Some major retailers and independent stores have introduced AI body scans, CCTV or facial recognition equipment to identify crimes like shoplifting.

Academic writing skills are crucial for students' success, but can feel overwhelming without proper guidance and practice, particularly when writing in a second language. Traditionally, students ask instructors or search dictionaries, which are not universally accessible. Early writing assistants emerged as rule-based systems that focused on detecting misspellings, subject-verb disagreements, and basic punctuation errors; however, they are inaccurate and lack contextual understanding. Machine learning-based assistants demonstrate a strong ability for language understanding but are expensive to train. Large language models (LLMs) have shown remarkable capabilities in generating responses in natural languages based on given prompts. Still, they have a fundamental limitation in education: they generate essays without teaching, which can have detrimental effects on learning when misused. To address this limitation, we develop CoachGPT, which leverages large language models (LLMs) to assist individuals with limited educational resources and those who prefer self-paced learning in academic writing. CoachGPT is an AI agent-based web application that (1) takes instructions from experienced educators, (2) converts instructions into sub-tasks, and (3) provides real-time feedback and suggestions using large language models. This unique scaffolding structure makes CoachGPT unique among existing writing assistants. Compared to existing writing assistants, CoachGPT provides a more immersive writing experience with personalized feedback and guidance. Our user studies prove the usefulness of CoachGPT and the potential of large language models for academic writing.

Now that alleged would-be Trump assassin Ryan Routh is in custody, the FBI and Florida police will have their hands full unraveling his planning process and what may have motivated him. Former NYPD investigator and security expert Patrick Brosnan told Fox News Digital that investigators will need to trawl through a litany of information in the coming weeks, including "all things cellular, online shopping; phone camera images, bank records, email correspondence, recent search engine inquiries, dating app activity, identification of any possible burner phones, footage from … city streets, UPS trucks, Amazon trucks or backup cameras, and all cell tower pings within a fixed distance." Using this information, investigators will build Routh's profile to answer these questions, according to Gene Petrino, a SWAT commander with nearly three decades in law enforcement and a master's degree in security management. Ryan W. Routh, suspected of attempting to assassinate Republican presidential nominee former President Trump at his West Palm Beach golf course, stands handcuffed after his arrest during a traffic stop near Palm City, Florida, Sept. 15, 2024. Petrino said investigators will obtain warrants to scour Routh's social media and speak with his family and associates to determine whether someone else was involved in planning his assassination attempt on Sunday afternoon or anyone who may have trained him beforehand.

There has been a growing interest in developing learner models to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted representations of tasks, thereby limiting the agent's ability to generalize skills across tasks. In this paper, we aim to enhance the generalization capabilities of agents in open-ended text-based learning environments by integrating Reinforcement Learning (RL) with Large Language Models (LLMs). We investigate three types of agents: (i) RL-based agents that utilize natural language for state and action representations to find the best interaction strategy, (ii) LLM-based agents that leverage the model's general knowledge and reasoning through prompting, and (iii) hybrid LLM-assisted RL agents that combine these two strategies to improve agents' performance and generalization. To support the development and evaluation of these agents, we introduce PharmaSimText, a novel benchmark derived from the PharmaSim virtual pharmacy environment designed for practicing diagnostic conversations. Our results show that RL-based agents excel in task completion but lack in asking quality diagnostic questions. In contrast, LLM-based agents perform better in asking diagnostic questions but fall short of completing the task. Finally, hybrid LLM-assisted RL agents enable us to overcome these limitations, highlighting the potential of combining RL and LLMs to develop high-performing agents for open-ended learning environments.

We introduce CheckEval, a novel evaluation framework using Large Language Models, addressing the challenges of ambiguity and inconsistency in current evaluation methods. CheckEval addresses these challenges by dividing evaluation criteria into detailed sub-aspects and constructing a checklist of Boolean questions for each, simplifying the evaluation. This approach not only renders the process more interpretable but also significantly enhances the robustness and reliability of results by focusing on specific evaluation dimensions. Validated through a focused case study using the SummEval benchmark, CheckEval indicates a strong correlation with human judgments. Furthermore, it demonstrates a highly consistent Inter-Annotator Agreement. These findings highlight the effectiveness of CheckEval for objective, flexible, and precise evaluations. By offering a customizable and interactive framework, CheckEval sets a new standard for the use of LLMs in evaluation, responding to the evolving needs of the field and establishing a clear method for future LLM-based evaluation.

From fake photos of Donald Trump being arrested by New York City police officers to a chatbot describing a very-much-alive computer scientist as having died tragically, the ability of the new generation of generative artificial intelligence systems to create convincing but fictional text and images is setting off alarms about fraud and misinformation on steroids. Indeed, a group of artificial intelligence researchers and industry figures urged the industry on March 29, 2023, to pause further training of the latest AI technologies or, barring that, for governments to "impose a moratorium." These technologies – image generators like DALL-E, Midjourney and Stable Diffusion, and text generators like Bard, ChatGPT, Chinchilla and LLaMA – are now available to millions of people and don't require technical knowledge to use. Given the potential for widespread harm as technology companies roll out these AI systems and test them on the public, policymakers are faced with the task of determining whether and how to regulate the emerging technology. The Conversation asked three experts on technology policy to explain why regulating AI is such a challenge – and why it's so important to get it right.

Organized by Divine Fuh, HUMA – Institute for Humanities in Africa at the University of Cape Town, South Africa and Fanny Chabrol, CEPED-IRD, France and funded by Carnegie Corporation of New York and the Wenner-Gren Foundation, this workshop is located within the framework of the project Future Hospitals: 4IR/AI and the Ethics of Care at HUMA – Institute for Humanities in Africa headed by Divine Fuh, and the "Hospital Multiple" at CEPED-IRD headed by Fanny Chabrol. The workshop aims at proposing new ethnographic methodological and conceptual tools to think and imagine the "hospital of the future" in Africa, in particular, the way artificial intelligence (AI) seeks to transform and is currently transforming access to health care in hospitals today and in the coming years. Our project aims to build a problematisation of the hospital of the future and an ethnographic method to critically analyse the ethical, regulatory, and political issues with respect to AI, healthcare, and hospitals on the continent. We consider the "hospital of the future" – through the digitalization and computer automation of healthcare – as a global promise that needs to be challenged by ethnographic methods within hospitals, engaging with persons interacting with them. The first line of inquiry will challenge the logic of adoption and Africa as a place where development policies are implemented, where infrastructure projects are developed, in which technological innovation, mainly coming from the West, is presented as the promise of better health for those in need.

As the capabilities of Artificial Intelligence systems increase everyday, government officials are under more pressure than ever to develop a comprehensive and robust set of policies and laws that holds these algorithms accountable for their decisions. The question on whether these algorithms should be held accountable has gained attention over the past few years through scandals such as Google's mislabeling of images and Microsoft Tay's racist tweets. In determining whether an algorithm should be held accountable or not, it is important to break the topic down into key questions. The first is what task is the algorithm completing? What are the implications to individuals/society resulting from the algorithm's decision.

The location of the drone strike is in a heavily built-up part of Kabul called Khaje Bughra, near the airport. Relatives and neighbours in the area have disputed the justification for the strike, telling journalists that US intelligence was wrong, and that there was no Islamic State presence in the area.

How does a machine learning project work? What are the different building blocks that go into making a machine learning or artificial intelligence (AI) system? This is a topic I personally struggled with during my initial days in the field. I knew how to make machine learning models but I had no clue how a real-world machine learning project actually worked. It was quite a revelation when I went through the process!