To What Extent Does the Perceived Obesity Level of Humanoid Robots Affect People's Trust in Them? Abstract-- Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regarding humans, Body Mass Index (BMI) is commonly used as an indicator of obesity, this scale is completely irrelevant in the context of robots, so it is challenging to operationalize the perceived obesity level of robots; indeed, while the effect of robot's size (or height) on people's trust in it was addressed in previous HRI papers, the perceived obesity level factor has not been addressed. This work examines to what extent the perceived obesity level of humanoid robots affects people's trust in them. To test this hypothesis, we conducted a within-subjects study where, using an online pre-validated questionnaire, the subjects were asked questions while being presented with two pictures of humanoids, one with a regular obesity level and the other with a high obesity level. The results show that humanoid robots with lower perceived obesity levels are significantly more likely to be trusted.

Background: Veterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives. Methods: BCS values were extracted from 4,415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT coercing the model to return the BCS information. Data were manually reviewed for comparison. Results: The precision of RegexT was higher (100%, 95% CI 94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall of ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of RegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is needed to improve ChatGPT output. Conclusions: Large language models create diverse opportunities and, whilst complex, present an intuitive interface to information but require careful implementation to avoid unpredictable errors.

Scientists have rubbished the theory that a child's personality is influenced by the gender of their siblings. Many think that children who grow up around multiple siblings of the opposite sex are influenced by them in terms of personality well into adulthood. For example, girls with brothers are seen as likely to become'tomboys', while boys with sisters are seen as likely to become'girlish', according to common belief. But a new study suggests this way of thinking is a misconception – and that sibling gender'does not systematically affect personality'. Our personality as adults is not determined by whether we grow up with sisters or brothers, the new study claims.

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

Instead of computing the success or failure of AI initiatives on a project-by-project basis, companies using the portfolio approach compute the ROI for all their AI initiatives. A portfolio approach works in other areas of business, and the same principles apply here. Take a look at three relevant examples and the lessons for AI. In the pharmaceutical world, developing a new drug takes an average of at least ten years and costs over $2.6 billion. Literally thousands and even millions of molecules and investigative drugs are studied during the initial drug discovery and preclinical trial phases of the R&D process.

A new way of training medical artificial intelligence (AI) systems has proven significantly more accurate at diagnosing illnesses than previous efforts. The AI system developed by researchers at University College London and Babylon Health, a medical service provider in the UK, relies on causation rather than correlation to pinpoint what could be wrong with people. It is more accurate than pre-existing AI systems and even outperformed real-life doctors in a small, controlled trial. Unlike traditional AI systems, which identify the most probable disease based on symptoms presented by a patient, the causal AI system more closely mimics the way a doctor diagnoses patients: by using counterfactual questions to narrow the range of possible conditions. A patient could present at a hospital with shortness of breath.

Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanatory modeling methods, capability of machine learning, and predictive modeling, in particular, remain mainly unexplored. In predictive modeling models are validated with previously unseen examples, giving a more accurate estimate of their performance and generalization ability in real-life scenarios. Objective: To find and review existing overweight or obesity research from the perspective of employing childhood data and predictive modeling methods. Methods: The initial phase included bibliographic searches using relevant search terms in PubMed, IEEE database and Google Scholar. The second phase consisted of iteratively searching references of potential studies and recent research that cite the potential studies. Results: Eight research articles and three review articles were identified as relevant for this review. Conclusions: Prediction models with high performance either have a relatively short time period to predict or/and are based on late childhood data. Logistic regression is currently the most often used method in forming the prediction models. In addition to child's own weight and height information, maternal weight status or body mass index was often used as predictors in the models.

Artificial Intelligence (AI) is making more decisions for us than ever before. AI is helping us keep our cars on the right lane, helping judges make the right decision, and even deciding who lives or dies on the battlefield. As AI proliferates in our daily lives, there is also a growing fear that humans lose control. The European Commission's current president, Ursula Von der Leyen, has pushed hard to start creating frameworks to regulate the use of AI, resulting in a document guidelining the requirements that AI systems need to meet in order to be trustworthy. One of the key elements of these guidelines is the notion that "AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned." What the European Commission really wants then, is Explainable AI (EAI): an AI where the logic for making the decision, or a summary of the logic is made available.

Scientists have created an AI that can detect obesity from space. The software scans satellite images and predicts how many residents are overweight based on the availability of parks, fast food stores and other buildings in the area. Researchers used deep learning to scan 150,000 high-resolution satellite images from Google Maps in order to identify patterns. They looked at data in six US cities - Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio. The team found that features of the built environment explained 64.8 per cent of the variation in obesity between cities.

A life-saving AI system that can identify the signs of malnutrition from a single photo of someone has been developed by a non-profit organisation. This system, called MERON (Method for Extremely Rapid Observation of Nutritional status) is still a prototype, but was 78 per cent accurate on the adults it was tested on. The technology reduces the need for lots of equipment and specialists in the field, making it easier to identify the symptoms of malnutrition, developers claim. By spotting the signs sooner, treatment can also be administered before the condition becomes critical. A non-profit organisation has developed technology which uses AI to identify the signs of malnutrition from a single photo of a person.