Holidaymakers typically rely on experienced tour guides and local companies to recommend excursions to medieval castles and spectacular waterfalls, but the world's biggest tour operator has said it will entrust the service to artificial intelligence instead. The German travel company Tui has started using ChatGPT in its app to provide holiday recommendations, in the latest sign of traditional businesses racing to harness AI. ChatGPT was released in November by the startup OpenAI, and became the fastest-growing consumer app ever as users flocked to try it out. The rapidly improving technology can produce comprehensible language and even photos and videos by crunching through and synthesising huge amounts of data. Tui's feature has been released to half of UK app users, with the aim of introducing it to all "in the next weeks", a spokesperson said.

The statement said that Japan's copyright law is more favorable to AI learning than those of other countries.

In its August edition, Resources Policy, an academic journal under the Elsevier publishing umbrella, featured a peer-reviewed study about how ecommerce has affected fossil fuel efficiency in developing nations. But buried in the report was a curious sentence: "Please note that as an AI language model, I am unable to generate specific tables or conduct tests, so the actual results should be included in the table." The study's three listed authors had names and university or institutional affiliations--they did not appear to be AI language models. But for anyone who has played around in ChatGPT, that phrase may sound familiar: The generative AI chatbot often prefaces its statements with this caveat, noting its weaknesses in delivering some information. After a screenshot of the sentence was posted to X, formerly Twitter, by another researcher, Elsevier began investigating.

Video conferencing service Zoom and transcription software provider Otter.ai recently rolled out generative AI meeting features that provide automated summaries and key points, action items to follow and the ability to share notes with the participants. Google is also working on releasing features and Microsoft is testing some with select customers. The hope is that AI will help workers to keep better track of what happens in meetings, whether they attend or not. AI could also help automatically schedule follow-up meetings or draft emails based on items from the meeting.

There's a big reason why every company hoping to deal in some way with artificial intelligence is either spending or raising billions upon billions of dollars right now, and it's not just investor hype. These stacks of cash are necessary for meeting the costs of building, training, and maintaining resource-intensive (and resource-lacking) power-sucking content generators like ChatGPT, as well as the resource-intensive power-sucking data sets, neural networks, and large language models, or LLMs, they're trained on--such as OpenAI's GPT-4, whose API was recently made public to paying customers with coding expertise. Someone who understands the energy issue all too well is OpenAI's CEO himself, Sam Altman. Back in May, while testifying to Congress about the challenges wrought by the A.I. arms race his company ushered into the world, Altman admitted something curious: that he'd prefer for his wildly popular ChatGPT bot, at that time the fastest-growing app in history, to have fewer users. "We're not trying to get them to use it more," he stated. "Actually, we'd love it if they use it less, because we don't have enough GPUs."

With the emergence of generative artificial intelligence, an increasing number of individuals and organizations have begun exploring its potential to enhance productivity and improve product quality across various sectors. The field of education is no exception. However, it is vital to notice that artificial intelligence adoption in education dates back to the 1960s. In light of this historical context, this white paper serves as the inaugural piece in a four-part series that elucidates the role of AI in education. The series delves into topics such as its potential, successful applications, limitations, ethical considerations, and future trends. This initial article provides a comprehensive overview of the field, highlighting the recent developments within the generative artificial intelligence sphere.

In the 2023-2024 academic year, the widespread availability of generative artificial intelligence, exemplified by ChatGPT's 1.6 billion monthly visits, is set to impact academic integrity. With 77% of high school students previously reporting engagement in dishonest behaviour, the rise of AI-driven writing assistance, dubbed 'AI-giarism' by Chan (arXiv:2306.03358v2), will make plagiarism more accessible and less detectable. While these concerns are urgent, they also raise broader questions about the revolutionary nature of this technology, including autonomy, data privacy, copyright, and equity. This paper aims to explore generative AI from a social justice perspective, examining the training of these models, the inherent biases, and the potential injustices in detecting AI-generated writing.

This study presents an innovative approach to the application of large language models (LLMs) in clinical decision-making, focusing on OpenAI's ChatGPT. Our approach introduces the use of contextual prompts-strategically designed to include task description, feature description, and crucially, integration of domain knowledge-for high-quality binary classification tasks even in data-scarce scenarios. The novelty of our work lies in the utilization of domain knowledge, obtained from high-performing interpretable ML models, and its seamless incorporation into prompt design. By viewing these ML models as medical experts, we extract key insights on feature importance to aid in decision-making processes. This interplay of domain knowledge and AI holds significant promise in creating a more insightful diagnostic tool. Additionally, our research explores the dynamics of zero-shot and few-shot prompt learning based on LLMs. By comparing the performance of OpenAI's ChatGPT with traditional supervised ML models in different data conditions, we aim to provide insights into the effectiveness of prompt engineering strategies under varied data availability. In essence, this paper bridges the gap between AI and healthcare, proposing a novel methodology for LLMs application in clinical decision support systems. It highlights the transformative potential of effective prompt design, domain knowledge integration, and flexible learning approaches in enhancing automated decision-making.

Large Language Models (LLMs) hold immense potential to generate synthetic data of high quality and utility, which has numerous applications from downstream model training to practical data utilisation. However, contemporary models, despite their impressive capacities, consistently struggle to produce both coherent and diverse data. To address the coherency issue, we introduce contrastive expert guidance, where the difference between the logit distributions of fine-tuned and base language models is emphasised to ensure domain adherence. In order to ensure diversity, we utilise existing real and synthetic examples as negative prompts to the model. We deem this dual-pronged approach to logit reshaping as STEER: Semantic Text Enhancement via Embedding Repositioning. STEER operates at inference-time and systematically guides the LLMs to strike a balance between adherence to the data distribution (ensuring semantic fidelity) and deviation from prior synthetic examples or existing real datasets (ensuring diversity and authenticity). This delicate balancing act is achieved by dynamically moving towards or away from chosen representations in the latent space. STEER demonstrates improved performance over previous synthetic data generation techniques, exhibiting better balance between data diversity and coherency across three distinct tasks: hypothesis generation, toxic and non-toxic comment generation, and commonsense reasoning task generation. We demonstrate how STEER allows for fine-tuned control over the diversity-coherency trade-off via its hyperparameters, highlighting its versatility.

Park's team tested 14 different chatbot models by asking them a series of political questions on topics such as immigration, climate change, the role of government and same-sex marriage. The research, released earlier this summer, showed that a series of models developed by Google called Bidirectional Encoder Representations from Transformers, or BERT, were more socially conservative, potentially because they were trained more on books compared to other models that leaned more on internet data and social media comments. Facebook's LLaMA model was slightly more authoritarian and right wing, while OpenAI's GPT-4, its most up-to-date technology, tended to be more economically and socially liberal.