With the recent global and regional socio-economic disruptions caused by the pandemic, industries such as retail, consumer products, manufacturing, pharmaceutical, and life sciences all struggle to align production and stocking with rapidly shifting purchasing demands. At the same time, some channels have surged ahead: online retailers, delivery services, and pharmacies are thriving. In this latest Data Science Central podcast, we discuss how injecting AI into existing business intelligence solutions can greatly enhance the ability of organizations to predict future demand for goods, even in uncertain and dynamic times.

First, The Data! • Over 1 million unique chat users • Over 150 million conversation data points focused on mental health and emotional wellbeing • Over 1.1 million manually labeled data points by our highly trained moderators • Over 1.2 million data points on moods and emotions • Over 480 thousand resources and services matched in real-time (patented) • Over 10 real-time AI engines Our own corpus and language models (patented) • Double feedback loop from users and moderators for ML and fine tuning • No hate/racism/trolls or other biases in the data, as all user chats supervised by our moderators Supportiv is driven by building a peer-to-peer mental well-being platform that helps users with their emotional needs 24/7/365 affordably. We are seeking a highly-analytical impact-oriented Ph.D. student to join our Data Science team. The role will serve as a core member within the team, focused on applying artificial intelligence (AI) to solve real business problems, along with analytics to drive insights from the user interactions on the platform to guide data-driven product development and business decisions. The Ph.D. student will work cross-functionally between various teams at Supportiv. We are not just a highly ambitious technology company with a soul.

Role requiring'No experience data provided' months of experience in None Combine two of the fastest-growing fields on the planet with a culture of performance, collaboration and opportunity and this is what you get.

In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments from the Open AI gym. There will be a strong focus on dealing with environments with continuous action spaces, which is of particular interest for those looking to do research into robotic control with deep reinforcement learning. Rather than being a course that spoon feeds the student, here you are going to learn to read deep reinforcement learning research papers on your own, and implement them from scratch. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers. Mastering the content in this course will be a quantum leap in your capabilities as an artificial intelligence engineer, and will put you in a league of your own among students who are reliant on others to break down complex ideas for them.

Experience with manipulating some datasets, even if in Excel only, will help. The course is suited to busy professionals and students who want to learn quickly and get to the important points without wasting time on long, boring videos. Also ideal for self-learners who need a solid "jump-start" for career acceleration, and interested in quickly working on real-life problems. Be able to complete machine learning projects from beginning to end, just like a professional working in the industry, for projects ranging from NLP, clustering, regression to computer vision. Learn how to learn and become independent to solve any future problems.

In the past decade, we have observed a growing interest in using technologies such as artificial intelligence (AI), machine learning, and chatbots to provide assistance to language learners, especially in second language learning. By using AI and natural language processing (NLP) and chatbots, we can create an intelligent self-learning environment that goes beyond multiple-choice questions and/or fill in the blank exercises. In addition, NLP allows for learning to be adaptive in that it offers more than an indication that an error has occurred. It also provides a description of the error, uses linguistic analysis to isolate the source of the error, and then suggests additional drills to achieve optimal individualized learning outcomes. In this paper, we present our approach for developing an Artificial Intelligence-based Arabic Language and Speech Tutor (AI-ALST) for teaching the Moroccan Arabic dialect. The AI-ALST system is an intelligent tutor that provides analysis and assessment of students learning the Moroccan dialect at University of Arizona (UA). The AI-ALST provides a self-learned environment to practice each lesson for pronunciation training. In this paper, we present our initial experimental evaluation of the AI-ALST that is based on MFCC (Mel frequency cepstrum coefficient) feature extraction, bidirectional LSTM (Long Short-Term Memory), attention mechanism, and a cost-based strategy for dealing with class-imbalance learning. We evaluated our tutor on the word pronunciation of lesson 1 of the Moroccan Arabic dialect class. The experimental results show that the AI-ALST can effectively and successfully detect pronunciation errors and evaluate its performance by using F_1-score, accuracy, precision, and recall.

The National Digital Library of India was conceptualized with an aim to bring equity of access to educational resources for every Indian through a single window access mechanism.

Students will have develop understanding of libraries used for Data Analysis like Pandas and Numpy. By creating these visualizations you will be able to derive better conclusions from data. After this course you will learn to build complete Data Science Pipeline from Data preparation to building the best Machine Learning Model. The course contains practical section after every new concept discussed and the course also has two projects at the end. Students will have develop understanding of libraries used for Data Analysis like Pandas and Numpy.

Video-aided grammar induction aims to leverage video information for finding more accurate syntactic grammars for accompanying text. While previous work focuses on building systems for inducing grammars on text that are well-aligned with video content, we investigate the scenario, in which text and video are only in loose correspondence. Such data can be found in abundance online, and the weak correspondence is similar to the indeterminacy problem studied in language acquisition. Furthermore, we build a new model that can better learn video-span correlation without manually designed features adopted by previous work. Experiments show that our model trained only on large-scale YouTube data with no text-video alignment reports strong and robust performances across three unseen datasets, despite domain shift and noisy label issues. Furthermore our model yields higher F1 scores than the previous state-of-the-art systems trained on in-domain data.

The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life. However, the digital medicine revolution also creates unforeseen and complex ethical, regulatory, and societal issues. In this article, we reflect on the ethical challenges facing digital medicine. We discuss the perils of ethical oversights in medical devices, and the role of professional codes and regulatory oversight towards the ethical design, deployment, and operation of digital medicine devices that safely and effectively meet the needs of patients. We advocate for an ensemble approach of intensive education, programmable ethical behaviors, and ethical analysis frameworks, to prevent mishaps and sustain ethical innovation, design, and lifecycle management of emerging digital medicine devices.