polkapolka

6 ms·

1.
by polkapolka 8y ago
Did you run into latency problems doing arbitrage this way? Or do you rely on API's?
2.
by polkapolka 8y ago
You bypass the API completely and fill orders "manually".
3.
by polkapolka 8y ago
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
4.
by polkapolka 8y ago
The identifying characteristics are signal for the minority, but noisy for genpop. Deployment/engineering constraints call for a single model. Realistic scenario.
5.
by polkapolka 8y ago
Eat your own dogfood. Let them douse their babies in asbestos, drink fracking water, or force them to watch Scientology advertisements.
6.
by polkapolka 8y ago
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 u
7.
by polkapolka 8y ago
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.
8.
by polkapolka 8y ago
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 ma
9.
by polkapolka 8y ago
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 w
10.
by polkapolka 8y ago
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
11.
by polkapolka 8y ago
Text data traditionally seen as unstructured. Try a simple MLP.
12.
by polkapolka 8y ago
For an accessible recent overview see: https://christophm.github.io/interpretable-ml-book/
13.
by polkapolka 8y ago
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
14.
by polkapolka 8y ago
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.
15.
by polkapolka 8y ago
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 pic
16.
by polkapolka 8y ago
I give Musk a little bit more credit than brutely calling someone a pedo without any proof. If you are as rich as Musk, there must be someone you can pay to dig up dirt on your opponents. Now there may be more trouble in showing that proof
17.
by polkapolka 8y ago
Csv and a permitive license like CC0. Provide good meta data to make provenance easier. Mark up your dataset with schema.org.
18.
by polkapolka 8y ago
Gone to sleep for the final time. Ash to ... And dust to dust.
19.
by polkapolka 8y ago
Dennett aims to solve this using heterophenemenology: https://en.m.wikipedia.org/wiki/Heterophenomenology In this framework, utterances can be studied without taking their truth value at face value.
20.
by polkapolka 9y ago
If you spend one year on applying deep learning, you can train a net on a 100 different data sets. That's where the intuition comes from. You'll debug a lot. People with zero experience with deep learning have ended in the top 10
21.
by polkapolka 9y ago
Articles like these should come with a disclosure: "the authors own bitcoin and augur" or something to that effect.