KDnuggets News 16:n23, Jun 29: Machine Learning Trends & Future of AI; Data Science Kaggle Walkthrough; Regularization in Logistic Regression

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Doing Data Science: A Kaggle Walkthrough Part 6 - Creating a Model Top Machine Learning Libraries for Javascript Improving Nudity Detection and NSFW Image Recognition History of Data Mining Predictive Analytics World in October: Government, Business, Financial, Healthcare Software 5 More Machine Learning Projects You Can No Longer Overlook BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark Achieving End-to-end Security for Apache Spark with Databricks Predicting purchases at retail stores using HPE Vertica and Dataiku DSS Tutorials, Overviews, How-Tos Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences Ten Simple Rules for Effective Statistical Practice: An Overview Mining Twitter Data with Python Part 3: Term Frequencies Opinions The Big Data Ecosystem is Too Damn Big An Inside Update on Natural Language Processing From Research to Riches: Data Wrangling Lessons from Physical and Life Science News Top Stories, June 20-26: New Machine Learning Book, Free Draft Chapters; Machine Learning Trends & Future of A.I. Webcasts and Webinars Webinar, Jun 30: Introducing Anaconda Mosaic: Visualize. Bank of Ireland: Senior Data Scientist within the Advanced Analytics Team DuPont Pioneer: Data Scientist - Encirca Academic U. of Iowa: Business Analytics & Information Systems, Lecturer U. of Iowa: Lecturer: Business Analytics & Information Systems Top Tweets Top KDnuggets tweets, Jun 15-21: Predicting UEFA Euro2016; Visual Explanation of Backprop for Neural Nets Quote "Everything at scale in this world is going to be managed by algorithms and data ... every business will be an algorithmic business."


If AI Destroys Jobs, It's Up To Us To Help People Affected

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When New York City introduced its first automated traffic lights in 1924, it was employing an army of 6,000 traffic cops to switch signals manually to keep the Studebakers and Model Ts from jamming intersections. Over the next two years, 92% of those jobs were automated out of existence. Yet most of the officers came out just fine, thanks to a retraining program that let them land new jobs fixing the very stoplights that took their old job. Almost a century later, we're seeing this pattern repeat itself, as it has countless times over the decades. This time the automation is coming from AI and machine learning, related technologies poised to make significant inroads on the 6.3 million jobs in finance.


Embrace big data and robots -- they're the future of work

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President Donald Trump's July 19 executive order establishing the President's National Council for the American Worker is directed at preparing Americans for the workplace of the future. Although short on specifics, the order sends a powerful message about the need for revitalizing educational opportunities if Americans are to thrive in the era of big data, robots and artificial intelligence. The president's intent is to lay the groundwork for tackling a national "skills crisis." His order accepts that Americans need additional skills to fill the current 6.7 million job vacancies. In fact, the executive order gives official imprimatur to what many in industry and academia have feared for some time: "The economy is changing at a rapid pace because of the technology, automation, and artificial intelligence," and existing programs have "prepared Americans for the economy of the past."


Homo Sapiens 2.0? We need a species-wide conversation about the future of human genetic enhancement

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Jamie Metzl is a Senior Fellow for Technology and National Security at the Atlantic Council. After 4 billion years of evolution by one set of rules, our species is about to begin evolving by another. Overlapping and mutually reinforcing revolutions in genetics, information technology, artificial intelligence, big data analytics, and other fields are providing the tools that will make it possible to genetically alter our future offspring should we choose to do so. Nearly everybody wants to have cancers cured and terrible diseases eliminated. Most of us want to live longer, healthier and more robust lives. Genetic technologies will make that possible. But the very tools we will use to achieve these goals will also open the door to the selection for and ultimately manipulation of non-disease-related genetic traits -- and with them a new set of evolutionary possibilities.


No Data in the Void: Values and Distributional Conflicts in Empirical Policy Research and Artificial Intelligence Economics for Inclusive Prosperity

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Economics has experienced an empirical turn in the last few decades. We have entered an era of big data, machine learning, and artificial intelligence. Experimental methods have greatly increased in importance in both the social and life sciences. And recent efforts at reforming the publication system promise to improve the replicability and credibility of published findings. One might be tempted to conclude that this increased availability of and reliance on quantitative evidence allows us to dispense with the normative judgements of earlier days. I will argue that the opposite is the case. The choice of objective functions, which define our goals, and of the set of policies to be considered matters ever more in all of these contexts. A famous example in debates about the dangers of artificial intelligence (AI) is the hypothetical AI system with the objective of producing as many paperclips as possible. If sufficiently capable, such an AI system might end up annihilating humanity in the pursuit of this objective. Another example is the design of experiments. The majority of experiments in the social and life sciences are designed based on the (implicit) objective of obtaining precise estimates of causal effects. Such experiments randomly assign treatments using fixed probabilities.