With generative artificial intelligence gaining rapid adoption worldwide, private-sector businesses in Japan are starting to embrace related information search technologies to enhance operational efficiency. Conventional information searches require users to enter relevant keywords and manually browse selected websites to locate the desired information. The process can be time-consuming, however, and users may not always find the exact content they need. By contrast, generative AI searches enable users to input queries using natural language or images. After interpreting the user's wishes, the AI retrieves relevant information from websites and other sources, providing concise, natural-sounding answers.

Summary: Examining the cognitive abilities of the AI language model, GPT-3, researchers found the algorithm can keep up and compete with humans in some areas but falls behind in others due to a lack of real-world experience and interactions. Researchers at the Max Planck Institute for Biological Cybernetics in Tübingen have examined the general intelligence of the language model GPT-3, a powerful AI tool. Using psychological tests, they studied competencies such as causal reasoning and deliberation, and compared the results with the abilities of humans. Their findings paint a heterogeneous picture: while GPT-3 can keep up with humans in some areas, it falls behind in others, probably due to a lack of interaction with the real world. Neural networks can learn to respond to input given in natural language and can themselves generate a wide variety of texts.

Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($<100$ models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories. This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10$^{\circ}$, 30$^{\circ}$, 60$^{\circ}$, and 120$^{\circ}$ degrees. The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.

The legal relationship between government and citizens is mediated by documents. This paper identifies four classes of cognitive assistants that could improve the experience of citizens and government officials in using and understanding government documents: self-filling forms; error-detecting forms; proactive information search; and deductive document synthesis. Each of these classes of cognitive assistants has the potential to significantly improve access to justice and delivery of information, services, and other benefits to citizens by improving the ability of citizens to understand and correctly fill out forms and to comprehend informational documents.

The growth of health literacy and patient empowerment movements has resulted in a more active and prominent role for patients as autonomous actors in decisions relating to their health. The Internet has become an important source of information for patients seeking to understand their health conditions and to evaluate possible treatments. However, in making autonomous healthcare decisions, the Internet can be viewed by patients as a decision support system. The Internet is poorly adapted to this task and may lead patients to make hasty, ill-informed, and even dangerous health choices. It is important, therefore, to guide patients to approach the Internet with appropriate skepticism and to temper their perceptions of autonomy.