2306 02781 A survey of Generative AI Applications

In our article, we’ll check out Generative AI examples and some companies using this technology. Generative AI for enterprises is used for creating personalized product recommendations. It also helps with automating content creation, predicting behavior, and enhancing data analysis. According to Gartner, by 2025, Generative AI will account for 10% of all data produced, up from less than 1% today. Social media platforms showed images created by models like DALL-E, and Stable Diffusion.

Early versions of generative AI required submitting data via an API or an otherwise complicated process. Developers had to familiarize themselves with special tools and write applications using languages such as Python. Artificial intelligence can also play a crucial role in generating NPCs or non-playable characters with unique behaviors and personalities.

Generative AI Use Cases and Applications

A data breach or hacking incident can reveal real-world data containing personal information about school age children. Generative AI offers teachers a practical and effective way to develop massive amounts of unique material quickly. By combining the power of machine learning with medical imaging technologies, such as CT and MRI scans, generative AI algorithms can accelerate precision in medical imaging with improved results. Some generative models like ChatGPT can perform data visualization which is useful for many areas.

Generative AI can significantly contribute to natural language processing and language translation. It can generate human-like text, making chatbots or virtual assistants more conversational and helpful. Additionally, generative models can aid in language translation by generating translations or suggestions based on existing parallel corpora, improving the efficiency and accuracy of translation systems. Large language Yakov Livshits models are a type of generative AI that are trained on a large dataset of text and are able to generate human-like text. Generative AI is a type of artificial intelligence that is able to create new content based on input data. It can be used to generate text or images, but also embeddings which make it a great way of solving many NLP related tasks such as classification, recommendations, semantic search and many more.

Content Generation at Scale

For example, a call center might train a chatbot against the kinds of questions service agents get from various customer types and the responses that service agents give in return. An image-generating app, in distinction to text, might start with labels that describe content and style of images to train the model to generate new images. Google was another early leader in pioneering transformer AI techniques for processing language, proteins and other types of content. Microsoft’s decision to implement GPT into Bing drove Google to rush to market a public-facing chatbot, Google Bard, built on a lightweight version of its LaMDA family of large language models. Google suffered a significant loss in stock price following Bard’s rushed debut after the language model incorrectly said the Webb telescope was the first to discover a planet in a foreign solar system.

It can automatically fill in the information where necessary, speeding up the process of creating these documents. 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. By leveraging generative AI to create a variety of fashion models, fashion companies can better serve their diverse customer base and accurately display their products in a more authentic manner.

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.

The array of readily available tools, frameworks, and blueprints simplifies the game creation process, a departure from the traditional method of building from scratch. Moreover, AI tools can generate lifelike, human-like voices suitable for video game avatars and animations. With the continuous advancements in AI, Generative AI is expected to evolve and influence every aspect of our lives. It holds immense potential in data augmentation, where it can generate additional training data for machine learning models, thereby enhancing their performance.

On top of it, you can also use generative AI for creating in-game assets and collectibles. The top examples of generative AI use cases in gaming sector include Unity Machine Learning Agents and Charisma AI. Midjourney is an innovative AI art generator in its beta stage, accessible via Discord.

Generative AI, known as enterprise search, can help companies find information more efficiently within their documents. Generative AI can securely read through a company’s documents, such as research reports or contracts, and then answer questions about them. Check out our free AI Text Generator and unlock the exciting world of AI-powered text generation. Whether you’re a developer, designer or artist, generative AI tools can enhance your productivity, boost your creativity and unlock new possibilities.

• We put our expertise and skills at the service of client business to pave their way to the industry leadership.
• Of course, AI can be used in any industry to automate routine tasks such as minute taking, documentation, coding, or editing, or to improve existing workflows alongside or within preexisting software.
• From music composition to video editing and voice synthesis, the full potential of Generative AI can be harnessed across film/music production, fashion, and gaming.

Specifically, it can produce standardized reports (such as in the figure below) that offer consistency in how findings are presented. Tools like ChatGPT can assist in creating content structure by generating outlines and organization suggestions for a given topic. This can be useful for SEO maximization because a well-structured and organized content not only provides a better user experience but also helps search engines understand the context and relevance of the content. By analyzing this data, generative AI tools can help you identify your target audience’s preferences, interests, and pain points, which can inform your marketing messaging, content, and product development.

Course design

In a recent Namecheap survey on generative AI, respondents revealed that they were most excited about generative AI for the purposes of content creation and marketing. The biggest concerns involved false information shared as fact and misuse of intellectual property. When a recent UK-based YouGov poll asked Americans about the impact of AI tools on society, 19% see the technology as a good thing for society, and 34% see it as a bad thing, with 24% neutral and 23% undecided. However, a closer look at the different age groups shows that younger people have a more positive attitude towards AI.

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. 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). Turing’s generative AI services are driven by in-depth expertise and continuous innovation that help us offer tailored solutions.