Machine learning is touching increasingly many aspects of our society, and its effect will only continue to grow. Given this, I and many others care about risks from future ML systems and how to mitigate them. When thinking about safety risks from ML, there are two common approaches, which I'll call the Engineering approach and the Philosophy approach: The Engineering approach tends to be empirically-driven, drawing experience from existing or past ML systems and looking at issues that either: (1) are already major problems, or (2) are minor problems, but can be expected to get worse in the future. Engineering tends to be bottom-up and tends to be both in touch with and anchored on current state-of-the-art systems. The Philosophy approach tends to think more about the limit of very advanced systems. It is willing to entertain thought experiments that would be implausible with current state-of-the-art systems (such as Nick Bostrom's paperclip maximizer) and is open to considering abstractions without knowing many details. It often sounds more "sci-fi like" and more like philosophy than like computer science. It draws some inspiration from current ML systems, but often only in broad strokes. I'll discuss these approaches mainly in the context of ML safety, but the same distinction applies in other areas. For instance, an Engineering approach to AI + Law might focus on how to regulate self-driving cars, while Philosophy might ask whether using AI in judicial decision-making could undermine liberal democracy. Original text: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.

In 1972, the Nobel prize-winning physicist Philip Anderson wrote the essay "More Is Different". In it, he argues that quantitative changes can lead to qualitatively different and unexpected phenomena. While he focused on physics, one can find many examples of More is Different in other domains as well, including biology, economics, and computer science. Some examples of More is Different include: Uranium. With a bit of uranium, nothing special happens; with a large amount of uranium packed densely enough, you get a nuclear reaction. DNA. Given only small molecules such as calcium, you can’t meaningfully encode useful information; given larger molecules such as DNA, you can encode a genome. Water. Individual water molecules aren’t wet. Wetness only occurs due to the interaction forces between many water molecules interspersed throughout a fabric (or other material). Original text: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.

Previously, I argued that we should expect future ML systems to often exhibit "emergent" behavior, where they acquire new capabilities that were not explicitly designed or intended, simply as a result of scaling. This was a special case of a general phenomenon in the physical sciences called More Is Different. I care about this because I think AI will have a huge impact on society, and I want to forecast what future systems will be like so that I can steer things to be better. To that end, I find More Is Different to be troubling and disorienting. I’m inclined to forecast the future by looking at existing trends and asking what will happen if they continue, but we should instead expect new qualitative behaviors to arise all the time that are not an extrapolation of previous trends. Given this, how can we predict what future systems will look like? For this, I find it helpful to think in terms of "anchors"---reference classes that are broadly analogous to future ML systems, which we can then use to make predictions. The most obvious reference class for future ML systems is current ML systems A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.

Previously, I've argued that future ML systems might exhibit unfamiliar, emergent capabilities, and that thought experiments provide one approach towards predicting these capabilities and their consequences. In this post I’ll describe a particular thought experiment in detail. We’ll see that taking thought experiments seriously often surfaces future risks that seem "weird" and alien from the point of view of current systems. I’ll also describe how I tend to engage with these thought experiments: I usually start out intuitively skeptical, but when I reflect on emergent behavior I find that some (but not all) of the skepticism goes away. The remaining skepticism comes from ways that the thought experiment clashes with the ontology of neural networks, and I’ll describe the approaches I usually take to address this and generate actionable takeaways. ## Thought Experiment: Deceptive Alignment Recall that the optimization anchor runs the thought experiment of assuming that an ML agent is a perfect optimizer (with respect to some "intrinsic" reward function R). I’m going to examine one implication of this assumption, in the context of an agent being trained based on some "extrinsic" reward function R∗ (which is provided by the system designer and not equal to R). Specifically, consider a training process where in step t, a model has parameters θt and generates an action at (its output on that training step, e.g. an attempted backflip assuming it is being trained to do backflips). The action at is then judged according to the extrinsic reward function R∗, and the parameters are updated to some new value θt+1 that are intended to increase at+1's value under R∗. Original text: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.

Previously, I argued that emergent phenomena in machine learning mean that we can’t rely on current trends to predict what the future of ML will be like. In this post, I will argue that despite this, empirical findings often do generalize very far, including across “phase transitions” caused by emergent behavior. This might seem like a contradiction, but actually I think divergence from current trends and empirical generalization are consistent. Findings do often generalize, but you need to think to determine the right generalization, and also about what might stop any given generalization from holding. I don’t think many people would contest the claim that empirical investigation can uncover deep and generalizable truths. This is one of the big lessons of physics, and while some might attribute physics’ success to math instead of empiricism, I think it’s clear that you need empirical data to point to the right mathematics. However, just invoking physics isn’t a good argument, because physical laws have fundamental symmetries that we shouldn’t expect in machine learning. Moreover, we care specifically about findings that continue to hold up after some sort of emergent behavior (such as few-shot learning in the case of ML). So, to make my case, I’ll start by considering examples in deep learning that have held up in this way. Since “modern” deep learning hasn’t been around that long, I’ll also look at examples from biology, a field that has been around for a relatively long time and where More Is Different is ubiquitous (see Appendix: More Is Different In Other Domains). Source: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO . --- A podcast by BlueDot Impact . Learn more on the AI Safety Fundamentals website.

In Future ML Systems Will Be Qualitatively Different, I argued that we should expect ML systems to exhibit emergent capabilities. My main support for…