The graph represents a network of 1,719 Twitter users whose tweets in the requested range contained "iot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 23 November 2022 at 12:43 UTC. The requested start date was Wednesday, 23 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 19-day, 11-hour, 51-minute period from Thursday, 03 November 2022 at 13:08 UTC to Wednesday, 23 November 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Upon automatic identifying logical reasoning phenomena in massive text corpus via detection heuristics, we train language models to predict the masked-out logical statements. Inspired by the facilitation effect of reflective thinking in human learning, we analogically simulate the learning-thinking process with an adversarial Generator-Verifier architecture to assist logic learning. LogiGAN implements a novel sequential GAN approach that (a) circumvents the non-differentiable challenge of the sequential GAN by leveraging the Generator as a sentence-level generative likelihood scorer with a learning objective of reaching scoring consensus with the Verifier; (b) is computationally feasible for large-scale pre-training with arbitrary target length. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets requiring general reasoning abilities, revealing the fundamental role of logic in broad reasoning, as well as the effectiveness of LogiGAN. Ablation studies on LogiGAN components reveal the relative orthogonality between linguistic and logic abilities and suggest that reflective thinking's facilitation effect might also generalize to machine learning.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.

Numerical simulations are ubiquitous in science and engineering. Machine learning for science investigates how artificial neural architectures can learn from these simulations to speed up scientific discovery and engineering processes. Most of these architectures are trained in a supervised manner. They require tremendous amounts of data from simulations that are slow to generate and memory greedy. In this article, we present our ongoing work to design a training framework that alleviates those bottlenecks. It generates data in parallel with the training process. Such simultaneity induces a bias in the data available during the training. We present a strategy to mitigate this bias with a memory buffer. We test our framework on the multi-parametric Lorenz's attractor. We show the benefit of our framework compared to offline training and the success of our data bias mitigation strategy to capture the complex chaotic dynamics of the system.

Many practical applications, such as recommender systems and learning to rank, involve solving multiple similar tasks. One example is learning of recommendation policies for users with similar movie preferences, where the users may still rank the individual movies slightly differently. Such tasks can be organized in a hierarchy, where similar tasks are related through a shared structure. In this work, we formulate this problem as a contextual off-policy optimization in a hierarchical graphical model from logged bandit feedback. To solve the problem, we propose a hierarchical off-policy optimization algorithm (HierOPO), which estimates the parameters of the hierarchical model and then acts pessimistically with respect to them. We instantiate HierOPO in linear Gaussian models, for which we also provide an efficient implementation and analysis. We prove per-task bounds on the suboptimality of the learned policies, which show a clear improvement over not using the hierarchical model. We also evaluate the policies empirically. Our theoretical and empirical results show a clear advantage of using the hierarchy over solving each task independently.

We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate Physically plausible Upper Body Animation (PUBA) using interaction in the image space, such as dragging their hand to various locations. We formulate a reinforcement learning problem to train a dynamic model that predicts the person's next 2D state (i.e., keypoints on the image) conditioned on a 3D action (i.e., joint torque), and a policy that outputs optimal actions to control the person to achieve desired goals. The dynamic model leverages the expressiveness of 3D simulation and the visual realism of 2D videos. PUBA generates 2D keypoint sequences that achieve task goals while being responsive to forceful perturbation. The sequences of keypoints are then translated by a pose-to-image generator to produce the final photorealistic video.

Artificial Intelligence helps you improve the business and the way the employees work. Learn AI online and enhance your understanding of interesting trends, facts, and insights. In this AI course, you will explore the relationship between AI and humans and the skills necessary to work with AI. Our expert trainers are always eager to solve your queries and help you identify your shortcomings by providing the best information followed in the industry. Our live instructor-led classes are designed to give you the best learning environment with classes being much more interesting and engaging.

There are many low-quality free courses and YouTube courses that provide no help in building strong machine learning fundamentals. You will end up even more confused and quit pursuing the career. I am a big advocate of paid courses, but you can also learn a lot from interactive free courses by Udacty, Coursera, and FastAI. These courses cover fundamentals and introduce you to supervised, unsupervised, and deep learning algorithms. You will be introduced to machine learning applications, examples, and building your first linear and logistic regression model on Jupyter Notebook.

When applying for a programming or data science job, machine learning certifications and certificates have the potential to help you stand out from the crowded pool of candidates. Whether you've just completed a course of study or passed an exam offered by a respected institution, obtaining a certificate or certification is a real accomplishment that indicates your knowledge, experience, and expertise in the field of machine learning. But, what certificates and certifications are right for you? In this article, you'll learn more about the difference between certificates and certifications and explore five of the most popular ones for machine learning available today. Though they are often confused, certificates and certifications are not the same.

Training and education are imperative in many facets of healthcare -- from understanding clinical systems, to improving technical skills, to understanding regulations and professional standards. Technology often presents unique training challenges because of the ways in which it disrupts existing workflow patterns, alters clinical practice, and creates both predictable and unforeseen challenges. The emergence of artificial intelligence (AI), its anticipated expansion in healthcare, and its sheer scope point to significant training and educational needs for medical students and practicing healthcare providers. These needs go far beyond developing technical skills with AI programs and systems; rather, they call for a shift in the paradigm of medical learning. An AMA Journal of Ethics article titled "Reimagining Medical Education in the Age of AI" discusses how traditional medical education -- which focuses on information acquisition, retention, and application -- is insufficient, counterproductive, and potentially harmful in the era of digital medicine.