Using null samples to shape decision spaces and defend against adversarial attacks

This post is a summary of a technical report we recently posted on Read the full report here: A copy of this post is also available on Medium. Introduction Shortly after the arrival of the new wave of neural network models for computer vision in roughly 2012 [1, 2], it was discovered that […]

Evolutionary approaches towards AI: past, present, and future

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 […]

Deep Learning versus Biological Neurons: floating-point numbers, spikes, and neurotransmitters

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 […]