Background: Wisdom is a superordinate construct that embraces perspective taking, reflectiveness, prosocial orientation, reflective empathetic action, and intellectual humility. Unlike conventional models of reasoning that are rigidly bound by binary thinking, wisdom unfolds in shades of ambiguity, requiring both graded evaluation and self-reflective humility. Current measures depend on self-reports and seldom reflect the humility and uncertainty inherent in wise reasoning. A computational framework that takes into account both multidimensionality and confidence has the potential to improve psychological science and allow humane AI. Method: We present a fuzzy inference system with Z numbers, each of the decisions being expressed in terms of a wisdom score (restriction) and confidence score (certainty). As part of this study, participants (N = 100) were exposed to culturally neutral pictorial moral dilemma tasks to which they generated think-aloud linguistic responses, which were mapped into five theoretically based components of wisdom. The scores of each individual component were combined using a base of 21 rules, with membership functions tuned via Gaussian kernel density estimation. Results: In a proof of concept study, the system produced dual attribute wisdom representations that correlated modestly but significantly with established scales while showing negligible relations with unrelated traits, supporting convergent and divergent validity. Contribution: The contribution is to formalize wisdom as a multidimensional, uncertainty-conscious construct, operationalized in the form of Z-numbers. In addition to progressing measurement in psychology, it calculates how fuzzy Z numbers can provide AI systems with interpretable, confidence-sensitive reasoning that affords a safe, middle ground between rigorous computation and human-like judgment.

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack of transparency in their reasoning (explainability), challenges in communication and commitment (cooperation), and risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom. Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive - through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition - the ability to reflect on and regulate one's thought processes - is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one's knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety. By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values - a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations.

This work introduces a framework to address the computational complexity inherent in Mixed-Integer Programming (MIP) models by harnessing the potential of deep learning. We compare the effectiveness of (a) feed-forward neural networks (ANN) and (b) convolutional neural networks (CNN) in approximating the active dimensions within MIP problems. We utilize multi-label classification to account for more than one active dimension. To enhance the framework's performance, we employ Bayesian optimization for hyperparameter tuning, aiming to maximize sample-level accuracy. The primary objective is to train the neural networks to predict all active dimensions accurately, thereby maximizing the occurrence of global optimum solutions. We apply this framework to a flow-based facility location allocation Mixed-Integer Linear Programming (MILP) formulation that describes long-term investment planning and medium-term tactical planning in a personalized medicine supply chain for cell therapy manufacturing and distribution.

Fox News medical contributor Dr. Marc Siegel joins'Fox & Friends' to discuss the benefits of artificial intelligence in the medical industry if used with caution. Music has long been shown to enhance athletic performance, whether that performance is on an NFL field or a treadmill at the gym. And now, with the help of artificial intelligence, music is helping surgeons achieve better results in the operating room. Backed by scientific studies, NextMed Health -- in collaboration with the data science company Klick Health -- has created the world's first AI-based health care radio station called Lifesaving Radio. It features more than 30 hours of hard rock music that's been carefully curated for peak surgical performance.

A drone flies outside the Massachusetts Institute of Technology's Kresge Auditorium during the 2018 Solve conference. The project connects tech entrepreneurs with leaders in government, business and academia to tackle world problems. MIT's recent billion-dollar commitment to its new AI-focused school, the Stephen A. Schwarzman College of Computing, represents an essential advance, not for its magnitude but for its plans to infect the rest of the university with AI. Announced earlier this month, MIT's new school's mission includes engaging across MIT to explore how AI might impact research across fields from engineering and social sciences to the humanities. MIT's president, Rafael Reif, explained the purpose of the school is to "educate bilinguals of the future."

At the 2010 Cannes Film Festival premiere of You Will Meet A Tall Dark Stranger, director Woody Allen was asked about aging. He replied with his characteristic, straight-faced pessimism. "I find it a lousy deal. There is no advantage in getting older. You don't get smarter, you don't get wiser ... Your back hurts more, you get more indigestion ... It's a bad business, getting old. I'd advise you not to do it if you can avoid it."