Hyperdimensional computing promises a new world in which computing is efficient and robust and machine-made decisions are entirely transparent. Via Quantanews
Once the network has generated hypervectors for each of the context images and for each candidate for the blank slot, another algorithm analyzes the hypervectors to create probability distributions for the number of objects in each image, their size, and other characteristics.
Another advantage of hyperdimensional computing is transparency: The algebra clearly tells you why the system chose the answer it did. The same is not true for traditional neural networks. Olshausen, Rahimi, and others are developing hybrid systems in which neural networks map things in the physical world to hypervectors, and then hyperdimensional algebra takes over. “Things like analogical reasoning just fall in your lap,” Olshausen said. “This is what we should expect of any AI system.
Despite such advantages, hyperdimensional computing is still in its infancy. “There’s real potential here,” Fermüller said. But she points out that it still needs to be tested against real-world problems and at bigger scales, closer to the size of modern neural networks.
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