lovboat

Naughty boy, indeed. In my post I say literally that she is wonderful at work and people that know her recognize so. A big net of friends and acquaintances provides plenty of opportunities of finding a good job. Her job don't require critical thinking skills.

Don't know about how to get a new job, but I think today is easy to learn many things. Many companies are looking for people that are able to learn by themselves and are motivated to do so. You would have to decide what you want to learn in order to increase the opportunities to get a new more interesting job. Today javascript and the concepts around it (node, promises, callback functions, reactive programming) seem to be useful tools. Another advice is to enhance your net of friends, they can give you good advice and perhaps can be a good first step to find the new job. Good luck.

The point is not that critical thinking is a useful tool, I agree here. The title is about being a necessary skill. Here I disagree, many people can learn the craft by accumulating a lot of experience. Is like machine learning, you only need to have a lot of data (experience). Human are good at learning by example. Creative people are necessary for our culture to progress, but is not something necessary. You can live your life doing a good job and contributing to the welfare of society without critical thinking. Many times the good ideas come from nowhere inside us. Many great mathematicians create ideas from insight not by reflexive critical thinking.

Thanks. police are (ok they are more than one). 50-year-old (why not 50-years-old ?).

It seems that nouns are created with "-" (merriam-webster agree with you).

a long-term solution is like a never-ending story.

Now, my question why neverending story (film) and not never-ending?

Show me the more advanced parts of the language, are they transducers, monads, functors, natural transformations, auto-compilers, self-replicating systems, high AI in action, ML, NLP. What is the honey spot for this new language?

Don't they feel sometimes defrauded because their long time worked question is left aside unanswered? Don't they feel that other student are being given full attention meanwhile they are not considered properly?, don't you think that asking and not answering my sound like not a fair game to play?

Education has many problems, and the majority of questions about it are unanswered or the real answer is today unknown.

When I was in my teens I read "Straight and crooked thinking", it seems to be online here: https://www.divinetruth.com/www/en/pdf/People/Other/Robert%2...

I must confess that I didn't read the original post, and I am being down voted. Perhaps there is something interesting beyond the title, but I would be grateful people down-voting me could give a hint about what they are thinking about, what their argument is. I know that is easier to move your finger than to make a real or sound argument, also I am awared that you must not ask why the down voting (a question of etiquette it seems). But anyway, some feedback is always welcome.

I think critical thinking is not necessary, depending of your personal circumstances you can be better off with a great net of friends that can help you to find a good job. For example, my wife found a good job because she, besides being wonderful at work, has a really splendid net of friends who can speak wonders of her. I don't think that being critical or having critical thinking skills is going to help you in your career, sometimes is better to be quiet and let the powers be. I remember a story of a very good broker with wonderful ability that didn't follow the herb, he make very good predictions but he lost his clients, financial markets are not for critical thinking, is like a beauty contest (in due context by Samuelson).

Another hypothesis I can adventure is that my karma will be soon below zero, at this moment I have six (6) points, not a big deal to me.

I must apologize, I think that surely people lives must not be so doom as in that series, but I find it difficult to watch a film in which people are really happy enjoying life. I can adventure an hypothesis: That many people has so high a goal that they never can reach that high mountain, hence is no wonder films are about killing criminals, war films, or enjoy life in prison.

At first sight I find amusing comparing Ruby to Perl 6. (a=. sort) (a.sort!) (a.what) (a.class) (say a) (puts a) (for @a=>item) (a.each {|item| ..} ) and the similarities, push, shift, splice, ...

I see nothing new here. But I must say that Perl 6 seems to be easier to learn than Perl 5 for beginners.

I am also learning English, don't know whether my suggestions or corrections are appropriate.

Also, sometimes I watch a TV series in which police is trying to solve a 50 years old closed case. What I find interesting is how sad the life of that people are, never a joke, sharp sound, short sentences, never a smile. Perhaps, it is a mirror of a society. In Spain the eyes of people are full of life, the doom is outside of our frontiers (or so I think).

