I'm not going to lie--when ChatGPT first came out and blew everyone's minds, I was pretty hesitant about it. I'm not going to say I was anti-AI, but I just figured I'd do the work myself to ensure it was right, especially since I'd heard a few of my coworkers complain about how ChatGPT could never give them perfect results. But in recent months, I've started getting so much more scrambled with work, and it's not super sustainable to rely on myself for all the answers. So, I finally started branching out and using ChatGPT, but ran into similar frustrations my coworkers did. Thankfully, I found this ChatGPT beginner course for only 9.99, and it's seriously upgraded how I understand the chatbot and create prompts.

Summary and Contributions: Update: My initial review noted two main issues with the paper: reliance on the initial model, and the use of task labels during the test phase. The author response addresses the first question, but misses the point on the second one. And this alone is not sufficient to strongly influence my overall rating. In my understanding, several previous methods, such as LwF, iCaRL highlighted in the author response, classify samples without the knowledge of which group of classes (i.e., old or new) they belong to. In other words, they only use a single framework that can identify samples from any of the old or the new classes, without additional information.

The paper proposes a continual learning approach for CNN models. This is achieved through spatial and channel-wise calibration modules, one for each new task. These calibration modules are introduced between each pair of consecutive layers in the original base model. The base model is learnt on the first task, and training data from the subsequent tasks is used to learn the calibration modules. Extensive experiments show the superiority of the proposed method in terms of accuracies, with minimal computation and storage overhead. It is important to emphasize that the proposed approach requires task labels in the test phase.

This work proposes algorithms for the online-within-online meta-learning setting as oppposed to the more prevalent statistical setting. In this particular meta-learning setting tasks arrive sequentially manner (outer loop) and then the learning per task itself happens in an online fashion. The aim is to have low average regret over tasks. The inner loop optimization is done via Online Mirror Descent (OMD). The inner algorithm design is carefully chosen to provide good approximations of (sub)-gradients of the outer meta objective.

This paper presents a method for online-within-online meta-learning where each task is revealed one after another and online learning is applied for within-task. The primal-dual online learning is the main ingredient. All of reviewers agree that the paper is well written and has valuable contributions, while a few relevant work is already available. During the discussion period, a reviewer with most negative review raised his/her score, enabling us to reach a consensus.

While this algorithm is specifically designed for detectors, Miech et al 2019 used unsupervised NCE losses (much like the ones in this paper) in order to understand the natural language descriptions associated with videos; the algorithm presented here seems like the most straightforward extension of this idea to bounding boxes. Little attention is given to demonstrating that the use of bounding boxes fundamentally changes the problem. Update The rebuttal addresses the following point regarding the accuracy of the evaluation. I had misunderstood the annotations that are available with epic kitchens, and therefore I am changing my review. I would encourage the authors to clarify the writing regarding what's available with epic kitchens.

This paper presents a method for learning a distribution of tasks to feed to an agent that's learning via meta RL, while simultaneously optimizing the agent to perform better more quickly on tasks sampled from this distribution. The task distribution is trained using an objective that maximizes mutual information between a latent task variable and the trajectories produced by the meta RL agent. The meta RL agent is trained to maximize this mutual information, more or less. The overall optimization relies on some variational lower bounds on mutual information, and on the RL 2 algorithm for meta RL. Experiments are provided which show that the task distributions and meta RL agents trained in this co-adaptive manner exhibit some potentially useful behaviors, e.g. an improved ability to quickly solve new tasks sampled from an "actual" task distribution -- i.e., a task distribution which is not equal to the one that's co-adapted with the agent.

This work makes progress in the unsupervised meta-learning domain with visual features. This work contributes a model for how to automatically learn useful data in an unsupervised sense, and to incorporate that into a meta learner. All three reviewers find the work novel and significant, and hence I recommend acceptance.

Design educators are finding ways to support students in skillfully using Generative Artificial Intelligence (GenAI) tools in their practices while encouraging the critical scrutiny of ethical and social issues around these technologies. However, the problem of environmental sustainability remains largely unaddressed. There is a lack of both resources to grasp the environmental costs of genAI in education and a lack of shared practices around the issue. This work contributes filling this gap by counting the energy costs of using genAI in design education and critically reflecting on the impact of these costs. We leverage the image data collected during a genAI workshop for designers held in 2023 with 49 students, to calculate the energy costs of these types of activities. The results reveal that a genAI workshop for designers can easily double the energy costs associated with students' use of computers, countering the efforts of educational institutions to minimize their energy expenditure. We critically reflect on this finding to distill a set of five alternative stances, with related actions, that can support a conscious use of genAI in design education, while respecting individual positions. The work contributes to the field of design pedagogy, and education more broadly, by bringing together ways for educators to reflect on their practices and informing the future development of educational programs around genAI.

This research assesses the effectiveness of state-of-the-art large language models (LLMs), including ChatGPT, Llama, Aya, Jais, and ACEGPT, in the task of Arabic automated essay scoring (AES) using the AR-AES dataset. It explores various evaluation methodologies, including zero-shot, few-shot in-context learning, and fine-tuning, and examines the influence of instruction-following capabilities through the inclusion of marking guidelines within the prompts. A mixed-language prompting strategy, integrating English prompts with Arabic content, was implemented to improve model comprehension and performance. Among the models tested, ACEGPT demonstrated the strongest performance across the dataset, achieving a Quadratic Weighted Kappa (QWK) of 0.67, but was outperformed by a smaller BERT-based model with a QWK of 0.88. The study identifies challenges faced by LLMs in processing Arabic, including tokenization complexities and higher computational demands. Performance variation across different courses underscores the need for adaptive models capable of handling diverse assessment formats and highlights the positive impact of effective prompt engineering on improving LLM outputs. To the best of our knowledge, this study is the first to empirically evaluate the performance of multiple generative Large Language Models (LLMs) on Arabic essays using authentic student data.