I am approaching the end of my first year of my EECS graduate program at MIT, and although a PhD typically stretches for years, I feel how acutely limited and precious this time is. It is a unique moment of academic freedom. What really interests me?
Creating a Programmable World
The problem that motivates me most is the problem of programmability. How can we put people in charge of our computational systems, instead of the other way around? I am very proud of my work in helping to implement programmable web standards, democratizing the internet by making web browsers radically easy to program. I am also proud of my work helping to teach young people to program. When I came to MIT, I had planned to continue work on programming tools geared toward making programming understandable to everyday users and beginners.
But in doing my required coursework, I was struck by a seductive new problem.
We all know that deep neural networks are making remarkable strides. For the first time, simple optimization techniques are automatically creating complexity that is worthy of being called artificial intelligence. We are training neural networks with dozens of layers and hundreds of millions of learned parameters, the equivalent of thousands of lines of automatically-generated code. And yet we do not know, really, how they work, why they fail when they do, or how to intentionally create beavhior within them.
Meanwhile, within the deep network community, understandable AI is generally perceived as an ineffective approach: people can only comprehend a few things at once, but neural networks are capable balancing thousands of signals at every neuron - and this capability seems essential to their function. For example, biasing a network towards sparse connections to aid understandability appears to penalize performance. So practitioners generally believe it is best to set aside human comprehension and let the algorithms optimize freely.
For my whole career, I have built tools to make it easier to for people to program, debug, and control increasingly complex software systems. So this is a disquieting moment. Whereas previously all my work has made human programmers progressively more capable, more aware, and more expressive, for the first time, the best way to program a computer is to take the human out of the loop. For the first time, it is best to let the computers devise their own algorithms.
Can Deep Neural Networks be Designed?
But does stocastic gradient descent really mean the end of human software engineering?
I think there are reasons to believe that opening the black box of deep neural networks is still worthwhile, even just measured with the metric of pursuing performance. Here are a few reasons.
- There has been significant success with transfer learning, where models trained on large data sets have been retrained on different problems with good success. Oquab et al found that hidden layers of a network pretrained on object recogntition were able to be reused and achieve state-of-the-art performance in other contexts.
- When examining the activations of individual hidden units, some interpretable meaning seems to emerge. Zhou and Khosla et al observe that object detectors emerge on a network trained only to classify places.
- There are relatively simple problems that are not amenable to direct learning by a deep network via stochastic gradient descent, but that are easy to learn when an intermediate goal is learned first. Gulcehre and Bengio demonstrate this on a problem of identifying images of pentominos in which all the pentominos in are of the same type.
- Biological brains seem to do a good job at creating single neurons that represent well-factored single concepts. Quiroga found individual neurons in the hippocampus of epilsepy patients that cleanly encoded concepts such as "Halle Berry."
These are all reasons to believe that even better neural networks could be created by combining the power of gradient descent with the power of human-driven software engineering.
But it might be a different flavor of software engineering, one more driven by measurement, experience, and learning rather than by types, axioms, and proofs. Programming might be more akin to biologists investigating gene expression and less like mathematicians preserving hard invariants in an argument. Is there structure nevertheless? Can neural networks have the equivalent of recombinant DNA?
What a neat problem!