People now do not actually look through telescopes. Instead, they are “looking” through large-scale, complex instruments which relay data to datacenters, and only then do they look at the information on their computers.
Note: Jim Gray on the impact of computational science on astronomy.
Another new idea is the use of functional programming languages. These languages embody a style of programming that mostly prohibits updates to program state. In other words, in these languages a variable can be given an initial value, but that value cannot be changed. Instead, a new variable is created with the new value. This style of programming is well suited to parallel programming because it eliminates the updates that require synchronization between two processors.
Note: The promise of FP, functional programming.
Over the past decade, scientific and engineering research via computing has emerged as the third pillar of the scientific process, complementing theory and experiment.
Note: This is a far-reaching statement. How widely held is the idea of a 3rd pillar?
  [2]  A. Halevy, P. Norvig, and F. Pereira, “The Unreasonable Effectiveness of Data,” IEEE Intell. Syst., vol. 24, no. 2, pp. 8–12. 2009, doi: 10.1109/MIS.2009.36.   [3]  C. Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired, vol. 16, no. 7, June 23, 2008, www.wired.com/science/discoveries/magazine/16-07/pb_theory.
Note: Abstract from the Halevy paper: "Problems that involve interacting with humans, such as natural language understanding, have not proven to be solvable by concise, neat formulas like F = ma. Instead, the best approach appears to be to embrace the complexity of the domain and address it by harnessing the power of data: if other humans engage in the tasks and generate large amounts of unlabeled, noisy data, new algorithms can be used to build high-quality models from the data."