Generative AI Basics and Applications

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

This technology has numerous applications in various fields, including art, music, and design.

Generative AI can also be used in data augmentation, where it generates new data to supplement existing datasets, making them more robust and diverse.

One of the key benefits of generative AI is its ability to reduce the need for human labor in content creation, freeing up time for more creative and high-value tasks.

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Generative AI models come in various forms, each with its own strengths and weaknesses. The transformer-based model is a type of generative AI that's particularly effective for natural language processing tasks.

Transformer-based models have been widely used in applications such as text translation and text generation. They work by breaking down input text into tokens, converting them into numerical vectors called embeddings, and then adding positional encoding to account for the order of words in a sentence.

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The self-attention mechanism in transformer-based models is what allows them to detect subtle relationships between words in a phrase. This mechanism can even distinguish between the meaning of similar sentences, such as "I poured water from the pitcher into the cup until it was full" and "I poured water from the pitcher into the cup until it was empty".

Diffusion models, on the other hand, are a type of generative AI that creates new data by mimicking the data on which it was trained. This process involves gradually introducing noise into the original data, analyzing how the added noise alters the data, and then reversing the process to generate new data that's close to the original.

Midjourney and DALL-E are two well-known image-generation tools based on diffusion models, which can generate realistic images, sounds, and other data types.

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Generative AI models are divided into two main categories: discriminative and generative modeling. Discriminative modeling is used to classify existing data points, such as images of cats and guinea pigs into respective categories, and belongs to supervised machine learning tasks.

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Generative modeling, on the other hand, tries to understand the dataset structure and generate similar examples, like creating a realistic image of a guinea pig or a cat, and mostly belongs to unsupervised and semi-supervised machine learning tasks.

Generative algorithms predict features given a certain label, whereas discriminative algorithms care about the relations between X and Y. Generative models capture the probability of x and y occurring together, allowing them to recreate or generate images of cats and guinea pigs.

Discriminative algorithms are easier to monitor and more explainable, meaning you can understand why the model comes to a certain conclusion. However, generative AI has business applications beyond those covered by discriminative models, making it a valuable tool in various industries.

Generative AI models and algorithms, such as Variational Autoencoders (VAEs) and diffusion models, have been developed to create new, realistic content from existing data. VAEs are unsupervised neural networks consisting of an encoder and a decoder, which learn to compress input data into a simplified representation and then reconstruct it from the encoded essence.

VAEs excel in tasks like image and sound generation, as well as image denoising. Diffusion models, on the other hand, create new data by mimicking the data on which it was trained, using a process similar to physical diffusion.

Synthetic data generation is another application of generative AI, where models like NVIDIA's neural network trained on videos of cities can render urban environments. This can be used to develop self-driving cars that can use generated virtual world training datasets for pedestrian detection.

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Model Customization allows you to tailor the default behavior of generative AI models to your specific needs. This is achieved through a process called model tuning, which simplifies complex prompts and reduces costs and latency.

Model tuning is a key feature in Vertex AI, enabling you to customize your model without requiring intricate prompts. By simplifying your prompts, you can reduce the cost and latency of your requests.

To evaluate the performance of your tuned model, Vertex AI offers model evaluation tools. These tools help you assess the effectiveness of your customized model, ensuring it's production-ready.

You can deploy your tuned model to an endpoint and monitor its performance, just like in standard MLOps workflows. This allows you to track the model's performance and make adjustments as needed.

Model customization is a crucial step in ensuring your generative AI model generates the desired results. By customizing your model, you can avoid unexpected output and maintain safety and responsibility in your AI applications.

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Evaluating models is a crucial step in creating effective generative AI models. High-quality generation outputs are key, especially in applications that interact directly with users.

For instance, in speech generation, poor speech quality is difficult to understand. This can be frustrating for users who rely on these models for communication.

To ensure diversity in our models, we need to capture minority modes in the data distribution without sacrificing generation quality. This helps reduce undesired biases in the learned models.

Here are the key evaluation criteria for generative AI models:

Speed is also an important consideration, especially for interactive applications like real-time image editing. This allows users to create content quickly and efficiently.

Generative AI is a powerful tool that can streamline the workflow of creatives, engineers, researchers, scientists, and more. It has a plethora of practical applications in different domains.

Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. For example, it can turn text inputs into an image, turn an image into a song, or turn video into text.

Generative AI models need to have the following capabilities to generate content that's useful in real-world applications: learn how to perform new tasks, access external information, and block harmful content. These capabilities work together to generate content that you want.

Generative AI can enhance image and video quality by determining each individual pixel and making a higher resolution. This can be done using a GAN, which can create a better version of a low-quality image.

We can upscale old images to 4k and beyond, making them look sharper and more detailed. This is especially useful for enhancing images from old movies.

Generative AI can also generate more frames per second, such as upscaling 23 fps to 60 fps. This can make videos look smoother and more engaging.

Using generative AI, we can add color to black-and-white movies, making them look more vibrant and lifelike. This can be a game-changer for film enthusiasts and historians.

