The present approach targets to assist decision-making by identifying and by-passing Cognitive Bias of speakers-participants and evaluators in regard to spoken texts.

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.

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch learning-to-learn, we consider a scenario where tasks are presented sequentially and the algorithm needs to adapt incrementally to improve its performance on future tasks. Key to this setting is for the algorithm to rapidly incorporate new observations into the model as they arrive, without keeping them in memory. We focus on the case where the underlying algorithm is ridge regression parameterized by a positive semidefinite matrix. We propose to learn this matrix by applying a stochastic strategy to minimize the empirical error incurred by ridge regression on future tasks sampled from the meta distribution. We study the statistical properties of the proposed algorithm and prove non-asymptotic bounds on its excess transfer risk, that is, the generalization performance on new tasks from the same meta distribution. We compare our online learning-to-learn approach with a state of the art batch method, both theoretically and empirically.

Consider a sample of $n$ points taken i.i.d from a submanifold $\Sigma$ of Euclidean space. We show that there is a way to estimate the Ricci curvature of $\Sigma$ with respect to the induced metric from the sample. Our method is grounded in the notions of Carr\'e du Champ for diffusion semi-groups, the theory of Empirical processes and local Principal Component Analysis.

CLEVELAND, Ohio - Computers are grading your child's state tests. After Ohio started using American Institutes for Research in 2015 to provide and score state tests, Artificial Intelligence (AI) programs have increasingly taken over grading. Computers are now scoring the entire test for about 75 percent of Ohio students, State Superintendent Paolo DeMaria and state testing official Brian Roget told the state school board recently. The other 25 percent are scored by people to help verify the computer's work. Ohio and AIR are not alone.

Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. It is an area that requires some previous experience of linear algebra and is focused on both the performance and precision of the operations. In this post, you will discover the fast.ai Computational Linear Algebra for Coders Review Photo by Ruocaled, some rights reserved. The course "Computational Linear Algebra for Coders" is a free online course provided by fast.ai.

A disgruntled Minecraft gamer is believed to be behind a bomb hoax email sent to more than 400 schools and colleges. Some students were evacuated from school and college buildings across the country on Monday after an email threatening to detonate a bomb if they refused to hand over cash was sent out. The email appeared to come from gaming network VeltPvP – a server which allows users to compete in the game Minecraft – but the US company said that the account had been "spoofed". Carson Kallen, the US firm's 17-year-old CEO, told the BBC he suspected the hoax emails had been sent by a disgruntled Minecraft player in a bid to damage VeltPvP's reputation. He said: "Everyone who plays it is between the ages of eight and 18 years old - it's all kids playing. "Every now and then we have a little rebel who will try to do something bad like this.

Design has consequences Carnegie Mellon University design students are exploring ways to enhance interactions with new technologies and the power of artificial intelligence. Assistant Professor Dan Lockton teaches the course, "Environments Studio IV: Designing Environments for Social Systems" in CMU's School of Design and leads the school's new Imaginaries Lab. "We want the designers of tomorrow to think about the overlap between the human world and AI. Many of our students are going to go work for companies like Facebook or Google, and they're going to be making decisions that might seem very small in the moment -- what text do we put on a button, how easy do we make it for someone to do this thing or that -- but those decisions are going to impact people's lives. We want them thinking through how their design has consequences."

DUBAI – Artificial intelligence could be the breakthrough that teachers have been waiting for. At the recently concluded GESS Dubai, experts showed a glimpse of the future for the teaching profession with the help of AI, and how it can contribute significantly to school improvement. Century Tech founder and CEO Priya Lakhani presented an AI platform for school improvement that presents real-time data on a student, entire class even a whole school to support timely and evidence-based interventions; as well as multimedia content that can be used in and out of the classroom with features that can also help automate certain tasks such as assessments and tracking of homework. "With teachers spending up to 60% of their time on administrative tasks and data management they need a solution which saves them time to do what they love: teach!" commented Lakhani, who also says the AI platform can also be used to improve outcome for learners as well as involve parents and guardians. Meanwhile, Sallyann dela Casa, lead Skills Hacker at GLEAC and head of Growing Leaders Foundation, says AI can be harnessed to develop outstanding schools.

Data efficiency, i.e., learning from small data sets, is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the data efficiency of learning algorithms by generalizing learned concepts from a set of training tasks to unseen, but related, tasks. Often, this relationship between tasks is hard coded or relies in some other way on human expertise. In this paper, we propose to automatically learn the relationship between tasks using a latent variable model. Our approach finds a variational posterior over tasks and averages over all plausible (according to this posterior) tasks when making predictions. We apply this framework within a model-based reinforcement learning setting for learning dynamics models and controllers of many related tasks. We apply our framework in a model-based reinforcement learning setting, and show that our model effectively generalizes to novel tasks, and that it reduces the average interaction time needed to solve tasks by up to 60% compared to strong baselines.