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Blog: Generative AI in action: real-world applications and examples

Generative AI prompt samples Vertex AI

One advantage of using generative AI to create training data sets is that it can help protect student privacy. A data breach or hacking incident can reveal real-world data containing personal information about school age children. It can also be used to generate text that is specifically designed to have a certain sentiment.

For example, a generative AI system could be used to generate social media posts that are intentionally positive or negative in order to influence public opinion or shape the sentiment of a particular conversation. It is also possible to use these visual materials for commercial purposes that make AI-generated image creation a useful element in media, design, advertisement, marketing, education, etc. An image generator, for example, can help a graphic designer create whatever image they need (See the figure below).

Video: short films and music videos

OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from boldface-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and Meta has released its Make-A-Video product based on generative AI. These companies employ some of the world’s best computer scientists and engineers.

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Generative AI can generate game content, such as levels, maps, and quests, based on predefined rules and criteria. This can help game developers to create more varied and interesting game experiences. Generative AI provides banks with a powerful tool to detect suspicious or fraudulent transactions, enhancing the ability to combat financial crime.

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More controls are likely to be required in the future, however — particularly as generative video creation becomes mainstream. One emerging application of LLMs is to employ them as a means of managing text-based (or potentially image or video-based) knowledge within an organization. The labor intensiveness involved in creating structured knowledge bases has made large-scale knowledge management difficult for many large companies.

examples of generative ai

Generative AI also has a feedback loop that allows models to be updated as new data is generated and used, meaning they are gradually improved. Some companies are exploring the idea of LLM-based knowledge management in conjunction with the leading providers of commercial LLMs. It seems likely that users of such systems will need training or assistance in creating effective prompts, and that the knowledge outputs of the LLMs might still need editing or review before being applied. Assuming that such issues are addressed, however, LLMs could rekindle the field of knowledge management and allow it to scale much more effectively. 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.

#3. Image generation and enhancement

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.

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. Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.

examples of generative ai

Therefore, it is possible to generate the needed visual material in a quick and simple manner. They are also showing potential as engines of misinformation and disinformation, as they can generate deepfake images of events that never happened or alter images of events that did happen. For example, Midjourney users generated and circulated in social media images of Pope Francis in a big, puffy white coat, Elon Musk hobnobbing with Alexandria Ocasio-Cortez and Donald Trump being dragged away by police. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. Dall-E is an AI image generator that creates images based on text descriptions.

Advanced AI technology has dramatically changed how the art and animation industry operates. Art creation has become easy, and a text prompt is enough to create artistic pieces. Generative model StyleGAN, for example, has made it possible to create real human faces and unique works of art in different styles.

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There are powerful generative AI tools that media houses and entertainment companies use to generate original content automatically. Generative artificial intelligence has made significant Yakov Livshits advancements in the healthcare industry. For example, AI scrutinizes medical records, symptoms, and images, to aid medical professionals in accurately diagnosing illnesses.

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Some examples of these highly repeatable processes include call centers, administrative functions, routine medical questions, etc. Generative artificial intelligence (AI) is a subfield that focuses on creating new data rather than only analyzing and classifying already-existing data. The term generative artificial intelligence (AI) refers to machine learning algorithms that are able to derive new meaning from existing content, such as text, images, and code.

But they are clearly derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years. As well as offering access to AI-generated synthetic data, Snowflake has created a number of tools based on generative AI for its customers to use.

  • New machine learning techniques developed in the past decade, including the aforementioned generative adversarial networks and transformers, have set the stage for the recent remarkable advances in AI-generated content.
  • Content creators, also called influencers, produce and share the material with their audience.
  • It’s also worth noting that generative AI capabilities will increasingly be built into the software products you likely use everyday, like Bing, Office 365, Microsoft 365 Copilot and Google Workspace.
  • OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from boldface-name donors.

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