The following is the second installment in a five-part series on the core issues facing academic science today that will comprise the foundation of our first book, targeted for publication later this year.
We know that knowledge builds on itself such that answering interesting questions typically leads to more questions. The type and style of questions that are asked and answered is highly dependent on the person doing the asking, thereby making the composition of the scientific research workforce incredibly important. There are a lot of scientists working in many different (and sometimes seemingly unrelated) spaces, and so our scientific understanding is constantly expanding in starts and stops, leaving many gaps in our understanding within and between fields. While it would be wonderful to systematically fill in these gaps, the reality is that many revolutionary advances in science can trace their origins to chance encounters between scientists from different areas of study that spark new ideas and thinking. People (and their relationships with each other) drive scientific advances.
It also becomes important to think about how we answer questions. Sometimes there are questions that we cannot answer because the appropriate tools simply do not exist. Such questions can stick around for a very long time while people strive to develop new tools; and when such breakthroughs occur, their impact reaches well beyond their initial intentions. For example, our earliest recorded discussions on artificial intelligence, date back to the sacred statues of ancient Egypt and Greece that craftsman reportedly imbued with consciousness, capable of wisdom and emotion. Hermes Trismegistus wrote that “by discovering the true nature of the gods, man has been able to reproduce it.” While the field of artificial intelligence was not formally founded as an academic discipline until 1956, required the invention of the computer, and increasingly powerful processors and electronics to establish; the questions Chinese, Indian and Greek philosophers raised about self-awareness and reasoning in 300-400 BCE still require the further development of multiple increasingly sophisticated programming languages to answer.
One of the most humbling exercises that just about every young scientist goes through at some point in their career is to read a paper from decades (or sometimes centuries!) ago that poses more-or-less the exact same question that they are currently struggling with. It’s more humbling still when we sometimes discover through interrogation of this literature that some our most difficult questions had been answered, but those answers were either not corroborated and fell out of favour with the latest theories of the time, or otherwise not publicized well enough and were subsequently forgotten. New technologies are therefore often used to re-ask “old” questions to see if the answers remain the same. Scientific “facts” are gradually established when answers to questions remain the same after a lot of interrogation over long periods of time by multiple scientists and tools.