In 2013, I wrote a book[1]. At the time, I wanted to explain neural networks in simple terms, I had high school students at my mind. I have expressed my concerns that machine learning was dominating the world, and people had no idea about it, smartphones were not popular in Brazil, and started go gain attention as personal computers. Deep learning started to gain momentum on 2012, and nowadays is kind of the rule. At the time, YouTube was bad, pretty bad a must say: I used to save the links to my videos, as so I could avoid passing through the main page. . Computational thinking is synonymous of algorithms. I cannot think a single computational routine which is not an algorithm; after all, “computers are stupid”, they need to be told what to do even when it is abstract (e.g., machine learning). What is computational think, though? Think like this, a thought experiment: Suppose you give your result, from your model, to someone. Do you believe the person would be able to tell the difference between your solution, from your algorithm, and a human? If not, this is computational thinking. It is a machine (i.e., an algorithm, a routine), doing human-thinking work. As we are going to see based on Kasabov’s work, we may actually be able to send ‘thinking loads’ to computers in the future. Initially, this book supposes to be called computational intelligence. Nonetheless, I thought, we do not necessarily need ‘intelligence’ to build models, not in the sense to artificial intelligence or even human intelligence. Furthermore, as we shall learn from Daniel Kahneman and colleagues, we can achieve nice models for decision making even with simple models, when compared to humans; imagine what we can do with machine learning + cloud computing + databases (such as MongoDB and Firebase)! Possible public Web developers wanting to expand their horizon; here I am being modest, I feel any web coder should learn computational thinking, as so they can add intelligence to their “dummy” apps; People from computational intelligence, waiting to learn new tricks; Computer scientists for sure! I would recommend to computational biologists, and anyone interested in bioinformatics; Applied mathematics, and computational mathematician for sure; Anyone that is opened to new ideas, but has a minimum computer programming background; Maybe, medical doctors and biologists; one of my PhD advisors was a surgeon, with a PhD in mathematics; thus, we may have this profile in medicine and, especially, in biology; External resources and tricks My GitHub profile; Our sandbox; I have used links to my LinkedIn profile, to posts related to the discussions. Feel free to start a conversation on LinkedIn, or to connect! Just comment on the posts, and I will be noticed; I have used several external links, to articles online; this is in addition to the classical/academic reference standard; With Special release of “My selected assays from Medium on Computer programming, Artificial Intelligence” [1] Redes Neurais em termos simples: como aprendemos, pensamos e modelamos. https://www.academia.edu/18365339/Redes_Neurais_em_termos_simples_como_aprendemos_pensamos_e_modelamos?fbclid=IwAR3NLQt003L5QXZQNLSePIxJxUf7NbqsthEjj8rb1zgfpgEgzkiqoNfO0RY. Accessed on 30/06/22.
