Many experts believe AI systems will, sooner or later, pose uninsurable risks, including existential risks (Grace et al., 2024; Bengio et al., 2024). If so, it will be impossible to hold accountable the parties liable for such harms (or their insurers). Weil (2024) proposes to solve this extreme judgment proof-problem by assigning punitive damages to harms that are correlated with uninsurable risks (where the correlation would be estimated by courts and juries). While of interest, this solution has several problems. First, is it's novelty: this would be an unprecedented application of punitive damages that may violate the Due Process Clause (2024, 40-44, 50-53), requiring a major doctrinal shift that would cut across all of tort law. Second, correlates of uninsurable risks might be difficult to find. Third, given the high uncertainty involved, correlation estimations by courts will likely be ad hoc, high variance, and fail to leverage all available information. Fourth and finally, punitive damages for correlated risks will send a very oblique and noisy signal to liable parties: its effectiveness at actually inducing greater care taken is doubtful. Liable parties might find powerful legal teams to be a safer investment than investments in safety.

Averaging predictions from multiple competing inferential models frequently outperforms predictions from any single model, providing that models are optimally weighted to maximize predictive performance. This is particularly the case in so-called $\mathcal{M}$-open settings where the true model is not in the set of candidate models, and may be neither mathematically reifiable nor known precisely. This practice of model averaging has a rich history in statistics and machine learning, and there are currently a number of methods to estimate the weights for constructing model-averaged predictive distributions. Nonetheless, there are few existing software packages that can estimate model weights from the full variety of methods available, and none that blend model predictions into a coherent predictive distribution according to the estimated weights. In this paper, we introduce the BayesBlend Python package, which provides a user-friendly programming interface to estimate weights and blend multiple (Bayesian) models' predictive distributions. BayesBlend implements pseudo-Bayesian model averaging, stacking and, uniquely, hierarchical Bayesian stacking to estimate model weights. We demonstrate the usage of BayesBlend with examples of insurance loss modeling.

Like most sectors that have seen consumer adoption of digital technologies accelerate since the pandemic, the insurance industry is undergoing a major transformation, with new technologies and business models making it possible for insurers to offer highly flexible and personalized coverage. For an industry that has historically moved slow in adopting technology, 2023 promises to be a challenging year for insurers – but one that will make a tremendous impact on their relationship with customers. In the next decade, the insurance industry as we know it will be unrecognizable. Cars, homes, and individuals will all be insured within highly flexible insurance programs as a matter of course. These programs will include sophisticated mechanisms to dynamically and automatically adjust coverage, ensuring that it is optimal and personalized at any given moment.

SAN FRANCISCO, Nov. 14, 2019 (GLOBE NEWSWIRE) -- Zendrive, a mission-driven company using data and analytics to make roads safer and insurance fairer, today announced John Kramer as Director of Insurance Sales. He brings with him nearly 20 years of insurance experience in underwriting, usage-based insurance, product management, and connected car technology. "Zendrive is an established leader in driving analytics and research, with the world's largest driving data set of over 180 billion miles," said John Kramer. "The company is thinking critically about how to apply its unique, predictive telematics factors and innovative technology solutions to the insurance industry. I'm proud to join such a passionate team powering a modern, data-driven future alongside our insurance provider partners."

Verisk Analytics (NASDAQ:VRSK) and Globant (NYSE:GLOB) are both business services companies, but which is the superior investment? We will contrast the two companies based on the strength of their profitability, dividends, institutional ownership, earnings, analyst recommendations, risk and valuation. This table compares Verisk Analytics and Globant's revenue, earnings per share (EPS) and valuation. Verisk Analytics has higher revenue and earnings than Globant. Verisk Analytics is trading at a lower price-to-earnings ratio than Globant, indicating that it is currently the more affordable of the two stocks.

Globant (NYSE:GLOB) and Verisk Analytics (NASDAQ:VRSK) are both computer and technology companies, but which is the superior investment? We will compare the two businesses based on the strength of their earnings, risk, dividends, analyst recommendations, profitability, institutional ownership and valuation. This table compares Globant and Verisk Analytics' net margins, return on equity and return on assets. This table compares Globant and Verisk Analytics' revenue, earnings per share (EPS) and valuation. Verisk Analytics has higher revenue and earnings than Globant.

Behavioural change is a very tricky thing. We humans are so fickle. We see a bright shiny wearable device that can track our every move and we think it's our "silver bullet", a "ticket" to achieving our health and fitness dreams. Only for guilt to set in, as after a short time, the wearable device winds up in our top drawer. We knew the fitness data was great, but we really didn't know what to do with it. The truth is, behaviour change requires much more than data.

Behavioural change is a very tricky thing. We humans are so fickle. We see a bright shiny wearable device that can track our every move and we think it's our "silver bullet", a "ticket" to achieving our health and fitness dreams. Only for guilt to set in, as after a short time, the wearable device winds up in our top drawer. We knew the fitness data was great, but we really didn't know what to do with it. The truth is, behaviour change requires much more than data. Many programs have realized the magnitude of the problem and created incentive programs to reward people for being active, so they get a small pay-off on the road to achieving fitness. But in spite of these rewards, the drop-out rate remains problematic.