Big Data Symposium: Generative AI Not After Your Job (Unless It Is)

Job security (and insecurity), regulatory policy, and the possible benefits of generative AI flavored the discussions among stakeholders and panelists at this week’s New Jersey Big Data Alliance (NJBDA) Symposium: Big Data in FinTech.

Speakers with ties to healthcare, fintech, and other industries shared insights on how they are making use of AI, ways it can be beneficial across organizations, and a few caveats for the technology that continues to build up momentum.

A panel on AI and machine learning applications was comprised of Bhupinder Bhullar, managing director and co-founder of Swiss Vault; Jason Cooper, CTO with Paradigm; Amitabh Patil, co-founder and CTO with Whiz AI. Rashmi Jain, professor of business analytics and information systems at Montclair State University, moderated. Jain is also director of affiliate industry membership with the NJBDA.

“Some form of AI has always existed for at least a decade or more,” Jain said. Around 2017 and 2018, she said, the first papers on generative AI emerged, “which is basically nothing but AI modeling based on NLP, which is natural language processing.”
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The way generative AI accelerates certain operations was touched on across the daylong, annual event at Seton Hall University in South Orange, NJ. Kjersten Moody, chief data officer at Prudential Financial, gave a keynote on combining technical knowledge and real-world experience to further transformation. She also shared a bit about Prudential Financial’s explorations of generative AI and how it fits with its current operational needs.

“We started looking at, and doing our research and development into, GPT and generative AI about a year and a half ago,” Moody said. “We have a commercial relationship with OpenAI.” Prudential Financial is not using the chat component yet with customers, she said, but AI along with data is leveraged to better understand customers with more personalized offerings based on their needs.

One recurring theme from the AI panel was a notion that generative AI would not necessarily put jobs at risk, but rather that it could enhance certain roles and possibly better inform some staffers on what they might do differently. For example, Jain said AI can be put to work in efficient, productive medical applications such as reading radiology reports.

Patil, whose company works with the pharmaceuticals sector, said AI is being used to help discover new molecules given the large amounts of data that typically must be processed. There are also business operational elements that software like Whiz AI can assist with, such as pointing out issues with documentation. “Once the drug has been approved, there’s commercialization and branding and marketing,” he said. “And that’s where we specialize the most. How do we take a drug to market? How do we commercialize it?”

But What About Creatives?

Despite assertions that jobs are not at risk because of AI, it must be noted that other industries are already grappling with the possibility of AI disruption. The current Writers Guild of America strike, for example, includes arguments about the use of generative AI in scriptwriting. The WGA wants usage of the technology to be regulated, and that AI content should not be deemed to be literary or source material, in order to preserve writers' jobs and salaries. Potential career-threatening scenarios point to the possibility of studios using AI to churn out scripts based on ideas, then only using limited human writers to just touch up such material.

The Alliance of Motion Picture and Television Producers reportedly pushed back against the idea of regulating AI usage in this way, claiming it could stifle innovation in the industry.

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In contrast with the entertainment space, the science-oriented sectors seem to be more receptive to how generative AI may be used in tandem with current jobs. Bhullar discussed uses cases of AI in agriculture and food production, where the technology can be put to work monitoring the growth of plants. “We developed edge networks that take sensor data in from soil,” he said. “We’re collecting all the data from the edge network on site so that analysts can analyze the data very quickly.”

His company is trying, through its applications, to help collect data to run with machine learning algorithms and understand impacts on agriculture. “We want to enable better food production,” Bhullar said.

For all the benefits AI may offer in science and healthcare spaces, Cooper warned there needs to be constant awareness of what AI is asked to do and how it might accomplish that task. “One of my favorite sayings is, ‘Tortured data will confess to anything,’” he said. “Just be careful with the questions that you’re asking and the methodologies that you’re using.”

Accurate data is still crucial for AI and machine learning, Cooper said, which might keep the door open for human workers to remain in the mix in collaboration with digital counterparts, though with some changes to their career paths. He cited a hospital in Ontario, Canada that went fully digital, including with machines to deliver medications when needed.

“Someone has to care and feed for the algorithms and those machines themselves,” Cooper said, “So you see a transition, potentially, from what I would call a more blue-collar service industry to a white-collar biomedical engineering type of job. I don't think AI takes your jobs -- I think it elevates and just creates different jobs for us.”