The latest Geophysical Tutorial came out this week in The Leading Edge. It's by my friend Gram Ganssle, and it's about neural networks. Although the example in the article is not, strictly speaking, a deep net (it only has one hidden layer), it concisely illustrates many of the features of deep learning.
Whilst editing the article, it struck me that some of the features of deep learning are really features of life. Maybe humans can learn a few life lessons from neural networks!
Activation functions are one of the most important ingredients in a neural network. They are the reason neural nets are able to learn complex, nonlinear relationships without a gigantic number of parameters.
Life lesson: look for nonlinearities in your life. Go to an event aimed at another profession. Take a new route to work. Buy a random volume at your local bookshop. Pick that ice-cream flavour you've never dared try (durian, anyone?).
Neural networks learn by repetition. They start with random guesses about what might work, then they process each data point a hundred, maybe 100,000 times, check the answer, adjust weights, and get a little better each time.
Life lesson: practice makes perfect. You won't get anything right the first time (if you do, celebrate!). The important thing is that you pay attention, figure out what to change, and tweak it. Then try again.
One of the things we know for sure about neural networks is that they work best when they train on a lot of data. They need to see as much of the problem domain as possible, including the edge cases and the worst cases.
Life lesson: seek data. If you're a geologist, get out into the field and see more rocks. Geophysicists: look at more seismic. Whoever you are, read more. Afterwards, share what you find with others, and listen to what they have learned.
Yes, well, I could probably go on. Convolutional networks teach us to create new things by mixing ideas from different parts of our experience. Long training times for neural nets teach us to be patient, and invest in GPUs. Hidden layers with many units teach us to... er, expect a lot of parameters in our lives...?
Anyway, the point is that life is like a neural net. Or maybe, no less interestingly, neural nets are like life. My impression is that most of the innovations in deep learning have come from people looking at their own interpretive and discriminatory powers and asking, "What do I do here? How do I make these decisions?" — and then trying to approximate that heuristic or thought process in code.
What's the lesson here? I have no idea. Enjoy your weekend!
Thumbnail image by Flickr user latteda, licensed CC-BY. The Leading Edge cover is copyright of SEG, fair use terms.