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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: More Is Different for AI, published by Jacob Steinhardt on January 4, 2022 on The AI Alignment Forum. 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. While Engineering and Philosophy agree on some things, for the most part they make wildly different predictions both about what the key safety risks from ML will be and how we should address them: Both Engineering and Philosophy would agree on some high-level points: they would agree that misaligned objectives are an important problem with ML systems that is likely to get worse. Engineering believes this because of examples like the Facebook recommender system, while Philosophy believes this based on conceptual arguments like those in Superintelligence. Philosophy is more confident that misaligned objectives are a big problem and thinks they could pose an existential threat to humanity if not addressed. Engineering and Philosophy would both agree that out-of-distribution robustness is an important issue. However, Philosophy might view most engineering-robustness problems (such as those faced by self-driving cars) as temporary issues that will get fixed once we train on more data. Philosophy is more worried about whether systems can generalize from settings where humans can provide data, to settings where they cannot provide data even in principle. Engineering tends to focus on tasks where current ML systems don't work well, weighted by their impact and representativeness. Philosophy focuses on tasks that have a certain abstract property that seems important, such as imitative deception. In my experience, people who strongly subscribe to the Engineering worldview tend to think of Philosophy as fundamentally confused and ungrounded, while those who strongly subscribe to Philosophy think of most Engineering work as misguided and orthogonal (at best) to the long-term safety of ML. Given this sharp contrast and the importance of the problem, I've thought a lot about which—if either—is the "right" approach. Coming in, I was mostly on the Engineering side, although I had more sympathy for Philosophy than the median ML researcher (who has ~0% sympathy for Philosophy). However, I now feel that: Philosophy is significantly underrated by most ML researchers. The Engineering worldview, taken seriously, actually implies assigning significant weight to thought experiments. On the...