Tech millionaire turned longevity pioneer Bryan Johnson devotes more than 6 hours a day to trialling different methods to turn back the clock. Can the rest of us learn anything from his radical approach? Bryan Johnson is finishing his 6.5-hour morning routine when I sign on to Zoom for my allotted 15-minute call with him (a constraint of what a member of his team describes as his "crazy" schedule). The tech millionaire turned longevity pioneer is standing in front of a cement wall in his California home, the coldness of which is relieved by green bursts of tropical houseplants. Wearing a helmet-like headset, a few wires trailing out and down past the screen, together with a black T-shirt bearing the words "Don't Die", the effect is somewhere between a luxury Balinese villa and a VR store designed by Apple.

As artificial intelligence (AI) continues to advance, understanding public perceptions -- including biases, risks, and benefits -- is critical for guiding research priorities, shaping public discourse, and informing policy. This study explores public mental models of AI using micro scenarios to assess reactions to 71 statements about AI's potential future impacts. Drawing on cross-cultural samples from Germany (N=52) and China (N=60), we identify significant differences in expectations, evaluations, and risk-utility tradeoffs. German participants tended toward more cautious assessments, whereas Chinese participants expressed greater optimism regarding AI's societal benefits. Chinese participants exhibited relatively balanced risk-benefit tradeoffs ($\beta=-0.463$ for risk and $\beta=+0.484$ for benefit, $r^2=.630$). In contrast, German participants showed a stronger emphasis on AI benefits and less on risks ($\beta=-0.337$ for risk and $\beta=+0.715$ for benefit, $r^2=.839$). Visual cognitive maps illustrate these contrasts, offering new perspectives on how cultural contexts shape AI acceptance. Our findings underline key factors influencing public perception and provide actionable insights for fostering equitable and culturally sensitive integration of AI technologies.

Artificial Intelligence (AI) is transforming diverse societal domains, raising critical questions about its risks and benefits and the misalignments between public expectations and academic visions. This study examines how the general public (N=1110) -- people using or being affected by AI -- and academic AI experts (N=119) -- people shaping AI development -- perceive AI's capabilities and impact across 71 scenarios, including sustainability, healthcare, job performance, societal divides, art, and warfare. Participants evaluated each scenario on four dimensions: expected probability, perceived risk and benefit, and overall sentiment (or value). The findings reveal significant quantitative differences: experts anticipate higher probabilities, perceive lower risks, report greater utility, and express more favorable sentiment toward AI compared to the non-experts. Notably, risk-benefit tradeoffs differ: the public assigns risk half the weight of benefits, while experts assign it only a third. Visual maps of these evaluations highlight areas of convergence and divergence, identifying potential sources of public concern. These insights offer actionable guidance for researchers and policymakers to align AI development with societal values, fostering public trust and informed governance.

Technology acceptance models effectively predict how users will adopt new technology products. Traditional surveys, often expensive and cumbersome, are commonly used for this assessment. As an alternative to surveys, we explore the use of large language models for annotating online user-generated content, like digital reviews and comments. Our research involved designing an LLM annotation system that transform reviews into structured data based on the Unified Theory of Acceptance and Use of Technology model. We conducted two studies to validate the consistency and accuracy of the annotations. Results showed moderate-to-strong consistency of LLM annotation systems, improving further by lowering the model temperature. LLM annotations achieved close agreement with human expert annotations and outperformed the agreement between experts for UTAUT variables. These results suggest that LLMs can be an effective tool for analyzing user sentiment, offering a practical alternative to traditional survey methods and enabling deeper insights into technology design and adoption.

