Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution (OOD) conditions. Here, we assessed predictive performance and uncertainty reliability for deep learning-based blood pressure (BP) estimation from photoplethysmography (PPG) signals under both in-distribution (ID) and OOD settings. Using an XResNet1D-50 trained on PulseDB and tested on four external datasets, we compared deep ensembles (DE) and Monte Carlo dropout (MCD) with Gaussian negative log-likelihood (GNLL) and mean squared error (MSE) losses, optionally followed by post-hoc recalibration via conformal prediction (CP), temperature scaling (TS), and isotonic regression (IR). The key findings of our study are as follows: (1) DE provides stronger predictive robustness under domain shift than MCD, an advantage that becomes clear primarily under external shift. (2) Recalibrated GNLL-based methods yield the best uncertainty calibration (e.g., GNLL+DE+CP for systolic blood pressure (SBP), GNLL+DE+TS for diastolic blood pressure (DBP)), while MSE-based uncertainty requires recalibration to become practically useful. (3) Across settings, CP and TS offer the most consistent gains, with IR remaining competitive in several cases. Overall, our results identify DE-based methods as most robust for predictive performance under domain shift, GNLL as strongest for native UQ, and recalibration as essential for making MSE-based uncertainty practical. These findings highlight the need to jointly assess predictive accuracy and calibration on external data for trustworthy cuffless BP estimation

Big Tech Says Generative AI Will Save the Planet. A new report finds that of 154 specific claims about how AI will benefit the climate, just a quarter cited academic research. A third included no evidence at all. A few years ago, Ketan Joshi read a statistic about artificial intelligence and climate change that caught his eye. In late 2023, Google began claiming that AI could help cut global greenhouse gas emissions by between 5 and 10 percent by 2030.

We present a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank--Wolfe algorithm (also called conditional gradient) with gradient-based steps, different from away steps and pairwise steps, but still achieving linear convergence for strongly convex functions, along with good practical performance. Our approach retains all favorable properties of conditional gradient algorithms, notably avoidance of projections onto P and maintenance of iterates as sparse convex combinations of a limited number of extreme points of P. The algorithm is lazy, making use of inexpensive inexact solutions of the linear programming subproblem that characterizes the conditional gradient approach. It decreases measures of optimality (primal and dual gaps) rapidly, both in the number of iterations and in wall-clock time, outperforming even the lazy conditional gradient algorithms of [arXiv:1410.8816]. We also present a streamlined version of the algorithm for the probability simplex.

Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious prototypical patient. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to all patients alike. The clinical outcomes associated with BCG treatment vary significantly among patients due to the biological and clinical complexity of the interaction between the immune system, treatments, and cancer cells. In this study, we take advantage of the patient's socio-demographics to offer a personalized mathematical model that describes the clinical dynamics associated with BCG-based treatment. To this end, we adopt a well-established BCG treatment model and integrate a machine learning component to temporally adjust and reconfigure key parameters within the model thus promoting its personalization. Using real clinical data, we show that our personalized model favorably compares with the original one in predicting the number of cancer cells at the end of the treatment, with 14.8% improvement, on average.

Can you scale artificial intelligence (AI) in your organization? Of course, the answer is yes assuming you want your company to survive and gain competitive advantage. Boston Consulting Group (BCG) has a great read on this: "Artificial Intelligence: Ready to Ride the Wave" as part of its Executive Perspectives series, a tsunami of trends, charts, lists and insights helping C-suite executives better understand AI as a must-have technology investment. Scaling AI matters, even more so because most companies fail at it. According to BCG's report, only 11% of companies have found value in AI.

Question: can you scale #AI at your organization? Of course, the answer is yesssssssss, assuming you want your company to survive and gain competitive advantage. Scaling AI matters, even more so because most companies fail at it. According to BCG's report, only 11% of companies have found value in AI. The answer is a bit complicated.

The metaverse can provide an additional channel of opportunity for organisations especially as more companies embrace Web3 capabilities. Setting a broad vision, creating a digital twin strategy and providing personalised attention with AI are some of the steps organisations need to implement when building out a metaverse strategy according to Boston Consulting Group. In a new report, BCG experts break down the ways organisations can build out a robust metaverse strategy. Get the latest insights and analysis delivered to your inbox. Firstly, set a broad vision, as the metaverse matures more companies will join in on the trend.

BCG's AI-based Trust Index enables companies to break down stakeholder perceptions of their trustworthiness. Analyses based on the Index have yielded valuable insights about what builds, sustains, or destroys trust. Most business leaders are only now beginning to realize the true importance of trust. More than a mere sentiment, trust has economic value--and in the digital age, its relevance continues to grow. At the macro level, it enables new disruptive products, services, and strategic moves; at the micro level, it smooths the way for smaller transactions at scale among a vastly greater number of buyers and sellers who have no prior relationship.

According to the Boston Consulting Group (BCG), companies have no option but to acquire a social license for AI. When Mary Shelley wrote Frankenstein in 1818, she was writing about technology. Dr. Victor Frankenstein created a man who becomes a monster – leaping over his creator's expectations and terrifying the townspeople until his creator shuts him down. One wonders, if Frankenstein had been given the right circumstances, would the story have ended in triumph? Reading Boston Consulting Group's article "Why AI Needs a Social License" reminded me of Shelley's classic.

As companies attempt to take sustainability to the next level and gain a more complete view of their greenhouse gas emissions, there's a growing need to quantify results and track progress. "If you can't measure it, you can't manage it," says Autumn Stanish, associate principal analyst at Gartner, Inc. "In order to take initiatives to the next level -- particularly as organizations look to expand beyond Scope 1 and Scope 2 tracking -- there's a need for more advanced and granular measurement tools." Boston Consulting Group (BCG) reports that while 85% of companies are interested in reducing their emissions, only 9% of companies measure their total emissions comprehensively. Worse, only 11% have reduced their emissions in line with their goals over the last five years. How can companies get a better handle on their carbon footprint?