Prompt engineering is critical for effective interaction with large language models (LLMs) such as ChatGPT. However, efforts to teach this skill to students have been limited. This study designed and implemented a prompt engineering intervention, examining its influence on undergraduate students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts. The intervention involved 27 students who participated in a 100-minute workshop conducted during their history course at a university in Hong Kong. During the workshop, students were introduced to prompt engineering strategies, which they applied to plan the course's final essay task. Multiple data sources were collected, including students' responses to pre- and post-workshop questionnaires, pre- and post-workshop prompt libraries, and written reflections. The study's findings revealed that students demonstrated a higher level of AI self-efficacy, an enhanced understanding of AI concepts, and improved prompt engineering skills because of the intervention. These findings have implications for AI literacy education, as they highlight the importance of prompt engineering training for specific higher education use cases. This is a significant shift from students haphazardly and intuitively learning to engineer prompts. Through prompt engineering education, educators can faciitate students' effective navigation and leverage of LLMs to support their coursework.

Many hyper-personalized AI systems profile people's characteristics (e.g., personality traits) to provide personalized recommendations. These systems are increasingly used to facilitate interactions among people, such as providing teammate recommendations. Despite improved accuracy, such systems are not immune to errors when making inferences about people's most personal traits. These errors manifested as AI misrepresentations. However, the repercussions of such AI misrepresentations are unclear, especially on people's reactions and perceptions of the AI. We present two studies to examine how people react and perceive the AI after encountering personality misrepresentations in AI-facilitated team matching in a higher education context. Through semi-structured interviews (n=20) and a survey experiment (n=198), we pinpoint how people's existing and newly acquired AI knowledge could shape their perceptions and reactions of the AI after encountering AI misrepresentations. Specifically, we identified three rationales that people adopted through knowledge acquired from AI (mis)representations: AI works like a machine, human, and/or magic. These rationales are highly connected to people's reactions of over-trusting, rationalizing, and forgiving of AI misrepresentations. Finally, we found that people's existing AI knowledge, i.e., AI literacy, could moderate people's changes in their trust in AI after encountering AI misrepresentations, but not changes in people's social perceptions of AI. We discuss the role of people's AI knowledge when facing AI fallibility and implications for designing responsible mitigation and repair strategies.

This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologists, and other organisational stakeholders in mind. They are founded on a comprehensive conception of AI compared to those in other AI adoption models and are also open-source artefacts. Thus, the AI-CAM and AI-CM present an accessible resource to help inform organisational decision-makers on the capability requirements for (1) AI-based data analytics use cases based on machine learning technologies; (2) Knowledge representation to engineer and represent data, information and knowledge using semantic technologies; and (3) AI-based solutions that seek to emulate human reasoning and decision-making. The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations. The AI-CM details the related individual and team-level capabilities needed to reach each level in organisational AI capability; it, therefore, extends and enriches existing perspectives by introducing knowledge and skills requirements at all levels of an organisation. It posits three levels of AI proficiency: (1) Basic, for operational users who interact with AI and participate in AI adoption; (2) Advanced, for professionals who are charged with comprehending AI and developing related business models and strategies; and (3) Expert, for computer engineers, data scientists, and knowledge engineers participating in the design and implementation of AIbased technologies to support business use cases. In conclusion, the AI-CAM and AI-CM present a valuable resource for practitioners, businesses, and technologists, looking to innovate using AI technologies and maximise the return to their organisations.

Google's acceleration comes as a cacophony of voices -- including notable company alumnae and industry veterans -- are calling for the AI developers to slow down, warning that the tech is developing faster than even its inventors anticipated. Geoffrey Hinton, one of the pioneers of AI tech who joined Google in 2013 and recently left the company, has since gone on a media blitz warning about the dangers of supersmart AI escaping human control. Pichai, along with the CEOs of OpenAI and Microsoft, will meet with White House officials on Thursday, part of the administration's ongoing effort to signal progress amid public concern, as regulators around the world discuss new rules around the technology.

To stay at the top of the field, you always have to learn new things. One of the easiest ways is to learn from these top YouTube channels for artificial intelligence and also one of the most effective ways to do that is to subscribe to the best YouTube channels for artificial intelligence. It's a great source of knowledge that imparts knowledge about the latest trends, and an easy way to develop new skills. In this article, we have curated a list of the top YouTube channels for artificial intelligence that tech fanatics can follow. Depending on the nature, It is the best YouTube channel for artificial intelligence, which has a combination of math and entertainment.

The presence of artificial intelligence in the federal workforce is poised to expand, with officials emphasizing the human component behind automation and machine learning technologies. Officials including Gil Alterovitz, the Veterans' Affairs National Artificial Intelligence Institute director, and Martin Stanley, the branch chief of Strategic Technology at the Cybersecurity and Infrastructure Security Agency, spoke during a Thursday panel and discussed digitization within their respective agencies. Alterovitz said that VA leadership has opened up new data scientist positions to serve as subject matter experts across the government. "We've been working toward building pathways toward developing and assessing that AI knowledge," he said. "We're working with a number of other agencies and really the idea there is to build that pipeline of talent with AI knowledge both from outside government [and] inside the government so that the result of that would be an agile and responsive federal workforce equipped with the necessary competencies for AI." Alterovitz also discussed the ethical parameters the VA has in place for its usage of automated technology.

Interest and investment in AI for Education is accelerating. So is concern about the issues that will arise when AI is widely implemented in educational technologies -- such as bias, fairness, and data security. With our team at the Center for Integrative Research in the Computing and Learning Sciences (CIRCLS), we see that organizations around the world--like UNESCO or the new EdSafe AI Alliance--are organizing people to tackle the issues. In the US, organizations like Stanford's HAI are addressing the issues of AI in healthcare, but not so much in education. Over the past year, my colleagues organized a working group in AI and education policy.

Chief Technology Officer at Integrity Management Services, Inc., where she is leading cutting-edge technology solutions (AI) for clients. According to a recent World Economic Forum report, 50% of all employees will need reskilling by 2025. In the U.S. government alone, 18.2% of the federal government retired in 2020. Another 34% will be eligible for retirement by the fiscal year 2023. Our workforce demands are urgent.

Every Board Director and CEO need to accelerate their Learning in AI and Machine Learning to Manage ... [ ] Risk. Time for Something New is Now! Are you keeping informed by reading on a regular basis, here are a few guide posts to help you speed up your AI learning as a board director, CEO or senior executive striving to advance your knowledge in the Intelligence Revolution - where AI is simply everywhere! With the speed of AI content proliferating the market, and media channels growing at over 50% a years, and by 2021, over 80% of all new emerging software technologies will apply AI in some fashion in their business models, according to Gartner Group. As of August 2020, IDC reported that the AI market - including software, hardware, and services, are forecast to grow 12.3 percent to $156.5 billion in 2020. Worldwide AI revenues will surpass $300 billion in 2024 with a five-year compound annual growth rate (CAGR) of over seventeen percent.

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