Generative artificial intelligence Wikipedia

Generative AI: What Is It, Tools, Models, Applications and Use Cases

Google BardOriginally built on a version of Google’s LaMDA family of large language models, then upgraded to the more advanced PaLM 2, Bard is Google’s alternative to ChatGPT. Bard functions similarly, with the ability to code, solve math problems, answer questions, and write, as well as provide Google search results. I think there’s huge potential for the creative field — think of it as removing some of the repetitive drudgery of mundane tasks like generating drafts, and not encroaching on their innate creativity.

These models rely on self-attention mechanisms, enabling them to capture complex relationships within the input data. Transformer models, such as GPT-3, are incredibly powerful for generating high-quality text and have numerous applications in chatbots, content generation, and translation. Generative AI leverages advanced techniques like generative adversarial networks (GANs), large language models, variational autoencoder models (VAEs), and transformers to create content across a dynamic range of domains. Generative AI is a technology that can create new and original content like art, music, software code, and writing.

What is generative AI? Artificial intelligence that creates

Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person. Because of the high effort required to train a foundation model from scratch, it’s common to rely on models trained by third parties, then apply customization. These can include fine-tuning, prompt-tuning, and adding customer-specific or domain-specific data. A streamlined pipeline is created to handle input data, process it through the generative model, and deliver the generated outputs. Generative AI has revolutionized the visual domain by enabling the generation of realistic images, videos, and visual effects. Generative adversarial networks (GANs) are widely used for visual generation tasks.

In 2021, the release of DALL-E, a transformer-based pixel generative model, followed by Midjourney and Stable Diffusion marked the emergence of practical high-quality artificial intelligence art from natural language prompts. Generative AI, as noted above, often uses neural network techniques such as transformers, GANs and VAEs. Other kinds of AI, in distinction, use techniques including convolutional neural networks, recurrent neural networks and reinforcement learning.

What is ChatGPT?

Over time, the program learns how to simplify the photos of people’s faces into a few important characteristics — such as size and shape of the eyes, nose, mouth, ears and so on — and then use these to create new faces. There are a variety of generative AI tools out there, though text and image generation models are arguably the most well-known. Generative AI models typically rely on a user feeding it a prompt that guides it towards producing a desired output, be it text, an image, a video or a piece of music, though this isn’t always the case. In 2022, Apple acquired the British startup AI Music to enhance Apple’s audio capabilities. The technology developed by the startup allows for creating soundtracks using free public music processed by the AI algorithms of the system. The main task is to perform audio analysis and create “dynamic” soundtracks that can change depending on how users interact with them.

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.

By analyzing customer feedback and purchasing patterns, generative AI can suggest new product features or designs that are likely to be well-received by customers. AI can be used to detect fraud by analyzing patterns and anomalies in financial transactions. The technology can learn from past data to detect new types of fraud and identify suspicious activities, helping financial institutions prevent fraud and reduce financial losses. These generative AI models are specifically designed to generate text by predicting the likelihood of words or phrases based on context. Today, generative AI continues to rapidly advance, finding applications in art, music, literature, fashion, architecture, gaming, and more. Its evolution promises to revolutionize technology creation and interaction as it becomes increasingly influential in society.

Software Automation Policy Guidelines

As we already mentioned NVIDIA is making many breakthroughs in generative AI technologies. One of them is a neural network trained on videos of cities to render urban environments. DLSS samples multiple lower-resolution images and uses motion data and feedback from prior frames to reconstruct native-quality images. To do this, you first need to convert audio signals to image-like 2-dimensional representations called spectrograms. This allows for using algorithms specifically designed to work with images like CNNs for our audio-related task.

For example, companies can produce curated content for customers, such as music playlists, book recommendations, and more. Salesforce has been exploring how to develop and deploy generative AI to support customer needs for years. For example, the company introduced CodeGen, which democratizes software engineering by helping users turn simple English prompts into executable code.

The GPT stands for “Generative Pre-trained Transformer,”” and the transformer architecture has revolutionized the field of natural language processing (NLP). The first neural networks (a key piece of technology underlying generative AI) that were capable of being trained Yakov Livshits were invented in 1957 by Frank Rosenblatt, a psychologist at Cornell University. Generative AI can produce outputs in the same medium in which it is prompted (e.g., text-to-text) or in a different medium from the given prompt (e.g., text-to-image or image-to-video).