Question Answering (QA) is a task in which a machine understands a given document and a question to find an answer. Despite impressive progress in the NLP area, QA is still a challenging problem, especially for non-English languages due to the lack of annotated datasets. In this paper, we present the Japanese Question Answering Dataset, JaQuAD, which is annotated by humans. JaQuAD consists of 39,696 extractive question-answer pairs on Japanese Wikipedia articles. We finetuned a baseline model which achieves 78.92% for F1 score and 63.38% for EM on test set. The dataset and our experiments are available at https://github.com/SkelterLabsInc/JaQuAD.

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

In selection processes such as hiring, promotion, and college admissions, implicit bias toward socially-salient attributes such as race, gender, or sexual orientation of candidates is known to produce persistent inequality and reduce aggregate utility for the decision maker. Interventions such as the Rooney Rule and its generalizations, which require the decision maker to select at least a specified number of individuals from each affected group, have been proposed to mitigate the adverse effects of implicit bias in selection. Recent works have established that such lower-bound constraints can be very effective in improving aggregate utility in the case when each individual belongs to at most one affected group. However, in several settings, individuals may belong to multiple affected groups and, consequently, face more extreme implicit bias due to this intersectionality. We consider independently drawn utilities and show that, in the intersectional case, the aforementioned non-intersectional constraints can only recover part of the total utility achievable in the absence of implicit bias. On the other hand, we show that if one includes appropriate lower-bound constraints on the intersections, almost all the utility achievable in the absence of implicit bias can be recovered. Thus, intersectional constraints can offer a significant advantage over a reductionist dimension-by-dimension non-intersectional approach to reducing inequality.

This is Moein Ud Din, I am instructor of mathematics, a programmer of Python, Artificial intelligence, machine learning, deep learning, image processing, NLP and an animation geek. It's my passion to pass my knowledge what I know to the desired entities. Kinda quintessential person, always hounding to make things ideal. I have plan in future to prepare more mathematics and IT courses, would love to make things change in my own certain way. Critics are most welcome as long as it helps me and my students.

It is clear that the pandemic has had a dramatic impact on education, which for many has meant an unplanned and rapid move to online and blended learning approaches. In the Summer of 2021 a'Machine Learning & AI' module was developed by PhD student Joyce Mahon at UCD in a collaboration between the SFI CRT in Machine Learning and industry partner Huawei. Joyce is supervised by Dr. Brett Becker and Dr. Brian Mac Namee of the UCD School of Computer Science; and for the duration of this project worked alongside Dr. Keith Quille of TU Dublin, and with student volunteers. 'Machine Learning & AI' module was added to the CS_LINC platform developed in 2020 by the CS_INC team in TU Dublin. CS_LINC provides formal computer science curricula through free and easily accessible online modules.

The answer is multifaceted, but I'll try to make it simple: Training can be demanding. For companies, it takes time to organize courses, curricula and other content; track progress; and demonstrate ROI. Plus, employees don't tend to enjoy standardized training that fails to cater to their needs and takes time from their already busy schedules. These arguably onerous tasks take a fundamental business process and turn it into something many people are reluctant to participate in -- a disadvantage for any company. I strongly believe that great learning keeps organizations from stagnation and irrelevance.

In this course, we aim to specialize in artificial intelligence by working on 14 Machine Learning Projects and Deep Learning Projects at various levels (easy - medium - hard). Before starting the course, you should have basic Python knowledge. Our aim in this course is to turn real-life problems that seem difficult to do into projects and then solve them using latest versions of artificial intelligence algorithms (machine learning algortihms and deep learning algorithms) and Python(3.8). This course was prepared in August 2021. We will carry out some of our projects using machine learning and some using deep learning algorithms.

Let me share my journey as Global Future Skills & Computer Science – Artificial Intelligence Expert. We have Served 7000 Students and 500 Teachers and 5000 Other students, I have done Global Future Skills Implementation and Future Skills Research for last 15 Years. I am Lifelong Lerner of Future Skills, Future Technologies. I have Spent 75000 USD on 1. Digital Skills 2. Future Skills 3. Freedom and Transformation Skills 4.Technologies Skills 5.Soft Skills and I have done 500 global digital and future skills trainings.

The previous article was all about forward propagation in neural networks, how it works and why it works. One of the important entities in forward propagation is weights. We saw how tuning the weights can take advantage of the non-linearity introduced in each layer to leverage the resultant output. As we said we are going to randomly initialize the weights and biases and let the network learn these weights over time. Now comes the most important question.