{"id":1511,"date":"2017-04-18T16:58:43","date_gmt":"2017-04-18T14:58:43","guid":{"rendered":"http:\/\/www.datascience.rs\/?p=1511"},"modified":"2017-04-18T16:58:43","modified_gmt":"2017-04-18T14:58:43","slug":"inner-life-machine-learning-search-delicate-balance-insights-sas-forum-2017-milan-italy","status":"publish","type":"post","link":"http:\/\/imuno-srbija.com\/data-science\/en\/2017\/04\/18\/inner-life-machine-learning-search-delicate-balance-insights-sas-forum-2017-milan-italy\/","title":{"rendered":"The Inner Life of Machine Learning in Search of a Delicate Balance: Insights from SAS Forum 2017, Milan, Italy"},"content":{"rendered":"
Goran S. Milovanovi\u0107, PhD<\/div>\n
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We are becoming an evidence driven culture: it’s all about data these days, but big and small data are nevertheless accompanied by big dilemmas in relation to their use and misuse in guiding our choices. Given the plenitude of statistical models and machine learning algorithms that we have at our disposal nowadays, how do we decide which one should decide in our place in the digital environment of evergrowing complexity? And when the recommendation that should help us guide further information search or decision making is automatically made, will we follow it blindly without reminding ourselves of the ultimate question of “why<\/span>” – a question that proved to be so central to our human existence? The quote (famously (mis)attributed to Albert Einstein) – “Every fool can know, the point is to understand<\/span>” – could prove to be the best piece of advice for anyone connected to the Internet, whenever and for whatever reason.<\/div>\n
Or to anything <\/span>online, perhaps? Our evidence driven culture will be driven mostly by autonomous algorithms doing the number crunching to provide for recommendations, risk estimates, classifications, and inferences. We as species could never possibly compute all these data products with our natural cognitive systems – simply because we were never evolutionary designed to integrate data on a scale that is characteristic of our contemporary digital environments. That is why our machines have to learn to learn<\/span>, and faster than we do: their adaption is our adaption indeed. But then, what is left for us once they master to recognize the most optimal of the structures that are found in the omnipresent data and infer the best course of action for us?<\/div>\n
Two answers come to mind. First, it us who define what is useful. Second – and maybe more important, because we can differ in our positions on what is ‘useful’ widely – we need to understand<\/span>, while the machines, in principle, do not. A sudden opportunity to attend the SAS Forum 2017 in Milan, Italy<\/span><\/a>, together with the SAS Adriatic<\/span><\/a> team, and as a representative of Data Science Serbia<\/span><\/a>, inspired another round of my questioning of the present situation in Data Science and Analytics along these lines.<\/div>\n
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Cellular automaton: Rule 193 with random conditions. Wikimedia Commons, 21 September 2013, 09:17:51, Author: Sofeykov.<\/div>\n<\/div>\n<\/figure>\n
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In his The difference between Statistical Modeling and Machine Learning, as I see it<\/span><\/a> (2016), Mr Oliver Schabenberger, EVP and Chief Technology Officer at SAS, has provided an attempt at a concise delineation between statistical modeling and machine learning, relying on the following proposal that differentiates between (a) statistical modeling<\/span>, (b) classic machine learning<\/span>, and (c) modern machine learning<\/span>:<\/div>\n
(a) The basic goal of Statistical Modeling <\/span>is to answer the question, \u201cWhich probabilistic model could have generated the data I observed?<\/span>\u201d<\/div>\n
(b) Classical machine learning is a data-driven effort, focused on algorithms for regression and classification, and motivated by pattern recognition. The underlying stochastic mechanism is often secondary and not of immediate interest […] the primary concern is to identify the algorithm or technique (or ensemble thereof) that performs the specific task.<\/div>\n
(c) A machine learning system is truly a learning system if it is not programmed to perform a task<\/span>, but is programmed to learn<\/span> to perform the task. […] Like the classical variant, it is a data-driven exercise. Unlike the classical variant, modern machine learning <\/span>does not rely on a rich set of algorithmic techniques. Almost all applications of this form of machine learning are based on deep neural networks.<\/div>\n
I was granted an opportunity to learn on some elaborations of this line of thinking from Mr Schabenberger directly during the SAS Forum 2017. In my interpretation – and this is necessary to stress, given the immense complexity of the topic under discussion – his words reassured me that the following trade-offs between (a) our understanding of what do we do with data analytics, and (b) simply being able to develop more and more complex methods to accomplish progressively complicated tasks hold:<\/div>\n