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We do. If you’re interested in the architecture particulars (I.e. which layer types, layer shapes, layer parameters, activation functions, etc) you’ll commonly see these in deep learning papers as a directed acyclic graph with layers represented by a collection of vertices or rectangles. Other architecture specifics are sometimes noted with text alongside layers. We also have more general representations of deep models which look like, and sometimes are exactly, Probabilistic Graphical Models. PGMs have their own formal language which makes it quite easy to write complex models describing a joint distribution very simply.

However, this is different than interpreting how a deep model works. “Interpreting” is an overloaded and poorly-defined term of active research. The sense I get from my friends in the interpretability/explainability research world is that despite the buzz, there is no common definition that lends to a clear set of requirements for an acceptable interpretation of a neural network.

I'm econ undergrad -> DS -> Machine learning. Econ is very useful for data science if you focus on the right subjects: statistics, math, and experimental design. You get all the hard skills you need to interact with data that a statistician or computer scientist gets, with the (significant, unique) benefit of learning how to ask the right question or design the right experiment given what is likely a messy, weird, social scientific question.

On the other hand, if you don't do any quantitative, empirical, or experimental economics -- i.e. you only do theory or political econ -- then you won't pick up these skills (as much).

How do you come to the conclusion that ML is a buzzword here? It's natural to publicize interesting new research that grounds a field -- as statisticial learning theory does for machine learning. Historically, Ben-David and co-authors have produced foundational work on the implications of SLT in ML.

There are a lot of talented ML researchers in China. This is a product of (a) the government and major companies (i.e. BAT) investing heavily in fundamental ML research (b) the population size (c) a long tradition of STEM-focused education in China. So, it's not surprising that would be the case.

The interesting questions are if China is uniquely focused on deep learning over other ML techniques, and Chinese research compares in terms of quality. Anecdotally (speaking as a researcher in the field) papers from Chinese institutions seem disproportionately focused on deep learning (whereas, for example, the UK does great work in Bayesian ML and the US does disproprotionately well in NLP). I'm not a deep learning researcher so I can't judge the technical merit, but I was just at NeurIPS in Montreal, and I saw about equal representation of Chinese institutions as South Korean ones. South Korea, with ~1/25 the population, punches way above its weight per capita.

I'm curious: if you think so much about climate change and its effects, and even plan your actions and location around it, have you considered directing your energy to combatting climate change with your skills on a systemic level? (For example, building sustainability companies, being part of climate activism, earning-to-donate?)

I'm in a similar spot but optimistic about systemic action.