For nearly 30 years, Pedro Domingos has been working within artificial intelligence communities, both as a researcher and a developer. In that time, the University of Washington professor has become an expert on all things machine learning. His new book, The Master Algorithm, traces the beginnings of machine learning, and provides a road-map to where it may be headed in the future. We talked to him by phone about what lies ahead for machine learning, how the master algorithm might change everything, and why he thinks the idea of the singularlity is overstated. Can you give me the elevator pitch on what "The Master Algorithm" is?

I have paid 29 for this course (Machine Learning by Andrew Ng) just now and it is written that "May 31st is the last date for purchasing a certificate, after which the course will be offered without a certificate option. As long as you purchase the certificate before May 31st, you can complete the course any time and earn the certificate." However the mailer I have got after enrolling is that I need to complete the course in 180 days. Kindly confirm if there is a 180 day limit or I can complete the course anytime and get a certificate?

John Holden is a journalist specializing in science, tech and innovation. His work has appeared mainly in the Irish Times. Cornered at a party by some bore talking about his unimaginative app that will "change the world." All seemingly convincing excuses to leave have already been used by the rest of the group (who were all there just a second ago). The last refuge of the rude scoundrel -- the smartphone -- is in your jacket pocket in the cloakroom all the way across the room. So you just have to suck it up and simulate enthusiasm for this guy's pitch.

Some of the most powerful people in the world have gathered this week to discuss the most pressing issues affecting humanity. And the overwhelming conclusion is that the robots are coming. At the Milken Institute's Global Conference in California, at least four panels focused ontechnology taking over markets to mining, and most importantly,jobs. Some of the most powerful people in the world have gathered this week to discuss the most pressing issues affecting humanity, and the overwhelming conclusion is the robots are coming. At the Milken Institute's Global Conference in California, four panels focused on technology taking over markets and jobs (stock image) 'Most of the benefits we see from automation is about higherquality and fewer errors, but in many cases it does reducelabor,' Michael Chui, a partner at the McKinsey GlobalInstitute, said on Tuesday during a panel on'Is Any Job TrulySafe?'

University lectures often offer online discussion forums for students to discuss and solve issues with other students and instructors. Correlating the participation of a student in a discussion forum to his performance in the course is subject of current research. Therefore, to qualify the different parts a student plays in a discussion, be it asking or answering a question, is sought in this paper. In current analysis of online discussion forums, such parts are annotated by hand. Thereby, identifying corresponding roles manually is a costly task, which requires the work of more than one person to annotate and approve the chosen roles. The desired step to a better understanding of student online discussion forums is the automated annotation of student roles. A student's role is determined by classifying the student's message into different speech act categories. This paper introduces a supervised speech act classification method for messages in German discussion forums that aims at solving the problem of manually detecting speech acts in online discussion for further discourse analysis. A comparative evaluation shows the significant improvements of the new classifier and its appropriateness for the German language.

This paper reports on an ongoing effort to provide computational linguistic support to scholars making use of the writings from the Third Dynasty of Ur, especially those trying to link reports of financial transactions together for the purpose of social networking. The computational experiments presented are especially addressed to the problem of identifying proper names for the ultimate purpose of reconstructing a social network of UR III society. We describe the application of established supervised learning algorithms, compare its results to previous work using unsupervised methods and propose future work based upon these comparative results.

In a digital classroom, analysis of students' interactions with the learning media provides important information about users' behavior, which can lead to a better understanding and thus optimizes teaching and learning. However, over the period of a course, students tend to forget the lessons learned in class. Learning predictions can be used to recommend learning objects users need most, as well as to give an overview of current knowledge and the learning level. The representation of time based data in such a format is difficult since the knowledge level of a user with a learning object changes continuously depending on various factors. This paper presents work in progress for a doctoral approach to extend the traditional user-item-matrix of a recommendation engine by a third dimension - the time value. Moreover, in this approach the learning need consists of different context factors each influencing the relevance score of a learning object.

This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding space that focuses the information relevant for a task in an ultradense subspace of a dimensionality that is smaller by a factor of 100 than the original space. We show that ultradense embeddings generated by DENSIFIER reach state of the art on a lexicon creation task in which words are annotated with three types of lexical information - sentiment, concreteness and frequency. On the SemEval2015 10B sentiment analysis task we show that no information is lost when the ultradense subspace is used, but training is an order of magnitude more efficient due to the compactness of the ultradense space.

High dimensionality and class imbalance are two important concerns when training tweet sentiment classifiers. Feature selection techniques reduce dimensionality by selecting an optimal subset of features. Class imbalance can be addressed by either using classifiers that are robust to the impact of class imbalance, such as those trained with an ensemble learning technique, or by using data sampling techniques to create a sampled training set with a more balanced class ratio. These separate techniques can be combined together to address both class imbalance and high-dimensionality; however, it is unclear if it is necessary to use data sampling and ensemble techniques together as both are used to target class imbalance. In our study, we investigate if the addition of random undersampling to Select-Boost (feature selection and boosting) significantly improves the performance of sentiment classifiers trained on imbalanced tweet data. We evaluate classifiers trained using four base learners and three feature subset sizes across two highly dimensional imbalanced datasets. Our results show, for tweet sentiment, the inclusion of random undersampling significantly improves classification performance and indicates this may be more noticeable on datasets with greater levels of class imbalance.