We can best view the method of science as the use of our sophisticated methodological toolbox

Metadata

• Author: OUP Academic
• Category: article
• URL: https://academic.oup.com/pnasnexus/article/3/4/pgae112/7626940?login=false

Highlights

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology.

scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study.

This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis.

Empirical evidence thus challenges the common view of the scientific method.

This provides a new perspective to the scientific method—embedded in our sophisticated methods and instruments—and suggests that we need to reform and extend the way we view the scientific method and discovery process.

In fact, hundreds of major scientific discoveries did not use “the scientific method”, as defined in science dictionaries as the combined process of “the collection of data through observation and experiment, and the formulation and testing of hypotheses” (1). In other words, it is “The process of observing, asking questions, and seeking answers through tests and experiments” (2, cf. 3).

In general, this universal method is commonly viewed as a unifying method of science and can be traced back at least to Francis Bacon's theory of scientific methodology in 1620 which popularized the concept

Science thus does not always fit the textbook definition.

Comparison across fields provides evidence that the common scientific method was not applied in making about half of all Nobel Prize discoveries in astronomy, economics and social sciences, and a quarter of such discoveries in physics, as highlighted in Fig. 2b. Some discoveries are thus non-experimental and more theoretical in nature, while others are made in an exploratory way, without explicitly formulating and testing a preestablished hypothesis.

We find that one general feature of scientific methodology is applied in making science's major discoveries: the use of sophisticated methods or instruments. These are defined here as scientific methods and instruments that extend our cognitive and sensory abilities—such as statistical methods, lasers, and chromatography methods. They are external resources (material artifacts) that can be shared and used by others—whereas observing, hypothesizing, and experimenting are, in contrast, largely internal (cognitive) abilities that are not material (Fig. 2).

Just as science has evolved, so should the classic scientific method—which is construed in such general terms that it would be better described as a basic method of reasoning used for human activities (non-scientific and scientific).

An experimental research design was not carried out when Einstein developed the law of the photoelectric effect in 1905 or when Franklin, Crick, and Watson discovered the double helix structure of DNA in 1953 using observational images developed by Franklin.

Direct observation was not made when for example Penrose developed the mathematical proof for black holes in 1965 or when Prigogine developed the theory of dissipative structures in thermodynamics in 1969. A hypothesis was not directly tested when Jerne developed the natural-selection theory of antibody formation in 1955 or when Peebles developed the theoretical framework of physical cosmology in 1965.

Sophisticated methods make research more accurate and reliable and enable us to evaluate the quality of research.

Applying observation and a complex method or instrument, together, is decisive in producing nearly all major discoveries at 94%, illustrating the central importance of empirical sciences in driving discovery and science.

How much are your 9's worth?

Metadata

• Author: Ross Brodbeck
• Category: rss
• URL: https://hross.substack.com/p/how-much-are-your-9s-worth

Highlights

All nines are not created equal. Most of the time I hear an extraordinarily high availability claim (anything above 99.9%) I immediately start thinking about how that number is calculated and wondering how realistic it is.

Human beings are funny, though. It turns out we respond pretty well to simplicity and order.

Having a single number to measure service health is a great way for humans to look at a table of historical availability and understand if service availability is getting better or worse. It’s also the best way to create accountability and measure behavior over time…

… as long as your measurement is reasonably accurate and not a vanity metric.

Cheat #1 - Measure the narrowest path possible.

This is the easiest way to cheat a 9’s metric. Many nines numbers I have seen are various version of this cheat code. How can we create a narrow measurement path?

Cheat #2 - Lump everything into a single bucket.

Not all requests are created equal.

Cheat #3 - Don’t measure latency.

This is an availability metric we’re talking about here, why would we care about how long things take, as long as they are successful?!

Cheat #4 - Measure total volume, not minutes.

Let’s get a little controversial.

