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Exploring the Role of Neuromodulators in AI | Brains@Bay Meetup
July 01, 2022
We highlight our top takeaways from the Brains@Bay meetup we hosted in spring, featuring Srikanth Ramaswamy, Jie Mei and Thomas Miconi. Our speakers did not disappoint, and the meetup was jam-packed with insights on how neuromodulators can lead to more flexible and adaptive AI.
How do I Pursue a Career in Brain-Based AI?
April 11, 2022
Interested in a career in brain-based AI? While there may not be a single answer or one clear path, I’ve gathered a list of tips and advice from our research team, designed to help anyone kick off their professional journey with brain-based AI.
Navigating the Pandemic: Numenta’s Approach
March 02, 2022
Like many companies, Numenta has found it challenging to cope with the COVID-19 pandemic over the past two years. Our CEO Donna Dubinsky shares how Numenta’s COVID-19 protocols and hybrid workplace model enables our team to be effective and safe.
AI Experts Dissect Sensorimotor Learning at Brains@Bay MeetUp
February 22, 2022
Numenta hosted a Brains@Bay meetup on December 15, 2021, entitled Sensorimotor Learning in AI. We’ve been fortunate to have some of the top experts in machine learning as guest speakers over the years, and the sensorimotor MeetUp followed in that tradition. Featuring Richard Sutton, Clément Moulin-Frier and Viviane Clay, their talks warrant a recap below.
The Path to Machine Intelligence: Classic AI vs. Deep Learning vs. Biological Approach
January 25, 2022
Six years ago, we wrote a blog about Classic AI, Simple Neural Networks, and Biological Neural Networks. Fast forward to today and it’s no surprise that the terms have continued to evolve. In this blog post, we’ll revisit these approaches, look at how they hold up today, and compare them to each other. We’ll also explore how each approach might address the same real-world problem.
Can Active Dendrites Mitigate Catastrophic Forgetting?
November 08, 2021
In our new pre-print titled “Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning”, we investigated how to augment neural networks with properties of real neurons, specifically active dendrites and sparse representations.