GENERATIVE AI

What is generative AI?

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent buzz around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds.

The technology, it should be noted, is not brand-new. Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of generative adversarial networks, or GANs -- a type of machine learning algorithm -- that generative AI could create convincingly authentic images, videos and audio of real people.

How does generative AI work?

Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.

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.

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.

Generative AI models

Generative AI models combine various AI algorithms to represent and process content. For example, to generate text, various natural language processing techniques transform raw characters (e.g., letters, punctuation and words) into sentences, parts of speech, entities and actions, which are represented as vectors using multiple encoding techniques. Similarly, images are transformed into various visual elements, also expressed as vectors. One caution is that these techniques can also encode the biases, racism, deception and puffery contained in the training data.

What are use cases for generative AI?

?Implementing chatbots for customer service and technical support.

?Deploying deepfakes for mimicking people or even specific individuals.

?Improving dubbing for movies and educational content in different languages.

?Writing email responses, dating profiles, resumes and term papers.

?Creating photorealistic art in a particular style.

?Improving product demonstration videos.

?Suggesting new drug compounds to test.

What are the benefits of generative AI?

Generative AI can be applied extensively across many areas of the business. It can make it easier to interpret and understand existing content and automatically create new content. Developers are exploring ways that generative AI can improve existing workflows, with an eye to adapting workflows entirely to take advantage of the technology. Some of the potential benefits of implementing generative AI include the following:

?Automating the manual process of writing content.

?Reducing the effort of responding to emails.

?Improving the response to specific technical queries.

?Creating realistic representations of people.

?Summarizing complex information into a coherent narrative.

?Simplifying the process of creating content in a particular style.

What are the limitations of generative AI?

Early implementations of generative AI vividly illustrate its many limitations. Some of the challenges generative AI presents result from the specific approaches used to implement particular use cases. For example, a summary of a complex topic is easier to read than an explanation that includes various sources supporting key points. The readability of the summary, however, comes at the expense of a user being able to vet where the information comes from.