In order to cheat the metric we want to choose the calculation that looks the best, since even though we might have been having a bad time for 3 hours (1 out of every 10 requests was failing), not every customer was impacted so it wouldn’t be “fair” to count that time against us.

Building more specific models of customer paths is manual. It requires more manual effort and customization to build a model of customer behavior (read: engineering time). Sometimes we just don’t have people with the time or specialization to do this, or it will cost to much to maintain it in the future.

We don’t have data on all of the customer scenarios. In this case we just can’t measure enough to be sure what our availability is.

Sometimes we really don’t care (and neither do our customers). Some of the pages we build for our websites are… not very useful. Sometimes spending the time to measure (or fix) these scenarios just isn’t worth the effort. It’s important to focus on important scenarios for your customers and not waste engineering effort on things that aren’t very important (this is a very good way to create an ineffective availability effort at a company).

Mental shortcuts matter. No matter how much education we try, it’s hard to change perceptions of executives, engineers, etc. Sometimes it is better to pick the abstraction that helps people understand than pick the most accurate one.

Data volume and data quality are important to measurement. If we don’t have a good idea of which errors are “okay” and which are not, or we just don’t have that much traffic, some of these measurements become almost useless (what is the SLO of a website with 3 requests? does it matter?).

What is your way of cheating nines? ;)

J.G. Ballard: My Favorite Books

Metadata

• Author: The MIT Press Reader
• Category: article
• URL: https://thereader.mitpress.mit.edu/j-g-ballard-my-favorite-books/

Highlights

In this respect I differed completely from my children, who began to read (I suspect) only after they had left their universities. Like many parents who brought up teenagers in the 1970s, it worried me that my children were more interested in going to pop concerts than in reading “Pride and Prejudice” or “The Brothers Karamazov” — how naive I must have been. But it seemed to me then that they were missing something vital to the growth of their imaginations, that radical reordering of the world that only the great novelists can achieve.

I now see that I was completely wrong to worry, and that their sense of priorities was right — the heady, optimistic world of pop culture, which I had never experienced, was the important one for them to explore. Jane Austen and Dostoyevsky could wait until they had gained the maturity in their 20s and 30s to appreciate and understand these writers, far more meaningfully than I could have done at 16 or 17.

Books:

• “The Day of the Locust,” Nathanael West
• “Collected Short Stories,” Ernest Hemingway
• “The Rime of the Ancient Mariner,” Samuel Taylor Coleridge
• “The Annotated Alice,” ed. Martin Gardner
• “The World through Blunted Sight,” Patrick Trevor-Roper
• “The Naked Lunch,” William Burroughs
• “The Black Box,” ed. Malcolm MacPherson
• “Los Angeles Yellow Pages”
• “America,” Jean Baudrillard
• “The Secret Life of Salvador Dalí,” by Dalí

Some article from the past ;)

Why We Love Music

Metadata

• Author: Jill Suttie
• Category: article
• URL: https://greatergood.berkeley.edu/article/item/why_we_love_music

Highlights

Using fMRI technology, they’re discovering why music can inspire such strong feelings and bind us so tightly to other people.

“A single sound tone is not really pleasurable in itself; but if these sounds are organized over time in some sort of arrangement, it’s amazingly powerful.”

There’s another part of the brain that seeps dopamine, specifically just before those peak emotional moments in a song: the caudate nucleus, which is involved in the anticipation of pleasure. Presumably, the anticipatory pleasure comes from familiarity with the song—you have a memory of the song you enjoyed in the past embedded in your brain, and you anticipate the high points that are coming.

During peak emotional moments in the songs identified by the listeners, dopamine was released in the nucleus accumbens, a structure deep within the older part of our human brain.

