What is Generative AI? Definition and Examples

In healthcare, one example can be the transformation of an MRI image into a CT scan because some therapies require images of both modalities. But CT, especially when high resolution is needed, requires a fairly high dose of radiation to the patient. It extracts all features from a sequence, converts them into vectors (e.g., vectors representing the semantics and position of a word in a sentence), and then passes them to the decoder. The discriminator is basically a binary classifier that returns probabilities — a number between 0 and 1.

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.

B. Text Generation and Language Modeling

Generative AI is a branch of artificial intelligence centered around computer models capable of generating original content. By leveraging the power of large language models, neural networks, and machine learning, generative AI is able to produce novel content that mimics human creativity. These models are trained using large datasets and deep-learning algorithms that learn the underlying structures, relationships, and patterns present in the data.

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.

Examples of generative AI tools

Generative AI can be run on a variety of models, which use different mechanisms to train the AI and create outputs. These include generative adversarial networks (GANs), transformers, and Variational AutoEncoders (VAEs). The model uses this data to learn styles of pictures and then uses this insight to generate new art when prompted by an individual through text.

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 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.

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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.

One notable application of Transformer models is the Transformer-based language model known as GPT (Generative Pre-trained Transformer). Models like GPT-3 have demonstrated impressive capabilities in generating coherent and contextually relevant text given a prompt. They have been used for various NLP tasks, including text completion, question answering, translation, summarization, and more. This sophisticated system of AI programmed to learn from examples is called a neural network. This transforms the given input data into newly generated data through a process involving both encoding and decoding.

• They described the GAN architecture in the paper titled “Generative Adversarial Networks.” Since then, there has been a lot of research and practical applications, making GANs the most popular generative AI model.
• This will make it easier to generate new product ideas, experiment with different organizational models and explore various business ideas.
• Innovations in architectures, regularization techniques, and training methods are expected to shape the future of generative modeling.
• For example, such breakthrough technologies as GANs and transformer-based algorithms.

Generative AI is best utilized as a tool to enhance and inspire human creativity. It can analyze medical images, such as X-rays or MRI scans, to assist doctors in diagnosing diseases or identifying abnormalities. Additionally, generative AI can aid in drug discovery by generating new molecule structures that have the potential to be used as pharmaceuticals. PCMag.com is a leading authority on technology, delivering lab-based, independent reviews of the latest products and services. Our expert industry analysis and practical solutions help you make better buying decisions and get more from technology. It includes altering an image’s external characteristics, such as its color, material, or shape while keeping its essential properties.

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 Yakov Livshits 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.

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 Yakov Livshits 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.

What is generative AI? Artificial intelligence that creates

What this means is that it basically predicts the next word in a sentence using a method called Transformer. It pays close attention to neighboring words to understand the context and establish a relationship between words. These are Generative Adversarial Networks (GAN), Variational Autoencoder (VAE), Generative Pretrained Transformers (GPT), Autoregressive models, and much more.

Generative AI has a wide range of applications in a variety of industries, including art, music, literature, and video games. The most popular examples of generative AI are in the field of language, where language models such as ChatGPT have become widely used. These models have been trained on vast amounts of text data and are able to generate new content that is often indistinguishable from content written by a human. ChatGPT is considered generative AI because it can generate new text outputs based on prompts it is given.

Humans are still required to select the most appropriate generative AI model for the task at hand, aggregate and pre-process training data and evaluate the AI model’s output. The traditional way this would work is that a human writer would take a look at all of that raw data, take notes and write a narrative. With generative AI, learning algorithms can review the raw data programmatically and create a narrative that appears to have been written by a human. The most commonly used generative models for text and image creation are called Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).