What is Generative AI: Definition, Examples, and Use Cases

With transformers, you could train one model on a massive amount of data and then adapt it to multiple tasks by fine-tuning it on a small amount of labeled task-specific data. In addition to natural language text, large language models can be trained on programming language text, allowing them to generate source code for new computer programs.[29] Examples include OpenAI Codex. Training involves tuning the model’s parameters for different use cases and then fine-tuning results on a given set of training data. For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return.

Generative AI refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on. Amongst all the Generative AI models, GPT is favored by many, but let’s start with GAN (Generative Adversarial Network). In this architecture, two parallel networks are trained, of which one is used to generate content (called generator) and the other one evaluates the generated content (called discriminator).

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First of all, generative artificial intelligence could help in serving advantages for coding as the tools can help in automation of different repetitive tasks, such as testing. GitHub features its individual artificial intelligence powered pair programmer, such as GitHub Copilot, which utilizes generative artificial intelligence to provide developers with suggestions for code development. Examples of generative AI also refer to tools like Stable Diffusion, which can create new videos from existing videos. The stable-diffusion-videos project on GitHub can provide helpful tips and examples for creating music videos. You can also find examples of videos that can transition between text prompts by using Stable Diffusion.

Our editors thoroughly review and fact-check every article to ensure that our content meets the highest standards. If we have made an error or published misleading information, we will correct or clarify the article. If you see inaccuracies in our content, please report the mistake via this form. Darktrace can help security teams defend against cyber attacks that use generative AI. With the capability to help people and businesses work efficiently, generative AI tools are immensely powerful. However, there is the risk that they could be inadvertently misused if not managed or monitored correctly.

Generative AI in Image Generation

In-context learning techniques include one-shot learning, which is a technique where the model is primed to make predictions with a single example. In few-shot learning, the model is primed with a small number of examples and is then able to generate responses in the unseen domain. Transformer-based models are designed with massive neural networks and transformer infrastructure that make it possible for the model to recognize and remember relationships and patterns in sequential data. The latest projects in the fields of generative AI have shown that we actually have finally learned to make something incredible.

Their propensity for “hallucinations,” or creating information that is factually inaccurate, can lead to a mass spread of misinformation. To be sure, generative AI’s promise of increased Yakov Livshits efficiency is another selling point. This technology can be used to automate tasks that would otherwise require manual labor — days of writing and editing, hours of drawing, and so on.

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Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

These models capture the statistical patterns of language and use them to generate text that is contextually relevant and appears as if it could have been written by a human. Generative AI can generate coherent and contextually relevant text by learning patterns and structures from a large corpus of text data. Models such as Recurrent Neural Networks (RNNs), Transformers, or Language Models are trained on textual data to understand the relationships between words and the context in which they are used.

These systems are trained to recognize patterns and relationships in massive datasets and can quickly generate content from this data when prompted by a user. These growing capabilities could be used in education, government, medicine, law, and other fields. The second element of the model (the discriminative NN) tries to distinguish between the real-world data and the ‘fake’ data generated by the model.

What Is a Generative AI Model?

It can generate new art, music, and even realistic human faces that never existed before. One of the most promising aspects of Generative AI is its ability to create unique and customized products for various industries. For example, in the fashion industry, Generative AI can be used to create new and unique clothing designs. In contrast, in interior design, it can help generate new and innovative home decor ideas. The process of simplification and democratization of human-machine interaction also positively influences the quality of the models itself since more people, including experts, are involved in their training.

For instance, VALL-E, a new text-to-speech model created by Microsoft, can reportedly simulate anyone’s voice with just three seconds of audio, and can even mimic their emotional tone. It’s worth noting, however, that much of this technology is not fully available to the public yet. Generative AI has also made waves in the gaming industry — a longtime adopter of artificial intelligence more broadly. Now, generative AI is transforming not only game development, but also game testing and even gameplay. The different examples of generative AI applications would also point toward gaming.

A generative adversarial network or GAN is a machine learning algorithm that puts the two neural networks — generator and discriminator — against each other, hence the “adversarial” part. The contest between two neural networks takes the form of a zero-sum game, where one agent’s gain is another agent’s loss. Say, we have training data that contains multiple images of cats and guinea pigs. And we also have a neural net to look at the image and tell whether it’s a guinea pig or a cat, paying attention to the features that distinguish them. Discriminative algorithms try to classify input data given some set of features and predict a label or a class to which a certain data example belongs.

Most of today’s foundation models are large language models (LLMs) trained on natural language. Generative AI has potential applications across a wide range of fields, including education, government, medicine, and law. Using prompts—questions or descriptions entered by a user to generate and refine the results—these systems can quickly write a speech in a particular tone, summarize complex research, or assess legal documents. Generative AI can also create artworks, including realistic images for video games, musical compositions, and poetic language, using only text prompts. In addition, it can aid complex design processes, such as designing molecules for new drugs or generating programming codes. Large Language Models are machine learning models which can help in processing and generating natural language text.

• Learn about generative AI from 100+ speakers and 200 AI leaders, and know their perspective towards the future of AI.
• Flow-based models utilize normalizing flows, a sequence of invertible transformations, to model complex data distributions.
• At the moment, there is no fact-checking mechanism built into this technology.
• A major concern is the ability to recognize or verify content that has been generated by AI rather than by a human being.
• Generative AI refers to unsupervised and semi-supervised machine learning algorithms that enable computers to use existing content like text, audio and video files, images, and even code to create new possible content.