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AI brings novel capabilities, at the cost of power- and compute-hungry algorithms. We engineer energy-efficient AI that can allow for operation on The Edge, or lower hardware and cloud computing demands.
Animals are immersed in the real world. Why can’t your AI be as well? We use neuroscience to engineer novel AI algorithms that accommodate operation in the real world.
Recent advances in AI are accelerating business. Yet there is tremendous hype as well. Binary Cognition can provide an honest, unbiased assessment of how state-of-the-art AI might (or might not) improve your business via enhanced data analyses or novel interactive capabilities.
The Neuro AI Blog
Exploring the intersection of neuroscience and AI
Can Darwinism revolutionize AI? A copy of this post is also available on Medium. Table of contents INTRODUCTION GENETICS AND NATURAL SELECTION EVOLUTIONARY COMPUTATION Evolution strategies Genetic algorithms with direct encoding Genetic algorithms with indirect encoding HyperNEAT Organism development Open-endedness (here’s where it gets really interesting!) What’s still missing? CONCLUSION REFERENCES Introduction Since roughly 2012 […]
Recurrence in biological and artificial neural networks: similarities, differences, and why it matters
Recurrence is an overloaded term in the context of neural networks, with disparate colloquial meanings in the machine learning and the neuroscience communities. The difference is narrowing, however, as the artificial neural networks (ANNs) used for practical applications are increasingly sophisticated and more like biological neural networks (BNNs) in some ways (yet still vastly different […]
In recent years, “deep learning” AI models have often been touted as “working like the brain,” in that they are composed of artificial neurons mimicking those of biological brains. From the perspective of a neuroscientist, however, the differences between deep learning neurons and biological neurons are numerous and distinct. In this blog post we’ll start […]
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