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.

The rapid development of deep learning methods has permitted the fast and accurate medical decision making from complex structured data, like CT images or MRI. However, some problems still exist in such applications that may lead to imperfect predictions. Previous observations have shown that, confounding factors, if handled inappropriately, will lead to biased prediction results towards some major properties of the data distribution. In other words, naïvely applying deep learning methods in these applications will lead to unfair prediction results for the minority group defined by the characteristics including age, gender, or even the hospital that collects the data, etc. In this paper, extending previous successes in correcting confounders, we propose a more stable method, namely Confounder Filtering, that can effectively reduce the influence of confounding factors, leading to better generalizability of trained discriminative deep neural networks, therefore, fairer prediction results. Our experimental results indicate that the Confounder Filtering method is able to improve the performance for different neural networks including CNN, LSTM, and other arbitrary architecture, different data types including CTscan, MRI, and EEG brain wave data, as well as different confounding factors including age, gender, and physical factors of medical devices etc.

We uncover a fairly general principle in online learning: If regret can be (approximately) expressed as a function of certain "sufficient statistics" for the data sequence, then there exists a special Burkholder function that 1) can be used algorithmically to achieve the regret bound and 2) only depends on these sufficient statistics, not the entire data sequence, so that the online strategy is only required to keep the sufficient statistics in memory. This characterization is achieved by bringing the full power of the Burkholder Method --- originally developed for certifying probabilistic martingale inequalities --- to bear on the online learning setting. To demonstrate the scope and effectiveness of the Burkholder method, we develop a novel online strategy for matrix prediction that attains a regret bound corresponding to the variance term in matrix concentration inequalities. We also present a linear-time/space prediction strategy for parameter free supervised learning with linear classes and general smooth norms.

Paperspace offers multiple templates for you to start. I recommend using the fast.ai This template is intended to provide a fully functional machine learning environment for interactive development. The template includes NVIDIA's libraries for using the GPU to run Machine Learning programs, as well as a variety of libraries for ML development (Anaconda Python distribution, Jupyter notebook, fast.ai Due to high demand, your request might take a few hours to be completed.