Robot Ants and Traumatized Rats: Highlights from NIPS Days 3/4

Cody Marie Wild
Dec 8, 2017 · 10 min read

By Wednesday morning, things at NIPS were starting to fall into a pattern: scribbling notes on the floor of overfull convention-center rooms, hearing casual discussion of eigenvectors and linear mappings in the hallway, becoming acutely aware of the inadequacies of the NIPS bulletin board app for lunch-meeting coordination.

My comrades-in-creative-sitting, the NIPS Floor Crew

Two days lie behind, two ahead. Maybe this is just life will be now, some part of you thinks: this heady mix of exhaustion, exhilaration, and new ideas. And, hey, maybe eventually the conference center will learn to moderate the room temperature correctly.

Robots Learning to Adapt

Peter Abbeel’s keynote talk began with an almost touching scene: a video of a robot delicately spoon-feeding a human. A moment later, Abbeel noted that this wasn’t an autonomous robot, but rather one controlled by a human operator; the mechanical engineering is there, he said, and now the burden is on the AI community to bring fully autonomous, usefully dexterous robots into being. He broke his talk into six areas, and, in each, discussed some directions of potential progress.

Respresenting the State of the World

Like many of the other spotlight talks, Yael Niv’s talk was interdisciplinary; in her specific case, she brought a neuroscientific and cognitive scientific lens, and applied it to the problem of learning encodings that represent the state of the world.

One key thing I took away from her talk was her theory about the way that inference about causality can impact our internal representations. For this, she gave a few examples. One experiment involved boxes appearing on screen, each of which contained some number of circles, and where the circles were either yellow or red. The human would guess, and afterwards the true number would be revealed. In one condition, the yellow boxes had 65 circles on average, the red 35. In the other world, yellow was still at 65, but red at 55. The investigators’ hypothesis, which was born out, was that in the former case, the subjects would learn that there really are two different underlying distributions, and their guesses for yellow circles would average out to 65. By contrast, the case where the yellow and red are very close together, the subjects wouldn’t be confident enough about separate causes to realize there were two different distributions in play, and on that task, their guesses for the yellow circles averaged out to 60, since that’s the average between the red and yellow cases. In other words, in the first case, the subject learned that keeping track of red vs yellow was relevant for the the task of counting circles, and in the second case, they did not.

Another fascinating experiment along similar lines involved rats, and the question of whether you can cause a rat to “unlearn” the link between a bell and a shock, as measured by the rat’s fear response. Somewhat counterintuitively, even if you switch the rat to a regime of bells with no shocks for a long time, a single bell + shock, or even just a shock, can very quickly re-instantiate the fear response connected to the bell. The theory here is that the rat never really unlearned that connection, it just assumed it was now operating in a new regime where that learning was no longer relevent. To test this, Niv’s lab tried out gradually scaling down the probability with which a bell led to a shock. And, indeed, they found that in this scenario, the rats truly “unlearned” the traumatic association, presumably because they updated their beliefs about a single regime, as opposed to collecting information related to a new regime.

I feel bad for the little guys, looking at this

Artificial Agents

The man I heard speak on Thursday night, David Runciman, was a statistically unusual fellow to be at a conference about statistical methods: he’s a lecturer in politics at Cambridge.

His talk argued that, instead of just focusing on Artifical Intelligences, we ought also be thinking about Artificial Agents, which he defined as non-human entities with decision-making powers and long spans of temporal continuity. The canonical examples he gave of such agents were states and corporations.

He further suggested that, much though we’re afraid of the digital singularity, in a meaningful way, we already saw a “singularity” with regard to artificial agents, that facilitated the exponential growth seen during the industrial revolution. The premise of this association, between the growth of modern states and corporations, and economic growth, was based on the premise that these entities could build projects of scale and complexity that a single human never could. Corporations and states can take on risk (because people expect them to be around to pay debt more robustly than a single human), make long term plans, and organize human effort in ways that weren’t possible before.

Even more saliently, the objectives of these actors (to gain profit and obey market incentives, in the corporate case, or to gain territory and prestige, in the state case) can be powerfully at odds with human flourishing, because these actors often aren’t designed to take account of negative side effects. (Or, in economics-speak: externalities).

Runciman’s “so what” out of this analogy was: in the past several hundred years of states and corporations becoming the dominant actors in our lives, shaping the lives of individual humans in dramatic ways, we haven’t really figured out the best ways to effectively regulate them. How do you effectively punish a state, or a corporation? How do you shape their behavior? These are questions that years and years of regulation and international relations literature has not really come up with a convincing answer to, although democratic control of states is probably one of the best solutions tried so far.

It’s certainly true that corporations are technically very different than states, and insofar as you care about the technical minutia of things like value alignment, the analogy becomes less useful. But I think that it has more relevance than we typically give it credit for

Other Interesting Ideas

Photo of the Day(s):

You can’t quite see it in this picture, but these magnets are all components of NNs (dropout, softmax, etc)

Quote of the Day(s) : “[if you need a model build] You could get a human expert in machine learning, but that might not work; from what I hear, all of us are at corporate parties all the time” — A session on self-tuning neural networks

Mood of the Day(s): “NIPS is a marathon, not a sprint”

Cody Marie Wild

Written by

machine learning data scientist; lover of cats, languages, and elegant systems; professional curious person.