A recent article in Techcrunch describes Twitter and Facebook issues: algorithms unable to detect fake news or hate speech. I wrote about how machine learning could be improved, and what can make implementations under-perform - or not perform at all. And a colleague shared with me an article about how Facebook really sucks at machine learning. You would think that machine learning simply does not work, at least not as advertised. Here, I actually claim that this is not the case, further explaining what the issues might be, and in short, that machine learning might not be the culprit.

A recent article in Techcrunch describes Twitter and Facebook issues: algorithms unable to detect fake news or hate speech. I wrote about how machine learning could be improved, and what can make implementations under-perform - or not perform at all. And a colleague shared with me an article about how Facebook really sucks at machine learning. You would think that machine learning simply does not work, at least not as advertised. Here, I actually claim that this is not the case, further explaining what the issues might be, and in short, that machine learning might not be the culprit.

Here I list my most interesting contributions published on Data Science Central. My plan is to categorize and aggregate this content to produce a few self-published books. The material below will always be available for free (from this webpage), but the books won't, or if they are, they will be free for members only. So you might want to bookmark this page. I also have written a number of academic papers, you can find some of them here.

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.

Here I list my most interesting contributions published on Data Science Central. My plan is to categorize and aggregate this content to produce a few self-published books. The material below will always be available for free (from this webpage), but the books won't, or if they are, they will be free for members only. So you might want to bookmark this page. I also have written a number of academic papers, you can find some of them here.

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A recent article in Techcrunch describes Twitter and Facebook issues: algorithms unable to detect fake news or hate speech. I wrote about how machine learning could be improved, and what can make implementations under-perform - or not perform at all. And a colleague shared with me an article about how Facebook really sucks at machine learning. You would think that machine learning simply does not work, at least not as advertised. Here, I actually claim that this is not the case, further explaining what the issues might be, and in short, that machine learning might not be the culprit. It seems that the issues appear in situations that are not critical - such as an ad badly targeted, a racist tweet that goes undetected, or a piece of fake news that goes viral.