Invictus Capital has pioneered the Titan AI Tool which identifies fraudulent and copycat content to address the rise of white paper plagiarism in the cryptocurrency space - and the resulting damage to the reputation of the industry. "Performing due diligence is vital for the health of the cryptocurrency community -- we need to stand together to prevent dubious and fraudulent projects from taking investor funds," says Daniel Schwartzkopff, CEO of Invictus Capital. The Titan AI Tool uses machine learning techniques to analyze and detect plagiarism in ICO white papers. It evaluates the originality and legitimacy of early-stage investment opportunities within the ICO space. Uploading white papers for comparison also helps to expand the Titan database and benefits the community.
Automated essay scoring (AES) is a broadly used application of machine learning, with a long history of real-world use that impacts high-stakes decision-making for students. However, defensibility arguments in this space have typically been rooted in hand-crafted features and psychometrics research, which are a poor fit for recent advances in AI research and more formative classroom use of the technology. This paper proposes a framework for evaluating automated essay scoring models trained with more modern algorithms, used in a classroom setting; that framework is then applied to evaluate an existing product, Turnitin Revision Assistant.
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.
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. In 2013 MIT Technology Review named him one of 35 top innovators under the age of 35.
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 D. (Arizona State University) | Crossley, Scott A. (Georgia State University) | Snow, Erica L. (Arizona State University) | Varner, Laura K. (Arizona State University) | McNamara, Danielle S. (Arizona State University)
Automated essay scoring tools are often criticized on the basis of construct validity. Specifically, it has been argued that computational scoring algorithms may be unaligned to higher-level indicators of quality writing, such as writers’ demonstrated knowledge and understanding of the essay topics. In this paper, we consider how and whether the scoring algorithms within an intelligent writing tutor correlate with measures of writing proficiency and students’ general knowledge, reading comprehension, and vocabulary skill. Results indicate that the computational algorithms, although less attuned to knowledge and comprehension factors than human raters, were marginally related to such variables. Implications for improving automated scoring and intelligent tutoring of writing are briefly discussed.
Computational indices related to n-gram production were developed in order to assess the potential for n-gram indices to predict human scores of essay quality. A regression analyses was conducted on a corpus of 313 argumentative essays. The analyses demonstrated that a variety of n-gram indices were highly correlated to essay quality, but were also highly correlated to the number of words in the text (although many of the n-gram indices were stronger predictors of writing quality than the number of words in a text). A second regression analysis was conducted on a corpus of 88 argumentative essays that were controlled for text length differences. This analysis demonstrated that n-gram indices were still strong predictors of essay quality when text length was not a factor.