Around midnight one Saturday in January, Sarah Jeong was on her couch, browsing Twitter, when she spontane ously wrote what she now bitterly refers to as "the tweet that launched a thousand ships." The 28-year-old journalist and author of The Internet of Garbage, a book on spam and online harassment, had been watching Bernie Sanders boosters attacking feminists and supporters of the Black Lives Matter movement. In what was meant to be a hyper bolic joke, she tweeted out a list of political carica tures, one of which called the typical Sanders fan a "vitriolic crypto racist who spends 20 hours a day on the Internet yelling at women." The ill-advised late-night tweet was, Jeong admits, provocative and absurd--she even supported Sanders. But what happened next was the kind of backlash that's all too familiar to women, minorities, and anyone who has a strong opinion online. By the time Jeong went to sleep, a swarm of Sanders supporters were calling her a neoliberal shill. By sunrise, a broader, darker wave of abuse had begun. She received nude photos and links to disturbing videos. One troll promised to "rip each one of [her] hairs out" and "twist her tits clear off." The attacks continued for weeks. "I was in crisis mode," she recalls. So she did what many victims of mass harassment do: She gave up and let her abusers have the last word.

Professor James J. (Jong Hyuk) Park received his Ph.D. degree in Graduate School of Information Security from Korea University, Korea. From December, 2002 to July, 2007, Dr. Park had been a research scientist of R&D Institute, Hanwha S&C Co., Ltd., Korea. From September, 2007 to August, 2009, He had been a professor at the Department of Computer Science and Engineering, Kyungnam University, Korea. He is now a professor at the Department of Computer Science and Engineering, Seoul National University of Science and Technology (SeoulTech), Korea. Dr. Park has published about 100 research papers in international journals and conferences.

Just last month on 22 and 23 June, the United Nations Public Service Forum 2017 took place in The Hague to celebrate public service delivery and to discuss how innovation is shaping the government of the future as well as how to accelerate such innovation. One of the technologies considered to be fundamental in shaping the government of the future is Artificial Intelligence (AI). With the ever-increasing amount of data collected through a wide variety of sources and sensors, the potential for computers to learn from this data and take over certain tasks is increasing. Although the potential of technological innovation is often overrated in the short term and underrated in the long term, it seems that AI is treated as the'shiny new typewriter' that we should all be a bit careful of. AI is seen as having the potential to do a lot of valuable work currently done by humans, specifically routine work.

Genetically modified babies may sound like something out of a science fiction movie, but recent innovations in both gene editing and artificial fertilization technology mean that this idea could become a reality. Scientists focus on gene editing to eliminate certain debilitating hereditary diseases, but the technology could accomplish a lot more. Recently, the team at Superdrug surveyed the public on what they would modify in their future children if they could, and the results are surprising. According to the survey, carried out by Superdrug Online Doctor, prospective parents who viewed baby modification as ethical explained that they would most likely alter their child to make them healthier and more intelligent, followed by increased creativity and attractiveness. When it came to specific physical characteristics, Europeans answered that they would genetically modify their child to be a blonde-haired blued-eyed girl of average height. Americans, on the other hand, identified their ideal child as a black-haired blued-eyed male of above average height.

How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.

Have you ever wondered how the Waze app knows shortcuts in your neighborhood better than you? It's because Waze acts like a superhuman air traffic controller -- it measures distance and traffic patterns, it listens to feedback from drivers, and it compiles massive data set to get you to your location as quickly as possible. Even as we grow more reliant on these kinds of innovations, we still want assurances that we're in charge, because we still believe our humanity elevates us above computers. Movies such as "2001: A Space Odyssey" and the "Terminator" franchise teach us to fear computers programmed without any understanding of humanity; when a human sobs, Arnold Schwarzenegger's robotic character asks, "What's wrong with your eyes?" They always end with the machines turning on their makers.

Here are 100 upcoming meetings and conferences, for July 2017 and beyond. You can also find the latest list on KDnuggets Meetings page Color code: Business-Oriented meetings in Blue, Research meetings (with calls for papers and program committee) in green Top countries: India, France, Australia: 3 New York City regained the top billing this month - here are the top cities for meetings: San Francisco, Boston: 8 Seattle, New Orleans, Berlin, Atlanta: 3 July 2017 Jul 5-8, 2017 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS). Register now and save 15% with code KDN15. Space is limited - register soon and save 20% extra w. Save 20% on most passes with discount code PCKDNG.

Editor's note: ODSC supports the self-education of data enthusiasts of all levels, building the access to information and the means to showcase their data driven passions. The author of this post is a premier example of one such intersection. Caspar is our youngest Data Science Associate yet, and you can come see his talk at ODSC Europe. I spent years programming, solving problems, and producing results, focusing on from computer vision to web development. It seemed right to explore what I think is one of the most magical and exciting methods in technology, neural networks. My excitement with Machine Learning stemmed from the fact I'm solving the problems of problems.

Big data is a big deal. The full launch of the English version of Samsung's Bixby voice control interface for the Galaxy S8 is still in a holding pattern, after originally expecting to launch in June. The delay comes from a handful of issues with the service but is driven by a lack of usage data that's ultimately required for the machine learning systems to work at their full potential. Although the service is already available in Samsung's home country of South Korea, evolving Bixby for use with U.S. English is proving tougher. Because of the vast number of potential commands and numerous pathways to accomplish those commands within the interface and apps on a phone, the only realistic way to make it all work is for machine learning algorithms to process large amounts of data and determine those links automatically. Samsung's engineers can of course set them on the right path, but in the end, you need real-world usage data to show the algorithms how people are using the service and how to best accomplish the commands.

How do you measure chatbot success? According to John Straw, senior advisor to McKinsey and Co and the opening speaker at Econsultancy's Supercharged event, it is simply when the user does not realise they are being served by a robot. Sounds pretty simple when you put it like that, right? Neither is convincing businesses that artificial intelligence is actually worth investing in, especially considering it is nearing the dreaded "trough of disillusionment" on the infamous Gartner Hype Cycle. Reflecting the various examples of brand chatbots we've seen throughout the past year or so, the conversation at Supercharged ranged from the inspiring to the silly.