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Human Brain vs Machine Learning – A Lost Battle?

Human (or any other animal for that matter) brain computational power is limited by two basic evolution requirements : survival and procreation. Our “hardware” (physiology) and “software” (hard-coded nature psychology) only had to evolve to allow us to perform a set of basic actions – identify Friend or Foe, obtain food, find our place in the social tribe hierarchy, ultimately find a mate and multiply. Anything beyond this point, or not directly leading to this point can be considered redundant, when viewed from the evolution perspective. To accomplish these “life” goals, our brains evolved to a certain physical limit (100 billion neurons per average brain, on average 7000 synaptic connections per neuron). Obviously, evolving beyond this limit was not beneficiary for survival and procreation in the African savannas. So, we are hard-limited by our “hardware”, with the hardware spec being 1.5 million years old.

Though, according to the saying, we all “Live and Learn” – we actually live for a relatively short period of time, and learn effectively for an even shorter period. So, the “training set” that each of us has been exposed to in his infancy and childhood is limited by time. Of course, we continue learning things and acquiring skills after we become teens and then adults – but at a much lower, if not negligible, efficiency. We may taste a few exotic fruits, see a new place, study a math subject or try to learn tango – but the truth is that we learned most of our necessary survival skills (telling a person from a tree from a lion etc.) by the age of 3. So, our brain’s “training set” is effectively limited in volume, and this limit is set by all the things we managed to see and do while we were infants, plus a long tail of things we picked up as adults.

So, we are limited by hardware and by the size of the training set. What about our artificial intelligence counterparts – our “machine tools”? Well, they are catching up and catching up fast! According to the following estimate (image taken from www.deeplearningbook.org, the blue dots are different artificial neural networks, #20 is GoogLeNet), computers will catch up with us in the neuron count game by the 2050’s, if not sooner.Human Brain vs Machine Learning – A Lost Battle?

What is somewhat ironic is that we are now at a civilization stage where it pays off greatly to invest in progress (you could say that this stage started with the industrial revolution and exploded exponentially in the past few decades and then past few years), as stronger computers with ever-improving machine learning algorithms are the driving force of the global economy in the past decade. I refer to Google, Facebook, Microsoft, Apple, Amazon, the rest of the Fortune 50’s and all their derivatives. It pays well to have the best algorithm, and it pays well to make a stronger computer, and it pays well to make progress.

It pays so well I have no doubt that we will continue improving and growing and progressing – until we (knowingly or unknowingly) pass that threshold so often mentioned in sci-fi books and movies, and we find ourselves surviving and procreating in a world where the machines – our “tools” – outperform us on every possible human task.

Add to this soup a pinch of “Internet Of Things”, put in the ever-growing spread of mobile always-on always-connected devices in our (and our children’s) lives and you may find the human race trapped on a tiny rocky planet run by sentient algorithms created by sentient algorithms derived from an algorithm based on an algorithm written by the last human researchers, right before they went obsolete.

The Matrix is real, or at least it will be in 2050.