polkapolka

Did you run into latency problems doing arbitrage this way? Or do you rely on API's?

You bypass the API completely and fill orders "manually".

You are thinking too US-centric here. There are jurisdictions where this is allowed.

Also don't stare yourself blind on the numbers.

Do you unfairly deny 5 minorities or erroneously deny 10 genpop?

This is reality for today's data scientists.

Right now in the US: You find an informative variable that acts as a proxy for race (such as Facebook likes). It is not forbidden by law to use it, but you have the data showing the proxy effect. Will you add it to the CTR model and get a raise, or do you act and speak up?

Will you let the trolley follow its course and run over 10.000 Chinese dissidents, or make the switch and run over 100 of your colleagues (and friends) who would benefit from a China expansion?

The identifying characteristics are signal for the minority, but noisy for genpop.

Deployment/engineering constraints call for a single model.

Realistic scenario.

Eat your own dogfood.

Let them douse their babies in asbestos, drink fracking water, or force them to watch Scientology advertisements.

The ads are a red herring (and uncomfortably visible for both parties).

The real activity was on Facebook groups, viralizing anti-immigration and far-left news, controling the narritive on 4chan pol and /r/the_donald, and botted up - and downvote brigades.

It was so blatant it was visible to a non-expert on information warfare like me.

The people doing this probably got paid about as much as a paid advertisement.

Statistical learning with connectionist architectures is driving current AI at scale.

To me, this paradigm is also the most promising: learn from data bottom-up, not from experts top-down.

Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere.

Bayesian models underperform to DL by a wide margin (though it is a step up from handwritten rules: if DEAR FRIEND then Spam score++.

Symbolic AI is machine programming. Connectionist AI is machine learning.

Machine programming simply does not scale. It is also not biologically plausible: it is not as if God put symbols into our brain, these formed, are prefaced by, and were learned from neural activity.

Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results.

Deep learning is the most promising direction AI went into since a long time. Finding flaws in DL does not point back to a programmer crafting handwritten rules to correct it. It merely points to more DL research needed to have machines correct this themselves. Preferrably differentiable.

Agreed. Logistic regression with poly kernel or good engineering interactions can equal or beat more complex models for a fraction of the budget.

All the more power to you if a solid simple logreg model (or even no ML at all) is your first deliverable.

Text data traditionally seen as unstructured. Try a simple MLP.

Yeah, it is a good first benchmark. But view interpretability as separate from accuracy. You can explain black box algorithms just fine these days.

Logistic regression is high bias low variance. If you were talking about fairness bias, then resistance to bias comes from logreg being too dumb to recognize complex non-linear patterns. Not necessarily a pro.

Sure there are constraint and pros and cons, but still: pick a neural network for unstructured data. Can always unsupervised pretrain and fine tune on a tiny dataset.

The image for logistic regression is hilariously wrong. It shows the sigmoid as a decision boundary.

Also dont get hung up about no free lunch theorem. That is a great result in computer science theory with little practical impact: just pick neural networks for unstructured and GBDT for structured data. For the vast majority of real-life problems (not all possible problems) these are the single best algorithms.