Source: The Creativity Post, Mar 2018
the first version of Model View Controller.
A simple, and highly flexible mean of connecting large quantities of data with users, in an intuitive way. The core principle was powerfully simple–map human mental models to computational behavior. That core idea single-handedly set the foundation for the field user interface (UI) and the technological framework for graphic user interfaces (GUI).
As time progressed–and especially once the internet opened up–MVC became flatter and more industrialized. In its original version MVC is a triangular relationship between a human, a machine and a tool. Keeping the tool (and any monetization of it) away from users, or data. In the new industrialist version it made more sense to guard the data, especially for its monetization potential, and offering the utility more as a mean to contain users within the boundaries of your system …
Source: Forbes, Dec 2016
Consider a famous shape, the Klein bottle, which is a bottle that loops into itself to create a shape with no inside and outside. You can obtain it by gluing together two Möbius strips along their edge.
Sadly, we can’t quite fit a Klein bottle in our 3-dimensional space, since it is not supposed to actually cut through itself. Like the ribbon above, the critical junction where it folds back in is meant to allow the two tubes to be completely separated.
But that’s quite easy to achieve using color, right? Here’s an example:
This uses the exact same approach as we had with the ribbon. The tube shifts into a different area in color space as it loops back in, and by the time it passes itself it is already very separated (green vs white). The Klein bottle sits very comfortably in 4-dimensional space, without any nasty self intersections.
Source: The Creativity Post, Mar 2018
Claude Shannon published his seminal paper A Mathematical Theory of Communication, establishing the thereafter held fact that everything–all information, in all of its forms–can be broken into 1’s and 0’s
What Shannon’s work highlights is the flexibility of abstraction. If before tools were designed based on the information they were carrying, the new binary norm lets us ignore that and know that whatever we want to say–no matter how long, in what language and whether it carries sense–could be communicated in bits.
Through the joint work of Shannon and Reenskaug we have written a status quo of extreme abstraction and rigid efficiency. The meaning of information is irrelevant, all data can be reduced to 1’s and 0’s.
Source: HBR, Mar 2018
building a culture focused on performance may not be the best, healthiest, or most sustainable way to fuel results. Instead, it may be more effective to focus on creating a culture of growth.
A culture is simply the collection of beliefs on which people build their behavior.
a true growth culture also focuses on deeper issues connected to how people feel, and how they behave as a result. In a growth culture, people build their capacity to see through blind spots; acknowledge insecurities and shortcomings rather than unconsciously acting them out; and spend less energy defending their personal value so they have more energy available to create external value. How people feel – and make other people feel — becomes as important as how much they know.
Building a growth culture, we’ve found, requires a blend of individual and organizational components:
- An environment that feels safe, fueled first by top by leaders willing to role model vulnerability and take personal responsibility for their shortcomings and missteps.
- A focus on continuous learning through inquiry, curiosity and transparency, in place of judgment, certainty and self-protection.
- Time-limited, manageable experiments with new behaviors in order to test our unconscious assumption that changing the status quo is dangerous and likely to have negative consequences.
- Continuous feedback – up, down and across the organization – grounded in a shared commitment to helping each other grow and get better.
A performance culture asks, “How much energy can we mobilize?” and the answer is only a finite amount. A growth culture asks, “How much energy can we liberate?” and the answer is infinite.
Source: INC, Mar 2018
Scientists and educators have long noted that kids who have a positive attitude towards math do better in the subject, but is that just because acing tests naturally makes you enjoy something, or does the arrow of causation point the other way? Does starting off with the expectation that you’ll enjoy and be good at math help you master numbers?
To start to tease this out a research team out of Stanford recently analyzed the math skills and attitudes of 240 kids aged seven to ten, as well as running 47 of them through an fMRI machine while asking them to do some basic arithmetic. What did they find?
As expected, kids who did well in math liked math more, both according to self reports and their parents, and kids who hated the subject did poorly. But the brain scans also turned up something much more fascinating. The images revealed that the hippocampus, a brain area linked with memory and learning, was significantly more active in kids with a positive attitude towards math.
It appears it’s not just that children like subjects they’re good at. It’s also that liking a subject helps students’ brain actually work better.
“Attitude is really important,” said Chen, “Based on our data, the unique contribution of positive attitude to math achievement is as large as the contribution from IQ.”
Source: Nature, Jan 2018
Science-fiction writer Isaac Asimov is widely credited with saying that “the most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ but ‘That’s funny’.”
how important is serendipity to science?
Yaqub gathered hundreds of historical examples. After studying these, he says, he has pinned down some of the mechanisms by which serendipity comes about. These include astute observation, errors and “controlled sloppiness” (which lets unexpected events occur while still allowing their source to be traced). He also identifies how the collaborative action of networks of people can generate serendipitous findings.
Source: Scientific American, Mar 2018
While I have found that a certain number of traits— including passion, perseverance, imagination, intellectual curiosity, and openness to experience– do significantly explain differences in success, I am often intrigued by just how much of the variance is often left unexplained.
In recent years, a number of studies and books–including those by risk analyst Nassim Taleb, investment strategist Michael Mauboussin, and economist Robert Frank— have suggested that luck and opportunity may play a far greater role than we ever realized, across a number of fields, including financial trading, business, sports, art, music, literature, and science.
some recent findings:
In the final outcome of the 40-year simulation, while talent was normally distributed, success was not. The 20 most successful individuals held 44% of the total amount of success, while almost half of the population remained under 10 units of success (which was the initial starting condition).
the most talented individuals were rarely the most successful. In general, mediocre-but-lucky people were much more successful than more-talented-but-unlucky individuals. The most successful agents tended to be those who were only slightly above average in talent but with a lot of luck in their lives.