flow-based models, which take complex data distributions and transform them into simple distributions. These models are typically used for image generation.

Here are some common types of generative AI models:

  • Generative Adversarial Networks (GANs)
  • Flow-based models
  • Neural networks with a decoder and encoder, such as variational autoencoders (VAEs)
  • Transformers, such as Google's Bidirectional Encoder Representations from Transformers (BERT)

These models are used to generate new content based on input, such as text or images. For example, a text-based generative AI model might be fed a sentence or a paragraph and then generate a new paragraph or story based on that input.

If this caught your attention, see: Can I Generate Code Using Generative Ai

Generative AI vs Traditional Methods

Generative AI is a powerful tool that creates new and original content, whereas traditional AI relies on predefined rules to process data and produce a result.

Generative AI uses techniques like transformers, GANs, and VAEs to generate new content, whereas traditional AI uses methods like convolutional neural networks, recurrent neural networks, and reinforcement learning.

Explore further: Generative Ai Content

Credit: youtube.com, The Evolution of AI: Traditional AI vs. Generative AI

The main difference between generative AI and traditional AI is that generative AI focuses on content generation, whereas traditional AI is task-specific.

Here's a comparison of the two:

For example, traditional AI can make ticketing systems more efficient, but generative AI can boost agent productivity by providing intelligent writing tools.

Keith Marchal

Senior Writer

Keith Marchal is a passionate writer who has been sharing his thoughts and experiences on his personal blog for more than a decade. He is known for his engaging storytelling style and insightful commentary on a wide range of topics, including travel, food, technology, and culture. With a keen eye for detail and a deep appreciation for the power of words, Keith's writing has captivated readers all around the world.

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