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Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-04T22:13:31.398Z · LW · GW

Thanks for making that distinction, Steve. I think the reason things might sounds muddled is that many people expect that (1) will drive (2).

Why might one expect (1) to cause (2)? One way to think about it is that, right now, most ML experiments optimistically given 1-2 bits of feedback to the researcher, in the form of whether their loss went up or down from a baseline. If we understand the resulting model, however, that could produce orders of magnitude more meaningful feedback about each experiment. As a concrete example, in InceptionV1, there are a cluster of neurons responsible for detecting 3D curvature and geometry that all form together in one very specific place. It's pretty suggestive that, if you wanted your model to have a better understanding of 3D curvature, you could add neurons there. So that's an example where richer feedback could, hypothetically, guide you.

Of course, it's not actually clear how helpful it is! We spent a bunch of time thinking about the model and concluded "maybe it would be especially useful on a particular dimension to add neurons here." Meanwhile, someone else just went ahead and randomly added a bunch of new layers and tried a dozen other architectural tweaks, producing much better results. This is what I mean about it actually being really hard to outcompete the present ML approach.

There's another important link between (1) and (2). Last year, I interviewed a number of ML researchers I respect at leading groups about what would make them care about interpretability. Almost uniformly, the answer was that they wanted interpretability to give them actionable steps for improving their model. This has led me to believe that interpretability will accelerate a lot if it can help with (2), but that's also the point at which it helps capabilities.

Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-03T00:10:44.024Z · LW · GW

Evan, thank you for writing this up! I think this is a pretty accurate description of my present views, and I really appreciate you taking the time to capture and distill them. :)

I’ve signed up for AF and will check comments on this post occasionally. I think some other members of Clarity are planning to so as well. So everyone should feel invited to ask us questions.

One thing I wanted to emphasize is that, to the extent these views seem intellectually novel to members of the alignment community, I think it’s more accurate to attribute the novelty to a separate intellectual community loosely clustered around Distill than to me specifically. My views are deeply informed by the thinking of other members of the Clarity team and our friends at other institutions. To give just one example, the idea presented here as a “microscope AI” is deeply influenced by Shan Carter and Michael Nielsen’s thinking, and the actual term was coined by Nick Cammarata.

To be clear, not everyone in this community would agree with my views, especially as they relate to safety and strategic considerations! So I shouldn’t be taken as speaking on behalf of this cluster, but rather as articulating a single point of view within it.

Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-02T23:17:28.390Z · LW · GW

Subscribed! Thanks for the handy feature.

Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-02T21:18:06.868Z · LW · GW

One thing I'd add, in addition to Evan's comments, is that the present ML paradigm and Neural Architecture Search are formidable competitors. It feels like there’s a big gap in effectiveness, where we’d need to make lots of progress for “principled model design” to be competitive with them in a serious way. The gap causes me to believe that we’ll have (and already have had) significant returns on interpretability before we see capabilities acceleration. If it felt like interpretability was accelerating capabilities on the present margin, I’d be a bit more cautious about this type of argumentation.

(To date, I think the best candidate for a capabilities success case from this approach is Deconvolution and Checkerboard Artifacts. I think it’s striking that the success was less about improving a traditional benchmark, and more about getting models to do what we intend.)

Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-02T21:12:37.623Z · LW · GW

I think that’s a fair characterization of my optimism.

I think the classic response to me is “Sure, you’re making progress on understanding vision models, but models with X are different and your approach won’t work!” Some common values of X are not having visual features, recurrence, RL, planning, really large size, and language-based. I think that this is a pretty reasonable concern (more so for some Xs than others). Certainly, one can imagine worlds where this line of work hits a wall and ends up not helping with more powerful systems. However, I would offer a small consideration in the other direction: In 2013 I think no one thought we’d make this much progress on understanding vision models, and in fact many people thought really understanding them was impossible. So I feel like there’s some risk of distorting our evaluation of tractability by moving the goal post in these conversations.

I’m not surprised by other people feeling like they have less traction. I feel like the first three or so years I spent trying to understand the internals neural networks involved a lot of false starts with approaches that ended up being dead ends (eg. visualizing really small networks, or focusing on dimensionality reduction). DeepDream was very exciting, but it retrospect I feel like it took me another two or so years to really digest what it meant and how one could really use it as a scientific tool. And this is with the benefit of amazing collaborators and multiple very supportive environments.

One final thing I’d add is that, if I’m honest, I’m probably more motivated by aesthetics than optimism. I’ve spent almost seven years obsessed with the question of what goes on inside neural networks and I find the crazy partial answers we learn every year tantalizingly beautiful. I think this is pretty normal for early research directions; Kuhn talks about it a fair amount in The Structure of Scientific Revolutions.

Comment by Christopher Olah (christopher-olah) on Chris Olah’s views on AGI safety · 2019-11-02T20:54:09.268Z · LW · GW

I'm curious what's Chris's best guess (or anyone else's) about where to place AlphaGo Zero on that diagram

Without the ability to poke around at AlphaGo -- and a lot of time to invest in doing so -- I can only engage in wild speculation. It seems like it must have abstractions that human Go players don’t have or anticipate. This is true of even vanilla vision models before you invest lots of time in understanding them (I've learned more than I ever needed to about useful features for distinguishing dog species from ImageNet models).

But I’d hope the abstractions are in a regime where, with effort, humans can understand them. This is what I expect the slope downwards as we move towards “alien abstractions” to look like: we’ll see abstractions that are extremely useful if you can internalize them, but take more and more effort to understand.

Is there an implicit assumption here that RL agents are generally more dangerous than models that are trained with (un)supervised learning?

Yes, I believe that RL agents have a much wider range of accident concerns than supervised / unsupervised models.

Later the OP contrasts microscopes with oracles, so perhaps Chris interprets a microscope as a model that is smaller, or otherwise somehow restricted, s.t. we know it's safe?

Gurkenglas provided a very eloquent description that matches why I believe this. I’ll continue discussion of this in that thread. :)