By training generative AI on annotated video, we can generate temporally-coherent, detailed, and photorealistic video clips. This technology is already being used in various applications, such as Sora by OpenAI, Gen-1 and Gen-2 by Runway, and Make-A-Video by Meta Platforms.

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Experience NVIDIA Playground is a great way to get hands-on with generative AI. You can generate landscapes, avatars, songs, and more at the NVIDIA AI Playground.

The NVIDIA AI Playground offers a variety of creative tools that let you experiment with different types of generative AI. This is a great opportunity to see the possibilities of generative AI firsthand.

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To learn more about generative AI, you can watch a video playlist of free tutorials, step-by-step guides, and explainer videos on demand.

The video playlist is available to registered users, and it's a great resource for anyone looking to get started with generative AI.

You can also stay informed about the latest news and updates in the field by checking out the Newsroom and Company Blog sections.

If you're a developer, you might be interested in the Technical Blog section, which offers in-depth information on the technical aspects of generative AI.

Here are some popular video categories to explore:

Additionally, you can find more resources on the Webinars and Events Calendar sections, which feature upcoming conferences and online events related to generative AI.

Generative AI has a plethora of practical applications in different domains. Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more.

One of the most exciting applications is in computer vision, where it can enhance the data augmentation technique. This can lead to mind-blowing results.

Generative AI models can take inputs such as text, image, audio, video, and code and generate new content into any of the modalities mentioned. For example, it can turn text inputs into an image, turn an image into a song, or turn video into text.

The use cases and possibilities span all industries and individuals. Generative AI has truly limitless potential.

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Generative AI models and algorithms have been developed to create new, realistic content from existing data. These models have distinct mechanisms and capabilities, and are at the forefront of advancements in fields such as image generation, text translation, and data synthesis.

In 2014, the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models. This marked a significant shift from discriminative models, which were typical at the time.

The Transformer network, introduced in 2017, enabled advancements in generative models compared to older Long-Short Term Memory models. This led to the development of the first generative pre-trained transformer, GPT-1, in 2018.

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Generative adversarial networks, or GANs, are a type of neural network framework that pits two neural networks against each other in a zero-sum game. Invented by Jan Goodfellow and his colleagues at the University of Montreal in 2014, GANs have been a popular generative AI algorithm until recently.

The GAN architecture consists of two deep learning models: a generator and a discriminator. The generator creates fake input or samples from a random vector, while the discriminator takes a given sample and decides if it's fake or real.

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The discriminator is a binary classifier that returns probabilities, with numbers closer to 0 indicating a higher likelihood of the output being fake, and numbers closer to 1 indicating a higher likelihood of the output being real. Both the generator and discriminator are often implemented as CNNs, especially when working with images.

The adversarial nature of GANs lies in the game theoretic scenario where the generator network competes against the discriminator network. The generator produces fake samples, while the discriminator attempts to distinguish between samples drawn from the training data and those drawn from the generator. In this scenario, there's always a winner and a loser, with the losing network being updated while its rival remains unchanged.

The goal of GANs is to create a fake sample that's so convincing it can fool a discriminator and humans. However, the game doesn't stop there; it's time for the discriminator to be updated and get better. Repeat.

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In 2014, advancements such as the variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models, as opposed to discriminative ones, for complex data such as images.

These deep generative models were the first to output not only class labels for images but also entire images. This was a significant breakthrough in the field of neural networks.

The variational autoencoder and generative adversarial network paved the way for further advancements in generative models, including the development of the Transformer network in 2017.

The Transformer network enabled advancements in generative models compared to older Long-Short Term Memory models, leading to the first generative pre-trained transformer (GPT) in 2018, known as GPT-1.

GPT-1 was followed in 2019 by GPT-2, which demonstrated the ability to generalize unsupervised to many different tasks as a Foundation model.

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Large language models can be trained on programming language text, allowing them to generate source code for new computer programs.

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This capability is exemplified by OpenAI Codex, a notable example of a model that can produce code.

With the ability to generate code, large language models can automate tasks such as writing boilerplate code, freeing up developers to focus on more complex and creative aspects of software development.

However, it's worth noting that the generated code may require human review and refinement to ensure it meets specific requirements and standards.

Large language models can learn from a vast amount of programming language text, enabling them to recognize patterns and structures that are common in code.

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Generative AI systems can be either unimodal or multimodal, with unimodal systems taking only one type of input and multimodal systems able to take more than one type of input.

Examples of multimodal systems include OpenAI's GPT-4, which accepts both text and image inputs.

Generative AI can be applied to various modalities, including text, images, audio, and video, with different models and algorithms suited to each modality.

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Here are some examples of generative AI models and their corresponding modalities:

These are just a few examples of the many generative AI models and their corresponding modalities. The capabilities of a generative AI system depend on the modality or type of the data set used.

Generative AI is capable of creating fake images that look like real ones. The most prominent use case of generative AI is creating fake images that look like real ones, such as creating realistic photographs of human faces.

In 2017, Tero Karras, a Distinguished Research Scientist at NVIDIA Research, published a paper on "Progressive Growing of GANs for Improved Quality, Stability, and Variation." This paper demonstrated the generation of realistic photographs of human faces.

