Generative AI: Transforming Creativity, One Algorithm at a Time

 How adept is this technology at mimicking human efforts at creative work? Well, for an example, the italicized text above was written by GPT-3, a “large language model” (LLM) created by OpenAI, in response to the first sentence, which we wrote. GPT-3’s text reflects the strengths and weaknesses of most AI-generated content. First, it is sensitive to the prompts fed into it; we tried several alternative prompts before settling on that sentence. Second, the system writes reasonably well; there are no grammatical mistakes, and the word choice is appropriate. Third, it would benefit from editing; we would not normally begin an article like this one with a numbered list, for example. Finally, it came up with ideas that we didn’t think of. The last point about personalized content, for example, is not one we would have considered.

Overall, it provides a good illustration of the potential value of these AI models for businesses. They threaten to upend the world of content creation, with substantial impacts on marketing, software, design, entertainment, and interpersonal communications. This is not the “artificial general intelligence” that humans have long dreamed of and feared, but it may look that way to casual observers.

What Is Generative AI?

Generative AI can already do a lot. It’s able to produce text and images, spanning blog posts, program code, poetry, and artwork (and even winning competitions, controversially). The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images. LLMs began at Google Brain in 2017, where they were initially used for translation of words while preserving context. Since then, large language and text-to-image models have proliferated at leading tech firms including Google (BERT and LaMDA), Facebook (OPT-175B, BlenderBot), and OpenAI, a nonprofit in which Microsoft is the dominant investor (GPT-3 for text, DALL-E2 for images, and Whisper for speech). Online communities such as Midjourney (which helped win the art competition), and open-source providers like HuggingFace, have also created generative models.

These models have largely been confined to major tech companies because training them requires massive amounts of data and computing power. GPT-3, for example, was initially trained on 45 terabytes of data and employs 175 billion parameters or coefficients to make its predictions; a single training run for GPT-3 cost $12 million. Wu Dao 2.0, a Chinese model, has 1.75 trillion parameters. Most companies don’t have the data center capabilities or cloud computing budgets to train their own models of this type from scratch.

But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data. This has led to specialized models of BERT — for biomedical content (BioBERT), legal content (Legal-BERT), and French text (CamemBERT) — and GPT-3 for a wide variety of specific purposes. NVIDIA’s BioNeMo is a framework for training, building and deploying large language models at supercomputing scale for generative chemistry, proteomics, and DNA/RNA.OpenAI has found that as few as 100 specific examples of domain-specific data can substantially improve the accuracy and relevance of GPT-3’s outputs.

To use generative AI effectively, you still need human involvement at both the beginning and the end of the process.

To start with, a human must enter a prompt into a generative model in order to have it create content. Generally speaking, creative prompts yield creative outputs. “Prompt engineer” is likely to become an established profession, at least until the next generation of even smarter AI emerges. The field has already led to an 82-page book of DALL-E 2 image prompts, and a prompt marketplace in which for a small fee one can buy other users’ prompts. Most users of these systems will need to try several different prompts before achieving the desired outcome.

Then, once a model generates content, it will need to be evaluated and edited carefully by a human. Alternative prompt outputs may be combined into a single document. Image generation may require substantial manipulation. Jason Allen, who won the Colorado “digitally manipulated photography” contest with help from Midjourney, told a reporter that he spent more than 80 hours making more than 900 versions of the art, and fine-tuned his prompts over and over. He then improved the outcome with Adobe Photoshop, increased the image quality and sharpness with another AI tool, and printed three pieces on canvas.

Generative AI models are incredibly diverse. They can take in such content as images, longer text formats, emails, social media content, voice recordings, program code, and structured data. They can output new content, translations, answers to questions, sentiment analysis, summaries, and even videos. These universal content machines have many potential applications in business, several of which we describe below.

Marketing Applications

These generative models are potentially valuable across a number of business functions, but marketing applications are perhaps the most common. Jasper, for example, a marketing-focused version of GPT-3, can produce blogs, social media posts, web copy, sales emails, ads, and other types of customer-facing content. It maintains that it frequently tests its outputs with A/B testing and that its content is optimized for search engine placement. Jasper also fine tunes GPT-3 models with their customers’ best outputs, which Jasper’s executives say has led to substantial improvements. Most of Jasper’s customers are individuals and small businesses, but some groups within larger companies also make use of its capabilities. At the cloud computing company VMWare, for example, writers use Jasper as they generate original content for marketing, from email to product campaigns to social media copy. Rosa Lear, director of product-led growth, said that Jasper helped the company ramp up our content strategy, and the writers now have time to do better research, ideation, and strategy.

Focus Keyword-

1) arrested faces eight charges

2) kidnapping