In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity, we build on four overlapping traditions of digital writing research in computers and composition that inform how we frame literacies, how we study social media rhetorics, how we engage iteratively and reflexively with methodologies and technologies, and how we blend computational methods with qualitative methods. Drawing on these four traditions, our paper shows our iterative research process through which we gathered and analyzed a dataset of 32,000 posts (formerly known as tweets) from X (formerly Twitter) about prompt writing posted between November 2022 to May 2023. We present five themes about these emerging AI literacy practices: (1) areas of communication impacted by prompt writing, (2) micro-literacy resources shared for prompt writing, (3) market rhetoric shaping prompt writing, (4) rhetorical characteristics of prompts, and (5) definitions of prompt writing. In discussing these themes and our methodologies, we highlight takeaways for digital writing teachers and researchers who are teaching and analyzing critical AI literacies.

The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.

This research introduces an innovative mathematical learning approach that integrates generative AI to cultivate a structured learning rather than quick solution. Our method combines chatbot capabilities and generative AI to offer interactive problem-solving exercises, enhancing learning through a stepby-step approach for varied problems, advocating for the responsible use of AI in education. Our approach emphasizes that immediate answers from ChatGPT can impede real learning. We introduce a reward-based system that requires students to solve mathematical problems effectively to receive the final answer. This encourages a progressive learning path from basic to complex problems, rewarding mastery with final solutions. The goal is to transition students from seeking quick fixes to engaging actively in a comprehensive learning experience.

A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls. Currently, when the orbit lock system fails operators must manually intervene. This paper develops a Machine Learning based fault detection methodology to identify orbit lock anomalies and notify accelerator operations staff of the off-normal behavior. Our method is unsupervised, so it does not require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to capture normal patterns and predict future values of monitoring sensors in the orbit lock system. Anomalies are detected when the prediction error exceeds a threshold. We conducted experiments using monitoring data from Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF). The results are promising: the percentage of real anomalies identified by our solution is 68.6%-89.3% using monitoring data of a single component in the orbit lock control system. The accuracy can be as high as 82%.

Reciprocal questioning is essential for effective teaching and learning, fostering active engagement and deeper understanding through collaborative interactions, especially in large classrooms. Can large language model (LLM), such as OpenAI's GPT (Generative Pre-trained Transformer) series, assist in this? This paper investigates a pedagogical approach of classroom flipping based on flipped interaction in LLMs. Flipped interaction involves using language models to prioritize generating questions instead of answers to prompts. We demonstrate how traditional classroom flipping techniques, including Peer Instruction and Just-in-Time Teaching (JiTT), can be enhanced through flipped interaction techniques, creating student-centric questions for hybrid teaching. In particular, we propose a workflow to integrate prompt engineering with clicker and JiTT quizzes by a poll-prompt-quiz routine and a quiz-prompt-discuss routine to empower students to self-regulate their learning capacity and enable teachers to swiftly personalize training pathways. We develop an LLM-driven chatbot software that digitizes various elements of classroom flipping and facilitates the assessment of students using these routines to deliver peer-generated questions. We have applied our LLM-driven chatbot software for teaching both undergraduate and graduate students from 2020 to 2022, effectively useful for bridging the gap between teachers and students in remote teaching during the COVID-19 pandemic years. In particular, LLM-driven classroom flipping can be particularly beneficial in large class settings to optimize teaching pace and enable engaging classroom experiences.

Detailed JD: • Minimum of 15 years of technical experience in Oracle ERP (Oracle Cloud/PeopleSoft) • Experience with preparation of data strategy, migration plan, object dependencies, etc. • Experience in Oracle Financials Cloud Schema and Data model • Experience with Master (customer, supplier, COA, etc.) and transaction (GL, PO, AP, etc.) data in Oracle Financials Cloud • Experience with conducting impact assessment on outbound data payload from Oracle Financials Cloud to data lake • Experience in creating design document for accommodating changes to the payload • Experience with optimizing data transfer (Extraction, Cleansing,Transformation, Loading and Validation) from Oracle Financials Cloud to data lake • Hands on with writing complex SQL • Excellent oral and written communication skills • Good understanding of PeopleSoft financial data model • Experience in data lake architecture Apply Here For Remote Business Architect/ Portland, OR ( Remote),6-12 months contract roles, visit Remote Business Architect/ Portland, OR ( Remote),6-12 months contract Roles

STERLING, Va., Dec. 5, 2019 /PRNewswire-PRWeb/ -- EnterWorks, a leading provider of Master Data Management (MDM) and Product Information Management (PIM) solutions, has announced a live webcast event featuring Michele Goetz, Principal Analyst, Business Insights, Information Architecture and Artificial Intelligence, at Forrester. The webinar, "How AI, Machine Learning and Data Strategy Can Enable Compelling New Products & Experiences," will take place on Tuesday, December 10, 2019 from 11:00 am to 12:00 pm EST. It is sponsored by EnterWorks; Amplifi, an information management consultancy that helps the world's leading brands, retailers and manufacturers to harness and unleash the power of their data; and Sisense, a business intelligence software and analytics platform. The webinar will inform participants how artificial intelligence, machine learning and data strategy can enable compelling new products and experiences, and how deploying AI and ML depends on effective master data and its proper governance. According to Forrester's Goetz, many companies have initiated AI and ML projects only to find that they have not established the foundation for success that comes with implementing a comprehensive data management strategy and the platforms needed to make replicable and scalable success possible.

A group of veterans inspired by the need to keep schools and public spaces safer have created a new technology they say can detect guns and send out alerts before shots are ever fired. Active shooter situations have played out across the country – a gunman opened fire inside a Florida high school, shots rang out at a Texas Walmart and multiple people were shot to death in an office building in Virginia Beach. The nation's most recent school shooting happened Thursday morning – when a 16-year-old high school student in Santa Clarita, California, opened fire in the campus quad, shooting five classmates and killing two. What if the gun was detected early – so early, the shooter was never able to get inside to hurt anyone? The technology to do that exists, and only WUSA9 was there when it was tested in Northern Virginia.

Artificial Intelligence (AI) or machine learning is being used by military intelligence and at the high strategic level. The question is whether this technology will ever filter down to the soldier actually doing the fighting on the ground? Science fiction novels and movies suggest a system that can communicate with a warfighter in real time and provide situational awareness, but how far is the fiction from reality? "The most important weapon is situation awareness, and there are AI-based tools to help a lot with this," explained Jim Purtilo, associate professor of computer science at the University of Maryland. "Visualization, communication, simple planning, these all go together at squad level," Purtilo told ClearanceJobs. "Being able to leverage augmented reality to'see' where the bad guys are, this is very cool. Being able to aim your squad weapon at a target and in effect use it as a pointer to call in indirect fire, even cooler."

New facial recognition technology has identified three impostors at Washington Dulles International Airport. Citing a U.S. Customs and Border Protection release, The Washington Post reports a woman arriving on a Monday flight from Accra, Ghana, presented a U.S. passport, but the facial recognition technology reported a mismatch. A secondary inspection and biometric examination identified her as a 26-year-old citizen of Cameroon, not the United States. The release says the Metropolitan Washington Airports Authority partnered with CBP to use biometric entry and exit technology using facial comparison to bolster security and efficiency for international travelers. Officers at Dulles previously intercepted a Congolese man using a French passport Aug. 22 and a Ghanaian woman using a U.S. passport Sept. 8. Posing as another person when entering the United States violates immigration law.