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The Future of AI: Embracing its Pros, Sidestepping its Cons

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Sensibly leveraging artificial intelligence has proven to be an exceptionally fruit-bearing process. (One not without its share of risks and ethical disputes, but fruitful all the same.) Text, imagery, audio; a new kingdom of AI applications await.

“This is as bleeding-edge as it gets,” said Eric Riz, CEO of VERIFIED, a 2014-founded data analytics firm known for its platform that verifies candidates’ resumes and varied social profiles via AI, ML, and the blockchain.

At this year’s Generative AI Expo (part of ITEXPO, held at the Broward County Convention Center in Fort Lauderdale, FL), Riz spoke at a session titled “Opportunities and Risks/Challenges in Generative AI” and dove into what foundational and functional technologies it, in his words, plays well with.

“But this hasn’t been the case for AI for years and years,” Riz continued. “In 2022, a ton of publications came out with lists of their Top 10 Disruptive Technologies. Can anyone guess how many lists Generative or Conversational AI, or even ChatGPT specifically, were on?”

The answer, as Riz’s audience found out, was zero.

“It wasn’t mainstream then. Now, it is. So let’s talk about it.”

Riz first covered replaceability versus irreplaceability. “Let me be clear,” he assured. “AI, in its current iterations, will not replace a myriad of our essential jobs; especially ones involving physical labor, services in mental health that aren’t chatbots, high-ticket sales positions and etcetera. That said, there was admittedly a recent instance where ChatGPT successfully passed a Google coding interview for an engineering role with a $183,000 salary.”

“Understandably, there’s many ways to feel about that,” he followed up. “But right now, we can still examine AI with a realistic, non-job-robbing lens. There’s a whole cosmos of opportunity for it, albeit with risks.”

Be it ChatGPT, DALL·E 2, Midjourney, Jasper, DeepMind or others, Generative AI rapidly accumulates data, compiles specific sets based on what’s being asked of it, and then spits back out results on whatever purposes it’s trained for.

The AI’s database. Users’ prompt inputs. Generation.

Those are Generative AI’s basic steps. But a human step to follow up with – one that must always enter the equation – is verification. (I penned a Future of Work Expo piece that covered this in detail.) Verifying, validating, authenticating (any of those terms will do) what Generative AI produces is quintessential because there isn’t a stamp assuring 100% accuracy on anything produced.

Riz then reviewed with the audience precisely what the name of his session pointed to: The opportunities for Generative AI, and the associated risks.

Opportunities:
 

  • Generating personalized email and messaging content for lead-nurturing campaigns
     
  • Creating compelling social media posts and high-quality, informative blog posts and articles for thought leadership
     
  • Crafting outlines for product descriptions and case studies for use on websites and in sales materials
     
  • Assisting with the development of video and voice-over scripts, as well as white papers, e-books, and other longer-form content
     
  • Churning out chatbot scripts and FAQs for customer service and support teams
     
  • Producing data organized from industry report summaries, plus transcriptions and captions for videos, webinars, etc.
     
  • Enhancing overall website user experiences by providing quickly customized recommendations, suggestions, and user-friendly content
     

Risks:
 

  • Context Understanding – AI lacks true context, so content that isn’t fully relevant or appropriate for an intended audience (but is generated anyway) necessitates contextual verification
     
  • Deeply Personalizing Content – Generative AI often misunderstands personal nuances; individuals and companies attempting to use it for these purposes may end up with messages that feel too generic or impersonal.
     
  • Customer Needs – Specific customer pain points may be outside the capability of AI to handle. Messaging that effectively addresses this requires human intervention.
     
  • Customer Behavior Analysis – ChatGPT, for example, isn’t currently able to fully understand the scope of evolving customer behaviors. (e.g. personal nuances) Is a customer just digitally window-shopping, or are they looking to do more than browse, for example? It can be difficult for AI to create on-the-fly messaging that matches customers’ shifts in behaviors.


“These, by no means, constitute complete lists,” Riz said. “After all, we’re in the thick of a fast-paced tech craze, at the moment. So as we progress through it, the verification of AI-generated content and the deployment of truly responsible AI-powered solutions will yield the best-possible results.”

For those interested, apart from ChatGPT the other AI listed above, Riz recommended keeping an eye on the following, as well:




Edited by Alex Passett
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