Cognitive assessments require normative data as essential benchmarks for evaluating individual performance. Hence, developing new cognitive tests based on novel image stimuli is challenging due to the lack of readily available normative data. Traditional data collection methods are costly, time-consuming, and infrequently updated, limiting their practical utility. Recent advancements in generative multimodal large language models (MLLMs) offer a new approach to generate synthetic normative data from existing cognitive test images. We investigated the feasibility of using MLLMs, specifically GPT-4o and GPT-4o-mini, to synthesize normative textual responses for established image-based cognitive assessments, such as the "Cookie Theft" picture description task. Two distinct prompting strategies-naive prompts with basic instructions and advanced prompts enriched with contextual guidance-were evaluated. Responses were analyzed using embeddings to assess their capacity to distinguish diagnostic groups and demographic variations. Performance metrics included BLEU, ROUGE, BERTScore, and an LLM-as-a-judge evaluation. Advanced prompting strategies produced synthetic responses that more effectively distinguished between diagnostic groups and captured demographic diversity compared to naive prompts. Superior models generated responses exhibiting higher realism and diversity. BERTScore emerged as the most reliable metric for contextual similarity assessment, while BLEU was less effective for evaluating creative outputs. The LLM-as-a-judge approach provided promising preliminary validation results. Our study demonstrates that generative multimodal LLMs, guided by refined prompting methods, can feasibly generate robust synthetic normative data for existing cognitive tests, thereby laying the groundwork for developing novel image-based cognitive assessments without the traditional limitations.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I am a proud late adopter of new technology. I had a StarTAC well into the 21st century, fearing the limitless access to digital information and services that smartphones would bring and the way they would rob us of our time and attention and humanity. Though this realization offers little solace as I stare into my phone hundreds of hours a day.) I traveled with my books of CDs and my Discman well into the era when Transportation Security Administration agents would look at them with curiosity and suspicion.

Colorectal cancer is the most common cancer in Singapore and the third most common cancer worldwide. Blood in a person's stool is a symptom of this disease, and it is usually detected by the faecal occult blood test (FOBT). However, the FOBT presents several limitations: the collection process for the stool samples is tedious and unpleasant, the waiting period for results is around two weeks and costs are involved. In this research, we propose a simple-to-use, fast and cost-free alternative - a stool recognition neural network that determines if there is blood in one's stool (which indicates a possible risk of colorectal cancer) from an image of it. As this is a new classification task, there was limited data available, hindering classifier performance. Hence, various generative adversarial networks (GANs) (DiffAugment StyleGAN2, DCGAN, Conditional GAN) were trained to generate images of high fidelity to supplement the dataset. Subsequently, images generated by the GAN with the most realistic images (DiffAugment StyleGAN2) were concatenated to the classifier's training batch on-the-fly, improving accuracy to 94%. This model was then deployed to a mobile app - Poolice, where users can take a photo of their stool and obtain instantaneous results if there is blood in their stool, prompting those who do to seek medical advice. As "early detection saves lives", we hope our app built on our stool recognition neural network can help people detect colorectal cancer earlier, so they can seek treatment and have higher chances of survival.

Soft robots have found extensive applications in the medical field, particularly in rehabilitation exercises, assisted grasping, and artificial organs. Despite significant advancements in simulating various components of the digestive system, the rectum has been largely neglected due to societal stigma. This study seeks to address this gap by developing soft circular muscle actuators (CMAs) and rectum models to replicate the defecation process. Using soft materials, both the rectum and the actuators were fabricated to enable seamless integration and attachment. We designed, fabricated, and tested three types of CMAs and compared them to the simulated results. A pneumatic system was employed to control the actuators, and simulated stool was synthesized using sodium alginate and calcium chloride. Experimental results indicated that the third type of actuator exhibited superior performance in terms of area contraction and pressure generation. The successful simulation of the defecation process highlights the potential of these soft actuators in biomedical applications, providing a foundation for further research and development in the field of soft robotics.

