Numenta, a company with backing from the Gates Foundation, on Wednesday released an open-source AI model it hopes will require less energy and data to create intelligent machines than existing ...

You started Numenta. You said that you’d been studying the brain for 25 years or so. It seemed to me that you knew an awful lot about the brain already.

Forget about smartphones. Two of the big brains behind those essential toys say they will build the basis of smart--really smart, like humans--machines, everywhere. Jeff Hawkins and Donna Dubinsky ...

Xilinx. Numenta, the Silicon Valley artificial intelligence firm founded by Palm Pilot creator Jeff Hawkins, has been able to achieve a dramatic speed up in conventional neural networks using ...

Numenta compared sparse and dense networks by running its algorithms on Xilinx FPGAs (Field Programmable Gate Array) for a speech recognition task using the Google Speech Commands (GSC) dataset.

In the software world, we’ve heard about the great promise of artificial intelligence for years and Hawkins has been working on Numenta since at least 2005. So there’s reason to be skeptical ...

Numenta’s sparse network utilizes highly sparse weights and activations, like in the neocortex. Their sparse-sparse techniques allow for extreme activation sparsity while maintaining model accuracy.

Numenta is building artificial intelligence that attempts to replicate the function of the human neocortex to solve hard problems. The software specializes in recognizing patterns within massive ...

REDWOOD CITY, Calif., January 07, 2025--Numenta’s Thousand Brains Project is now an independent nonprofit, fostering brain-inspired AI research with open-source code and a global community.

Numenta’s Grok for IT product, built upon the company’s novel HTM technology, is one of the most impressive AI products available. If you’re in IT you need to check Grok for IT out; ...

REDWOOD CITY, Calif., May 21, 2021 — Numenta, Inc. announced it has achieved greater than 100x performance improvements on inference tasks in deep learning networks without any loss in accuracy. In a ...