COULD WETWARE WIN? NEURONS VERSUS TRANSISTORS: BUT WHICH IS BETTER?

Is there something special about the human mind that gives it an inherent advantage over any conventional, binary computer? In simple numerical terms, brains are still far superior. Composed of something like 100 billion neurons, there’s complexity beyond any existing computer chip, even before you acknowledge that neurons are far more complex than binary transistors.

It’s thought each neuron can be connected to as many as 10,000 other neurons, the net result of which is up to 1,000 trillion synaptic connections. Try simulating that on a mining cluster of RTX 3090 GPUs. Of course, the brain’s detailed functionality isn’t something that can be easily reduced to numbers. But it’s still remarkable to think about all that power and connectivity, and still consider that the brain’s power consumption tops out at about 20 watts. How do you like them apples, Intel mobile CPU?

While we can debate the true computing power of biological brains, there’s little doubt we can learn a lot from them in terms of efficiency. As for other learnings, biology obviously informs the disciplines of machine learning and AI. That alone may be enough to create new software paradigms that accelerate effective computing power in a way that keeps the implied impact of Moore’s Law—an exponential growth in computing power—alive.

A parallel pursuit in this field is biological computers, or computers built from living cells. Yes, it’s been happening for years. In 2016, researchers from MIT created a machine based on cells that can perform simple computational operations, as well as store and recall data. It’s early days for this area of research and there’s no telling where it will lead. But it’s not impossible that biological computers could step in just when more conventional efforts involving silicon or carbon nanotubes reach their physical limits.