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.
DeepMind’s cofounder: Generative AI is just a phase. What’s next is interactive AI.
“This is a profound moment in the history of technology,” says Mustafa Suleyman.
AI hype is built on high test scores. Those tests are flawed.
With hopes and fears about the technology running wild, it's time to agree on what it can and can't do.
You need to talk to your kid about AI. Here are 6 things you should say.
As children start back at school this week, it’s not just ChatGPT you need to be thinking about.
AI language models are rife with different political biases
New research explains you’ll get more right- or left-wing answers, depending on which AI model you ask.
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