KCFforecast

I have just posted a long post that probably will be located near the bottom because is lengthy. So here is a short one with my 2 cents: 1) I like to read quality post, I think quality is the main source to sustain a post. 2) Don't shallow your post if you want to have an open road for future success. 3) Your post is a drop in an ocean of knowledge in which content is interrelated. Perhaps you can be successful posting in a niche but I think that niche won't last. 4) I think that in the future quality post will get more recognition.

Concepts and names are kind. To give and example, in a recent post of jeremykun.com blog about math and computing the author explains a concrete case of conjugate prior without jargon. But in fact, if you know what that post is about, that is the magic word aka math jargon conjugate prior, you will find that en.wikipedia gives you many insights and links about that concept. Wikipedia is getting better and better and now there are an enormous quantity of good resources to learn form. Also informative comments, like those here in HN, are making blogging more and more difficult because your post is going to be only a drop in an ocean of knowledge where parts are interrelated. No post should reject math jargon, since those words, like conjugate priors, are the key to looking for rich and complete information about it. To give another example, if someone try to explain monads he/she should not try to avoid the term monad since monad importance relies on is capacity to model many rich and differences sceneries ranging from security to multiprocessing.

Openai or distill aim is to explain or visualize ml ideas, but I think that more than displaying animations or pretty graphics, we need clear concepts and definitions, and to inform the reader that there is no royal road to understanding, you must pay the prize to understand the main concepts, that the only real road to understanding.

To not be categorical, perhaps there should be some posts about explaining in very shallow terms what's the meaning of something, but in the end many times you end up without a real meaning of the concept and you can look hundred of same level posts and waste your time, because the concept required to be framed on its appropriate level.

Edit: Edited for clarify and grammar.

Just googling for some related ideas I found:

1) The Axel age, https://en.wikipedia.org/wiki/Axial_Age, 2) The concept of liminarity: https://en.wikipedia.org/wiki/Liminality

I think Hume ideas, http://www.iep.utm.edu/hume/, for example about utilitarity, and his idea that there is no permanent self. Obvious, is your dynamic self is shaped by rituals then you can apply practice makes perfect techniques. A recent comment about good study habits suggested small time habits better than a strict but non-real schedule that in the end you are not going to adhere to. Also there is a very old book, whose name I cannot recall now, about Chinese culture and the art of living, in which the main insight is that Chinese culture is not about a formal system of rules, but about embracing nature and learning practical skills, that is you learn to live your life by living (repl) read (sensor model) eval (action model) print (show off yourself in society and take liability of your acts) and loop (think about how to improve). In the interview the interviewer notes that the author use the words great, good question, ...

So my takeaway is that a professor of 101 philosophy should motivate (positively) students to question their assumptions , that's the role of philosophy, and that is good to sell books. On the other hand, a bad cop is needed to say students that they know nothing and that perhaps this book, beyond motivating them to think about philosophy, is null. But the ultimate role of philosophy is to make you think by yourself, so a null thing can be a good think, the media is the message.

Edit: Recalled the book name: Author Lin Yutang, China - The Art of Living. Don't follow if you need a pay check.

a table of similar function using R and the other R: https://msdn.microsoft.com/en-us/microsoft-r/scaler/compare-...

Try this version instead: There is a rocket to be launched for people to upload problems to solve, the rocket travels in such a way that it is able to solve all the problems and come back to tell people the solution. The way it solves the problem is by orbiting around a tiny planet full of computers devoted to compute the problems they are communicated of. The problem seems to be how to locate the observers.

Edited: time dilation and computation: https://arxiv.org/pdf/0907.1579.pdf

You don't solve the problem by yourself, that problem is solved for you on system A, in system A a million years have lapsed but in your life only a few days have passed.

I think that you can have a cheap copy of yourself if you only simulate the main factors. The problem is now to know have sensible is the system to perturbations and how to control deviations. It seems that our genes and body are programmed to recover and repair, in such a way that for example all those bad mutated cells are eliminated and we survive for 80 years. Like a flame, the laws of the university generate stable system like life.

How to solve any problem quickly: Program your computer to solve a problem in a million years time, then go for a travel at the speed of light, come back to your home and consult the solution of your problem, you have been traveling only a few days but your computer has been doing computation a million of years, so you now have the solution, a quick one.

Just alter that system to use the relative time to have computer working millions of years to solve problems for you and you can have the solution in days. Call it photon computation to give the system a name.

You are right, more information here: http://uk.practicallaw.com/1-520-8264

Under Spain protection of personal data law, any file with personal information must be accessible for the person, and the person has the right to know, modify and delete that file, and to deny the access to that information for any purpose.

I think you have emphasized the main point: a lot of work for a low reward. Research is more above exploring the state of the art and new venues, divulgation and graphics is more akin to book sellers (for example Nielsen open science, and other interesting books, but for young researcher the most important and rewarding goal is to publish.

In this concrete case, how do you do transfer learning? what is the domain you have experience to transfer to the energy of energy? Also, Bayes's naive algorithm can be used in spam detection and usually it gives good results, is RL such a great tool in spam filtering when there is moderate data?

I don't want to give any hint to the DeepMind, since I an inclined to think that in this field an expert assessment can be better that DeepMind advice. Just to give a simple question or example of the kind of knowledge involved in those predictions: Since energy generation and demand depends a lot of weather conditions, do they have any state of the art machine learning model to forecasting weather conditions?, can they predict the evolution of energy prices?, can they measure the impact of new improvement in reducing the cost in renewable energy, what about the impact of the brexit in the energy market?, are they first class experts in times series or are they simple to apply current technologies like profet or Hyndman's R package for ts. How can they defend that technologies applied at go game like reinforcement learning can be successfully applied to forecasting? Perfect games simulations allow you to get almost infinite data, current world market and weather has only very limited amount of information to be supplied for a model, how can they justify the application of big data techniques to a small available data world. I hope that some of those questions can be addressed, unfortunately while writing this I haven't read the post yet.