Science
Interdisciplinary
Which half?
Scientific writing
A tiny rivulet in a distant forest
The downgrading of experience
Humility
Art and science
The Structure of Scientific Revolutions
BLDGBLOG
The Art of Doing Science and Engineering: Learning to Learn
The illustrated guide to a Ph.D.
evermore, and other beautiful things
An Article by Linus the SephistIf all evidence of civilization on Earth was destroyed, and humans had to re-build society from the ground up, what would be different? Feynman reckons that pivotal scientific moments, like the discovery of the atom, will still happen in the same way. Perhaps mathematics will be similarly rediscovered.
Someone told me once in response to this question, no artwork would ever be recreated. The art we create – music, stories, dance, film – isn’t a fundamental element of the universe, or even of humanity. It’s unique to each artist. If you choose to create art, you leave something in the world that has never had a chance to exist before, and will never again have a chance to exist. There will never be another Beatles or Studio Ghibli or Picasso. Art, in its infinite variations of originality, is cosmically unique in a way the sciences will never be. Art immortalizes human experiences that would otherwise vanish in time.
Reality is Very Weird and You Need to be Prepared for That
An EssayWe might be closer than we think to cures for depression, hypertension, and yes, even obesity.
The answer to scurvy was just one thing, plus a few wrinkles — mostly “not all citrus has the antiscorbutic property” and “most animals can’t get scurvy”. This was only difficult because people weren’t prepared to deal with basic wrinkles, but we can do better by learning from their mistakes.
This means don’t give up easily. It suggests that there is lots of low-hanging fruit, because even simple explanations are easily missed.
Lots of theories have been tried, and lots of them have been given up because of something that looks like contradictory evidence. But the evidence might not actually be a contradiction — the real explanation might just be slightly more complicated than people realized. Go back and revisit scientific near-misses, maybe there’s a wrinkle they didn’t know how to iron out.
Tortured phrases
An Article by Holly ElseIn April 2021, a series of strange phrases in journal articles piqued the interest of a group of computer scientists. The researchers could not understand why researchers would use the terms ‘counterfeit consciousness’, ‘profound neural organization’ and ‘colossal information’ in place of the more widely recognized terms ‘artificial intelligence’, ‘deep neural network’ and ‘big data’.
Further investigation revealed that these strange terms — which they dub “tortured phrases” — are probably the result of automated translation or software that attempts to disguise plagiarism. And they seem to be rife in computer-science papers.
Why Most Published Research Findings Are False
A Research Paper by John P.A. IoannidisThere is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance.
A hypothesis is a liability
A Research Paper by Itai Yanai & Martin LercherThere is a hidden cost to having a hypothesis. It arises from the relationship between night science and day science, the two very distinct modes of activity in which scientific ideas are generated and tested, respectively [1, 2]. With a hypothesis in hand, the impressive strengths of day science are unleashed, guiding us in designing tests, estimating parameters, and throwing out the hypothesis if it fails the tests. But when we analyze the results of an experiment, our mental focus on a specific hypothesis can prevent us from exploring other aspects of the data, effectively blinding us to new ideas.
Deadlines are bullshit
In software development deadlines are a necessary evil. It is important to understand when they are necessary, and it is important to understand why they are evil.
- External vs. internal deadlines
- Why are internal deadlines evil?
- Engineers who love their work
External vs. internal deadlines
When are deadlines necessary?
- Contractual obligations
- Technical liabilities (e.g., dependency EOL)
- Compliance, government, investors, and other external stakeholders
What do all of these deadlines have in common? They are all important. They are all deadlines that cannot be missed. They are all external.
When are deadlines evil?
- Your manager says you have a deadline
- Your software development methodology says you have deadlines
What do all of these deadlines have in common? None of them are important. They are arbitrary. They are all internal. They are all bullshit.
Why are internal deadlines evil?
- Estimation: When estimating engineering work a substantial time investment is required by an engineer in order to get an accurate estimate.
- Misaligned Incentives: There is an incentive to lie and give estimates much longer than the feature is truly expected to take.
- Low Morale: Deadlines are likely to be missed often. Repeated failure has a cost to the morale of the team.
- Micromanagement: Deadlines are wielded by middle managers as a whip to harass and annoy engineers working on features.
- High Stress: When engineers feel the pressure of other stakeholders holding deadlines over their heads it creates an environment of high stress.
- High Turnover: On teams with high turnover rates the best engineers have an easy time finding new work and leave quickly, the worst engineers have a difficult time finding work and remain. This selects for a lower quality team over time.
Engineers who love their work
The resolution is simple. Never have internal deadlines. Operate on a prioritized and ordered list of features. Estimate only when necessary to prioritize and do so in a t-shirt sizing way. Trust your engineers and they will begin to love their work. Engineers who love their work are happy and productive.