Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instruction on the early-stop of the online experiment and timely feedback from the environment. Policy evaluation in online learning thus attracts increasing attention by inferring the mean outcome of the optimal policy (i.e., the value) in real-time. Yet, such a problem is particularly challenging due to the dependent data generated in the online environment, the unknown optimal policy, and the complex exploration and exploitation trade-off in the adaptive experiment. In this paper, we aim to overcome these difficulties in policy evaluation for online learning. We explicitly derive the probability of exploration that quantifies the probability of exploring the non-optimal actions under commonly used bandit algorithms. We use this probability to conduct valid inference on the online conditional mean estimator under each action and develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy in online learning. The proposed value estimator provides double protection on the consistency and is asymptotically normal with a Wald-type confidence interval provided. Extensive simulations and real data applications are conducted to demonstrate the empirical validity of the proposed DREAM method.

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors’ routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner–instructor interaction (inter alia, communication, support, and presence) has a profound impact on students’ satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner–instructor interaction, capturing students’ and instructors’ concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

Machine learning technology can autonomously identify malignant tumors, pilot Teslas, and subtitle videos in real-time. The term "autonomous" is tricky here because machine learning still requires a lot of human ingenuity to get these jobs done. It works like this: An algorithm scans a massive dataset. Engineers don't tell it exactly what to look for in this initial dataset, which could consist of images, audio clips, emails, and more. Instead, the algorithm conducts a freeform analysis.

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NIT Warangal has invited applications from researchers and faculty for a 5-day online course on artificial intelligence and machine learning.

At a large private university in Northern California, a business professor uses an avatar to lecture on a virtual stage. Meanwhile, at a Southern university, graduate students in an artificial intelligence course discover that one of their nine teaching assistants is a virtual avatar, Jill Watson, also known as Watson, IBM's question-answering computer system. Of the 10,000 messages posted to an online message board in one semester, Jill participated in student conversations and responded to all inquiries with 97% accuracy. At a private college on the East Coast, students interact with an AI chat agent in a virtual restaurant set in China to learn the Mandarin language. These examples provide a glimpse into the future of teaching and learning in college.

The machine learning field is quite interesting and is constantly evolving. In the modern world, you will find its application in every aspect of your lives starting from Facebook feed to Google Maps for navigation and so on. It is a subfield of artificial intelligence and involves learning computer algorithms that improve automatically through experience. Its demand is gradually rising because it can make high-value predictions to guide better decisions and smart actions in real-time without human intervention. So, to benefit our readers, we have created a comprehensive list of the best online machine learning courses and certifications from the leading educational platforms and renowned universities.

Data will control the twenty-first century. Every company, big or small, is attempting to use data to their advantage. Data-driven insights could aid businesses in transforming and targeting new markets, addressing customer pain points, increasing revenue, and more. As a result, a growing number of companies are concentrating on data collecting, interpretation, and application. of India sees significant digitisation of its industries and services, making it the second-largest data science hub. Analysts estimate that by 2026, the country will have around 11 million job openings.

If the authors of a recent Stanford report (Bommasani et al., 2021) on the opportunities and risks of "foundation models" are to be believed, these models represent a paradigm shift for AI and for the domains in which they will supposedly be used, including education. Although the name is new (and contested (Field, 2021)), the term describes existing types of algorithmic models that are "trained on broad data at scale" and "fine-tuned" (i.e., adapted) for particular downstream tasks, and is intended to encompass large language models such as BERT or GPT-3 and computer vision models such as CLIP. Such technologies have the potential for harm broadly speaking (e.g., Bender et al., 2021), but their use in the educational domain is particularly fraught, despite the potential benefits for learners claimed by the authors. In section 3.3 of the Stanford report, Malik et al. argue that achieving the goal of providing education for all learners requires more efficient computational approaches that can rapidly scale across educational domains and across educational contexts, for which they argue foundation models are uniquely well-suited. However, evidence suggests that not only are foundation models not likely to achieve the stated benefits for learners, but their use may also introduce new risks for harm.

Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master's of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada.