Whether we like it or not, robots are coming for our jobs. Self-driving cars will be the start, but rest assured, if it can be automated, it will be. Robots are even starting to edit movie trailers. As you plan out your career, it would be wise keep an eye on automation both for the ways it can speed up your own workflow and also for the ways it might make your job obsolete. While we are nowhere near losing many jobs to automation in film yet, the new EPICOLOR plugin from Lemke Software for FCPX and Resolve gives us a hint of what is coming with its automated grading tool that can be useful for small projects with tight turnarounds where a professional colorist might not be an option.
Artificial intelligence has improved beyond recognition. With breakthroughs in machine learning happening every day, more and more column inches are being devoted to the prospect that super-intelligent computers are right around the corner--and that they'll soon take over people's jobs. While that future is still on the horizon, though, there are multiple ways that A.I. has already started to transform our experiences, in industries that most might not have predicted. Take, for instance, the webpages you see every day on the Internet. Unbounce is one of the first companies to use machine learning to determine what creates the most attractive pages on the net.
Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy.
What's the best way to prove you "know" something? A. Multiple choice tests B. Essays C. Interviews D. None of the above Go ahead: argue with the premise of the question. Oh yeah, you can't do that on multiple-choice tests. Essays can often better gauge what you know. Writing is integral to many jobs. But despite the fact that everyone can acknowledge that they're a more useful metric, we don't demand students write much on standardized tests because it's daunting to even imagine grading millions of essays.
Anthony Goldbloom is cofounder and CEO of Kaggle, a platform for machine-learning competitions. Almost 500,000 of the world's top data scientists compete on Kaggle to solve important problems for industry, government, and academia. Kaggle has catalyzed breakthroughs in areas ranging from automated essay grading to automated disease diagnosis from medical images. Before cofounding Kaggle in 2010, Anthony was an econometrician at the Australian treasury.
Given the large number of new musical tracks released each year, automated approaches to plagiarism detection are essential to help us track potential violations of copyright. Most current approaches to plagiarism detection are based on musical similarity measures, which typically ignore the issue of polyphony in music. We present a novel feature space for audio derived from compositional modelling techniques, commonly used in signal separation, that provides a mechanism to account for polyphony without incurring an inordinate amount of computational overhead. We employ this feature representation in conjunction with traditional audio feature representations in a classification framework which uses an ensemble of distance features to characterize pairs of songs as being plagiarized or not. Our experiments on a database of about 3000 musical track pairs show that the new feature space characterization produces significant improvements over standard baselines.
Roscoe, Rod (University of Memphis) | Varner, Laura (University of Memphis) | Cai, Zhiqiang (University of Memphis) | Weston, Jennifer (University of Memphis) | Crossley, Scott (Georgia State University) | McNamara, Danielle (University of Memphis)
Research on automated essay scoring (AES) indicates that computer-generated essay ratings are comparable to human ratings. However, despite investigations into the accuracy and reliability of AES scores, less attention has been paid to the feedback delivered to the students. This paper presents a method developers can use to quickly evaluate the usability of an automated feedback system prior to testing with students. Using this method, researchers evaluated the feedback provided by the Writing-Pal, an intelligent tutor for writing strategies. Lessons learned and potential for future research are discussed.
Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.
In this article, we describe a deployed educational technology application: the Criterion Online Essay Evaluation Service, a web-based system that provides automated scoring and evaluation of student essays. Criterion has two complementary applications: (1) CritiqueWriting Analysis Tools, a suite of programs that detect errors in grammar, usage, and mechanics, that identify discourse elements in the essay, and that recognize potentially undesirable elements of style, and (2) e-rater version 2.0, an automated essay scoring system. Critique and e-rater provide students with feedback that is specific to their writing in order to help them improve their writing skills and is intended to be used under the instruction of a classroom teacher. All of these capabilities outperform baseline algorithms, and some of the tools agree with human judges in their evaluations as often as two judges agree with each other.