Tag Archives: future

Ethics of the dangerous present and the hypothetical future

Embarcadero_27I need to reiterate an obvious truism: science peels back the unknown, producing new knowledge and changing our perception of the possible. Every bit of new information is individually inert and blameless, but when humans choose to act on scientific knowledge, fundamental facets of nature are seen in a whole new light. Over the latter half of the 20th century, perhaps no field was more emblematic of the dichotomy between great good and great danger than nuclear physics; the same knowledge that lead to abundant nuclear power also lead to ruinous nuclear weapons. Though the developments of other fields may not be so dramatic and poetic, the ethics underlying technological advancement are an important issue. I am frustrated that scientific ethics so often abandons the issues resulting from present-day science to dance through the realm of the science fictional.

Recently, I read this article by Huw Price in the New York Times with a mixture of excitement and disappointment. His particular focus is on artificial intelligence research and thus on the possibility of a technological singularity-like event. Sometimes derided as the “Rapture of the Nerds,” the singularity refers to a time when humans first develop an AI smarter than themselves, which will (in theory) exponentially improve itself. This massive intelligence could potentially render humans themselves superfluous—or at least, no longer the dominant will on Earth. In any case, the implications of such an event are, of course, enormous.
READ MORE ARTICLES

Science and the Data Revolution

The scientific method is obsolete.

This is a bold statement in a room filled with scientists who have spent the better portion of their lives striving for the gold standard of science set forth by 3rd grade science fair projects:

  1. Observe the world.
  2. Ask a question.
  3. Make a hypothesis.
  4. Devise a testable experiment with one variable and everything else controlled.
  5. Compile the data and analyze the results.
  6. Revise the experiment or hypothesis and repeat as necessary until they match.
  7. Form a conclusion.
  8. Justify how your manageable (relatively simple) model makes predictions on a much wider scale.

But the predominating focus on producing data to answer a question, which up until now has served our scientific community faithfully, may no longer be the best method for useful discovery, according to Stanford scientist Atul Butte, MD, PhD.  We are amidst a data revolution that necessitates that good science be best performed backwards; instead of questions demanding data, we now have data demanding questions.
READ MORE ARTICLES