For effective interactions with the open world, robots should understand how interactions with known and novel objects help them towards their goal. A key aspect of this understanding lies in detecting an object's affordances, which represent the potential effects that can be achieved by manipulating the object in various ways. Our approach leverages a dialogue of large language models (LLMs) and vision-language models (VLMs) to achieve open-world affordance detection. Given open-vocabulary descriptions of intended actions and effects, the useful objects in the environment are found. By grounding our system in the physical world, we account for the robot's embodiment and the intrinsic properties of the objects it encounters. In our experiments, we have shown that our method produces tailored outputs based on different embodiments or intended effects. The method was able to select a useful object from a set of distractors. Finetuning the VLM for physical properties improved overall performance. These results underline the importance of grounding the affordance search in the physical world, by taking into account robot embodiment and the physical properties of objects.

When classrooms and conference rooms abruptly moved online three years ago, we all experienced moments of technical frustration. Whether dealing with connectivity issues or clumsy virtual interactions, which were sometimes accompanied by awkward background noises, we persisted. Fortunately, the education sector had time to smooth out some of these wrinkles, especially with improved connectivity and advancing technology such as artificial intelligence (AI). Having seen such positive changes firsthand, Elliott Levine, director of worldwide public sector and education at Qualcomm Technologies, Inc. is excited about the newest technologies and their impact on the learning experience. Before transitioning to EdTech, Levine enjoyed 30 years working in various positions in K-12 and higher ed.

With machine learning on the rise, businesses are relying on machine learning models and algorithms to derive insights from data and make predictions. Serg Masís, data scientist and author of Interpretable Machine Learning with Python from Packt Publishing Ltd., believes that in order to know how and why those algorithms make predictions, they must be both interpretable and explainable. In this Q&A, Masís discusses these concepts, coined "interpretablility" and "explainability," and how they are more than just buzzwords or theory by explaining their value in real-world scenarios. Editor's note: The following interview was edited for length and clarity. What near-future trends in machine learning will emerge, and will they adhere to the advice in this book about interpretability and explainability?

For the past 10 years, Sonia Grego has been thinking about toilets – and more specifically what we deposit into them. "We are laser-focused on the analysis of stool," says the Duke University research professor, with all the unselfconsciousness of someone used to talking about bodily functions. "We think there is an incredible untapped opportunity for health data. And this information is not tapped because of the universal aversion to having anything to do with your stool." As the co-founder of Coprata, Grego is working on a toilet that uses sensors and artificial intelligence to analyse waste; she hopes to have an early model for a pilot study ready within nine months.

An artificial intelligence tool being developed by Duke scientists can be added to the standard toilet to help analyze patients' stool and give gastroenterologists the information they need to provide appropriate treatment for chronic issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). The work is being done by Duke University's Center for Water, Sanitation, Hygiene and Infectious Disease (WaSH-AID), and was presented Saturday at the virtual conference Digestive Disease Week 2021. "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, associate professor of medicine at Duke University and one of the lead authors on the study. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process," Fisher said. "The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."

A research has found that an artificial intelligence tool under development at Duke University can be added to the standard toilet to help analyse patients' stool and give gastroenterologists the information they need to provide appropriate treatment. The research was selected for presentation at Digestive Disease Week (DDW) 2021. The new technology could assist in managing chronic gastrointestinal issues such as inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). "Typically, gastroenterologists have to rely on patient self-reported information about their stool to help determine the cause of their gastrointestinal health issues, which can be very unreliable," said Deborah Fisher, MD, one of the lead authors on the study and associate professor of medicine at Duke University Durham, North Carolina. "Patients often can't remember what their stool looks like or how often they have a bowel movement, which is part of the standard monitoring process. The Smart Toilet technology will allow us to gather the long-term information needed to make a more accurate and timely diagnosis of chronic gastrointestinal problems."