Several faculty members in CMS/W have expertise, both technological and pedagogical, in what the use of ChatGPT and other A.I. tools may mean for the instruction of academic writing. Because instructors at MIT and elsewhere have expressed some urgency in better understanding what the effects and ethics of ChatGPT may be, two pairs of our faculty have provided an advisory memo and a response. It should be noted that with tools like ChatGPT being both so new and so quickly evolving, these pieces are the faculty's take and don't yet represent official guidance from CMS/W or MIT. First is an excerpt from "Advice Concerning the Increase in AI-Assisted Writing", a memo from Edward Schiappa, Professor of Rhetoric, and Nick Montfort, Professor of Digital Media. The full document is available at Montfort's website.

The Covid-19 pandemic may not truly be over, but the boom it spawned in online learning and tutoring startups sure is. Now, with kids back in classrooms and venture capital funding for edtech down near pre-pandemic levels, entrepreneurs and venture capitalists have turned their attention to virtual reality, short form video and, first and foremost, artificial intelligence. "Investors are going gaga over artificial intelligence," says Tony Wan, head of platform at Reach Capital, a VC firm that invests in dozens of edtech companies. The education industry has been flirting with AI for half a dozen years, he notes, but suddenly, the relationship has turned serious. "Every business in ed tech--if it's not an AI business--needs to have an AI component," echoes Michael Moe, founder and CEO of GSV Holdings, a VC firm focused on the education and workforce skills sectors.

A great deal of ink has been spilt recently following the launch of ChatGPT and the advent of AI that can generate text and answers of a sufficient standard to be used by students in their assignments. Responses, in my opinion, have thus far been rather predictably, if not troublingly, conformist. Writers here in THE and elsewhere have variously suggested that if we can't beat it, we ought to join it and that hybrid or asynchronous communication ought to be embraced and integrated as part of the brave new world of human-machine creativity. More informal discussions with colleagues have suggested that resistance seems futile and that we ought to embrace AI to equip students to operate in a hybrid world of the artificial and the real for the purposes of employability. Some slightly more ambitious voices have suggested that AI-generated assignments make the case for authentic assessment or a more "human" form of assessment even more urgent, but how this might transpire in ways other than falling back on the adage of assessment for learning, constructive feedback and alignment with skills seems less clear.

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While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.

Deep Neural Networks are vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the driving tools of deep neural networks, demonstrates superior performance in prediction accuracy in various machine learning applications. However, it is unclear how it performs against adversarial attacks. Given the presence of a robust teacher, it would be interesting to investigate if NAS would produce robust neural architecture by inheriting robustness from the teacher. In this paper, we propose Robust Neural Architecture Search by Cross-Layer Knowledge Distillation (RNAS-CL), a novel NAS algorithm that improves the robustness of NAS by learning from a robust teacher through cross-layer knowledge distillation. Unlike previous knowledge distillation methods that encourage close student/teacher output only in the last layer, RNAS-CL automatically searches for the best teacher layer to supervise each student layer. Experimental result evidences the effectiveness of RNAS-CL and shows that RNAS-CL produces small and robust neural architecture.

This paper introduces a novel Learning from Demonstration framework to learn robotic skills with keyframe demonstrations using a Dynamic Bayesian Network (DBN) and a Bayesian Optimized Policy Search approach to improve the learned skills. DBN learns the robot motion, perceptual change in the object of interest (aka skill sub-goals) and the relation between them. The rewards are also learned from the perceptual part of the DBN. The policy search part is a semiblack box algorithm, which we call BO-PI2 . It utilizes the action-perception relation to focus the high-level exploration, uses Gaussian Processes to model the expected-return and performs Upper Confidence Bound type low-level exploration for sampling the rollouts. BO-PI2 is compared against a stateof-the-art method on three different skills in a real robot setting with expert and naive user demonstrations. The results show that our approach successfully focuses the exploration on the failed sub-goals and the addition of reward-predictive exploration outperforms the state-of-the-art approach on cumulative reward, skill success, and termination time metrics.

As they have a vital effect on social decision-making, AI algorithms not only should be accurate and but also should not pose unfairness against certain sensitive groups (e.g., non-white, women). Various specially designed AI algorithms to ensure trained AI models to be fair between sensitive groups have been developed. In this paper, we raise a new issue that between-group fair AI models could treat individuals in a same sensitive group unfairly. We introduce a new concept of fairness so-called within-group fairness which requires that AI models should be fair for those in a same sensitive group as well as those in different sensitive groups. We materialize the concept of within-group fairness by proposing corresponding mathematical definitions and developing learning algorithms to control within-group fairness and between-group fairness simultaneously. Numerical studies show that the proposed learning algorithms improve within-group fairness without sacrificing accuracy as well as between-group fairness.

Abstract--Natural language BERTs are trained with language corpus in a self-supervised manner. An example of generated image descriptions. Given different condition flags, our proposed UCM model is able to generate diverse image descriptions, such as COCO caption, dense caption, and questions. It's clear that the generated contents have different styles. Large scale pretraining has become the dominating approach in various natural language processing tasks. The success of large scale pretraining is due to a large amount of language setting. Although these models can be finetuned to perform training data available everywhere and the self-training algorithm. In this paper, we Second, current common practice in vision language BERT propose a self-training approach that allows to pretrain VL-pretraining uses various image descriptions to train, such as BERTs using unlabeled image data. Those image Self-training is usually done by iterating the following three descriptions have significant differences, making it difficult for steps: 1) training with labeled data, 2) generating pseudo labels an unconditional model to learn to generate adequate pseudo for unlabeled data, 3) mixing the labeled data and unlabeled captions for unlabeled images. However, the has shown its effectiveness in various tasks [4], [5], how to self-training of vision language BERTs is nontrivial due to use it effectively in training vision language BERTs is not yet the following reasons. First, although auto-encoding models studied.

Several approaches have been proposed and studied, such as Intrinsic Motivation, co-evolution of agents and tasks, and multi-agent competition. In particular, the interplay between multiple agents has proven to be capable of generating human-relevant emergent behaviour that would be difficult or impossible to learn in single-agent settings. In this work, an extensible competitive environment for multi-agent interplay was developed, which features realistic physics and human-relevant semantics. Moreover, several experiments on different variants of this environment were performed, resulting in some simple emergent strategies and concrete directions for future improvement. The content presented here is part of the author's thesis "Multi-Agent Interplay in a Competitive Survival Environment" for the Master's Degree in Artificial Intelligence and Robotics at Sapienza University of Rome, 2022.