Thinking Complete

High-level analysis of reinforcement learning Building machines that learn and think like people . Lake et al. think that human learners are doing something fundamentally different from machine learners: we carry out tasks in the context of many years of related tasks, whereas ML systems cannot. So, they ask, "how do we bring to bear rich prior knowledge to learn new tasks and solve problems so quickly?" The core ingredients they identify are intuitive models of physics and psychology, which exist from infanthood, and the capacity for rapid building of mental models which can be used for classification, prediction, communication, explanation, and composition. The skill of model-building can be distinguished from pattern recognition, which the authors suggest makes up much of the recent progress in deep learning. In particular, notions of composition, causation and hierarchy which are central to models may be weak or non-existent in pattern recognition. Further, they note ...

...you can download machine learning papers, which is almost the same thing, and I have so many papers on my to-read list. I like being able to write summaries of the most interesting ones, but when I hit 200 open tabs of interesting papers, I realised that the backlog was only going to keep piling up. So I've spent the day skimming as many as I could. There are many I'd like go to back and reread in more detail, but for now the summaries below will have to do. Deep learning theory Approximation by superpositions of a sigmoidal function . The original paper showing that sufficiently-wide one-hidden-layer neural networks can approximate any function. Why does unsupervised pre-training help deep learning?  Another classic paper, offering an explanation for why unsupervised pre-training improves performance: apparently it's an "unusual form of regularisation". (How widely is it still used?) Do deep nets really need to be deep?  Ba and Caruna show that shall...

In this post I summarise four lines of argument for why we should be skeptical about the potential of deep learning in its current form. I am fairly confident that the next breakthroughs in AI will come from some variety of neural network, but I think several of the objections below are quite a long way from being overcome. Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution -  Pearl, 2018 Pearl describes three levels at which you can make inferences: association, intervention, and counterfactual. The first is statistical, identifying correlations - this is the level at which deep learning operates. The intervention level is about changes to the present or future - it answers questions like "What will happen if I do y?" The counterfactual level answers questions like "What would have happened if y had occurred?" Each successive level is strictly more powerful than the previous one: you can't figure out what the effects ...

This essay consists of summaries, explanations, and discussions of several papers which provide high-level arguments and intuitions about why, conceptually, deep learning works. Particular areas of investigation are "Which classes of functions can deep neural nets approximate well in principle?"; "Why can they quickly learn functions which have very small training loss?"; and "Why do the functions they learn generalise so well?". Understanding deep learning requires rethinking generalisation -  Zhang, Bengio, Hardt, Recht and Vinyals, 2017 Machine learning in general is about identifying functions in high-dimensional spaces based on finitely many samples from them. In doing so, we navigate between two potential errors: learning a function which is too simple to capture most of the variation in our data (underfitting) and learning a function which matches the data points well, but doesn't generalise (overfitting). Underfitting implies higher train...