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In an article of Northeastern University News, Lisa Feldman Barrett, University Distinguished Professor of Psychology, is quoted saying that we are at a moment in history when it is very important to have a working knowledge of science. Luckily there a lot of scientists how are trying to communicate in simple terms that are adapted to the general public.

According to journalist David Wallace-Wells, the most important thing that we can do at this point to try to stop climate change is political action. Other measures may not be able to counteract the failure to change state policies so that they will effectively result in reducing the global warming effect. His interview in The Guardian.

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... or why we hold on to our beliefs even if we are presented with strong proofs that they are wrong. Here is the New Yorker article about this. As you may have already noticed we have a tendency to take into account the proofs that are in favor of our current beliefs but to dismiss the ones that disproves them. Another way of putting it is that we like to be right and we hate to be wrong. So we do our best to make ourselves feel good. Even if it is counter intuitive we should try to overcome this fear of being wrong and not hesitate of changing opinion if evidences point in that direction. In an interview Jeff Bezos points that doing this is a valuable trait. Or from a Richard Feynman quote : "We have found it of paramount importance that in order to progress we must recognize our ignorance and leave room for doubt."

... if you put them in practice. In the end, you cannot say that an idea is good only by reasoning about it. You may always miss something when you're simulating the future in your head. You need to actually do it to test if it works or not, usually multiple times so that you know that if you got the expected results this was not by chance. Another way of thinking about it is that let's say you are reading about some rule. You shouldn't think it is true or that it applies in any conditions because you trust who wrote it. You have to think yourself about it maybe more than you usually do by instinct when you are encounter anything new. Which basically means to think critically. So ideas are good if you put them to practice. But if you apply this pattern to the process of reasoning about the patterns themselves it just means to think critically since the patterns are about thinking about how to think.  

As in why do I write? Just because I believe that most of the time the reason we are not happy is that we get caught up in unhealthy thinking. And this unhealthy thinking happens because we focus so much on the immediate inconveniences that we forget some good thinking rules that we found in more happier times. And I feel that if I just write them down this thinking patterns may just become habits so that I remember to use them when I need them the most. So basically I write so that I don't forget what I think them to be general patterns of good thinking. Because they usually don't teach you in school (enough about) this kind of things, more precisely how to think. I guess it is implied that since you are thinking all day long then obviously you know how to do it. But actually it doesn't mean that you are doing it right! And it may not be obvious how to correct your way of thinking until you somehow find out from someone wiser. As an example when confronting with a ma...

While deep learning (or other types of machine learning) seems to be the answer to most of the hard problems in computer science like computer vision, prominent figures of the field admit that advancing in the area of general intelligence will require entirely different methods. Geoffrey Hinton says that he is "deeply suspicious" about back-propagation, the technique used to train neural networks, and that "My view is throw it all away and start again" . Yoshua Bengio, in a conference presentation called " Creating Human Level AI ", says that we are "still far away". "All industrial successes are based on pure supervised learning" "Still learning supeficial clues that do not generalize well outside of training contexts and make it easy to fool trained networks: Current models cheat by picking on surface regularities, e.g. , background greenery implies that probably an animal is present " "Still unable to do a good...