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You make good points. I think it's a typical trait of the way computer scientists and programmers tend to think. Computer science has made great strides over th
by mtlmtlmtlmtl 3y ago
You make good points. I think it's a typical trait of the way computer scientists and programmers tend to think. Computer science has made great strides over the decades through abstraction, as well as distillation of complex systems into simpler properties that can easily be computed.
As a result of the combination of this method of thinking and the Dunning-Kruger effect, people in our field tend to apply this to the entire world, even where it doesn't fit very well, like biology, geopolitics, sociology, psychology, etc.
You see a lot of this on HN. People who seem to think they've figured out some very deep truth about another field that can be explained in one hand-waving paragraph, when really there are lots of important details they're ignoring that make their ideas trivially wrong.
Economists have a similar thing going on, I feel. Though I'm not an economist.
- mjburgess 3y agoRemember always, computer science is just discrete mathematics with some automatic whiteboards. It is not science. And that's the heart of the problem. The CSci crowd have a somewhat well-motivated inclination to treat abstractions as real objects of study; but have been severely misdirected by learning statistics without the scientific method. This has created a monster: the abstract objects of study are just the associations statistics makes available. You mix those two together and you have flat-out pseudoscience.
- andoando 3y agoNot sure I agree in this regard. We are after all, aiming to create a mental model which describes reproducible steps for creating general intelligence. That is, the product is ultimately going to be some set of abstractions or another. I am not sure what more scientific method you could propose. And we can, in this field produce actual reproducible experiments. Really, more so than any other field.
- mjburgess 3y agoThere's nothing to replicate. ML models are associative statistical models of historical data. There are no experimental conditions, no causal properties, no modelled causal mechanisms, no theories at all. "Replication" means that you can reproduce an experiment designed to validate a causal hypothesis. Fitting a function to data isnt an experiment, it's just a way of compressing the data into a more efficient representation. That's all ML is. There are no explanations here (of the data) to assess.
- dartos 3y agoI don’t think that’s true either. Take the research into Loras for example. Surely the basic scientific method was followed when developing it. You can see that from the paper. Obviously the results can be reproduced. Unlike in many other fields, reproducibility can be pretty trivial in CS. Training a model isn’t really a science, but the work gone into creating the models surely is.
- dartos 3y agoAlso there’s literally a causal relationship between model topology and quality of output. This can be plainly seen when trying to get a model to replicate its input. Some models perform better in fewer steps, some perform worse for many steps, then suddenly much better. How is discovering these properties of statistical models NOT science?
- CPLX 3y agoAs an aside both my parents are prominent economists, I myself have a degree in economics, and I have spent much of my life with a birds eye view of the economics profession and I can emphatically confirm that your feeling is correct.
- mjburgess 3y agoEconomics is zoology presented in the language of physics. Economists are monkeys who've broken into the uniform closet and are now dressed as zookeepers. I aspire, at best, to be one of the children outside the zoo laughing. I fear I might be the monkey who stole the key...