Elon Musk wants to push technology to its absolute limit, from space travel to self-driving cars -- but he draws the line at artificial intelligence. The billionaire first shared his distaste for AI in 2014, calling it humanity's'biggest existential threat' and comparing it to'summoning the demon.' At the time, Musk also revealed he was investing in AI companies not to make money but to keep an eye on the technology in case it gets out of hand. His main fear is that in the wrong hands, if AI becomes advanced, it could overtake humans and spell the end of mankind, which is known as singularity. That concern is shared among many brilliant minds, including the late Stephen Hawking, who told BBC in 2014: 'The development of full artificial intelligence could spell the end of the human race.

International Journal of Educational Technology in Higher Education is calling for submissions to our Collection on New advances in artificial intelligence applications in higher education. There has been growing interest in the educational potential of Artificial Intelligence (AI) applications within the field of educational technology for the past decade. Despite the recent peak of excitement towards advanced features and techniques of AI-driven language models and OpenAI's ChatGPT, their actual impact on higher education (HE) institutions and participants have been largely unknown. Thus, the discussions in the field have continuously remained, mainly consisting of overstated hype and untested hypotheses, either optimistic or pessimistic, about the impact of AI applications. About three years ago, the editors of the ETHE Special Issue "Can artificial intelligence transform higher education?" However, a lot has happened since then.

Artificial intelligence offers exciting new ways to work and learn, but there are reasons to be careful. In nature, sometimes the prey becomes the predator. But in the case of larvae of the Epomis beetle, it wriggles around to attract frogs, then latches on and sucks the life out of them. This is how I'm feeling about artificial intelligence and ChatGPT in higher education just now. The positives are blinding us to the risks.

Inquiry-based learning (actuated by inquiry-based teaching questions) and project-based learning have long been recognized as powerful ways to engage students in meaningful, authentic learning experiences. By empowering students to ask questions, explore ideas, and create their own solutions, these approaches foster curiosity, creativity, and critical thinking skills that can benefit students long after they leave the classroom. As a teacher, I've seen firsthand the transformative impact that project-based learning and inquiry teaching can have on students. But I've also recognized the challenges that come with implementing these approaches effectively. From designing engaging projects to managing student inquiry, there are many factors that can make or break the success of these teaching strategies.

The Enterprise Data team works alongside multiple departments to help them get the most out of their data. As an Analytics Engineer, you'll work with stakeholders to build models and serve as a subject-matter guide on the best processes surrounding analysis. Peloton is looking for a talented individual to build and maintain foundational data infrastructure crucial to gaining insights. The base salary range represents the low and high end of the anticipated salary range for this position based at our New York City headquarters. The actual base salary offered for this position will depend on numerous factors including individual performance, business objectives, and if the location for the job changes.

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks.

We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it does not have access to the true modes of individual training sequences. The state-of-the-art continual learning approaches cannot handle this setup, because parameter transfer suffers from catastrophic interference and episodic memory design requires the knowledge of the ground-truth modes of sequences. We devise a novel continual learning method that overcomes both limitations by maintaining a descriptor of the mode of an encountered sequence in a neural episodic memory. We employ a Dirichlet Process prior on the attention weights of the memory to foster efficient storage of the mode descriptors. Our method performs continual learning by transferring knowledge across tasks by retrieving the descriptors of similar modes of past tasks to the mode of a current sequence and feeding this descriptor into its transition kernel as control input. We observe the continual learning performance of our method to compare favorably to the mainstream parameter transfer approach.

Current robotic minimally invasive surgery (RMIS) platforms provide surgeons with no haptic feedback of the robot's physical interactions. This limitation forces surgeons to rely heavily on visual feedback and can make it challenging for surgical trainees to manipulate tissue gently. Prior research has demonstrated that haptic feedback can increase task accuracy in RMIS training. However, it remains unclear whether these improvements represent a fundamental improvement in skill, or if they simply stem from re-prioritizing accuracy over task completion time. In this study, we provide haptic feedback of the force applied by the surgical instruments using custom wrist-squeezing devices. We hypothesize that individuals receiving haptic feedback will increase accuracy (produce less force) while increasing their task completion time, compared to a control group receiving no haptic feedback. To test this hypothesis, N=21 novice participants were asked to repeatedly complete a ring rollercoaster surgical training task as quickly as possible. Results show that participants receiving haptic feedback apply significantly less force (0.67 N) than the control group, and they complete the task no faster or slower than the control group after twelve repetitions. Furthermore, participants in the feedback group decreased their task completion times significantly faster (7.68%) than participants in the control group (5.26%). This form of haptic feedback, therefore, has the potential to help trainees improve their technical accuracy without compromising speed.

Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition benchmarks and directly applied to situated interactions. However, the conditions in situated human-robot interactions differ significantly from the training data and settings of these models. Thus, there is a need to deepen our understanding of how AC solutions can be best leveraged, customised and applied for situated HRI. This paper, while critiquing the existing practices, presents four critical lessons to be noted by the hitchhiker when applying AC for HRI research. These lessons conclude that: (i) The six basic emotions categories are irrelevant in situated interactions, (ii) Affect recognition accuracy (%) improvements are unimportant, (iii) Affect recognition does not generalise across contexts, and (iv) Affect recognition alone is insufficient for adaptation and personalisation. By describing the background and the context for each lesson, and demonstrating how these lessons have been learnt, this paper aims to enable the hitchhiker to successfully and insightfully leverage AC solutions for advancing HRI research.

The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification (+2.8% / +3.3% on COIN / EPIC-Kitchens) and step forecasting (+7.4% on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL.