That's is a great and pithy characterization. To expand on that a little:

If there existed an underlying model with a finite number of parameters, then you could potentially find it will a finite number of measurements. But if there is no underlying model, the only viable approach is using non-parametric estimation. If you try to truncate a non-parametric model to a finite/fixed dimensional model, you are introducing a strong source of bias. (the "bias" of assuming that there actually exists a not-crazily-complicated underlying model)

Yes, interestingly the idea of preference for simplicity is a medieval invention - not a universal truth. It served the rational program well for eight hundred years, but now we have infernal engines we can abandon it. I think that the late enlightenment test of theories that generate a clutch of non trivial testable predictions is going to prove more useful in the future.

There are many reasons to prefer simplicity, not just that prior to ubiquitous computing complicated models were intractable. Off the top of my head:

A simple model can be "robust" to errors because the errors tend to cancel out. More complex models are typically more sensitive to slight changes in the input (and therefore noise), so even if your model is exactly correct, small errors in the input can be amplified yielding large errors in the predictions.

Or it can be preferable because it tends to fail in certain characteristic ways. For example, Newtonian mechanics breaks down at both the high and low mass/energy scales, but it's usually possible to determine whether you'll have to employ GR or QM (and perhaps despair if both are required simultaneously).

Also, it's much easier to test simple models (and typically also to train them if we're talking ML)[0]. Furthermore, if your first approximation is well-understood, then you can complicate it to address new data.

Simplicity has many virtues; just because we can employ more complicated methods now doesn't mean that we should, or that the sole reason previous generations of scholars opted for simple models is because they couldn't perform the calculations needed by more complex ones. In general we can't expect to know the underlying truth with certainty, but maybe a question is so important and so well investigated that it merits a seriously convoluted answer. For less well-studied or relatively unimportant problems, a model that is obviously wrong but easy to use is usually preferable. It's called the bias-variance trade-off for a reason (although you should still try to get a good deal, which in this case means minimizing error or perhaps risk).

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0. This point can be particularly subtle-- we generally have more computing power available than (quality) data. I can try millions of variations on a given hypothesis for a small data set relatively quickly and find a handful of results with publishable p-values. This is how you get replication crises. Even if one of my hypotheses is invalidated, I can generate many more that are still consistent with the data. Take string theory for example-- a naive approach to testing the various parameterizations/permutations of the basic idea would run out of time before the heat death of the universe, so absent theoretical work constraining the possibility space, it would be effectively unfalsifiable.

I don't have ML or deep learning background, but I second what you said base on my experience with backtesting trading systems. We will collect market data and design algorithms that seem to produce the kind of outcomes we want. Then test on some other data sets which the algorithms have never been applied on. Many iterations later, you can get a decent profitable algorithm. And if the 'holy grail' algo is run in market long enough, eventually there will be severe drawdown and going bust. The quality of the algo and I assume the deep learning model lies in the quality (breadth and depth) of the data, and how honest with himself the person choose to model it. There will be time and again new 'black swan' or edge events happening (remember LTCM), because using machine learning is like using the past to predict the future.

I guess as long as the users' expectations are correct it can be useful in some very specific areas. Referencing the AlphaGo game last year, I was a Go player for more than a decade. But yet AlphaGo's weird move inspires new insights that break the conventional structure / thinking-framework of a Go player. From that angle, I do think that even though DL is somewhat a blackbox, humans can pick up new insights because it explores areas which are normally ridiculous to a human with 'common sense' to explore.

trading systems are very difficult to model because there are more variable than you can imagine. So basically you are creating a model with less information from the domain than you rather need. But this is not the case in all domain.

With AlphaGo there are several things you have to consider. It is not only using deep learning it also uses monte carlo tree search, basically this algorithms is good at exploring search spaces like games. But the key factor in AlphaGo was deep learning evaluating the states.

What I want to say is that neural networks are not meant to explore but to discover patterns. It is something very different. They are very interesting because they work as our brains (discovering and fulfilling patterns), but they as bad in searching as us.

For the branch of trading technical analysis which assumes that all information is factored into price and price action, the variables will drastically be reduced probably to something like volume, low, high, open, close etc. That makes it ideal to develop algos. But as the fundamentalists would say, assuming that everything (demand from insider news etc) has been factored into price is an overly simplistic way of looking at markets.

