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Deep Learning versus Biological Neurons: floating-point numbers, spikes, and neurotransmitters – from Matthew Roos

Peng Liu

Original post from https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9

Conjoined Dichotomy by Melting Miltons

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 by describing a few key characteristics of biological neurons, and how they are simplified to obtain deep learning neurons. We’ll then speculate on how these differences impose limits on deep learning networks, and how the movement toward more realistic models of biological neurons might advance AI as we currently know it.

The details can be found from the original https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-numbers-spikes-and-neurotransmitters-6eebfa3390e9

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Evolutionary approaches towards AI: past, present, and future – from Matthew Roos

Peng Liu

Can Darwinism revolutionize AI?

original: https://towardsdatascience.com/evolutionary-approaches-towards-ai-past-present-and-future-b23ccb424e98

Open environments and competition among species are two driving forces of Darwinian evolution that are largely absent in recent works on evolutionary approaches toward AI models. Within a given generation, faster impalas and faster cheetahs are more likely to survive (and reproduce) than their slower counterparts—leading to the evolution of faster and faster impalas and cheaters over time. Can these and other principles of genetics and natural selection guide us towards major advances in AI?

A copy of this post is also available on Medium.

https://youtu.be/8wl8ZxAaB2E
Due in part to co-evolution, wolves have evolved a social intelligence that allows them to hunt in packs—thereby killing animals much larger than themselves and earning a food reward that benefits the entire pack, not just individual wolves.

Indeed, a very recent study by OpenAI demonstrates the emergence of complex interactive behaviors among agents trained by reinforcement learning, despite a seemingly impoverished reward system. Notably, rewards were given for team (akin to species) performance, not individual performance—much like all the wolves in a pack are rewarded when a large kill is made.

Given the simple task of playing hide-and-seek, these agents learn to collaborate to perform the complex tasks that at first blush, seem to have little to do with the primary hide-and-seek objective. Competition between teams is a driving factor in the emergence of these collaborative within-team behaviors. Note that these agents were trained by reinforcement learning rather than evolutionary computation. However, the relevant point is that competition between teams of agents (or species) can promote novel and complex behaviors.
Baby wildebeests gain agile muscle control and navigation abilities with minutes of birth. Clearly evolution has given them an innate sense of gravity, physics, objectness, and an advanced sensorimotor control system. They do no need to learn these concepts and capabilities through their reinforcement learning system.

What’s still missing?

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