{"id":4174,"date":"2020-12-08T06:33:55","date_gmt":"2020-12-08T06:33:55","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2020\/12\/08\/data-science-central-weekly-digest-7-dec-2020\/"},"modified":"2020-12-08T06:33:55","modified_gmt":"2020-12-08T06:33:55","slug":"data-science-central-weekly-digest-7-dec-2020","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2020\/12\/08\/data-science-central-weekly-digest-7-dec-2020\/","title":{"rendered":"Data Science Central Weekly Digest, 7 Dec 2020"},"content":{"rendered":"

Author: Kurt Cagle<\/p>\n

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Data Science Central Weekly Digest, 7 Dec 2020<\/strong><\/span><\/td>\n<\/tr>\n
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Welcome to the DSC Weekly Digest, a production of Data Science Central. Every week, we pick out the newest and brightest articles on the topic of data … and how it informs our lives, powers our applications, and provides insights into our world.<\/div>\n
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From the Editor’s Desk<\/strong><\/div>\n
One of the key concepts in data science (and indeed in physics) is the nature of noise and randomness. The two are connected, though not the same thing. Randomness is an abstract concept that can be described in any number of ways, but a useful way of thinking about is that when two variables are completely uncorrelated to one another, a phase space diagram of the two will ultimately fill a unit square if the variables are normalized.<\/div>\n
True randomness is surprisingly rare. Noise, on the other hand, is not. Noise is seldom truly random, but instead, can be thought of as the signals of the universe that are not of interest to you. Sometimes that noise can be treated as random (television static is a good example, as that signal may be everything from the cosmic background radiation to interactions of the Earth’s electromagnetic fields to … who knows, ghosts in the machine, so to speak. A surprising amount of such noise comes in fact from recursive fractals that seem to fill nature, and may in fact be artifacts of any complex system. There’s a growing amount of evidence to suggest that contexts are themselves fractals, which is perhaps why AI keeps bumping up against the fractal walls.<\/div>\n
One of the roles of most data scientists and data analysts is to extract the signal from the noise, to understand what is relevant from what is not. Neural Networks, when you get right down to it, exist primarily to boost signals – to both find and enhance the signals from noise, and to then use that to predict or verify behavior based upon known signals.<\/div>\n
Vincent Granville writes about randomness<\/a> in this issue of the DSC Weekly Digest. Ajit Jaokar attempts to parse the signal from the noise<\/a> as well in the first of several explorations of what 2021 looks likely to bring in the field of AI, while Kirsten Lloyd digs into the rise of Model Ops<\/a> , a synthesis of DevOps and Modeling, as a way to make navigating through that noise easier for modern companies. These are just a few of the articles new this week in Data Science Central<\/a>.  <\/div>\n
Data Science Central<\/a> is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles<\/a> and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think<\/a>.<\/div>\n
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