WORDS OF CAUTION
Still, while AI and generative AI hold great promise for businesses and individuals, industry experts advise caution in expecting too much from these algorithms at this time. “ChatGPT and related technologies—such as Google’s Bard—are still very new and works-in-progress. Caution is needed as these technologies develop,” said Sglavo. “But they also are great examples of analytics for the people—you don’t need to be a data scientist or statistician to benefit from AI, NLP, RL, and related technologies.”
AI “is not yet within reach in the enterprise,” said Sudhakar. “With the advent of ChatGPT, businesses are now looking for opportunities to introduce AI into their processes and products, but they are running into issues understanding how to scale this technology to their specific domains to meet their specific needs.”
Direct access to emerging AI “should be viewed as a positive technological advancement—but only to an extent,” said Su. “Bringing AI into everyone’s reach introduces the technology to millions who may otherwise not go near it, allowing individuals to familiarize themselves with the use cases and develop a better understanding of what it is, hopefully lessening the fear that AI is coming for jobs. What the general public may not fully understand are the ethical considerations and conversations that must come with the increased usage of AI. Take Generative AI, for example, which can hallucinate false answers. These incorrect responses may go unnoticed, unbeknownst to any given individual. So while consumer-friendly AI has emerged and captured our attention, educating the public on its limitations must be a top priority.”
USE CASES
Tellingly, open generative AI is seeing practical use cases emerging as well. “It’s not all hype—the early adopters have already found ways to use it in their daily activities and are unlikely to slow applications,” said Kroupenev. “At Augury, industrial AI technology is a core part of how we solve big customer problems. We’re now embedding genAI into our own daily structures, which is fundamental to improving the way people work and ultimately serving our customers better.”
Generative AI “is also creating inroads for enterprise use cases,” said Sudhakar. “In a very short time, companies realized how much this technology could do: support customer services, code and develop, execute marketing activations and write content, assist the IT help desk, and aid in compliance.”
Chandramouli, who is an AI researcher and developer, noted that “open sourcing of deep learning models has enabled me to leverage and apply models that are otherwise not feasible for smaller companies due to enormous data and computing requirements. As a result, smaller companies can utilize powerful artificial intelligence models to solve problems effectively.”
As with most things, “the democratization of AI will have good aspects as well as bad, but I think on net, this is a positive change,” said Endicott. “With AI tools like ChatGPT now broadly available to the public, we are seeing a massive surge in this use of generative text for all sort of purposes. I know recruiters who are using ChatGPT to write job descriptions. My brother is an artist who is using ChatGPT to write blog articles about painting techniques. I know software engineers who are using ChatGPT to write code, for example converting code in one development langue to another one. On the other hand, the quality of output can be highly dependent on the input given to the model, and broad adoption is going to generate output of varying quality and consistency.”