This study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and User Experience. Although previous studies in simulated environments provided insights into human-automation interaction, real-world experiences can differ significantly. The research was conducted on a segment of German interstate with 30 participants lacking familiarity with Level 3 ADS. Pre- and post-driving questionnaires were used to assess changes in acceptance and confidence. Supplementary metrics included post-driving ratings for usability and user experience. Findings reveal a significant increase in acceptance and trust following the first contact, confirming results from prior simulator studies. Factors such as Performance Expectancy, Effort Expectancy, Facilitating Condition, Self-Efficacy, and Behavioral Intention to use the vehicle were rated higher after initial contact with the ADS. However, inadequate communication from the ADS to the human driver was detected, highlighting the need for improved communication to prevent misuse or confusion about the operating mode. Contrary to prior research, we found no significant impact of general attitudes towards technological innovation on acceptance and trust. However, it's worth noting that most participants already had a high affinity for technology. Although overall reception was positive and showed an upward trend post first contact, the ADS was also perceived as demanding as manual driving. Future research should focus on a more diverse participant sample and include longer or multiple real-traffic trips to understand behavioral adaptations over time.

A prominent theory of affective response to music revolves around the concepts of surprisal and expectation. In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences. To date, however, these models have been limited to compute exact probabilities through hand-crafted features or restricted to linear models which are likely not sufficient to represent the complex conditional distributions present in music. In this work, we propose to use modern deep probabilistic generative models in the form of a Diffusion Model to compute an approximate likelihood of a musical input sequence. Unlike prior work, such a generative model parameterized by deep neural networks is able to learn complex non-linear features directly from a training set itself. In doing so, we expect to find that such models are able to more accurately represent the 'surprisal' of music for human listeners. From the literature, it is known that there is an inverted U-shaped relationship between surprisal and the amount human subjects 'like' a given song. In this work we show that pre-trained diffusion models indeed yield musical surprisal values which exhibit a negative quadratic relationship with measured subject 'liking' ratings, and that the quality of this relationship is competitive with state of the art methods such as IDyOM. We therefore present this model a preliminary step in developing modern deep generative models of music expectation and subjective likability.

Figure from Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity, Adyasha Maharana and Elaine Okanyene Nsoesie. Image on the right represents actual obesity prevalence; on the left, cross-validated estimates of obesity prevalence based on features of the built environment extracted from satellite images. Figure reproduced under CC-BY licence. The 11th International Conference on Learning Representations (ICLR) took place last week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program included workshops, contributed talks, affinity group events, and socials.

It's early autumn in central China, and the streets of Ding Qingzi's village are turning into gold. Thousands of husked corncobs lie in orderly rectangles in front of homes, their kernels drying in the sun. The harvest is one of the heartbeats of rural life in Anhui Province, a constant that Ding, 35, has known since childhood. Yet few other rhythms remain. Except for the corn, the streets are almost empty. The sounds of children have faded. And for years, Ding struggled to find a wife. Few young women still live in the village. Fewer still would marry a welder unable to buy a house or pay a bride-price. "My family is not rich," Ding says.

A higher GDP per capita generally means that citizens have more disposable income, which can be used (Corresponding visualization is the first plot in Figure 1) to purchase goods and services that improve their health [Prompt] Generate an engaging caption for a scatter plot and wellbeing. The outlier in this data is Swaziland, titled GDP per capita VS Healthy life expectancy with which has a lower healthy life expectancy than would the x-axis labeled as GDP per capita and the y-axis labeled be expected of its GDP per capita. This is likely due as Healthy life expectancy. Other columns from to the high prevalence of HIV/AIDS in the country, as well as other factors such as poor access to healthcare, the data set include Social support, Perceptions of corruption, sanitation, and nutrition. Generosity, Overall rank, Score, Country or region, and Freedom to make life choices. The range [Added prompt] What is the reason for Swaziland's poor of GDP per capita is 0.0 to 1.684.

Our recent study using historic data of paddy yield and associated conditions include humidity, luminescence, and temperature. By incorporating regression models and neural networks (NN), one can produce highly satisfactory forecasting of paddy yield. Simulations indicate that our model can predict paddy yield with high accuracy while concurrently detecting diseases that may exist and are oblivious to the human eye. Crop Yield Prediction Using Regression and Neural Networks (CYPUR-NN) is developed here as a system that will facilitate agriculturists and farmers to predict yield from a picture or by entering values via a web interface. CYPUR-NN has been tested on stock images and the experimental results are promising.