In the ChatGPT era, evaluating student performance through traditional term papers no longer makes sense. The best solution to this most modern of dilemmas is a return to the past. There's no need to seek out a technological remedy when history's oldest teaching style points to a better way. When the Socratic method is used in place of lecturing, students are forced to trade their passive role in the classroom for an active one in which participation is the primary measure of mastery. Named for Socrates, the ancient Greek philosopher, the Socratic method employs skillful questioning to test the limits of each student's grasp of the material while the instructor and students explore the difficult ideas and concepts together.

This course is aimed to IT Pros and is supposed to give the viewer the information they need to know to get started with Powershell and how to manage Windows Server 2016 Active Directory with its help. The goal is to provide coverage of AD DS server's tasks including topics like Managing, configuring and modifying Users, Computers, Groups, Group Policies in detail with PowerShell The course is targeted to help to automate and script daily tasks. There are lots of live demonstrations how to use PowerShell commands and a Server's GUI. I hope it will help to do your job more efficiently.

For five years or so, I have been running around as a pale imitation of Paul Revere, yelling, "The robots are coming! At schools, social settings, with family and friends, or even to complete strangers with whom I fell into conversations, I have uttered the same warning: "It's critical that you or your children identify a career -- now -- that won't be taken over by robots and artificial intelligence." My particular midnight ride started well before the pandemic reared its ugly head. But the pandemic may have planted a seed in the minds of certain CEOs that human beings are the weakest link on their chain to profit and prosperity. When the first "Terminator" movie was released -- eerily enough, in 1984 -- the world was introduced to Cyberdyne Systems and its "Skynet" artificial superintelligence system, which not only gained self-awareness but realized it could do everything infinitely faster and better than its human creators. Well, ever since that movie got people asking, "What if," the fictional theme -- and warnings about AI -- have been morphing into reality. The latest example of a technology poised to replace a human workforce is ChatGPT, the chatbot auto-generative system created by Open AI for online customer care. It is a pre-trained generative chat, which makes use of natural language processing, or NLP. The source of its data is textbooks, websites and various articles, which it uses to model its own language for responding to human interaction. It's certainly not a stretch to believe that any number of CEOs might think, "Interesting… A self-teaching artificial intelligence system that won't call in sick, doesn't need to be fed or to take bathroom breaks, does not require health care, but can and will work 24/7/365." Not shockingly, it has been reported that Microsoft, which is laying off 10,000 people, announced a "multiyear, multibillion-dollar investment" in this revolutionary technology, which apparently is growing smarter by the day. Pengcheng Shi, an associate dean in the Department of Computing and Information Sciences at Rochester Institute of Technology, warned in an interview with the New York Post: "AI is replacing the white-collar workers.

Bytecode Security is an AI training institute based in India, providing world-class training in Artificial Intelligence. The institute is renowned for its high-quality training and experienced faculty that provide students with the necessary skills to become industry-ready professionals. Bytecode Security boasts of a wide range of AI related courses, ranging from basic to advanced levels. The courses are designed to equip students with the knowledge and skills required to excel in the field. The institutes' curriculum is focused on covering all the major aspects of AI, starting from basic concepts to advanced topics such as Machine Learning, Deep Learning and Natural Language Processing.

In 1969, Marvin Minsky and Seymour Papert published Perceptrons: An Introduction to Computational Geometry. In it, they showed that a single-layer perceptron cannot compute the XOR function. The main argument relies on linear separability: Perceptrons are linear classifiers, which essentially means drawing a line to separate input that would result in 1 versus 0. You can do it in the OR and AND case, but not XOR. Of course, we're way past that now, neural networks with one hidden layer can solve that problem. The solution in essence is analogous to composing AND, OR, and NOT gates, which can be represented by single-layer networks, to form the required function. Depth is important for certain functions.

Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, link-level, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.

Connected decision boundaries are useful in several tasks like image segmentation, clustering, alpha-shape or defining a region in nD-space. However, the machine learning literature lacks methods for generating connected decision boundaries using neural networks. Thresholding an invex function, a generalization of a convex function, generates such decision boundaries. This paper presents two methods for constructing invex functions using neural networks. The first approach is based on constraining a neural network with Gradient Clipped-Gradient Penality (GCGP), where we clip and penalise the gradients. In contrast, the second one is based on the relationship of the invex function to the composition of invertible and convex functions. We employ connectedness as a basic interpretation method and create connected region-based classifiers. We show that multiple connected set based classifiers can approximate any classification function. In the experiments section, we use our methods for classification tasks using an ensemble of 1-vs-all models as well as using a single multiclass model on larger-scale datasets. The experiments show that connected set-based classifiers do not pose any disadvantage over ordinary neural network classifiers, but rather, enhance their interpretability. We also did an extensive study on the properties of invex function and connected sets for interpretability and network morphism with experiments on simulated and real-world data sets. Our study suggests that invex function is fundamental to understanding and applying locality and connectedness of input space which is useful for various downstream tasks.

The recent explosion of question answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or by combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer candidates. Through quantitative and qualitative experiments we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches in both in-domain and out-of-domain scenarios, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future developments of multi-agent systems on https://github.com/UKPLab/MetaQA.

Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past few years, plenty of studies have leveraged various forms of external knowledge to augment the reasoning capabilities of deep models, tackling challenges such as effective knowledge integration, implicit knowledge mining, and problems of tractability and optimization. However, there is a dearth of a comprehensive technical review of the existing knowledge-enhanced reasoning techniques across the diverse range of application domains. This survey provides an in-depth examination of recent advancements in the field, introducing a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We systematically discuss these methods and highlight their correlations, strengths, and limitations. Finally, we elucidate the current application domains and provide insight into promising prospects for future research.

Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.