Graphics Processing Units (GPUs), once known primarily for powering video games, have evolved into the backbone of today’s most advanced artificial intelligence (AI) systems.

At AFROTECH™ 2024, Seyi (Shay) Ogebule, Ph.D., product lead at Intel and a seasoned expert in IoT and tech manufacturing, took the Cybersecurity Stage to share how GPUs are revolutionizing edge AI.

“What powers AI? How does AI work? What’s behind the scenes?” Dr. Ogebule asked the audience. “So, I spent a lot of my career and my work over a decade-plus behind the scenes trying to create those specialized chips so that you can enjoy the benefits of AI.”

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During the panel, viewers learned about the latest breakthroughs in GPU technology, which is used to process massive volumes of data, and how such advancements are making general computing and AI more efficient, accessible, and impactful.

As Ogebule explained, the growing demand for real-time, intelligent decision-making across sectors like health care, retail, and industrial tech is pushing AI away from centralized cloud servers and toward the edge — closer to where data is generated and decisions must be made instantly. By the end of 2025, it’s projected that 75% of all data will be processed at the edge.

“I hope you all see that Edge AI is here to stay. This is exactly where AI is moving,” Ogebule said. “The cloud has been great. The cloud has [benefited] us today a lot. But we need to start fine-tuning, building models, and building devices around the edge, and making sure it applies to just not me.”

For people worried about AI taking their jobs, Ogebule delivered a clear message: AI “is coming to take your job” — from people who don’t scale up and learn how to use it for the benefit of the world and themselves.

She urged communities — particularly underrepresented ones — not just to use AI but to help build it.

“I don’t want us to restrict and limit ourselves to just what we see with security surveillance within our communities,” Ogebule concluded. “I want us to be able to generate that data and gather that data because as we train these models, the models recognize the disparities, they recognize the bias, and they themselves become biased themselves. So the more we can get involved, the more we can be involved in these fields, the better it is for our world and the better it is for our community.”