A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely microTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of microTC along with an extensive experimental comparison with relevant state-of-the-art methods. mircoTC was compared on 30 different datasets. Regarding accuracy, microTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

Artificial intelligence (AI) is advancing at lightning pace, but there has been an inconclusive focus on its real impact on employment and the way we learn. Whilst these new technologies can improve the speed, quality and cost of available goods and services, we don't yet know the extent to which they may also displace large numbers of workers. According to Oxford University economists Dr Carl Frey and Dr Michael Osborne, 40% of all jobs are at risk of being lost to computers in the next two decades. Understandably, headlines like these are unsettling and leave many people worried about what will happen if robots do end up taking multiple jobs from humans. In the education sector, there are further predictions that intelligent machines could replace the best teachers of the future.

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success in either offline (training) or online (testing) phases. Our problem is motivated by real-world applications where observations are time-consuming and/or expensive. We develop a knowledge gradient policy using an online Bayesian linear classifier to guide the experiment by maximizing the expected value of information of labeling each alternative. We provide a finite-time analysis of the estimated error and show that the maximum likelihood estimator based produced by the KG policy is consistent and asymptotically normal. We also show that the knowledge gradient policy is asymptotically optimal in an offline setting. This work further extends the knowledge gradient to the setting of contextual bandits. We report the results of a series of experiments that demonstrate its efficiency.

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping.

Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.

Technology will replace the best teachers of the future with intelligent machines, according to the head of one of the UK's most famous public schools. Sir Anthony Shelden, the master of Wellington college, predicts that the change will happen within the next 10 years and will completely transform the education system. Teachers will remain in classrooms to set up equipment and maintain discipline according to Sir Anthony, but they will simply be assistants while the real education is done by artificial intelligence.

Wondering what Artificial Intelligence, or AI, is all about? Where does data science leave off? And where and how does machine learning apply? AI will likely define the next generation of software. Given all the talk and confusing terminology out there, we've got the perfect overview course for those of you who are just getting started.

Many people find mathematics daunting. If true, this piece is for you. If not, this piece is still for you. What do you think of when you think about mathematics? Perhaps you think about x's and y's, intractable fractions, or nonsensical word problems.

Five years ago, in 2012, I decided to experiment in improving my teaching by creating a flipped classroom (and semi-MOOC) for my course "Business Analytics Using Data Mining" (BADM) at the Indian School of Business. I initially designed the course at University of Maryland's Smith School of Business in 2005 and taught it until 2010. When I joined ISB in 2011 I started teaching multiple sections of BADM (which was started by Ravi Bapna in 2006), and the course was fast growing in popularity. Repeating the same lectures in multiple course sections made me realize it was time for scale! I therefore created 30 videos, covering various supervised methods (k-NN, linear and logistic regression, trees, naive Bayes, etc.) and unsupervised methods (principal components analysis, clustering, association rules), as well as important principles such as performance evaluation, the notion of a holdout set, and more.

The Daily Roundup is our comprehensive coverage of the VR industry wrapped up into one daily email, delivered directly to your inbox. Today's educational system is static, generalized and puts less focus on individual self-development than it perhaps should. To make matters worse, students often don't understand why they are learning the things that they're learning, which makes certain classes feel arbitrary and purposeless in the face of their personal ambitions. Lucas Rizzotto is an award-winning XR creator, industry speaker, and entrepreneur working on the the realities to come. You can follow his creations and thoughts on Facebook, Twitter, Medium or Instagram.