"I'm still not great at English but I surely improved over 15 years": => (I have surely improved) or: I haven't mastered English yet, but I am sure I have improved a lot in the last 15 years.

Without to mention how trivial is to go back in the learning process as long as you stop talking / listening for a couple of weeks… => To top it all, if you stop talking/listening for a couple of weeks you begin going backward in the learning process.

My long term hope is that soon or later => My long term hope is that sooner or later.

Another reason I find myself. => Another reason why I find myself.

NEVER learn a new word without learning what is its sound. => (how it sounds)

In order to use p-values properly, that is to make decision, you should explain what are you going to do with the information that the p-value provides. Rejecting the null hypothesis when the p-value < 0.05 is a sensible thing, but making a strong decision when the p-value is < 0.0001 doesn't make sense in many circumstances. You should have a scheme for the decision before having the p-value.

I don't have any Google Hangout chat messages to run the first example of using jupyter. I know that you are not going to share your data, but it should be handy if some fake conversations could be included. People like me like to first install the applications and then run it to see whether it works as claimed. I installed the conda distribution and the jupyter notebook works correctly. (I installed conda in ubuntu and then seaborn, PyMC3 and panda (PyMC3 and seaborn with pip since conda install 2.3 of PyMC3). It works.

I should say that the first step is to clone:

cd where_you_want_the_data_to_be_copied git clone ....

# and now start jupyter notebook with

jupyter notebook

# go to File/open/ and select the first section.

I see that I can edit the markdown. I translated the introduction to section 0, here it goes. Thanks for this tutorial. The graphics are nice.

### Sección 0: Introducción Bienvenido a "Bayesian Modelling in Python" - un tutorial para personas interesadas en técnica de estadística bayesiana con Python. La lista de secciones del tutorial se encuentra en la página web del projecto [homepage](https://github.com/markdregan/Hangout-with-PyMC3).

La estadística es un tema que en mis años de universidad nunca me gustó . Las técnicas frecuentistas que nos enseñaron (p-values, etc.) parecían rebuscadas y en última instancia di la espalda a este tema en el que no estaba interesado.

Esto cambió cuando descubrí la estadística Bayesiana - una rama de la estadística bastante diferente a la estadística frecuentista que se suele enseñar en la mayoría de las universidades. Mi aprendizaje se inspiró en numerosas publicaciones, blogs y videos. A los que se inician en la estadística bayesiana les recomendaría fervientemente los siguientes:

- [Doing Bayesian Data Analysis](http://www.amazon.com/Doing-Bayesian-Analysis-Second-Edition...) by John Kruschke - [Python port](https://github.com/aloctavodia/Doing_Bayesian_data_analysis) of John Kruschke's examples by Osvaldo Martin - [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programmin...) fue para mí una gran fuente de inspiración para aprender estadística bayesiana. En reconocimiento de la gran influencia que ejerció en mí, he adoptado el mismo estilo visual que se usa en BMH. - [While My MCMC Gently Samples](http://twiecki.github.io/) blog de Thomas Wiecki - [Healthy Algorithms](http://healthyalgorithms.com/tag/pymc/) blog de Abraham Flaxman - [Scipy Tutorial 2014](https://github.com/fonnesbeck/scipy2014_tutorial) de Chris Fonnesbeck

He creado este tutorial con la esperanza de que otros lo encontrarán útil y que les servirá para aprender técnicas bayesianas de la misma forma que me ayudaron a mí. Cualquier aportación de la comunidad corrección/comentario/contribución será bienvenida.

Using a server to decide what kind of data has real significance provides a mean to reduce data. For example if you are in a gym doing hard physical work the data must be compared with what happen in that circumstances, you can filter and weigh the data depending of the circumstances. Using AI to classify contexts in which precision must be increased is the best way to reduce the too much data problem.

