In a wonderfully concise passage in his 1940 preface to Adolfo Bioy Casares’ The Invention of Morel, Jorge Luis Borge — taking issue with Ortega y Gasset’s elevation of “psychological” fiction over the “fantastic” — offers a devastating critique of the pretensions of a great deal of modern “psychological realism”:
The Russians and their disciples have demonstrated, tediously, that no one is impossible. A person may kill himself because he is so happy, for example, or commit murder as an act of benevolence. Lovers may separate forever as a consequence of their love. And one man can inform on another out of fervor or humility. In the end such complete freedom is tantamount to chaos. But the psychological novel would also be a “realistic” novel, and have us forget that it is a verbal artifice, for its uses each vain precision (or each languid obscurity) as a new proof of realism.
Wendell Berry in an essay entitled “Preserving Wildness” collected inHome Economics makes the case for what may be called an economy of attentiveness (as opposed to an economy of mere attention).
The good worker loves the board before it becomes a table, loves the tree before it yields the board, loves the forest before it gives up the tree. The good worker understands that a badly made artifact is both an insult to its user and a danger to its source. We could say, then, that good forestry begins with the respectful husbanding of the forest that we call stewardship and ends with well-made tables and chairs and houses, just as good agriculture begins with stewardship of the fields and ends with good meals.
The temperature is an artifact—an indirect measurement—of the energy level in the fluid.
“One popular misconception [about machine learning] is that people think they have enough data when they don’t. When people say machine learning, a very large segment of predictions are based on existing data. And in order for that to work, you generally have to have a big labeled set of data,” says Hillary Green-Lerman of Codecademy.
Emphasis on labeled.
“People often don’t realize how much of machine learning is getting data into a format so that you can feed it into an algorithm. The algorithms are actually usually available pre-baked,” Hillary said. “In a lot of ways, you need to know how to pick the best linear regression for your data, but you don’t really need to know the intricacies of how it’s programmed. You do need to work the data into a format where each row is a data point, the kind of thing you’d want to pick.
“Why do people assume a discipline they don’t know is less complicated than the one they do know?”
— Yung-Hsing Wu
> One of the largest obstacles in constructing effective temporal queries is the English language. Our natural language for describing interval-event relationships completely masks the underlying computational complexity.
There will be no humans elsewhere. Only here. Only on this small planet. We are a rare as well as an endangered species. Every one of us, in the cosmic perspective, precious. If a human disagrees with you, let him live. In a hundred billion galaxies, you will not find another.
A book is made from a tree. It is an assemblage of flat, flexible parts (still called “leaves”) imprinted with dark pigmented squiggles. One glance at it and you hear the voice of another person, perhaps someone dead for thousands of years. Across the millennia, the author is speaking, clearly and silently, inside your head, directly to you. Writing is perhaps the greatest of human inventions, binding together people, citizens of distant epochs, who never knew one another. Books break the shackles of time ― proof that humans can work magic.
[rnelsonee offered the best explanation][r] of the imperial measurement system I have ever read:
> Imperial is similar to metric if you constrain yourself to one type of measurement. Like liquid volume uses power of 2 instead of 10:
1 dram x 2 ** 2 = 1 Tbsp
1 Tbsp x 2 = 1 fl oz
1 fl oz x 2 = 1 jig
1 jig x 2 = 1 gill
1 gill x 2 = 1 cup
1 cup x 2 = 1 pint
1 pint x 2 = 1 quart
1 quart x 2 ** 2 = 1 gallon
[*The notation “2 ** 2” should be read as “two squared [that is, 4] or “two to the second power.*]
> But then Imperial gets all weird because entire different scales get mixed together. For example, a mile isn’t a terrible unit – it’s just a thousand paces (hence miles), and is more intuitive/easier to measure (when walking) than km. I like the foot and inch (thumb size) as well, even though people obviously have different sized feet (but hey, it’s not like the meter is easy to recreate with no tools). But no one has any business mixing inches and miles (at least they didn’t 1,000 years ago) because you’d measure troop movements in miles and your dick in inches. It wasn’t until we started doing a lot of ‘extreme’ levels/measurements with physics that we needed metric to easily convert between the two scales.
Of course, this was after this:
from Josh Bazell’s “The Wild Things”
And then this:
Imperial versus Metric by Bar Graph
[Kevin Kelly tells][kk] those of us interested in the conjunction of the internet, the internet of things, artificial intelligence, and sensors that we are not late:
> Because here is the other thing the greybeards in 2044 will tell you: Can you imagine how awesome it would have been to be an entrepreneur in 2014? It was a wide-open frontier! You could pick almost any category X and add some AI to it, put it on the cloud. Few devices had more than one or two sensors in them, unlike the hundreds now. Expectations and barriers were low. It was easy to be the first. And then they would sigh, “Oh, if only we realized how possible everything was back then!”
Good thing I spent the weekend with an Arduino board.