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A Guide to Solving Social Problems with Machine Learning

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You sit down to watch a movie and ask Netflix for help. Zoolander 2?") The Netflix recommendation algorithm predicts what movie you'd like by mining data on millions of previous movie-watchers using sophisticated machine learning tools. And then the next day you go to work and every one of your agencies will make hiring decisions with little idea of which candidates would be good workers; community college students will be largely left to their own devices to decide which courses are too hard or too easy for them; and your social service system will implement a reactive rather than preventive approach to homelessness because they don't believe it's possible to forecast which families will wind up on the streets. You'd love to move your city's use of predictive analytics into the 21st century, or at least into the 20th century. You just hired a pair of 24-year-old computer programmers to run your data science team. But should they be the ones to decide which problems are amenable to these tools? Or to decide what success looks like?


Current Trends in Deep Learning – Knowitlabs

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In the last decade, the area of artificial intelligence (AI) has exploded with interesting and promising results. With major achievements in image recognition, speech recognition and highly complex games, AI continues to disrupt society. This blog post will discuss practical applications of AI, optimization and interpretability of deep learning models and reinforcement learning (RL), based on the 2018 REWORK Deep Learning Summit in Toronto. Four software engineers from Knowit had the pleasure of travelling to Canada to attend this conference, and with renowned speakers such as Geoff Hinton attending, it turned out be an insightful experience. Today, the addition of learning is also in place, but due to the non-deterministic nature of the real world, decisions cannot be made purely from the facts that are given. Further development of AI will require improvements in a variety of areas.


The FBI Says Its Photo Analysis is Scientific Evidence. Scientists Disagree.

Mother Jones

This story was originally published by ProPublica. At the FBI Laboratory in Quantico, Virginia, a team of about a half-dozen technicians analyzes pictures down to their pixels, trying to determine if the faces, hands, clothes or cars of suspects match images collected by investigators from cameras at crime scenes. The unit specializes in visual evidence and facial identification, and its examiners can aid investigations by making images sharper, revealing key details in a crime or ruling out potential suspects. But the work of image examiners has never had a strong scientific foundation, and the FBI's endorsement of the unit's findings as trial evidence troubles many experts and raises anew questions about the role of the FBI Laboratory as a standard-setter in forensic science. FBI examiners have tied defendants to crime pictures in thousands of cases over the past half-century using unproven techniques, at times giving jurors baseless statistics to say the risk of error was vanishingly small. Much of the legal foundation for the unit's work is rooted in a 22-year-old comparison of bluejeans. Studies on several photo comparison techniques, conducted over the last decade by the FBI and outside scientists, have found they are not reliable. Since those studies were published, there's no indication that lab officials have checked past casework for errors or inaccurate testimony. Image examiners continue to use disputed methods in an array of cases to bolster prosecutions against people accused of robberies, murder, sex crimes and terrorism. The work of image examiners is a type of pattern analysis, a category of forensic science that has repeatedly led to misidentifications at the FBI and other crime laboratories. Before the discovery of DNA identification methods in the 1980s, most of the bureau's lab worked in pattern matching, which involves comparing features from items of evidence to the suspect's body and belongings. Examiners had long testified in court that they could determine what fingertip left a print, what gun fired a bullet, which scalp grew a hair "to the exclusion of all others." Research and exonerations by DNA analysis have repeatedly disproved these claims, and the U.S. Department of Justice no longer allows technicians and scientists from the FBI and other agencies to make such unequivocal statements, according to new testimony guidelines released last year. Though image examiners rely on similarly flawed methods, they have continued to testify to and defend their exactitude, according to a review of court records and examiners' written reports and published articles.


This Ikea kitchen might teach industrial robots to be less dumb and more helpful

MIT Technology Review

For all the recent progress in artificial intelligence, industrial robots remain amazingly dumb and dangerous. Sure, they can perform arduous tasks precisely and repetitively, but they cannot respond to variations in their environment or tackle something new. That severely limits just how useful robots can be in the workplace. Nvidia wants to use machine learning to help solve this problem. The world's leading producer of the specialistcomputer chips that are crucial to artificial intelligenceis opening a new robotics lab in Seattle to make the robots that work alongside humans--co-bots-- smarter and more capable.


Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2018 - insideBIGDATA

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Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This paper provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. The reader is assumed to be familiar with basic machine learning concepts.


Artificial Intelligence Research and Application Advancement

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Recent advances in the field of artificial intelligence are gaining widespread attention from the world because of the impact that they can have on our lives. From speech recognition, virtual home assistants to learning platforms, things have gotten very interesting in the tech industry. Tech-giants have been racing against each other to incorporate AI aspects into their newest creations so as to make the human experience much more comfortable. By adding characteristics that understand children, empathy and work routines, artificial intelligence technology is set to become a revolution. Here are some of the recent advances in the field of artificial intelligence, in terms of research and technology.




Machine Learning 101

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We are pleased to announce a basic introduction to machine learning presentation that provides an overview of the basic algorithms by Donald Woodlock, InterSystems VP of HealthShare Platforms.


The Past, Present, and Future of Automated Machine Learning SciPy 2018 Randal Olson

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Automated Machine Learning (AutoML) has been described as a "quiet revolution in AI" that is poised to dramatically change the data science landscape by using AI to automate many of the time-consuming aspects of applying Machine Learning to real-world problems. Academic researchers, startups, and tech giants alike have begun developing AutoML methods and tools ranging from simple open source prototypes to industry-scale software products. Yet beyond all the hype and vague tech jargon, many are left wondering: What is AutoML, really? In this talk, I will draw from my AutoML research experience to discuss the benefits of AutoML and highlight some promising future directions of the field, including Python packages and other existing tools that offer AutoML solutions.