Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while previous attempts aimed at automating nutrition coaching lack the personalization needed to address these diverse challenges. This paper introduces a novel LLM-powered agentic workflow designed to provide personalized nutrition coaching by directly targeting and mitigating patient-specific barriers. Grounded in behavioral science principles, the workflow leverages a comprehensive mapping of nutrition-related barriers to corresponding evidence-based strategies. A specialized LLM agent intentionally probes for and identifies the root cause of a patient's dietary struggles. Subsequently, a separate LLM agent delivers tailored tactics designed to overcome those specific barriers with patient context. We designed and validated our approach through a user study with individuals with cardiometabolic conditions, demonstrating the system's ability to accurately identify barriers and provide personalized guidance. Furthermore, we conducted a large-scale simulation study, grounding on real patient vignettes and expert-validated metrics, to evaluate the system's performance across a wide range of scenarios. Our findings demonstrate the potential of this LLM-powered agentic workflow to improve nutrition coaching by providing personalized, scalable, and behaviorally-informed interventions.

In the competitive realm of sports, optimal performance necessitates rigorous management of nutrition and physical conditioning. Specifically, in badminton, the agility and precision required make it an ideal candidate for motion analysis through video analytics. This study leverages advanced neural network methodologies to dissect video footage of badminton matches, aiming to extract detailed insights into player kinetics and biomechanics. Through the analysis of stroke mechanics, including hand-hip coordination, leg positioning, and the execution angles of strokes, the research aims to derive predictive models that can suggest improvements in stance, technique, and muscle orientation. These recommendations are designed to mitigate erroneous techniques, reduce the risk of joint fatigue, and enhance overall performance. Utilizing a vast array of data available online, this research correlates players' physical attributes with their in-game movements to identify muscle activation patterns during play. The goal is to offer personalized training and nutrition strategies that align with the specific biomechanical demands of badminton, thereby facilitating targeted performance enhancements.

The beginning of a new year is a great time to imagine new possibilities, both for ourselves and for the world. And this year, as Sam Altman put it while accepting the 2023 Stephen Hawking Fellowship at Cambridge: "We stand on the threshold of a brave new world. It's an exciting yet precarious place to be." At the moment the AI conversation is focused on how to align AI with human values. But the possibility I'm most excited about isn't how AI can become more human -- it's how AI can help humans become more human.

Kurt Knutsson looks ahead to the seven emerging trends and innovations in tech that will no doubt transform our lives over the next year. I'm like a kid in a candy store this time of year because CES, the Consumer Technology Association's annual trade show, is in full swing in Las Vegas. From the world's first wireless transparent OLED TV by LG that can transform your viewing experience to a smart lock that recognizes your face for seamless entry and even bone-conduction headphones with built-in AI coach, there's no shortage of cutting-edge gadgets to tell you about this year. Here are the top 10 product reveals that wowed us the most right out of the gate at CES 2024, each pushing the boundaries of what's possible in innovation. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Imagine watching your favorite shows and movies on a see-through TV that is almost invisible.

In a pre-post experiment (n = 41), we test the impact of an AI Coach's explanatory communications modeled after the instructions of human driving experts. Participants were divided into four (4) groups to assess two (2) dimensions of the AI coach's explanations: information type ('what' and 'why'-type explanations) and presentation modality (auditory and visual). We directly compare how AI Coaching sessions employing these techniques impact driving performance, cognitive load, confidence, expertise, and trust in an observation learning context. Through interviews, we delineate the learning process of our participants. Results show that an AI driving coach can be useful for teaching performance driving skills to novices. Comparing between groups, we find the type and modality of information influences performance outcomes. We attribute differences to how information directed attention, mitigated uncertainty, and influenced overload experienced by participants. These, in turn, affected how successfully participants were able to learn. Results suggest efficient, modality-appropriate explanations should be opted for when designing effective HMI communications that can instruct without overwhelming. Further, they support the need to align communications with human learning and cognitive processes. Results are synthesized into eight design implications for future autonomous vehicle HMI and AI coach design.

AI Coach enables Genpact to develop confident top-performing contact center agents through voice and chat simulations that provide highly realistic immersive learning experiences. Integrating the AI technology with Genpact's Cora Banking ecosystem allows Genpact to elevate its performance for more than 700 global clients. AI Coach is transforming how contact centers develop confident, prepared new hires before their first call and close skill gaps for experienced agents. The simulation training platform creates hyper-realistic simulations of any voice or chat scenario, allowing agents to learn through practicing, solving problems and navigating errors. By providing a platform to build proficiencies, risks and costs are minimized in the short and long term for clients. "Genpact is helping freshly hired agents improve their confidence before their first call.

Together, the companies will highlight how Zenarate's AI Coach has become an integral part of ERC employee experience and retention during the webinar, "Develop Top Performing Agents Through AI Conversation Simulation." ERC leverages AI Conversation Simulation to develop top-performing customer-facing teams right out of training and continues to close underperforming tenured agent skill gaps. With Zenarate AI Coach, ERC's customer-facing teams are able to master real scenarios they encounter with customers by applying best practices. Trainers can also easily track individual and team proficiency. "This partnership allowed ERC to expedite the time and resource-intensive process of onboarding new agents and upskilling tenured agents, leading to significant improvements in performance with AI Conversation Simulation," said Brian Tuite, CEO of Zenarate.

Abstract--Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., aviation, healthcare), lack of shared mental models can lead to preventable errors and harm. Towards the goal of mitigating such preventable errors, here, we present a Bayesian approach to infer misalignment in team members' mental models during complex healthcare task execution. As an exemplary application, we demonstrate our approach using two simulated team-based scenarios, derived from actual teamwork in cardiac surgery. In these simulated experiments, our approach inferred model misalignment with over 75% recall, thereby providing a building block for enabling computer-assisted interventions to augment human cognition in the operating room and improve teamwork.

What does Lululemon see when it looks into (the) Mirror? A future where artificial intelligence coaching changes the game. Lululemon recently paid $500m (€423m) to acquire Mirror, the maker of a $1,500 vertical home mirror with an embedded training service to guide you through your workouts. The incredible valuation of this startup fires a starting gun for the race to create personal remote coaches, powered by computer vision, for everyone who needs a little motivation and guidance at home. "Home fitness products like Peloton and streaming classes have boomed during the [Covid-19] pandemic," writes the New York Times about Lululemon's acquisition of Mirror.

Chatbots are one of the most widely adopted iterations of artificial intelligence (AI), as is the idea of creating a chatbot for therapeutic dialog [1-2]. But, when combined with today's ever-advancing natural language processing (NLP) and other modes of AI that make more sensitive communication with human users possible, emotionally supportive chatbots are anything but retrograde. X2 is a company that creates customized chatbots (AI coaches) for an array of use-cases, most of which focus on exploring and uplifting emotional well-being. This technology is highly scalable, easy to use, available on demand, and swiftly adaptable across languages, cultures, and other important contexts. This means that a supportive AI coach can complement conventional mental health care and even reach users in times and places where other modes of care cannot.