Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort. While performing annotation work on crowdsourcing platforms, we hypothesize that such heuristic use among annotators cascades on to data quality and model robustness. In this work, we study cognitive heuristic use in the context of annotating multiple-choice reading comprehension datasets. We propose tracking annotator heuristic traces, where we tangibly measure low-effort annotation strategies that could indicate usage of various cognitive heuristics. We find evidence that annotators might be using multiple such heuristics, based on correlations with a battery of psychological tests. Importantly, heuristic use among annotators determines data quality along several dimensions: (1) known biased models, such as partial input models, more easily solve examples authored by annotators that rate highly on heuristic use, (2) models trained on annotators scoring highly on heuristic use don't generalize as well, and (3) heuristic-seeking annotators tend to create qualitatively less challenging examples. Our findings suggest that tracking heuristic usage among annotators can potentially help with collecting challenging datasets and diagnosing model biases.

We establish a non-deterministic model that predicts a user's food preferences from their demographic information. Our simulator is based on NHANES dataset and domain expert knowledge in the form of established behavioral studies. Our model can be used to generate an arbitrary amount of synthetic datapoints that are similar in distribution to the original dataset and align with behavioral science expectations. Such a simulator can be used in a variety of machine learning tasks and especially in applications requiring human behavior prediction.

Humans can learn to operate the user interface (UI) of an application by reading an instruction manual or how-to guide. Along with text, these resources include visual content such as UI screenshots and images of application icons referenced in the text. We explore how to leverage this data to learn generic visio-linguistic representations of UI screens and their components. These representations are useful in many real applications, such as accessibility, voice navigation, and task automation. Prior UI representation models rely on UI metadata (UI trees and accessibility labels), which is often missing, incompletely defined, or not accessible. We avoid such a dependency, and propose Lexi, a pre-trained vision and language model designed to handle the unique features of UI screens, including their text richness and context sensitivity. To train Lexi we curate the UICaption dataset consisting of 114k UI images paired with descriptions of their functionality. We evaluate Lexi on four tasks: UI action entailment, instruction-based UI image retrieval, grounding referring expressions, and UI entity recognition.

Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017--2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.

Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment. In most robotic systems, perception is typically independent of motion planning. For example, traditional object detection is passive: it operates only on the images it receives. However, we have a chance to improve the results if we allow planning to consume detection signals and move the robot to collect views that maximize the quality of the results. In this paper, we use reinforcement learning (RL) methods to control the robot in order to obtain images that maximize the detection quality. Specifically, we propose using a Decision Transformer with online fine-tuning, which first optimizes the policy with a pre-collected expert dataset and then improves the learned policy by exploring better solutions in the environment. We evaluate the performance of proposed method on an interactive dataset collected from an indoor scenario simulator. Experimental results demonstrate that our method outperforms all baselines, including expert policy and pure offline RL methods. We also provide exhaustive analyses of the reward distribution and observation space.

Reinforcement learning (RL) is a paradigm of AI methodologies in which an agent learns to interact with its environment in order to maximize the expectation of reward signals received from its environment. Unlike supervised learning, in which the agent is given labeled examples and learns to predict an output based on input, RL involves the agent actively taking actions in its environment and receiving feedback in the form of rewards or punishments. This feedback is used to adjust the agent's behavior and improve its performance over time. RL has been applied to a wide range of domains, including robotics, natural language processing, and finance. In the gaming industry, RL has been used to develop advanced game-playing agents, such as the AlphaGo [1] algorithm that defeated a human champion in the board game Go.

Universities should stop panicking and embrace students' use of artificial intelligence, AI experts say. South Australia's three main universities have updated their policies to allow the use of AI as long as it is disclosed. The advent of ChatGPT, a language processing chatbot that can produce very human-like words, sparked fears students would use it to write essays. Anti-plagiarism software wouldn't pick it up because ChatGPT isn't plagiarising anything, it's producing new work in response to prompts from users. Flinders University, the University of Adelaide and the University of South Australia have adjusted their policies to allow AI use under strict controls.

This week, Terwiesch released a research paper in which he documented how ChatGPT performed on the final exam of a typical MBA core course, Operations Management. The A.I. chatbot, he wrote, "does an amazing job at basic operations management and process analysis questions including those that are based on case studies." It did have shortcomings, he noted, including being able to handle "more advanced process analysis questions." But ChatGPT, he determined, "would have received a B to B- grade on the exam." Elsewhere, it has also "performed well in the preparation of legal documents and some believe that the next generation of this technology might even be able to pass the bar exam," he noted.

Fortune ranked the University of Alabama at Birmingham's in-person master's degree in cybersecurity as the No. 1 program in the country. According to Fortune, there are nearly 770,000 cybersecurity job openings in the United States. "We are proud to be recognized for academic excellence by Fortune and named the nation's leading institution for graduate studies in cybersecurity," said UAB Provost and Senior Vice President for Academic Affairs Pam Benoit. "UAB's Department of Computer Science has created an outstanding collaborative master's degree program that prepares students to lead careers solving the world's most challenging cybersecurity problems." Fortune's first-ever ranking of in-person cybersecurity master's degree programs compared 14 programs across the United States in three components: Selectivity Score, Success Score and Demand Score.

The AI can write essays and poems and answer questions about many topics. "If I thought about using ChatGPT for creating an essay, the answer could be ok – I might use it, but I will also be using these other resources because my interest as a student is to create something that can be for the benefit of others," said Moya who is a research assistant with a new University of Calgary research project investigating the ethical use of AI in post-secondary learning and teaching. "I want to be creative and create opportunities for others." Known as ChatGPT and created by a company called OpenAI, the software is designed to generate human-like responses to a wide range of inputs by using algorithms. "It was interesting to see that this tool could provide some particular insights that could become the starting point of something," Moya said.