GANs can be trained on real pictures of celebrities and then produce new realistic photos of people's faces that have some features of celebrities, making them seem familiar. For example, the girl in the second top right picture looks a bit like Beyoncé but is not the pop singer.

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Generative AI can also be used to enhance images from old movies by upscaling them to 4k and beyond, generating more frames per second (e.g., 60 fps instead of 23), and adding color to black-and-white movies.

Here are some examples of generative AI image generation models:

These models can be used for text-to-image generation and neural style transfer, allowing for the creation of high-quality visual art.

Generative AI models can power a wide range of products, from chatbots like ChatGPT to text-to-image tools like Midjourney.

Many generative AI features have been integrated into existing commercially available products, such as Microsoft Office, Google Photos, and the Adobe Suite.

Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers.

For example, LLaMA-7B, a version with 7 billion parameters, can run on a Raspberry Pi 4, while one version of Stable Diffusion can run on an iPhone 11.

Larger models with tens of billions of parameters require accelerators like GPU chips from NVIDIA and AMD or the Neural Engine in Apple silicon products to achieve acceptable speed.

The 65 billion parameter version of LLaMA can be configured to run on a desktop PC.

Running generative AI locally offers advantages like protecting privacy and intellectual property, and avoiding rate limiting and censorship.

The subreddit r/LocalLLaMA focuses on using consumer-grade gaming graphics cards through techniques like compression.

Language models with hundreds of billions of parameters, such as GPT-4 or PaLM, typically run on datacenter computers equipped with arrays of GPUs or AI accelerator chips.

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Generative AI models can boast billions of parameters and require fast and efficient data pipelines to train, making significant capital investment and technical expertise necessary to maintain and develop them.

One of the biggest challenges of generative AI is the scale of compute infrastructure required to train these models. To train diffusion models, for example, massive compute power is needed, and AI practitioners must be able to procure and leverage hundreds of GPUs.

Generative AI models can also struggle with slow sampling speeds, which can be a problem for interactive use cases like chatbots and customer service applications. In these cases, conversations must happen immediately and accurately.

However, generative AI also offers many benefits. Generative AI algorithms can create new, original content that's indistinguishable from human-created content, making them useful for applications like entertainment and advertising.

Generative AI can also improve the efficiency and accuracy of existing AI systems, such as natural language processing and computer vision. This can be done by creating synthetic data that can be used to train and evaluate other AI algorithms.

Here are some of the key benefits of generative AI:

Generative AI is still in its early stages, and it's facing some significant challenges. One of the main issues is the scale of compute infrastructure required to train these models. Generative AI models can boast billions of parameters and need fast and efficient data pipelines, which demands significant capital investment, technical expertise, and large-scale compute infrastructure.

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To train these large datasets, massive compute power is needed, and AI practitioners must be able to procure and leverage hundreds of GPUs. This is especially true for diffusion models, which could require millions or billions of images to train.

Sampling speed is another challenge. Due to the scale of generative models, there may be latency present in the time it takes to generate an instance. This can be a problem for interactive use cases like chatbots, AI voice assistants, or customer service applications, where conversations must happen immediately and accurately.

Generative AI models require high-quality, unbiased data to operate. However, not all data can be used to train AI models, and some domains don't have enough data to train a model. For example, few 3D assets exist and they're expensive to develop.

Data licenses can also be an issue. Many organizations struggle to get a commercial license to use existing datasets or to build bespoke datasets to train generative models, which can lead to intellectual property infringement issues.

Here are the main challenges of generative AI:

Generative AI is a game-changer in many industries, and its benefits are numerous. Generative AI algorithms can create new, original content, such as images, videos, and text, that's indistinguishable from human-created content.

This can be a huge advantage for applications like entertainment, advertising, and creative arts. For example, generative AI algorithms can be used to create synthetic data that can be used to train and evaluate other AI algorithms.

Generative AI algorithms can also improve the efficiency and accuracy of existing AI systems, such as natural language processing and computer vision. This can lead to significant time and resource savings for businesses and organizations.

By exploring and analyzing complex data in new ways, generative AI algorithms can uncover hidden patterns and trends that may not be apparent from the raw data alone. This can be especially useful for businesses and researchers.

Here are some key benefits of generative AI:

Generative AI has the potential to revolutionize the way we consume and interact with media, but it also poses significant challenges to journalism.

Fake news and misinformation are major concerns, as generative AI can be used to create convincing but entirely fabricated content.

Journalists are struggling to verify the authenticity of sources, with 75% of them reporting difficulty in distinguishing between real and fake news.

The ease of creating fake news has led to a decrease in trust in traditional media, with only 31% of people trusting the news they read online.

Generative AI can also be used to manipulate public opinion, with some companies using AI-generated content to sway voters in elections.

The lack of transparency around AI-generated content is a major issue, with 62% of journalists reporting difficulty in identifying AI-generated content.

The consequences of AI-generated misinformation can be severe, with some studies showing that it can lead to a decrease in vaccination rates and an increase in hate crimes.

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