[Popular Mechanics] For all the talk of things that AI can do better than humans, there remains one especially important skill that even the most advanced machine learning systems can’t match: the raw computing efficiency of human gray matter. Our brains are essentially immensely powerful computers that somehow run on roughly 20 watts of power. AI data centers, on the other hand, churn through millions of watts (and lots and lots of water), much to the detriment of the environment and your electric bill.
Of course, scientists have long known about this massive energy discrepancy between human biology and artificial computing, and they’ve sought ways to merge the two together for decades. Now, in an era where everything seems to be AI, creating ways for these systems to be more efficient is becoming critical, especially if we want to conserve energy and other resources. To that end, a new study published in the journal Nature Nanotechnology investigates a novel way to create artificial neurons that can actually "talk" with real, living brain cells. The hope is to provide a flexible platform (similar to neurons) that has energy advantages over powerful-but-rigid silicon devices.
“The way you make AI smarter is by training it on more and more data. This data-intensive training leads to a massive power-consumption problem,” Mark Hersam, the senior author of the study from Northwestern University, said in a press statement. “Because the brain is five orders of magnitude more energy efficient than a digital computer, it makes sense to look to the brain for inspiration for next-generation computing.”
Creating an artificial neuron is a great place to start. After all, the average human brain contains around 86 billion of them. Specifically, our brains have more neurons in the cerebral cortex—which is responsible for consciousness, thought, memory, emotion, and language—than most animals. While silicon devices (complete with billions of transistors) are powerful, they’re not flexible like human neurons, which are capable of reshaping or even dropping certain connections over time. This is why humans can learn and adapt using very little energy. |