That was the theme of a recent conference hosted by Swiss Cognitive, a global community of AI experts from across industries. The conference featured 20 of those experts, all talking about the impact of large language models and generative AI-based tools (like ChatGPT). The thought leaders discussed the possibilities, the risks, and what needs to happen so we can best leverage generative AI to revolutionize processes.
In this article, I give you some of the top takeaways from this event related to generative AI and large language models. To get the full story, I encourage you to watch the entire conference. It’s free.
Generative AI Virtual Conference in Brief
Generative AI provides a crucial steppingstone to leverage our available data in ways not possible before. When used properly, generative AI and large language models can help us break down boundaries across industries and arrive at destinations sooner than we could with human brain power alone.
How Generative AI Propels Business
The first panel I caught discussed opportunities for generative AI and red flags.
Andreas Welsch, VP and Head of Marketing & Solution Management, AI for SAP, and founder and president Machine Intelligence Institute of Africa, talked about how AI is shaping customer engagement and how businesses can leverage AI in that way.
He emphasized the importance of role-playing with AI: ask it detailed questions to uncover the top three to five things your target audience cares about. Craft copy and value statements that address these issues. Tip: Always fact-check AI’s responses.
Jair Riberio, Analytics & Insights Leader at Volvo Group, discussed the potential of generative AI in manufacturing. These tools can discover new materials — like a new compound for a stronger tire, perhaps. They can also analyze large amounts of data to improve efficiency, reduce cost, enhance safety, and add a new layer of understanding to help leadership and everyday activities to make better decisions.
Noelle Silver Russell, Global AI Solutions Lead at Accenture, highlighted various possibilities in marketing, finance, legal, and sales. But the value closest to her heart was how generative AI can improve patient care coordination.
A caregiver to her father and mom to a child with special needs, Russell said AI-based tools are already making it a little bit easier for caregivers to coordinate doctors’ appointments, pay medical bills, and manage all the other details that come with this role. She also saw the potential for individualized education plans for kids.
Imagine an education plan tailored to a child’s learning style and level vs. sitting in a classroom bored because the material is either too easy or the child has trouble paying attention to a lecture.
The panelists also discussed what needs to happen for these possibilities to become reality. Silver Russel emphasized the importance of high-quality data.
Ludik advised businesses to plan for AI in the design phase. Make a UX designer your best friend.
Welsch warned businesses to be aware of ethical implications. For example, do not copy and paste AI-generated text without verifying the accuracy of its information. Be a good fact-checker. If you’re using generative AI, you are responsible for double-checking the output.
Riberio cautioned leaders to remember: you can affect millions to billions of people by using generative AI-based information. Act responsibly.
He also encouraged leaders to educate employees who will be impacted by this. If AI could replace their job, for example, train them on how to use AI. Employees must also take responsibility to educate themselves.
Risk and Challenges of Generative AI and How to Mitigate Them
The next panel discussed the risks generative AI poses and how we can mitigate those risks.
Ori Goshen, cofounder and co-CEO of AI21 Labs, noted that while large language models can spark creativity, they lack reasoning. This makes them unreliable for business applications that require reasoning and logic. As a potential solution, he envisions a hybrid approach with a system embedded in an enterprise context where reliability and explainability are important.
Sean McClain, founder and CEO, Absci, an AI-based drug discovery company, addressed the application of generative AI-based tools in scientific research. As with radiology and other medical roles, AI will not replace scientists. AI-based tools can, however, replace certain wet lab tasks to give scientists the time to focus on bigger, broader problems related to the biology of the drugs or compounds.
AI can’t tell us what the biology is, he explained. We need researchers to dive into these problems.
With an AI-based scientific process, pharma and biopharma companies can potentially get products to patients faster than ever, and at a lower cost. Those are both problems pharma and healthcare have been trying to solve for years.
There’s so much potential in generative AI to transform industry in positive ways. The key is to use these tools ethically, develop approaches to mitigate bias, and train employees on how to use AI-based tools so they can do their jobs differently than before, and take businesses to new levels of productivity and possibility.
How are you using AI in your business? If you’re not? How will you?