Brains suggest an alternate way to build AI—one that will replace deep learning as the central technology for creating artificial intelligence. Read the full article published in Fast Company.
For truly intelligent AI, we need to mimic the brain’s sensorimotor principles Read More »
Retrieval-augmented generation may provide a big step forward in addressing many of the issues that keep enterprises from adopting AI.
How RAG Will Usher In the Next Generation of LLMs and Generative AI Read More »
Today’s cutting-edge artificial intelligence is based on neuroscience from the ‘50s and ‘60s. Imagine what A.I. could do if it incorporates the latest breakthroughs. This article originally appeared on Fortune.com.
AI Needs Neuroscience More than Ever Read More »
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.
How do I Pursue a Career in Brain-Based AI? Read More »
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.
The Path to Machine Intelligence: Classic AI vs. Deep Learning vs. Biological Approach Read More »
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.
Can Active Dendrites Mitigate Catastrophic Forgetting? Read More »
Are you a machine learning researcher looking for better learning algorithms? Interested in how neuroscience research can help inform the development of artificial intelligence systems? Brains@Bay may be the Meetup group for you! Brains@Bay is a meetup hosted by Numenta with the goal of bringing together experts and practitioners at the intersection of neuroscience and AI.
Brains@Bay: A Meetup on Brain-Inspired Machine Learning Algorithms Read More »
We’ve been getting a lot of questions lately as to the differences between Hinton’s GLOM model and Numenta’s Thousand Brains Theory. In this blog, we will outline the commonalities and main differences of both models at a high level.
Comparing Hinton’s GLOM Model to Numenta’s Thousand Brains Theory Read More »
In this post, Karan describes the technicalities of why neural networks do not learn continually, briefly discusses how the brain is thought to succeed at learning task after task, and finally highlights some exciting work in the machine learning community that builds on fundamental principles of neural computation to alleviate catastrophic forgetting.
Why Neural Networks Forget, and Lessons from the Brain Read More »
Numenta Visiting Research Scientist Vincenzo Lomonaco, Postdoctoral Researcher at the University of Bologna, gives a machine learner’s perspective of HTM (Hierarchical Temporal Memory). He covers the key machine learning components of the HTM algorithm and offers a guide to resources that anyone with a machine learning background can access to understand HTM better.
A Machine Learning Guide to HTM (Hierarchical Temporal Memory) Read More »