Machine Learning Is Not Like Your Brain Part One: Neurons Are Slow, Slow, Slow
Artificial intelligence is not all that intelligent. While today’s AI can do some extraordinary things, the functionality underlying its accomplishments has very little to do with the way in which a human brain works to achieve the same tasks.
A biological brain understands that physical objects exist in a 3D environment which is subject to various physical attributes. It interprets everything in the context of everything else it has previously learned. In stark contrast, AI – and specifically machine learning (ML) – analyzes massive data sets looking for patterns and correlations without understanding any of the data it is processing. An ML system requiring thousands of tagged samples is fundamentally different from the mind of a child, which can learn from just a few experiences of untagged data. Even the recent “neuromorphic” chips rely on capabilities which are absent in biology.
This is just the tip of the iceberg. The differences between ML and the biological brain run much deeper, so for today’s AI to overcome its inherent limitations and evolve into its next phase – artificial general intelligence (AGI) – we should examine the differences between the brain, which already implements general intelligence, and the artificial counterparts.
With that in mind, this nine-part series will explain in progressively greater detail the capabilities and limitations of biological neurons and how these relate to ML. Doing so will shed light on what ultimately will be needed to replicate the contextual understanding of the human brain, enabling AI to attain true intelligence and understanding.
To understand why machine learning is not much like your brain, you need to know a little about how brains work and about their primary component, the neuron. Neuron cells are complex, electro-chemical devices and this brief description just scratches the surface. I will be as brief and succinct as possible, so bear with me then we’ll get back to ML.
The neuron has a cell body and a long axon (avg. length ~10mm) which connects via synapses to other neurons. Each neuron accumulates an internal charge (the “membrane potential”) which can change. If it reaches a threshold, the neuron will “fire.”
The neuron’s internal charge can be modified through the addition of neurotransmitter ions which can increase or reduce the internal charge (depending on the neurotransmitter ion’s charge). These neurotransmitters are contributed from incoming synapses when the connected neuron fires in an amount we’ll call the “weight” of each synapse. As the weight changes, the synapse contributes more or fewer ions to the target neuron’s internal charge. When the neuron fires, it sends a neural spike down its axon and contributes neurotransmitters through each synapse to each connected neuron. After firing, the voltage returns to its resting state and the neurotransmitters return to their initial locations to be reused.
This is significantly different than the process used by the theoretical perceptron which underlies most ML systems.
