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

Generative artificial intelligence Wikipedia

Text-to-image generation protocols rely on creating images that represent the content in a prompt. The potential of generative artificial intelligence for transforming content creation across different industries is only one aspect of the capabilities for innovation with generative artificial intelligence. The increasing interest in generative AI models is clearly visible in the millions of dollars being poured into a new wave of startups working on generative AI. Let us learn more about generative Artificial Intelligence in the following post with a detailed explanation of how it works. DALL-E is an example of text-to-image generative AI that was released in January 2021 by OpenAI. It uses a neural network that was trained on images with accompanying text descriptions.

For example, generative AI can be used to create 3D models of products, which can then be used to simulate how the products would perform in the real world. This can help to identify potential design flaws and improve the overall performance of the product. Initially created for entertainment purposes, the deep fake technology has already gotten a bad reputation. Being available publicly to all users via such software as FakeApp, Reface, and DeepFaceLab, deep fakes have been employed by people not only for fun but for malicious activities too. Typically, it starts with a simple text input, called a prompt, in which the user describes the output they want.

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Embracing these advanced technologies will be key for businesses and individuals looking to stay ahead of the curve in our rapidly evolving digital landscape. China and other Nations, such as Israel, have also invested in AI and chatbots. Baidu, in particular, has developed several chatbots for different applications, including healthcare and customer support. Tencent, another Chinese company, Yakov Livshits has created a chatbot called Xiaowei for reservations and ticket purchases, while Israel has developed a military chatbot called Tzayad. Currently, ChatGPT has implemented in Alpha for some users a plug-in that allows the artificial intelligence to work with current data. Flow-based models utilize normalizing flows, a sequence of invertible transformations, to model complex data distributions.

Through training, VAEs learn to generate data that resembles the original inputs while exploring the latent space. Some of the applications of VAEs are Image Generation, anomaly detection, and latent space exploration. Like other forms of artificial intelligence, generative AI learns how to take actions from past data.

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Text-based models, such as ChatGPT, are trained by being given massive amounts of text in a process known as self-supervised learning. Here, the model learns from the information it’s fed to make predictions and provide answers. Machine learning refers to the subsection of AI that teaches a system to make a prediction based on data it’s trained on. An example of this kind of prediction is when DALL-E is able to create an image based on the prompt you enter by discerning what the prompt actually means. Additionally, diffusion models are also categorized as foundation models, because they are large-scale, offer high-quality outputs, are flexible, and are considered best for generalized use cases.

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.

This information can then be used to create financial models that can help to predict future market movements. In summary, while both Generative AI and Traditional AI have their roots in understanding and processing data, their end goals differ significantly. Traditional AI seeks to understand and categorize the world, while Generative AI aims to contribute to it by creating new, original content.

Every time the first model succeeds in fooling the second one, it gets rewarded. Generative AI is a powerful tool that holds immense potential for a variety of industries. However, it’s crucial to understand its complexities, benefits, and challenges to harness its capabilities effectively. As the technology Yakov Livshits continues to evolve, it is likely to transform the way we generate and interact with content, offering new opportunities for innovation and creativity. In the manufacturing industry, generative AI is being used to design new products, optimize production processes, and improve quality control.

  • A specific style that is unique to the artist can, therefore, end up being replicated by AI and used to generate a new image, without the original artist knowing or approving.
  • NLP algorithms can be used to analyze and respond to customer queries, translate between languages, and generate human-like text or speech.
  • Engineers can produce more effective and economical designs while reducing the time and resources needed for developing products by employing generative AI for developing things.
  • Deep learning is a subset of machine learning that utilizes neural networks, especially deep neural networks with many layers, to analyze and process data.
  • One of the most important things to keep in mind here is that, while there is human intervention in the training process, most of the learning and adapting happens automatically.

Generative AI utilizes deep learning, neural networks, and machine learning techniques to enable computers to produce content that closely resembles human-created output autonomously. These algorithms learn from patterns, trends, and relationships within the training data to generate coherent and meaningful content. The models can generate new text, images, or other forms of media by predicting and filling in missing or next possible pieces of information. In the realm of artificial intelligence (AI), generative models have emerged as powerful tools capable of creating new and imaginative content. By leveraging sophisticated algorithms and deep learning techniques, these models enable machines to generate realistic images, texts, music, and even videos that mimic human creativity.

Since each feature is a dimension, it’ll be easy to present them in a 2-dimensional data space. The line depicts the decision boundary or that the discriminative model learned to separate cats from guinea pigs based on those features. In healthcare, X-rays or CT scans can be converted to photo-realistic images with the help of sketches-to-photo translation using GANs.

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