What is generative AI? Artificial intelligence that creates
The models ‘generate’ new content by referring back to the data they have been trained on, making new predictions. Generative AI is a type of artificial intelligence that can produce various types of data — images, text, video, audio, etc. — after being fed large volumes of training data. While GPT-4 promises more accuracy and less bias, the detail getting top-billing is that the model is multimodal, meaning it accepts Yakov Livshits both images and text as inputs, although it only generates text as outputs. Right now, an AI text generator tends to only be good at generating text, while an AI art generator is only really good at generating images. AI developers assemble a corpus of data of the type that they want their models to generate. This corpus is known as the model’s training set, and the process of developing the model is called training.
And these are just a fraction of the ways generative AI will change how we work. Despite their promise, the new generative AI tools open a can of worms regarding accuracy, trustworthiness, bias, hallucination and plagiarism — ethical issues that likely will take years to sort out. Microsoft’s first foray into chatbots in 2016, called Tay, for example, had to be turned off after it started spewing inflammatory rhetoric on Twitter. OpenAI, an AI research and deployment company, took the core ideas behind transformers to train its version, dubbed Generative Pre-trained Transformer, or GPT. Observers have noted that GPT is the same acronym used to describe general-purpose technologies such as the steam engine, electricity and computing.
Examples of generative AI
This allows for using algorithms specifically designed to work with images like CNNs for our audio-related task. Here, a user starts with a sparse sketch and the desired object category, and the network then recommends its plausible completion(s) and shows a corresponding synthesized image. So, the adversarial nature of GANs lies in a game theoretic scenario in which the generator network must compete against the adversary.
Deep Reinforcement Learning (DRL) models combine reinforcement learning algorithms with deep neural networks to generate intelligent and adaptive behaviors. These models learn through trial and error, exploring different actions in an environment and receiving feedback in the form of rewards. DRL models have been applied in game playing, robotics, recommendation systems, and autonomous driving, among other areas, generating sophisticated and goal-oriented actions. Generative AI systems trained on words or word tokens include GPT-3, LaMDA, LLaMA, BLOOM, GPT-4, and others (see List of large language models). Moreover, innovations in multimodal AI enable teams to generate content across multiple types of media, including text, graphics and video. This is the basis for tools like Dall-E that automatically create images from a text description or generate text captions from images.
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Retailers can use AI to create descriptions for their products, promotional content for social media, blog posts, and other content that improves SEO and drives customer engagement. Generative programming tools can be used to automate game testing, such as identifying bugs and glitches, and providing feedback on gameplay balance. This can help game developers to reduce testing time and costs, and improve the overall quality of their games. It is essential for decision makers and loan applicants to understand the explanations of AI-based decisions, including why the loan applications were denied.
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.
- Then, it takes the bold step of creating something original that fits within those understood frameworks.
- GANs have made significant contributions to image synthesis, enabling the creation of photorealistic images, style transfer, and image inpainting.
- However, after seeing the buzz around generative AI, many companies developed their own generative AI models.
- These models do not appropriately understand context and rhetorical situations that might deeply influence the nature of a piece of writing.
- These are just some of the types of generative AI models, and there is ongoing research and development in this field, leading to the emergence of new and more advanced generative models over time.
A major concern is the ability to recognize or verify content that has been generated by AI rather than by a human being. Another concern, referred to as “technological singularity,” is that AI will become sentient and surpass the intelligence of humans. Many generative AI systems are based on foundation models, which have the ability to perform multiple and open-ended tasks. When it comes to applications, the possibilities of generative AI are wide-ranging, and arguably, many have yet to be discovered, let alone implemented. In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications.
Netflix uses generative models to curate personalized lists of recommended shows and movies for each user. Exploring real-world applications of generative AI not only illuminates its capabilities but also helps us understand its broader impact on society, industry, and science. Here, we examine specific case studies that showcase the diverse uses of generative AI in various domains, from healthcare to entertainment. You’ll be running your chosen algorithm on your dataset numerous times, adjusting various parameters to improve its performance.
The results, whether it’s a whimsical poem or a chatbot customer support response, can often be indistinguishable from human-generated content. Generative AI is a technology that can create new and original content like art, music, software code, and writing. When users enter a prompt, artificial intelligence generates responses based on what it has learned from existing examples on the internet, often producing unique and creative results. Variational AutoEncoders (VAEs) are a type of generative model, similar to Generative Adversarial Networks (GANs).
VAEs consist of two neural networks, encoders, and decoders, that work together to create the most effective generative models. The encoder network learns to represent the data more efficiently, while the decoder network learns to regenerate the original dataset more efficiently. VAEs are generative models that learn to encode Yakov Livshits data into a latent space and then decode it back to reconstruct the original data. They learn probabilistic representations of the input data, allowing them to generate new samples from the learned distribution. VAEs are commonly used in image generation tasks and have also been applied to text and audio generation.
Finding a place in Gartner’s 2022 trends, it’s predicted that generative AI will account for 10% of all data production by 2025 (that’s substantially higher from less than 1% today). The best generative AI tool may vary depending on the requirements and use cases at hand. The most popular generative AI tools include ChatGPT, GPT-4 by OpenAI, AlphaCode by DeepMind, etc.