I see.. Thanks for sharing. I assume you mean evaluating game board states, finding valuations objectively and figuring out the pattern of where to move that can lead to higher probability of winning? 20 years ago I made an Othello game that use a search tree (I think configurable from 3 to 9 levels deep), assign valuations to different board positions and let the computer assume that the player would make the best move to his advantage. It turns out can beat human players easily, but that is such a small search space and valuation weights are very clear. Thus I was amazed at the computer winning with such a large game board where even for professional players, it is arguable which state is a better state. EDIT - ok I just googled monte carlo tree search, I must have accidentally implemented that search at that time, base on common sense and what I can do with the programming langauge I had at that time (Visual Basic).

Yes I meant that.

I find very interesting how some trade algorithms are using deep learning to extract features form news apart from the variables you mentioned.

About the search, in case you are interested. You have the basic tree search, then you have A* (a-start) which uses an heuristic to decide which is the next node to expand (very used in path finding in games, where the heuristic is the euclidean distance to the target point). For games you use a search where in one state you maximize the heuristic (your move) and in the next one you minimize it (your opponent move) (sorry I don't remember the name). And monte carlo tree search what it does is to no evaluate some states, it just do some random moves and evaluates the final state, this way it tries to improve the exploration/exploitation of the search (and works pretty well).

> For the branch of trading technical analysis which assumes that all information is factored into price and price action, the variables will drastically be reduced probably to something like volume, low, high, open, close etc.

I'd call that quantitative analysis, not technical analysis. The difference between them being the difference between astronomy and astrology. Technical analysis refers to classic trading strategies of visual patterns in the price charts like double top or bottom, head and shoulders, etc; these patterns and their traders suffer from massive hindsight bias. Feeding prices into computers looking for patterns is not technical analysis unless you've programmed the computer to look for said human found patterns. If the computer is actually searching for real patterns and you're testing them properly with forward tests on fresh data, you're doing quantitative analysis not technical analysis.

TA is practiced by manual traders, Quants are generally automated traders or traders doing proper statistics rather than relying on the visual patterns manual traders think they see.

I also think that, even if all the information is efficiently baked into price, price isn't the predictor - the information doing the baking is. If you use the "predictant" as the predictor, you won't get very good results.

Not to say that people don't, but as mentioned, they don't last. How long they do last is an even more difficult prediction problem.

Yes, this is both uninteresting and true. The whole field of ML exists precisely because many things are too complex to model directly.

It is uninteresting for those who know :)

too complex for humans to create the models unassisted?

Definitely.

Look at the ImageNet ICVLR competition. Hand-built models can't approach SoTA results built with CNNs.

In what scenario is it ever possible to understand the underlying model, if the model was naturally occurring and not invented by the scientist?

May I ask how you reached this insight? What field do you work in?

classic statistics is much more interested in the explanatory power of models to describe phenomena.

ML is mainly interested in prediction (correlation instead of causation), typically over some data that just fell in your lap.

In a sense Data Science is like the Cult of the MBA. MBAs believe a trained manager can manage anything because management skills are generic. A data scientist believes they can analyse anything because analysis is generic. Both fail in the real world because they discount domain knowledge.

Is there a field that does not discount domain knowledge? Or is that just "judgment" and custom analysis? I am trying to understand how all fields map together. Thank you.

The divisions are very confused. I think that sensible people all wish to use domain knowledge if possible. There are two tiers of this, firstly the use of domain knowledge in the manual or procedural construction of the insight system. Secondly the use of formalised knowledge in the creation of models that can then be fused with data.

The first case is where data science has got a bad name; people swing into domains and companies full of cocksure ideas, produce insights that are risible or obvious and get ejected. Sometimes it takes years for sufficient knowledge to be acquired by analysts to deal with difficult domains.

Lots of people use Bayesian inference to do the second. Tools like Stan and PyMC3 are really popular and effective.

I simply don't recognise that characterisation of ML. I think that "data driven AI" fits far better. ML emerged in a number of ways over the years, but a strong driver of the last iteration was the knowledge engineering bottleneck encountered in fifth generation computing and surrounding the demise of the last turing center.

I invite you to read Chris Bishop's or Stephen Muggleton's books.

Anyone who works with data will find it hard to imagine data that "fell into your lap", all the data I've ever used successfully required slogging and grinding.

data that "fell into your lap" is a euphemism for a process in which the data was not generated by a controlled experiment in which a hypothesis is created first, and then the data is gathered according to a procedure using the proper controls and testing.

"fell into your lap" has nothing to do with how hard the work is, it's about the difference between a controlled experiment and an observational study. the bulk of ML is observational in nature (focusing on prediction) and therefore has nothing to say about causation or understanding the causal variables of the underlying reality.

Yes, it's observational data. But this is quite common in many sciences. Observations of stella events, measurements of ecosystems and weather events for example. These have lead to theories with explanatory power and machine learning tools can and do as well.

One big deal is applications to dynamic domains.