Background: Neuromorphic computer chips are designed to work like the human brain. Instead of being controlled by binary, on-or-off signals like most current chips, neuromorphic chips weight their outputs, mimicking the way different neurons fire at different strengths through their synapses.
What’s new: Artificial synapses have proved tricky to create. But MIT researchers now say they can precisely control one that can be used to train neural networks. What’s more, they’ve used the design to build a chip of synapses, and they’ve found that it’s able to recognize handwriting samples with 95 percent accuracy.
What it means: Artificial neural networks are already loosely modeled on the brain. The combination of neural nets and neuromorphic chips could let AI systems be packed into smaller devices and run a lot more efficiently.
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Yann LeCun has a bold new vision for the future of AI
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The hype around DeepMind’s new AI model misses what’s actually cool about it
Some worry that the chatter about these tools is doing the whole field a disservice.
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