What the history of traditional science can teach computer scientists

The Double Helix

The Double Helix

  • Before a new technology can take off in a big way, it needs to be explainable in layman’s terms:
  • Crick, ever the talker, could not help but tell everyone we had just found the “secret of life”

  • You don’t need to be regarded as an expert in the field in order to change the future
  • The essence of the molecule was left to be discovered by a two-man team, a biologist and a physicist, neither of whom possessed a detailed command even of undergraduate chemistry. But paradoxically, this was, at least in part, the key to our success

  • It’s essential to be able to think at different levels of abstraction and develop a good understanding of what is the most appropriate level of abstraction for the task in hand
  • Great works are often not given due credit initially, not appreciated due to a lack of understanding of their significance. For example, Mendel presented his results in 1865, published them in 1866, and died in 1884
  • They would have been unintelligible to most scientists of the era

    The work was rediscovered in 1902, Sutton-Bowers chromosome theory of inheritance.

  • All sciences are susceptible of being hijacked by a mixture of flawed statistics and emotion. Henry Goddard introduced flawed IQ tests which added a lot of weight to existing impressions that the human population was getting stupider. This and other flawed science lead to compulsory sterilisation laws in the USA and adopted by Hitler shortly after coming to power in 1933
  • To some extent, we must be comfortable with only partial knowledge
  • The mathematics of both computers and life are rooted in binary numbers. Life is coded by a sequence of DNA base pairs. Computers are coded by a sequence of bits. There is a difference in that in computer science we usually use bytes, with 256 permutations, whereas DNA has a triplet code with 64 permutations, but both are based on a power of 2.
  • We should be wary of making predictions when exploring uncharted waters, because actual results have the ability to surprise us.
  • A solution to a problem doesn’t need to be the best in order to become ubiquitous
  • Once natural selection has solved a problem, it tends to stick with that solution, in effect following the maxim ‘if it ain’t broke, don’t fix it’

  • Specialising in one area isn’t enough to understand the full system:
  • Scientists for the most part still specialise on one gene or on the genes involved in one biochemical pathway. But the parts of any machine do not operate independently. If I were to study the carburetor of my car engine, I would still have no idea about the overall function of the engine, much less the engine car.

  • Concerns regarding privacy of our digital data overlap with the risks of providing our genetic data. Data protection policies tend to change over time, and ever more sophisticated analyses of our data become viable along with new opportunities for monetising that data.

All quotes by E.O. Wilson in his critically acclaimed book DNA

One thought on “What the history of traditional science can teach computer scientists

  1. Pingback: Developer On Fire Retrospective | Zombie Code Kill

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