Advancements in generative artificial intelligence (AI) and large language models (LLMs) have fueled the development of many educational technology innovations that aim to automate the often time-consuming and laborious tasks of generating and analysing textual content (e.g., generating open-ended questions and analysing student feedback survey) (Kasneci et al., 2023; Wollny et al., 2021; Leiker et al., 2023). LLMs are generative artificial intelligence models that have been trained on an extensive amount of text data, capable of generating human-like text content based on natural language inputs. Specifically, these LLMs, such as Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) and Generative Pre-trained Transformer (GPT) (Brown et al., 2020), utilise deep learning and self-attention mechanisms (Vaswani et al., 2017) to selectively attend to the different parts of input texts, depending on the focus of the current tasks, allowing the model to learn complex patterns and relationships among textual contents, such as their semantic, contextual, and syntactic relationships (Min et al., 2021; Liu et al., 2023). As several LLMs (e.g., GPT-3 and Codex) have been pre-trained on massive amounts of data across multiple disciplines, they are capable of completing natural language processing tasks with little (few-shot learning) or no additional training (zero-shot learning) (Brown et al., 2020; Wu et al., 2023). This could lower the technological barriers to LLMs-based innovations as researchers and practitioners can develop new educational technologies by fine-tuning LLMs on specific educational tasks without starting from scratch (Caines et al., 2023; Sridhar et al., 2023). The recent release of ChatGPT, an LLMs-based generative AI chatbot that requires only natural language prompts without additional model training or fine-tuning (OpenAI, 2023), has further lowered the barrier for individuals without technological background to leverage the generative powers of LLMs. Although educational research that leverages LLMs to develop technological innovations for automating educational tasks is yet to achieve its full potential (i.e., most works have focused on improving model performances (Kurdi et al., 2020; Ramesh and Sanampudi, 2022)), a growing body of literature hints at how different stakeholders could potentially benefit from such innovations.

Due to the cultural and governance differences of countries around the world, there currently exists a wide spectrum of AI regulation policy proposals that have created a chaos in the global AI regulatory space. Properly regulating AI technologies is extremely challenging, as it requires a delicate balance between legal restrictions and technological developments. In this article, we first present a comprehensive review of AI regulation proposals from different geographical locations and cultural backgrounds. Then, drawing from historical lessons, we develop a framework to facilitate a thorough analysis of AI regulation proposals. Finally, we perform a systematic analysis of these AI regulation proposals to understand how each proposal may fail. This study, containing historical lessons and analysis methods, aims to help governing bodies untangling the AI regulatory chaos through a divide-and-conquer manner.

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared with many published self-supervised surveys on computer vision and natural language processing, a comprehensive survey for time series SSL is still missing. To fill this gap, we review current state-of-the-art SSL methods for time series data in this article. To this end, we first comprehensively review existing surveys related to SSL and time series, and then provide a new taxonomy of existing time series SSL methods by summarizing them from three perspectives: generative-based, contrastive-based, and adversarial-based. These methods are further divided into ten subcategories with detailed reviews and discussions about their key intuitions, main frameworks, advantages and disadvantages. To facilitate the experiments and validation of time series SSL methods, we also summarize datasets commonly used in time series forecasting, classification, anomaly detection, and clustering tasks. Finally, we present the future directions of SSL for time series analysis.

IRS Agent Joseph Ziegler joins'Special Report' to respond to critiques of hearing, letters from Del. prosecutor. The IRS special agent-turned-whistleblower formerly known as "Mr. X." spoke out to Fox News on Friday following an at-times contentious congressional hearing earlier this week. Joseph Ziegler, who came forward to Congress along with his colleague, Gary Shapley, said his team uncovered "a ton of evidence" that proved Hunter Biden allegedly willfully evaded or fraudulently filed his taxes, which set him apart from a typical IRS investigatory subject that would be faced with civil fines. Ziegler also told "Special Report" the ultimate reason he came forward as a whistleblower was that he saw many instances where federal officials were not following proper procedures.

White House says it's a 'critical step' towards the responsible development of the technology.

OpenAI's trust and safety lead, Dave Willner, has left the position, as announced via a Linkedin post. Willner is staying on in an "advisory role" but has asked Linkedin followers to "reach out" for related opportunities. The former OpenAI project lead states that the move comes after a decision to spend more time with his family. Yes, that's what they always say, but Willner follows it up with actual details. "In the months following the launch of ChatGPT, I've found it more and more difficult to keep up my end of the bargain," he writes.

Center for A.I. Safety Director Dan Hendrycks explains concerns about how the rapid growth of artificial intelligence could impact society. President Biden said Friday that his White House would continue to put out executive actions aimed at regulating and guiding the use of artificial intelligence but also said those actions won't end the need for Congress to pass AI legislation. "These commitments are a promising step, but we have a lot more work to do together," Biden said at the White House as he announced that seven AI development companies would work within a voluntary set of guidelines aimed at creating safe, secure and trustworthy AI systems. "Realizing the promise of AI by managing the risks is going to require new laws, regulations and oversight," Biden added. "In the weeks ahead, I'm going to continue to take executive action and help America lead the way to responsible innovation."

Fox News congressional correspondent Aishah Hasnie has more on the bipartisan effort to prevent AI overreach and the dangers of tech innovation on'Special Report.' The Pulitzer Prize-winning biographer of physicist J. Robert Oppenheimer has endorsed legislation that would keep artificial intelligence away from nuclear weapons. Kai Bird, a co-author of "American Prometheus: The Triumph and Tragedy of J. Robert Oppenheimer" -- which serves as the main inspiration for Christopher Nolan's new film, "Oppenheimer," opening this weekend -- met with Sen. Ed Markey, D-Mass., on Thursday to discuss the intersecting threats of nuclear war and artificial intelligence. Markey is one of the sponsors of a bipartisan amendment to the National Defense Authorization Act that would prohibit AI from making nuclear launch decisions. During their meeting, Bird and Markey spoke about their shared concerns over emerging AI technologies and what guardrails are needed for their use in the national defense sector, as well as the risks of using nuclear weapons in South Asia and elsewhere.