Cardiovive is a modern metabolic wellness formula created to support the body’s natural ability to regulate glucose and maintain stable daily energy. In today’s fast-moving world, frequent sugar exposure, irregular eating patterns, and chronic stress can gradually interfere with the body’s delicate glucose balance. Cardiovive follows the HumanRise philosophy - restoring internal metabolic harmony so your body can manage blood sugar with greater consistency and resilience. Instead of forcing sudden metabolic reactions or relying on harsh stimulants, Cardiovive works by supporting your body’s natural glucose communication system. Its carefully selected botanical nutrients interact with metabolic pathways that influence how cells absorb, process, and utilize glucose for steady energy production. At the center of Cardiovive is the principle of metabolic balance. When insulin signaling, cellular energy conversion, and digestive efficiency operate together smoothly, the body becomes better equipped to maintain stable glucose levels throughout the day. This internal stability can help reduce unwanted energy crashes, excessive cravings, and the feeling of fatigue that often follows blood sugar fluctuations. Cardiovive also includes plant-derived compounds known for their antioxidant properties. These nutrients help defend cells from oxidative stress that may arise from modern dietary habits and environmental pressures. By supporting insulin responsiveness and metabolic flexibility, the formula encourages the body to manage nutrients more efficiently and sustain healthier metabolic rhythms. Carefully developed with quality, safety, and daily usability in mind, Cardiovive integrates easily into modern lifestyles. Non-habit forming and designed for consistent support, it helps individuals move toward better metabolic awareness, steadier energy, and a renewed sense of internal balance - empowering the body to function with clarity, stability, and long-term vitality.

Position: Is machine learning good or bad for the natural sciences? Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology-in which only the data exist-and a strong epistemology-in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here, we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they introduce strong confirmation biases.

For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics. It is an understatement to say that machine learning (ML) is having a big impact across the sciences. A significant fraction of all scientific papers in the natural sciences now employ ML in part (or all) of their analyses. We will define ML below in Section 2). However, when we ask what scientific breakthroughs have been enabled by this influx of new tools and methods, there isn’t a long list. The success of the AlphaFold projects in protein structure (Jumper et al., 2021) are often raised.

But these are successes in a very specific challenge-problem setting in which performance is valued over understanding. In the natural sciences we almost exclusively care about understanding, in the long run. The natural sciences are concerned with understanding the world, and naturally occurring mechanisms in play in that world. We make progress by discovering new kinds of objects and phenomena, and explaining (and, even better, predicting) qualitatively new kinds of objects and phenomena. Our most successful investigations are judged in terms of the questions they answer, or the new questions they raise, or both. The question here is: How will ML contribute to this mission? In contrast to natural science, ML research and ML methods are concerned with making accurate predictions for, or descriptions of, data. A ML method is considered successful if it performs well on held-out training data, even if the latent structure of the model is generic and the internals are impossible to interpret.