Wheebox, one of the global company in online AI driven Remote Proctored Assessments has launched a solution for all modern educators who are adapting to the online methods of cheat-proof testing. Wheebox launched a Low-Code/No-Code (LCNC) AI-Driven Proctoring Solution for all Learning Management Solution Companies. The application can be integrated into any existing Learning Management System (LMS) in one single touch. This integration is suited for certification platforms and many other LTI-compliant applications such as Moodle, Blackboard, and Canvas. The Plug-and-Play, Extension-Based Integration offers an all-in-one proctoring solution fortified with Microsoft Cognitive Services; bundled with features such as face tracking, live stream, face recognition, on-demand proctors, 360 degree room scan, object and noise detection, and auto ID card-based authentication for highly reliable and cheat-proof examinations. Wheebox has partnered with University of Kelaniya, a State University in Colombo, Sri Lanka, to conduct its assessments on its learning and assessment application hosted on Moodle.

The artificial intelligence revolution is sweeping the globe, and it's gaining momentum. The rate of progress in artificial intelligence is one of the most disputed aspects of the constant increase in teaching computers and robots how to observe the world, make sense of it, and eventually execute complex tasks in both the physical and virtual environments. And not just real product breakthroughs and research milestones are used to gauge how rapidly the industry is advancing, but also the predictions and expressed worries of AI executives, futurists, academics, economists, and legislators. The world will be changed by AI, but how and when are still unknown. In a continuous effort to assist address those questions, the conclusions of a group of specialists were published today.

Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches. We also present approaches that use pre-trained language models to generate data for training augmentation or other purposes. We conclude with discussions on limitations and suggested directions for future research.

The largest dataset of Arabic speech mispronunciation detections in Egyptian dialogues is introduced. The dataset is composed of annotated audio files representing the top 100 words that are most frequently used in the Arabic language, pronounced by 100 Egyptian children (aged between 2 and 8 years old). The dataset is collected and annotated on segmental pronunciation error detections by expert listeners.

Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods achieve good out-of-distribution (OOD) generalizability with a considerable sacrifice of the in-distribution (ID) performance. Therefore, they are only applicable in domains where the test distribution is known in advance. In this paper, we present a novel debiasing method called Introspective Distillation (IntroD) to make the best of both worlds for QA. Our key technical contribution is to blend the inductive bias of OOD and ID by introspecting whether a training sample fits in the factual ID world or the counterfactual OOD one. Experiments on visual QA datasets VQA v2, VQA-CP, and reading comprehension dataset SQuAD demonstrate that our proposed IntroD maintains the competitive OOD performance compared to other debiasing methods, while sacrificing little or even achieving better ID performance compared to the non-debiasing ones.

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of "task" that might be desirable: (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists. We conclude with an empirical study that corroborates and illustrates our theoretical findings.

Dubai, UAE: Rochester Institute of Technology (RIT) Dubai and Stallion AI have joined forces to launch a new initiative that will enable students of all disciplines to become Certified Artificial Intelligence (AI) Citizens. The program, known as 365 Digital AI Citizenship, will provide participants with a custom-made learning plan, along with access to an exclusive global community of experts, to help them explore their future career path in the AI economy. The launch of the program comes as 86% of employers report that artificial intelligence is already mainstream technology in their day-to-day business operations. As AI increasingly pervades across industries and professions, its ability to transform productivity is expected to contribute $15.7 trillion to the global economy by 2030. Explaining the importance of the initiative Samer Obeidat, CEO of Stallion AI, said, "All future jobs will involve AI in some form, so everyone needs some understanding of the field. The biggest barrier to optimising the technology is the lack of knowledge and skills, so introducing young people to AI through this Citizenship program will ensure that they are highly sought after employees of the future, with the ability to both utilise and enhance AI functions in the workplace."

We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers share information to synthesize new knowledge in real time. Our chatbot pipeline accomplishes this modelling in three broad stages. The first stage translates user utterances into a symbolic predicate representation. The second stage then uses this structured representation in conjunction with a larger knowledge base to synthesize new predicates using efficient graph matching. In the third and final stage, our bot selects a small subset of predicates and translates them into an English response. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.

With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention. Our previous study(Lu et al. 2020) on building and interpreting the KT model mainly adopts the ASSISTment dataset(Feng, Heffernan, and Koedinger 2009),, whose size is relatively small. In this work, we perform the similar tasks but on a large and newly available dataset, called EdNet(Choi et al. 2020). The preliminary experiment results show the effectiveness of the interpreting techniques, while more questions and tasks are worthy to be further explored and accomplished.

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