I think the unix philosophy is the model to follow here. Design an input method just capable of receiving the information and let other applications process your input. Input is also about context, in which case you need to process an input to context prior to an input in a context.

Example: Context = Numeric question about integer numbers and four operators, +, - ,, /

input = r'[0-9,\+,\-,\,\/]'

I can think of many applications of data. For example can to improve the learning rate of MOOCs, how to teach effectively, how to detect great performers (for example for hiring), how to meet the kind of people able to inspire others (analyzing cliques in graph of social relations), how to get feedback, how to get a first impression of what products are the market demanding and how much are they going to pay for it, and many others. A gold mine for those that can extract the real value and significance of data, that is data analysts.

It seems that the author think that sometime young people need to broke the social rules or experiment something new (perhaps not socially allowed) in order to grow up, to construct their own identity.

Perhaps my idea is that machine learning and AI is now a way to sell snake oil, specially when you try to give recipes and avoid the foundation. Today computer programs can do many task as for example predictions using models which require little o no tuning, so the user just press a button or write two or three lines of code. Someone can think that he is at the cutting edge of research pressing those buttons, perhaps that naive feeling is what I was fighting again.

If AIs are not able to communicate we us properly then those are programs but not real AIs. I didn't find any interesting idea here and the sheer act of proposing that program as a language for AI is really humorist to say the least. People are using R and Python to construct a solid foundation for machine learning, the NLP and the IA will evolve to communicate with us by its own means, that's the real AI.

An explanation of the following useful fact:A fraction p/q is equal to a finite length floating point number in base b <=> q divides b^n <=> the prime factors of q are prime factors of b.

Proof if q divides b^n, then q * c = b^n => p/q = (p * c)/(q * c) = (p * c)/b^n = (p * c)e-n in base b.

Example: Express the fraction 7/18 (base 10), in base 60 and convert it to a floating point in base 60.

60 = 2^2 * 3^1 * 5^1, 18 = 2^1 * 3^2, look for c such that 18 * c is a power of 60. Observe that the factors of 60^2 all have exponents that are greater or equal than those of 18, so we can multiply 18 by the required factors to get (60^2). This way: 60^2 = (2^2 * 3 * 5)^2 = 2^4 * 3^2 * 5^2 = 18 * c = (2^1 * 3^2) * (2^3 * 3^0 * 5^2)= 18200, so we need to multiply by 200 the numerator and denominator of the initial fraction to get a new fraction whose denominator is a power of 60. Now 7/18 = (7 200)/(18 * 200) = 1400/(60^2) and 1400 = 23 * 60 + 20.

Finally 7/18 = 1400/(60^2) = 0.2360 in base 60.

We use the notation 00,01,02, ... 59 for the digits of base 60, so 2360 is a two digits number in base 60.

The asterisk used for multiplication is not shown in HN, don't know why.

You can shrink an interval [a,b] by two methods [a,b'] with b'<b or [a',b] with a'> a, what you see is [a,b'] (NP-complete seem easier or lower in the difficulty scale), what I see is [a',b] that is P cover a lot more ground, I think P will cover all NP-complete problems and then the interval becomes a unique point.

If [P,NP-c] = [0,1] I see GI as 1/4, if GI is proven to be in P then I should say [P,NP-c] is now [1/4,1].

In order to reduce more that interval one should find a problem that people think has a probability of 0.5 to be in P and 0.5 to be in NP, if it is proven that problem is in P then the interval get reduced a lot more. Just trying to explain my perception of the intuitive complexity of algorithms.

copy and paste but before do

cat -A - and here paste text, you can see what is going on.

There are 140 comments in the blog of Scot Aaronson about this topic: http://www.scottaaronson.com/blog/?p=2521#comments

Personal Opinion: Perhaps the Complexity Hierarchy is going to collapse since intuition is not so clear and perhaps the distance from P to NP is shrinking.