Exploring the Possibilities of Generative AI in 2023

As technology advances, so do conversations around what impact innovation has on human labor. This has been the case since at least the 16th century. Though the conversation has never entirely ceased, its volume has peaks and valleys.

Presently, we’re at a peak, which began growing in 2017 when the Google Brain team announced their Large Language Models (LLMs) breakthrough, and the conversation has reached what feels like a pinnacle as a result of OpenAI’s launch of ChatGPT.

In just five days, one million users flocked to ChatGPT (for context, it took Netflix 3 years to achieve this feat, and Facebook 10 months), and since its inception, generative AI has taken the world by storm.

As its name indicates, generative AI can generate net new text, speech, images, music, video, and code by leveraging foundation models. This differs from traditional AI approaches which can only describe, predict, or prescribe something based on existing content.

While there is plenty of hysteria that generative AI will soon take over the world and displace job functions in mass, we think of it from a different perspective. We think it’s the combination of humans and machines that makes us stronger: making it easier to get things done, amplifying human creativity, and broadening the accessibility of software.

Modern Use Cases of Generative AI

Generative AI has a wide array of use cases from marketing tactics like brainstorming and developing campaign strategies and content at scale, to CRO strategies like structuring and generating sales descriptions based on past successful sales posts, to CX strategies like segmenting and personalizing content based on the preferences of individual users.

Let’s explore a few other unique applications:

• Text:  ChatGPT, by AI research and deployment company OpenAI, is a major springboard of innovation across all industries, helping accomplish tasks like, creating PowerPoint presentations, drafting blog posts, and otherwise reducing creative blocks for in-house marketing teams. LLMs’ are also being used as an enhancement for modern-day chatbots, opening the doors to implementation-capable tactics as well. For example, Evolv AI’s latest text-to-variant product, currently in Beta, leverages the messaging framework for consumers to communicate CRO ideas, then implement outcomes to design for rapid experimentation and digital asset modification, without the help of developers or designers.
• Image: Text-to-image tools have made it possible to create images on the fly, some even winning art competitions. Brands are taking this innovation to the next level by identifying opportunities to combine personalization with Open AI tools. For example, Stitch Fix is experimenting with DALL-E 2 to create visualizations of clothing based on requested customer preferences for color, fabric, and style.
• Audio: Tech startup Adthos recently launched a platform that uses AI to generate scripts for audio ads making it possible to customize content with components like voiceovers, and sound effects to create fully produced ads quickly
• Video: In a similar vein as the Adthos example, Stability AI is disrupting the motion picture industry by making it possible to synthesize locations, backgrounds, faces, costumes and other assets - meaning film crews don’t need to be “on set” to produce the same effect.
• Code: By generating code in response to a command, developers can adapt the models for a wide range of use cases, with little fine-tuning required for each task. For example, scientists used GPT2 to create novel protein sequences based on the principles of natural ones.

Despite the vastly different use cases mentioned above, there is one commonality between all applications of generative AI. Generative AI still requires human input, but because AI handles some of the more tedious aspects of modern labor, humans can focus on creativity: prompting, editing, and iterating at scale, and with fewer resources.

According to a study conducted by Dell Technologies, 82% of leaders expect humans and machines will work as integrated teams within their organization within five years, and 42% of respondents believe they’ll have more job satisfaction in the future by offloading the tasks they don’t want to do to machines - freeing up time to do other tasks.

The Excitement Has Just Begun

The most powerful aspect of generative AI is its human-in-the-loop partnership. Content becomes stronger with the addition of user intent rather than content provided by stringent parameters and criteria, allowing for more personalized and tailored experiences for end users.

We anticipate as the race for the latest and greatest in generative AI presses on, we will continue to create more opportunities for humans to meet their full creativity capabilities, both by enabling non-technical users to do more with less, and by prompting humans with more starter ideas. This massive technological advance will play a huge role in driving human progress and growing our economies.