Deep Learning And The Limits Of Learning By Correlation Rather Than Causation
AI development today has become fixated on singular monolithic models trained end-to-end without any human assistance and encapsulating an almost general intelligence-like variety of tasks together. The resulting models have struggled in areas like content moderation to sufficiently abstract beyond their limited training data. Yet, as Waymo reminds us, the most successful complex AI systems combine multiple deep learning models with traditional hand-coded algorithms to address one of the greatest challenges confronting today’s deep learning systems: their inability to abstract from correlation to causation.
Waymo put it best this past December when the company noted that “deep learning identifies correlations in the training data, but it arguably cannot build causal models by purely observing correlations … knowing why an expert driver behaved the way they did and what they were reacting to is critical to building a causal model of driving. For this reason, simply having a large number of expert demonstrations to imitate is not enough.”
In Waymo’s case, the company addresses this through two primary means.
The first is hand-coding some rules, like simply telling the vehicle to stop at red stoplights, rather than forcing it to learn this rule from observation. This mimics the human driving experience in which new drivers learn through a combination of memorizing the written rules of the road and observation and experiential driving.
The second is the use of simulators to offer the algorithms a wide array of unusual experiences not ordinarily found in its training data. As the company puts it, “for obvious reasons, we don’t want our expert drivers to get into near-collisions or climb curbs just to show a neural network how to recover in these cases” so it relies on teaching its algorithms about these experiences through simulated driving. Once again, this is how humans are typically taught to recover from exceptional circumstances, whether in pilot simulators or simulators designed to mimic new vehicular interiors and experiences, so brings the AI learning process closer to that used by humans.
Yet, these simulators also serve a second purpose beyond merely helping the algorithms experience rare driving events: they build up the diversity of its statistical inferences to help it compensate for its inability to abstract upwards from its training data towards causal understanding.
The inability to reason at a causal level is perhaps the greatest challenge confronting the future of deep learning.
No matter how hauntingly accurate their results, today’s deep learning algorithms are still at the end of the day nothing more than statistical inferencing engines. They reduce data to minute building blocks, but rather than assemble those building blocks into higher order semantic relationships and causal models like humans, today’s algorithms merely record correlative probabilities between collections of input building blocks and output outcomes.
This means that a deep learning image recognition algorithm fed a library of dog photographs taken exclusively in dog parks and cat photographs taken exclusively in living rooms will naturally gravitate towards the most statistically distinguishing feature: the grassy outdoors versus the furnished indoors.
Learning algorithms optimize for accuracy and in such a training scenario, the location of the photograph is the most distinguishing feature that guarantees near perfect accuracy.
In reality, the algorithm isn’t even learning the concept of “indoors” versus “outdoors.” Under the hood, a typical convolutional neural network merely reduces the image to a set of patterns and colors and assesses the statistical correlation of each of those building blocks with the label “dog” or “cat.”
Humans recompose visual scenes upwards to understand them as objects.