This finding suggested to her that when people listen to unfamiliar music, their brains process the sounds through memory circuits, searching for recognizable patterns to help them make predictions about where the song is heading. If music is too foreign-sounding, it will be hard to anticipate the song’s structure, and people won’t like it—meaning, no dopamine hit. But, if the music has some recognizable features—maybe a familiar beat or melodic structure—people will more likely be able to anticipate the song’s emotional peaks and enjoy it more. The dopamine hit comes from having their predictions confirmed—or violated slightly, in intriguing ways.

On the other hand, people tend to tire of pop music more readily than they do of jazz, for the same reason—it can become too predictable.

Her findings also explain why people can hear the same song over and over again and still enjoy it. The emotional hit off of a familiar piece of music can be so intense, in fact, that it’s easily re-stimulated even years later.

“Musical rhythms can directly affect your brain rhythms, and brain rhythms are responsible for how you feel at any given moment,” says Large.

“If I’m a performer and you’re a listener, and what I’m playing really moves you, I’ve basically synchronized your brain rhythm with mine,” says Large. “That’s how I communicate with you.”

He points to the work of Erin Hannon at the University of Nevada who found that babies as young as 8 months old already tune into the rhythms of the music from their own cultural environment.

“Liking is so subjective,” he says. “Music may not sound any different to you than to someone else, but you learn to associate it with something you like and you’ll experience a pleasure response.”

Interesting findings

Some appetizers for the book on breaking Enigma.

Have enjoyed The New York Trilogy

Filosofą išgarsinusi „Nuovargio visuomenė“ (vok. Müdigkeitsgesellschaft) Vokietijoje išleista 2010 m. Šioje knygoje jis dešimtmečiu aplenkė šiandien visuotinai pripažįstamą perdegimo kultūros įsigalėjimą, ypač būdingą vadinamajai tūkstantmečio kartai (gimusiesiems 1981–1996 m.). Kasdien patiriama tokia stipri stimuliacija, ypač internete ir socialiniuose tinkluose, kad sunkiai begebama jausti ar savarankiškai mąstyti. Ironiška, kad Hano knygos populiarinamos iš lūpų į lūpas būtent per internetą.

Highlights

COBOL remains crucial to businesses and institutions around the world.

It is estimated $3 trillion in daily commerce flows through COBOL systems, while 95% of ATM swipes and 80% of in-person banking transactions rely on COBOL code.

when unemployment claims suddenly spiked due to the pandemic, these archaic systems could not keep up, which means that benefits are not being distributed.

The spike in unemployment claims exposed another new problem: there is no one around to repair these legacy systems.

Although a few universities still offer COBOL courses, the number of people studying it today is extremely small.

COBOL Cowboys’ business model is more akin to the gig economy rather than to that of the companies at which these industry veterans spent their careers. It is staffed with mostly older freelancers, everyone is an independent consultant, and there is no promise of any work. The company’s slogan is “not our first rodeo.”

“A lot of us want to spend time with our grandkids, but we also want to keep busy.”

Hinshaw was in contact with the state of New Jersey at the beginning of the current crisis, and quickly saw that the unemployment claims issue wasn’t a back-end problem. Every claim that was sent to the host (the back-end mainframe) was processed.

“They all have the same problem on the front end,” says Hinshaw, adding that these organizations’ Web sites were not designed to handle that kind of volume, while the back-end mainframes typically can.

IBM, which sold many of the mainframes on which COBOL systems run, has been scrambling to launch initiatives in order to meet the urgent need for COBOL programmers to address the overloaded unemployment systems.

While these measures should eventually help to alleviate the shortage in COBOL programming expertise, it is clear that the past approach of “if it isn’t broken, don’t fix it” has contributed to the current problem.

Are you learning COBOL already? ;)

Looking forward to reading the book someday.

How to calculate SLO

cross-posted from: https://feddit.it/post/7752642

A week of downtime and all the servers were recovered only because the customer had a proper disaster recovery protocol and held backups somewhere else, otherwise Google deleted the backups too

Google cloud ceo says "it won't happen anymore", it's insane that there's the possibility of "instant delete everything"