During the Parkland, Florida, school shooting in 2018, the shooter was caught on a security camera pulling his rifle out of a duffle bag in the staircase 15 seconds before discharging the first round. However, the School Resource Officer didn't enter the building because he wasn't confident about the situation, and the Coral Springs Police Department had no idea what the shooter even looked like until 7 minutes and 30 seconds after the first round was fired. If the video system had included technology to recognize the gun threat in real time, alerts could have been sent to the security team. An announcement could have been made right away for all students and faculty in Building 12 to barricade their doors, and law enforcement could have responded a lot faster to a real-time feed of timely and accurate information. Aegis AI offers such a technology, which the company says enables existing security cameras to automatically recognize gun threats and notify security in real-time.

In an attempt to revamp the education system at a primary level, the Central Board of Secondary Education (CBSE) has signed partnerships with Microsoft India and IBM to focus on tech reskilling for teachers and AI curriculum in schools. In its partnership with Microsoft India, CBSE is looking to conduct capacity building programmes for high school teachers with an aim to integrate cloud-powered technology in K12 teaching and inculcating digital teaching skills in educators through curriculum as well as extra-curricular training. The programme for teachers of grades VIII to X will be conducted in 10 cities across the country, starting September 11. The CBSE Microsoft association is expected to provide teachers better access to the latest information and communication technology (ICT) tools and help them integrate technology into teaching and the curriculum in a smart manner. The selected 1000 teachers nominated by CBSE will be undergoing a three-day project-based training for practical, hands-on knowledge of Microsoft 365 tools such as OneNote, Flipgrid, Teams, Outlook, Minecraft and Paint3D.

Prominent digital companies such as Google, Amazon, and Netflix are making tremendous strides in the machine learning landscape, and this means the tide is drawing everyone toward the machine learning horizon. Therefore, you would be doing yourself a favor by mastering the field of machine learning. Soon, the phrase "I know a little about machine learning" won't cut it; experts and knowledgeable engineers are needed to usher in the future of machine learning. We have tips to help you become a top-notch machine learning engineer so you're not left alone on the shore. Thankfully, the emergent push toward more sophisticated machine learning systems has led many engineers and academics to learn the basics of machine learning systems, and many qualified individuals offer classes about machine learning on the internet.

The vast potential of AI to improve efficiencies and provide optimized solutions along with machine learning in supply chain boosts organization management capabilities. These technologies not only enable quick response and faster query resolution but also are greatly adaptable to change when exposed to new data. Some of the key players of the supply chain analytics market are JDA Software Group, Inc.; Aera Technology; Accenture PLC; Birst, Inc.; Genpact Limited; Capgemini SA; IBM; Kinaxis; Manhattan Associates; Lockheed Martin Corporation; and Maersk Group. These companies are inclined to develop new portfolio of solutions designed to help build a connected supply chain and allow easy access to users thereby enabling faster decisions.

India News: Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children.

When Norio Ichihashi is involved in a project, anyone with any sense had better pay attention. After all, Ichihashi is one of the co-founders of Mobileye (MBLY), which was just recently purchased by Intel for $15.3 billion. Ichihashi now has his sights set on a new venture: a new artificial intelligence educational gaming app called Zangu that enables children to learn a new language in as little as 30 days. "There are serious problems with the way children learn a second language," says Kenichi Kainuma, the CEO of AI Teach U Shanghai, which created Zangu, along with Ichihashi. "There's always been problems with children – and even adults – learning a new or a second language."

Incorporating second order curvature information in gradient based methods have shown to improve convergence drastically despite its computational intensity. In this paper, we propose a stochastic (online) quasi-Newton method with Nesterov's accelerated gradient in both its full and limited memory forms for solving large scale non-convex optimization problems in neural networks. The performance of the proposed algorithm is evaluated in Tensorflow on benchmark classification and regression problems. The results show improved performance compared to the classical second order oBFGS and oLBFGS methods and popular first order stochastic methods such as SGD and Adam. The performance with different momentum rates and batch sizes have also been illustrated. Keywords: Neural networks · stochastic method · online training · Nesterov's accelerated gradient · quasi-Newton method · limited memory · Tensorflow 1 Introduction Neural networks have shown to be effective in innumerous real-world applications.

Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in contemporary settings where item responses are being continuously collected, such as Massive Open Online Courses (MOOCs), interest will naturally be on the dynamics of ability, thus complicating usage of traditional IRT models. We propose DynAEsti, an augmentation of the traditional IRT Expectation Maximization algorithm that allows ability to be a continuously varying curve over time. In the process, we develop CurvFiFE, a novel non-parametric continuous-time technique that handles the curve-fitting/regression problem extended to address more general probabilistic emissions (as opposed to simply noisy data points). Furthermore, to accomplish this, we develop a novel technique called grafting, which can successfully approximate distributions represented by graphical models when other popular techniques like Loopy Belief Propogation (LBP) and Variational Inference (VI) fail. The performance of DynAEsti is evaluated through simulation, where we achieve results comparable to the optimal of what is observed in the static ability scenario. Finally, DynAEsti is applied to a longitudinal performance dataset (80-years of competitive golf at the 18-hole Masters Tournament) to demonstrate its ability to recover key properties of human performance and the heterogeneous characteristics of the different holes. Python code for CurvFiFE and DynAEsti is publicly available at github.com/chausies/DynAEstiAndCurvFiFE. This is the full version of our ICDM 2019 paper.

--Meta-learning algorithms for active learning are emerging as a promising paradigm for learning the "best" active learning strategy. However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning. This is contrary to the nature of active learning which typically starts with a small number of labeled samples. The unavailability of large amounts of labeled samples for training meta-learning models would inevitably lead to poor performance (e.g., instabilities and overfitting). In our paper, we tackle these issues by proposing a novel learning-based active learning framework, called Learning T o Sample (L TS). This framework has two key components: a sampling model and a boosting model, which can mutually learn from each other in iterations to improve the performance of each other . Within this framework, the sampling model incorporates uncertainty sampling and diversity sampling into a unified process for optimization, enabling us to actively select the most representative and informative samples based on an optimized integration of uncertainty and diversity. T o evaluate the effectiveness of the L TS framework, we have conducted extensive experiments on three different classification tasks: image classification, salary level prediction, and entity resolution. The experimental results show that our L TS framework significantly outperforms all the baselines when the label budget is limited, especially for datasets with highly imbalanced classes. In addition to this, our L TS framework can effectively tackle the cold start problem occurring in many existing active learning approaches. I NTRODUCTION Sampling is a fundamental technique for acquiring training data in machine learning applications. However, obtaining large amounts of manually labeled samples is often expensive or simply infeasible in practice.

Lecture notes on optimization for machine learning, derived from a course at Princeton University and tutorials given in MLSS, Buenos Aires, as well as Simons Foundation, Berkeley.