Beyond 6G networks may unlock AI with real-world adaptability and reasoning abilities Virginia Tech researchers believe AI-native networks are the key to AGI Wireless technology could help AI bridge the gap to human intelligence
Artificial intelligence has advanced rapidly, yet researchers at Virginia Tech believe that achieving artificial general intelligence (AGI) depends on equipping AI tools with common sense, enabling it to think, imagine, and plan beyond its training data.
A recent study published in the Proceedings of the IEEE Journal
The researchers propose that AI-native wireless systems beyond 6G could provide a framework for AI to learn from real-world interactions, mimicking human cognition using the most powerful computers to process vast amounts of data.
Next-generation AI could drive a wireless revolution
Virginia Tech researchers argue next-generation AI is the missing link in future wireless advancements, moving beyond the efficiency-focused AI of early 6G developments to systems that can actively reason and make decisions like humans.
Unlike current AI, which relies on pattern recognition and statistical analysis, this new approach would introduce AI capable of understanding physical principles, predicting events, and adapting to unforeseen circumstances.
However, achieving this remains a long-term goal that will require the best small business servers to ensure seamless data processing and integration.
“We’re looking at least 10 or 15 years down the line before we have a wireless network with artificial general intelligence [AGI] that can think, plan, and imagine,” said Walid Saad, a professor in the Bradley Department of Electrical and Computer Engineering at the university.
“We have a blueprint and concrete road map. The entire vision might not be immediately deployable, but pieces of it can be implemented now. We’re trying to position this paper in a way to tell the community that there is a path to something really revolutionary — step by step we can work toward a living, thinking wireless network.”
By processing vast real-time data and interacting with digital twins, AI could develop intuition, enabling it to predict outcomes, make logical decisions, and bridge the gap between computational processing and human reasoning.
“Simply put, the current level of AI is good at extracting statistical relationships from data, but it’s very bad at reasoning and generalizing to novel, unexpected situations – things that most humans master perfectly.”
To make this vision a reality, the researchers argue that wireless networks must evolve from merely transmitting data to actively learning from it.
“The missing link is really the wireless network and its components like digital twins, because we can use a twin that exists as a basis for a world model thereby enabling human-level-like thinking and integrating these ‘thought’ processes in the wireless network now,” Saad said.
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