"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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.
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.
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.
Researchers at the University of Wollongong, Deakin University, Monash University and Kyushu University have developed a framework that could be used to build a smart, AI-powered agile project management assistant. Their paper, pre-published on arXiv, has been accepted at the 41st International Conference on Software Engineering (ICSE) 2019, in the New Ideas and Emerging Results track. "Our research was driven by our experience working in and with the industry," Hoa Khanh Dam, one of the researchers who carried out the study, told TechXplore. "We saw the real challenges in running agile software projects and the serious lack of meaningful support for software teams and practitioners. We also saw the potential of AI in offering significant support for managing agile projects, not only in automating routine tasks, but also in learning and harvesting valuable insights from project data for making predictions and estimations, planning and recommending concrete actions."
In this blog post, we will talk about deep learning: its use and business implications. We will then give an overview of the R&D efforts that Qubole is conducting in this area with respect to GPU support and distributed training. The last few years have seen considerable interest in artificial intelligence (AI) and machine learning (ML), specifically in the area of deep learning. These terms are used interchangeably and very often confused with each other. Let's take a look at the relationship between these technologies and the reason for the popularity of deep learning.
This book is an introductory overview of Ethem's detailed text on ML. The text itself has gotten mostly mixed or bad reviews due to a lot of math and algorithms notated without a lot of detailed explanations, however, this is a general reader intro and doesn't go into math, algos in detail, trees, Bayesian logic or even pseudocode, it is more an up to date overview of the field as it exists at this writing. Alpaydin's expensive text, btw, is also available in a very inexpensive Asian edition here on Amazon if you want to brave that difficult book without a lot of investment (Introduction To Machine Learning 3Rd Edition). The present volume is sortof a "ML for Dummies" only updated for the current craze with big data management. There is a lot of history and background that an experienced ML person will find too basic, but as a High School intro or general interested reader intro it is excellent.