Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improve value alignment in these tools, comparatively less work has centered concerns around the fundamental justifiability of using these tools. This work seeks to center validity considerations in deliberations around whether and how to build data-driven algorithms in high-stakes domains. Toward this end, we translate key concepts from validity theory to predictive algorithms. We apply the lens of validity to re-examine common challenges in problem formulation and data issues that jeopardize the justifiability of using predictive algorithms and connect these challenges to the social science discourse around validity. Our interdisciplinary exposition clarifies how these concepts apply to algorithmic decision making contexts. We demonstrate how these validity considerations could distill into a series of high-level questions intended to promote and document reflections on the legitimacy of the predictive task and the suitability of the data.

The topic diversity of open-domain videos leads to various vocabularies and linguistic expressions in describing video contents, and therefore, makes the video captioning task even more challenging. In this paper, we propose an unified caption framework, M&M TGM, which mines multimodal topics in unsupervised fashion from data and guides the caption decoder with these topics. Compared to pre-defined topics, the mined multimodal topics are more semantically and visually coherent and can reflect the topic distribution of videos better. We formulate the topic-aware caption generation as a multi-task learning problem, in which we add a parallel task, topic prediction, in addition to the caption task. For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos. The topic prediction provides intermediate supervision to the learning process. As for the caption task, we propose a novel topic-aware decoder to generate more accurate and detailed video descriptions with the guidance from latent topics. The entire learning procedure is end-to-end and it optimizes both tasks simultaneously. The results from extensive experiments conducted on the MSR-VTT and Youtube2Text datasets demonstrate the effectiveness of our proposed model. M&M TGM not only outperforms prior state-of-the-art methods on multiple evaluation metrics and on both benchmark datasets, but also achieves better generalization ability.

Model Theft: Because models represent a significant investment in Intellectual Property, they can be a valuable target for theft. And like other software assets, they are tangible and can be stolen. Model theft happens when a model is taken outright from a storage location or re-created through deliberate query manipulation. An example of this type of attack was demonstrated by a research team at UC Berkeley who used public endpoints to re-create language models with near-production state-of-the-art translation quality. The researchers were then able to degrade the performance and erode the integrity of the original machine learning model using data input techniques to compromise the integrity of the original machine learning model (see Data Poisoning above).

Rather than banning students from using labor-saving and time-saving AI writing tools, we should teach students to use them ethically and productively. Educators are worried about students turning to ChatGPT to help them complete assignments. One proposed solution is to make students write exam essays using pen and paper, without the use of any Internet-connected electronic devices. The University of California, Los Angeles is considering making it an honor code violation to use ChatGPT for taking an exam or writing a paper. That is the wrong approach.

Note, average Entropy is the weighted average of all the sub nodes that a parent node splits into. Thus, in our example this would be 2 sub nodes for Working Status and 3 sub nodes for Student Background.

Furthering its range of expertise, ChatGPT scored a 50 percent accuracy rate on the multiple-choice component of the Bar Exam, or the Multistate Bar Examination (MBE). The Bar Exam is the test that law school graduates need to pass in order to officially practice law and is composed of three parts, with the MBE being the first. GPT-3.5 reached the average passing rate for Torts and Evidence, which are two of the seven subject areas. The researchers concluded that due to these results, a large language model such as GPT will be able to "pass the MBE component of the Bar Exam in the near future."

Former English teacher, Peter Laffin, predicts OpenAI's new artificial intelligence chatbot will lead to a learning crisis and force teachers to rethink education. Artificial intelligence is sparking concerns about plagiarism in schools worldwide. Still, the evolving technology poses tremendous benefits for creators and could soon be accepted in the classroom alongside tools like the calculator, according to professors and AI experts. Harvard Business School Assistant Professor Edward McFowland III compared generative AI, like ChatGPT, to other educational tools, such as the calculator and Wikipedia, with the former's benefits and the latter's disadvantages. ChatGPT has already been found to produce questionable results, with papers and responses sometimes including significant statistical or historical errors.

This is a beginner-friendly course for anyone who wants to develop their mathematical fundamentals for a career in machine learning and data science. A high-school level of mathematics will help learners get the most out of this class. Enroll now and take your career to the next level!

Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predicted environment change, which provides agent-centric exploration signal. We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills, and perform tasks specified via goal images. Website at https://robo-explorer.github.io/

The classic literature on two-sided matching [Gale and Shapley, 1962, Roth and Xing, 1997, Haeringer and Wooders, 2011, e.g.], encompassing applications including long-and short-term labor markets, dating and marriage, school choice, and more, has typically focused on situations where agents are aware of their own preferences. The problem of learning preferences while participating in a repeated matching market first started receiving attention in the AI literature in the work of Das and Kamenica [2005], and the general idea of two-sided matching under unknown preferences has since been studied in economics and operations research as well Lee and Schwarz [2009], Johari et al. [2022]. This area of research has received renewed attention in the last few years, along with novel theoretical insights into convergence properties of upper-confidence-bound style algorithms Liu et al. [2021], Kong et al. [2022], Zhang et al. [2022]. The two-sided matching problem involves agents on two sides of a market who have preferences for each other but cannot communicate explicitly. The goal is to create a matching process that ensures stability, where no pairs of agents would rather be matched with each other over their current match. Gale and Shapley [1962] famously demonstrated, constructively, the existence of such matchings. The Gale-Shapley algorithm is structured around one side of the market proposing and the other side choosing whether to accept proposals. This theory has been applied to various markets, like matching medical students to residencies Roth and Peranson [1999] and students to schools Abdulkadiroğlu et al. [2005], with the assumption that agents know their own preferences. There has also been considerable interest in the AI community on two-sided matching in the presence of various constraints, e.g.