Hundreds of astronomers have warned that satellite constellations like Elon Musk's Starlink network could prove "extremely impactful" to astronomy and scientific progress. A report by the Satellite Constellations 1 (Satcon1) workshop found that that constellations of bright satellites will fundamentally change ground-based optical and infrared astronomy and could impact the appearance of the night's sky for stargazers around the world. The research brought together more than 250 astronomers, satellite operators and dark-sky advocates to better understand the astronomical impact of large satellite constellations. "We find that the worst-case constellation designs prove extremely impactful to the most severely affected science programs," stated the report, which was published on Tuesday. Elon Musk's SpaceX plans to launch more than 30,000 Starlink satellites in order to beam high-speed internet down to Earth.

Disregard for how the research could undermine the tribe's interests led to a lawsuit and out-of-court settlement. Science is a social enterprise. Many scientific programs interact with a wide range of communities and stakeholders to secure various types of access and permission, to seek cooperation and collaboration for scientific studies, to fulfill regulatory and ethical requirements, and to try to shape research strategies and to improve the translation of their findings into policy or practice. But these interactions are motivated disproportionately by the interests and goals of the scientific programs and less by the need to elicit and understand their implications for stakeholders. However, there is increasing recognition that substantive community and stakeholder engagement (CSE) can improve the performance, and even make or break the success, of some science programs by providing a means of navigating, and responding to, the complex social, economic, cultural, and political settings in which science programs are conducted.

Science has always relied on a combination of approaches to derive an answer or develop a theory. The seeds for Darwin's theory of natural selection grew under a Herculean aggregation of observation, data, and experiment. The more recent confirmation of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) was a decades-long interplay of theory, experiment, and computation. Certainly, this idea was not lost on the U.S. Department of Energy's (DOE) Argonne National Laboratory, which has helped advance the boundaries of high-performance computing technologies through the Argonne Leadership Computing Facility (ALCF). Realizing the promise of exascale computing, the ALCF is developing the framework by which to harness this immense computing power to an advanced combination of simulation, data analysis, and machine learning.

If you've succumbed to the hype around machine learning, you've likely heard hundreds of ML evangelists claim that data-driven decision-making is inevitable for companies that want to thrive in the near future. And a number of questions will arise as you consider how to employ the technology in your business. How can you estimate return on investment? Can you leverage the existing data to yield game-changing insights? Should you even try to get on that train right now?