With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V\&L) tasks, scholars have introduced an abundance of deep learning models in this research domain. Furthermore, in recent years, transfer learning has also shown tremendous success in Computer Vision for tasks such as Image Classification, Object Detection, etc., and in Natural Language Processing for Question Answering, Machine Translation, etc. Inheriting the spirit of Transfer Learning, research works in V\&L have devised multiple pretraining techniques on large-scale datasets in order to enhance the performance of downstream tasks. The aim of this article is to provide a comprehensive revision of contemporary V\&L pretraining models. In particular, we categorize and delineate pretraining approaches, along with the summary of state-of-the-art vision-and-language pretrained models. Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective into V\&L pretraining. Lastly, we decided to take a further step to discuss numerous directions for future research.

Europe's focus on business practices of American tech titans is evident in the agenda for Breton's trip. The morning after the stress test, he will host a launch event for the European Union's San Francisco office, a physical foothold for regulators in the tech industry's backyard. He'll also meet with a host of tech executives shaping the future of AI, including Meta chief executive Mark Zuckerberg, chip maker Nvidia chief executive Jensen Huang and OpenAI chief executive Sam Altman. During those meetings, he plans to discuss a new "AI Pact," a voluntary pledge to ensure the responsible development of AI until the AI Act takes effect. Google chief executive Sundar Pichai agreed to take the pledge recently, Breton said.

Artificial Intelligence is very much on the news agenda right now. The unstoppable rise of ChatGPT and the seemingly imminent prospect of generalised AI able to re-create broad human thinking processes has seen concerns raised by everyone from major business CEOs to Geoffrey Hinton, one of the godfathers of AI research. AI has been an element of video game design and production for at least two decades, but now with AI art programs and the rise of procedurally generated game dialogue, there are growing questions over how AI is going to effect not just the content of games, but the teams that make them. Talking at the Xbox games showcase in Los Angeles recently, Xbox chief Phil Spencer played down concerns that AI could be used to streamline the game production process and therefore lead to smaller teams. "Actually, that isn't an area we're thinking about a ton with AI," he said.

No wonder some of them may be turning to tools like ChatGPT to maximize their earning potential. To find out, a team of researchers from the Swiss Federal Institute of Technology (EPFL) hired 44 people on the gig work platform Amazon Mechanical Turk to summarize 16 extracts from medical research papers. Then they analyzed their responses using an AI model they'd trained themselves that looks for telltale signals of ChatGPT output, such as lack of variety in choice of words. They also extracted the workers' keystrokes in a bid to work out whether they'd copied and pasted their answers, an indicator that they'd generated their responses elsewhere. They estimated that somewhere between 33% and 46% of the workers had used AI models like OpenAI's ChatGPT.

About 32% of university students polled in Japan said they have used the artificial intelligence-powered ChatGPT chatbot, with many saying it enhances their thinking abilities, according to a recent survey by a Japanese research group. ChatGPT was used the most by students in the departments of science, technology and agriculture, at 45.5% overall, and far more by men, at 44.8%, than women, at 27.1%, the online survey found. Carried out between May 24 and June 2, the survey received responses from 4,000 students enrolled in universities across the nation. It was conducted amid growing worries that the use of ChatGPT could hurt students' critical thinking skills and creativity. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generating tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we utilize natural language processing approaches to investigate the public attitudes towards ChatGPT by applying sentiment analysis and topic modeling techniques to Twitter data. Our result shows that the overall sentiment is largely neutral to positive, which also holds true across different occupation groups. Among a wide range of topics mentioned in tweets, the most popular topics are Artificial Intelligence, Search Engines, Education, Writing, and Question Answering.

Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these models for entity matching are that (i) the models require significant amounts of fine-tuning data for reaching a good performance and (ii) the fine-tuned models are not robust concerning out-of-distribution entities. In this paper, we investigate using ChatGPT for entity matching as a more robust, training data-efficient alternative to traditional Transformer models. We perform experiments along three dimensions: (i) general prompt design, (ii) in-context learning, and (iii) provision of higher-level matching knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model, reaching a zero-shot performance of 82.35% F1 on a challenging matching task on which RoBERTa requires 2000 training examples for reaching a similar performance. Adding in-context demonstrations to the prompts further improves the F1 by up to 7.85% when using similarity-based example selection. Always using the same set of 10 handpicked demonstrations leads to an improvement of 4.92% over the zero-shot performance. Finally, we show that ChatGPT can also be guided by adding higher-level matching knowledge in the form of rules to the prompts. Providing matching rules leads to similar performance gains as providing in-context demonstrations.

Analysis of innovation has been fundamentally limited by conventional approaches to broad, structural variables. This paper pushes the boundaries, taking an LLM approach to patent analysis with the groundbreaking ChatGPT technology. OpenAI's state-of-the-art textual embedding accesses complex information about the quality and impact of each invention to power deep learning predictive models. The nuanced embedding drives a 24% incremental improvement in R-squared predicting patent value and clearly isolates the worst and best applications. These models enable a revision of the contemporary Kogan, Papanikolaou, Seru, and Stoffman (2017) valuation of patents by a median deviation of 1.5 times, accounting for potential institutional predictions. Furthermore, the market fails to incorporate timely information about applications; a long-short portfolio based on predicted acceptance rates achieves significant abnormal returns of 3.3% annually. The models provide an opportunity to revolutionize startup and small-firm corporate policy vis-a-vis patenting.

Artificial intelligence has made significant progress in natural language processing, with models like GPT-3 demonstrating impressive capabilities. However, these models still have limitations when it comes to complex tasks that require an understanding of the user, such as mastering human comedy writing strategies. This paper explores humor generation using GPT-3 by modeling human comedy writing theory and leveraging step-by-step thinking instructions. In addition, we explore the role of cognitive distance in creating humor.

Efficient human activity recognition (HAR) using sensor data needs a significant volume of annotated data. The growing volume of unlabelled sensor data has challenged conventional practices for gathering HAR annotations with human-in-the-loop approaches, often leading to the collection of shallower annotations. These shallower annotations ignore the fine-grained micro-activities that constitute any complex activities of daily living (ADL). Understanding this, we, in this paper, first analyze this lack of granular annotations from available pre-annotated datasets to understand the practical inconsistencies and also perform a detailed survey to look into the human perception surrounding annotations. Drawing motivations from these, we next develop the framework AmicroN that can automatically generate micro-activity annotations using locomotive signatures and the available coarse-grain macro-activity labels. In the backend, AmicroN applies change-point detection followed by zero-shot learning with activity embeddings to identify the unseen micro-activities in an unsupervised manner. Rigorous evaluation on publicly available datasets shows that AmicroN can accurately generate micro-activity annotations with a median F1-score of >0.75. Additionally, we also show that AmicroN can be used in a plug-and-play manner with Large Language Models (LLMs) to obtain the micro-activity labels, thus making it more practical for realistic applications.