No technology remains dominant forever.
new AI architecture seeking to challenge the transformer, claims to tackle both of these shortcomings.
Rus is one of the world’s leading roboticists, and liquid neural networks appear to be particularly well-suited for robotics applications, including autonomous vehicles and drones. They only work with time-series data , meaning that they cannot be applied to images or other static data modalities. This work is still early and unproven. It remains far from certain whether these new architectures will succeed in replacing the transformer. But if they do, the implications for the world of AI will be enormous.
Yet it is not preordained that this trend toward unification—toward “one AI architecture to rule them all”—will continue indefinitely.
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