50 Useful Generative AI Examples in 2023

What is ChatGPT, DALL-E, and generative AI?

In design, generative AI can help create countless prototypes in minutes, reducing the time required for the ideation process. In the entertainment industry, it can help produce new music, write scripts, or even create deepfakes. Generative AI has the potential to revolutionize any field where creation and innovation are key.

• Recognizing the unique capabilities of these different forms of AI allows us to harness their full potential as we continue on this exciting journey.
• Generative Pre-trained Transformer (GPT), for example, is the large-scale natural language technology that uses deep learning to produce human-like text.
• But in the long run, they hold the potential to automatically learn the natural features of a dataset, whether categories or dimensions or something else entirely.
• Wizdom is an AI solution that analyzes vast amounts of data from the global research ecosystem to offer valuable insights for decision-making.

This technology allows generative AI to identify patterns in the training data and create new content. Part of the umbrella category of machine learning called deep learning, generative AI uses a neural network that allows it to handle more complex patterns than traditional machine learning. Inspired by the human brain, neural networks do not necessarily require human supervision or intervention to distinguish differences or patterns in the training data. Data augumentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering. The goal is to increase the diversity of training data and avoid overfitting, which can lead to better performance of machine learning models. GPT-3 in particular has also proven to be an effective, if not perfect, generator of computer program code.

Generating user-friendly explanations for loan denial

These tools use neural networks to create art automatically based on a prompt from the user (e.g., “an elephant painted in the style of Goya”). Most generative AI is powered by deep learning technologies such as large language models (LLMs). These are models trained on a vast quantity of data (e.g., text) to recognize patterns so that they can produce appropriate responses to the user’s prompts. Generative AI is a type of artificial intelligence technology that broadly describes machine learning systems capable of generating text, images, code or other types of content, often in response to a prompt entered by a user. A neural network is a type of model, based on the human brain, that processes complex information and makes predictions.

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do. Researchers Yakov Livshits appealed to GANs to offer alternatives to the deficiencies of the state-of-the-art ML algorithms. GANs are currently being trained to be useful in text generation as well, despite their initial use for visual purposes. Creating dialogues, headlines, or ads through generative AI is commonly used in marketing, gaming, and communication industries.

Have a language expert improve your writing

Generative AI is a type of artificial intelligence that can produce content such as audio, text, code, video, images, and other data. Whereas traditional AI algorithms may be used to identify patterns within a training data set and make predictions, generative AI uses machine learning algorithms to create outputs based on a training data set. Over two billion dollars have already been invested in generative artificial intelligence, a rise of 425 percent since 2020. Machine learning is one of the more popular applications of generative artificial intelligence.

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 example of a Transformer-based model is the GPT-3 language model, which can generate coherent and contextually relevant text when given a prompt. In other words, they try to understand the structure of the data and use that understanding to generate new data similar to the original data. There are powerful generative AI tools that media houses and entertainment companies use to generate original content automatically. Generative artificial intelligence has made significant advancements in the healthcare industry. For example, AI scrutinizes medical records, symptoms, and images, to aid medical professionals in accurately diagnosing illnesses.

Turning sketches into color images

By using AI to enhance the resolution of these materials, they can be brought up to modern standards and be more engaging for students who are used to high-quality media. ChatGPT and other similar tools can analyze test results and provide a summary, including the number of passed/failed tests, test coverage, and potential issues. Tools like ChatGPT can convert natural language descriptions into test automation scripts. Understanding the requirements described in plain language can translate them into specific commands or code snippets in the desired programming language or test automation framework. Generative AI can also be used to make the quality checks of the existing code and optimize it either by suggesting improvements or by generating alternative implementations that are more efficient or easier to read.

Machine learning is the ability to train computer software to make predictions based on data. Similarly, users can interact with generative AI through different software interfaces. This has been one of the key innovations in opening up access and driving usage of generative AI to a wider audience. Currently, AI image technology only understands English text prompts and input. This will most likely change in the future, but until then you can use free online translator tools like DeepL to translate your prompts. Bard is Google's chatbot and content generation tool, developed as a response to ChatGPT.

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This technique can be used to generate new images, videos, or text based on training data. ChatGPT, DALL-E 2, and Bing AI are just some of the popular examples of generative AI tools. Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data.

Generative Adaptive Networks, or GANs, are also a type of neural network used in machine learning to generate new data from existing information. This network takes as input 100 random numbers drawn from a uniform distribution (we refer to these as a code, or latent variables, in red) and outputs an image (in this case 64x64x3 images on the right, in green). As the code is changed incrementally, the generated images do too—this shows the model has learned features to describe how the world looks, rather than just memorizing some examples. These images are examples of what our visual world looks like and we refer to these as “samples from the true data distribution”. We now construct our generative model which we would like to train to generate images like this from scratch.