We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.

Inductive Logic Programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set Programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset.

After my first post on Anomaly Detection for Time Series post, I would like to continue presenting what I did during the course at for the Data Science for IoT Course at Department of Continued Education of the University of Oxford with Ajit Jaokar. In line with what I wrote previously, this second post will be about predictive maintenance. The post will conclude the initial exploration of the topics I covered at Oxford. When researching materials to cover this course, I had a general idea of what to look for. Having worked already in industrial environments, I had a good idea of what predictive maintenance should be and how it could be used.

This post is authored by Xibin Gao, Wei Guo, and Debraj GuhaThakurta at Microsoft. Microsoft Machine Learning Services were a key highlight of our SQL Server 2017 CTP 2.0 release in April this year. It allows Python scripts to run within SQL Server or be embedded in SQL scripts and be deployed as stored procedures. This feature essentially brings Python visualization and predictive analytics capabilities close to the data stored within SQL Server. Data scientists can combine the powers of SQL and Python and build end-to-end machine learning solutions with much greater ease.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? We distinguish between AI and machine learning (ML) throughout this article when appropriate. At TechEmergence, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI. All machine learning is AI, but not all AI is machine learning. Our enumerated examples of AI are divided into Work & School and Home applications, though there's plenty of room for overlap.

When it launched, the Udacity Artificial Intelligence Nanodegree program became a kind of landmark in this history of AI. There was no precedent for the program's groundbreaking combination of content, platform, partners, and services. That simply did not exist before. The first term of students to enroll and start the program got to experience something genuinely new. They were risk-takers in every sense of the term.

Summary: Looking beyond today's commercial applications of AI, where and how far will we progress toward an Artificial Intelligence with truly human-like reasoning and capability? This is about the pursuit of Artificial General Intelligence (AGI). There is no question that we're making a lot of progress in artificial intelligence (AI). So much so that we are rapidly approaching or have already arrived at a plateau in development where more effort is being put into commercializing existing AI capabilities than in improving it. As far back as November 2014 Kevin Kelly, cofounder of Wired magazine and prolific futurist observed "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Well Kevin, you're right.

Beginning in fall 2017, some students and educators at the University of Michigan may be getting help on writing assignments from computers. Campus Technology reports that a team of educators developed a writing-to-learn tool called M-Write, which uses automated text analysis (ATA) to identify the strengths of a writing submission. Developed by two professors, the tool was initially meant to help students grow their conceptual learning skills in large courses and to help streamline the grading process, reports a UMich article. ATA works by "using a variety of text analysis techniques, such as vocabulary matching or topic matching, which the algorithm detects." Using M-Write also lets educators identify the students who are going to need help.

Biography: Professor Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth where he was Director of the Institute for Quantitative Biomedical Sciences. Prior to Dartmouth he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University. He has a PhD in Human Genetics and an MSc in Applied Statistics from the University of Michigan.