Congratulations on winning the Merck contest! That was an impressive demonstration.
About 12 years ago, I switched from a Bio major to CS. I hoped to major in AI, but after taking 2 upper level classes, one focusing on symbolic AI and the other focusing on Bayesian networks, I was completely turned off.
Our brains are massively parallel redundant systems that share practically nothing in common with modern Von Neumann CPUs. It seemed the only logical approach to AI was to study neurons. Then try to discover the basic functional units that they form in simple biological life forms like insects or worms. Keep reverse engineer brains of higher and higher life forms until we reach human level AI.
Whenever I tried to relate my course material in AI to what was actually going on in a brain, my profs met my questions with disdain and disinterest. I learned more about neurons in my high school AP Bio class than either of my AI classes. In their defense, we've come a long ways, with new tools like MRIs and neural probes.
The answers are all locked up in our heads. It took nature millions of years of natural selection to engineer our brains. If we want to crack this puzzle in our lifetimes, we to copy nature, not reinvent it from scratch. Purely mathematical theories like Bayesian statistics that have no basis in Biological systems might work in specific cases, but are not going to give us strong AI.
Are these new deep learning algorithms for neural networks rooted in biological research? Do we have to necessary tools yet to start reversing engineering the basic functional units of the brain?
About 12 years ago, I switched from a Bio major to CS. I hoped to major in AI, but after taking 2 upper level classes, one focusing on symbolic AI and the other focusing on Bayesian networks, I was completely turned off.
Our brains are massively parallel redundant systems that share practically nothing in common with modern Von Neumann CPUs. It seemed the only logical approach to AI was to study neurons. Then try to discover the basic functional units that they form in simple biological life forms like insects or worms. Keep reverse engineer brains of higher and higher life forms until we reach human level AI.
Whenever I tried to relate my course material in AI to what was actually going on in a brain, my profs met my questions with disdain and disinterest. I learned more about neurons in my high school AP Bio class than either of my AI classes. In their defense, we've come a long ways, with new tools like MRIs and neural probes.
The answers are all locked up in our heads. It took nature millions of years of natural selection to engineer our brains. If we want to crack this puzzle in our lifetimes, we to copy nature, not reinvent it from scratch. Purely mathematical theories like Bayesian statistics that have no basis in Biological systems might work in specific cases, but are not going to give us strong AI.
Are these new deep learning algorithms for neural networks rooted in biological research? Do we have to necessary tools yet to start reversing engineering the basic functional units of the brain?