The human brain is often hailed as the ultimate parallel processing machine. It juggles memory, learning, and bodily functions all at once—an evolutionary marvel that even our most advanced computers struggle to match.
But despite its complexity, scientists have long grappled with a fundamental question: How many tasks can the human brain truly handle at once?
Now, groundbreaking research has put a number on it.
According to neuroscientist Harris Georgiou from the National Kapodistrian University of Athens, the human brain processes approximately 50 independent tasks in parallel, even when engaged in complex activities.
At first glance, this number may seem underwhelming.
After all, our brains consist of roughly 100 billion neurons, each capable of forming up to 10,000 connections with its neighbors.
With such vast potential, why does our multitasking capability seem so… limited?
A High-Performance System Running on 20 Watts
To understand how our brains process multiple tasks, Georgiou turned to functional magnetic resonance imaging (fMRI).
This technology tracks brain activity by measuring oxygen levels in blood flow, pinpointing which regions are most active.
The brain is then mapped into three-dimensional pixels called voxels, each roughly five cubic millimeters in size.
Participants in Georgiou’s study were asked to complete two tasks:
- A complex visuo-motor task—identifying a red or green box on a screen and responding by raising a specific finger.
- A recognition task—spotting repeated images of objects like faces, houses, and chairs.
The results?
The more complex the task, the more processes were engaged. During the visuo-motor task, up to 50 independent processes were identified.
The recognition task, being simpler, required fewer.
This discovery challenges a long-standing assumption: that parallel processing occurs at the level of individual neurons.
Instead, Georgiou’s findings suggest that neurons work together in highly structured groups, forming distinct processing units—akin to the CPU cores of a computer.
As MIT Technology Review puts it:
“[The research] implies that parallelism in the brain does not occur on the level of individual neurons but on a much higher structural and functional level, and that there are about 50 of these.”
The Contrarian View: Our Brain Is Not the Supercomputer We Thought
For years, the prevailing belief was that the brain operates as an infinitely scalable parallel processor, effortlessly juggling countless tasks.
But Georgiou’s research suggests otherwise—we’re limited to around 50 parallel processes at a given time.
This realization is both humbling and illuminating.
We often marvel at human intelligence, assuming our brains are vastly superior to artificial processors.
Yet, a modern GPU can run thousands of parallel processes simultaneously, far surpassing our meager 50.
However, raw computing power is only part of the equation. What makes the brain truly remarkable isn’t the number of tasks it handles, but how efficiently it does so—all while operating at a mere 20 watts of power.
That’s less than a dim light bulb.
This efficiency has long fascinated computer scientists, who dream of replicating biological cognition in artificial systems.
But if Georgiou is right, we may not need neural networks with billions of nodes to mimic human intelligence.
Instead, a well-optimized system of 50 parallel processes could be enough to achieve brain-like cognition.
What This Means for AI and Computing
The implications of this research extend far beyond neuroscience.
As AI engineers strive to build neuromorphic processors—hardware that mimics brain function—this discovery suggests that scalability may not be the key to artificial intelligence.
Instead of endlessly increasing the number of connections in a neural network, future AI models might achieve human-like cognition through strategic, hierarchical processing, mirroring the 50-task limitation of the brain.
Georgiou himself speculates:
“In theory, an artificial equivalent of a brain-like cognitive structure may not require a massively parallel architecture at the level of single neurons, but rather a properly designed set of limited processes that run in parallel on a much lower scale.”
If this holds true, we could see a revolution in AI efficiency, with future models requiring far less energy and computing power than today’s deep learning systems.
Evolution Still Has Us Beat (For Now)
Despite all our technological advancements, the brain remains an enigma—a structure the size of a party cake that outperforms our most advanced chips in efficiency.
While computers may outmatch us in sheer speed and scale, they have yet to replicate the adaptive, power-efficient intelligence that has kept humans thriving for millennia.
For budding AI engineers and neuroscientists alike, Georgiou’s research is a wake-up call.
The future of artificial intelligence may not lie in brute-force computing, but in understanding and mirroring the elegant constraints of human cognition.
And until we crack that code, evolution remains the undisputed champion of intelligent design.
Sources: MIT Technology Review, io9
