Definition of Generative AI Gartner Information Technology Glossary

The encoder transforms input data into a lower-dimensional latent space representation, while the decoder reconstructs the original data from the latent space. Through training, VAEs learn to generate data that resembles the original inputs while exploring the latent space. Some of the applications of VAEs Yakov Livshits are Image Generation, anomaly detection, and latent space exploration. 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.

This helps businesses save time and resources while providing fast and efficient support to customers. Companies can also use generative AI to analyze customer behavior and use that analysis internally to develop potential areas of improvement for their own business practices. One of the key features of generative AI is its ability to learn and improve over time. The more data that is collected by the algorithms, the more refined the recommendations become.

A. Advancements in generative model architectures and techniques

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. With recent advances, companies can now build specialized image- and language-generating models on top of these foundation models. Most of today’s foundation models are large language models (LLMs) trained on natural language.

In the marketing, gaming, and communications sectors, generative AI is often utilized to generate dialogues, headings, and ads. These capabilities may be used in real-time chat boxes with consumers or for the creation of product details, blogs, and social media materials. During the training phase, a restricted number of parameters are provided to these AI models. Essentially, this strategy challenges the model to formulate its own judgments on the most significant characteristics of the training data. One of the biggest concerns is the ethical implications of using this technology to generate content without proper attribution or consent.

DALL-E 2

This Spotlight examines the technology behind these systems that are surging in popularity. Secondly, there are generative AIs capable of creating images from prompts (texts entered by the user). For example, Midjourney, the direct competitor of DALL-E generates high quality images through the Discord platform. Flow-based models directly model the data distribution by defining an invertible transformation between the input and output spaces. Coming to the “pretrained” term in GPT, it means that the model has already been trained on a massive amount of text data before even applying the attention mechanism.

Large Language Models are machine learning models which can help in processing and generating natural language text. The noticeable advancement in creating large language models focuses on access to large volumes of data with the help of social media posts, websites, and books. The data can help in training models, which can predict and generate natural language responses Yakov Livshits in different contexts. Generative AI is a type of artificial intelligence that uses machine learning algorithms to generate new content. Unlike traditional AI, which is programmed to respond to specific inputs, generative AI is designed to be creative and produce original outputs. This can include anything from art and music to text and even entire virtual worlds.

The rise of deep generative models

For example, designers can use tools like designs.ai to quickly generate logos, banners, or mockups for their websites. New and seasoned developers alike can utilize generative AI to improve their coding processes. Generative AI coding tools can help automate some of the more repetitive tasks, like testing, as well as complete code or even generate brand new code. GitHub has its own AI-powered pair programmer, GitHub Copilot, which uses generative AI to provide developers with code suggestions. And GitHub also has announced GitHub Copilot X, which brings generative AI to more of the developer experience across the editor, pull requests, documentation, CLI, and more. The impact of generative AI is quickly becoming apparent—but it’s still in its early days.

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.

Gain insight from top innovators and thought leaders in the fields of IT, business, enterprise software, startups, and more. Generative AI is a specific use case for AI that is used for sophisticated modeling with a creative goal. It takes existing patterns and combines them to be able to generate something that hasn’t ever existed before.

Techniques include VAEs, long short-term memory, transformers, diffusion models and neural radiance fields. The created material is examined to determine its caliber and degree of conformity to the intended attributes. Depending on the application, evaluation metrics and human input may be used to improve the generated output and develop the model. Iterative feedback loops contribute to the improvement of the content’s diversity and quality. Generative AI is an exciting new development in the field of artificial intelligence, and it has the potential to create significant positive impacts across various industries.

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. Some people are concerned about the ethics of using generative AI technologies, especially those technologies that simulate human creativity.

It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace. This is effectively a “free” tier, though vendors will ultimately pass on costs to customers as part of bundled incremental price increases to their products. Generative AI provides new and disruptive opportunities to increase revenue, reduce costs, improve productivity and better manage risk.

Are AI tools advanced enough for product documentation?

Traditional AI seeks to understand and categorize the world, while Generative AI aims to contribute to it by creating new, original content. Generative AI has the potential to create and propagate misinformation or malicious content. If misused, AI-generated content could spread false information, damage reputations, or even incite harm. It is crucial to develop robust mechanisms for verifying and validating AI-generated content to mitigate these risks. Artists can leverage generative AI models to create unique and captivating artworks, exploring new possibilities and pushing the boundaries of creativity. These models can generate paintings, sculptures, or even virtual art pieces, inspiring artists to collaborate with AI and produce innovative masterpieces.

• This is done through a process called “training” or “deep learning,” where neural networks are trained on large datasets of images, videos, or text.
• The AI is fed immense amounts of data so that it can develop an understanding of patterns and correlations within the data.
• Another concern, referred to as “technological singularity,” is that AI will become sentient and surpass the intelligence of humans.
• Its ability to generate content autonomously has captured the interest of various industries, offering new possibilities for creativity and automation.
• Decoders sample from this space to create something new while preserving the dataset’s most important features.
• With its ability to create new content, it has the potential to transform the industries listed above and more.

This is in contrast to most other AI techniques where the AI model attempts to solve a problem which has a single answer (e.g. a classification or prediction problem). Next, Transformers were introduced in 2017, offering a new method for natural language understanding – leading to significant advances in machine translation and text generation. They use natural language processing techniques commonly known as NLP(Natural Language Processing in English), including the attention mechanism, to understand meaning.

This will make it easier to generate new product ideas, experiment with different organizational models and explore various business ideas. Recent progress in LLM research has helped the industry implement the same process to represent patterns found in images, sounds, proteins, DNA, drugs and 3D designs. This generative AI model provides an efficient way of representing the desired type of content and efficiently iterating on useful variations.

At its core, Generative AI operates through a special class of algorithms known as generative models. These models, using principles from probability theory and statistics, generate a plausible set of data as output based on the input data they’re trained on. Various techniques, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and more are used to train these models, each suited to different types of tasks. The capabilities of generative AI are one of the biggest pointers for thinking about its potential to address some of the existing problems. For example, generative AI applications could help in creating rich academic content. On the other hand, synthetic data by generative AI could present complicated concerns in cybersecurity.