The Melancholy of Infinite Space

We live at the very beginning of the Universe.

As we peer back with our telescopes toward the beginning of time, and measure the age of the universe, we are beginning to find that the universe is closer to ten billion years old than to fifty; that the oldest of the stars we see around us are, in fact, as old as any star can be; as old as the universe itself. Looking outward, we are finding that the gravity of the universe is not enough to pull it back together in some future cataclysmic big-crunch. The universe will expand forever.

Ten billion years. A mere eyeblink in cosmic time. We stand at the beginning of time, looking outward into the void of infinite time.

And what of us?

We have no guarantees. Humanity has spread across the globe; populated ecosystems from the equatorial rain forest to the polar ice, but as a species we are new, a species barely a hundred thousand years old. This is far too young for us to begin to guess whether Darwin’s awful mill will judge us a success, or whether we will be wiped away as another dead end, one of a million failed experiments. The Earth has no memory for the dead-ends of evolution. In a few hundred thousand years glaciers would grind our works and our bones into gravel, would grind the gravel into sand, and in a few hundred million years the movement of continents erase the last of any trace of our brief existence, save perhaps for a handful of deeply-buried and enigmatic fossils.

But some species survive, and perhaps we will be among them. A species might last a million years, even ten million years, and who can say that we will not be among those rare evolutionary successes, with success judged by that cruel god who knows no mercy or kindness, only death or survival? And in a million years, or even ten thousand years, who can tell what we shall become? All we can say is that we will become something unguessable, possibly unimaginable.

Very few species last more than ten million years, and those few are the living fossils, the ones frozen by evolution into some marginal niche. A genus may last longer, and perhaps genus homo will last a hundred million years or more. There would, then, in time be other species of humans, radiating into other ecological niches. But even genera evolve or are supplanted; and in life, nothing lasts. It is unlikely that genus homo will last a billion years. A billion years ago, even multi-celled life had yet to evolve; there were no plants, no animals, no fungus, only primitive bacteria. A billion years hence, we cannot guess what life will be like, but it will no more be us than we are those primitive bacteria.

Source: The Melancholy of Infinite Space

 

Israel Proves the Desalination Era is Here – Scientific American

Ten miles south of Tel Aviv, I stand on a catwalk over two concrete reservoirs the size of football fields and watch water pour into them from a massive pipe emerging from the sand. The pipe is so large I could walk through it standing upright, were it not full of Mediterranean seawater pumped from an intake a mile offshore.

“Now, that’s a pump!” Edo Bar-Zeev shouts to me over the din of the motors, grinning with undisguised awe at the scene before us. The reservoirs beneath us contain several feet of sand through which the seawater filters before making its way to a vast metal hangar, where it is transformed into enough drinking water to supply 1.5 million people.

We are standing above the new Sorek desalination plant, the largest reverse-osmosis desal facility in the world, and we are staring at Israel’s salvation. Just a few years ago, in the depths of its worst drought in at least 900 years, Israel was running out of water. Now it has a surplus. That remarkable turnaround was accomplished through national campaigns to conserve and reuse Israel’s meager water resources, but the biggest impact came from a new wave of desalination plants.

Source: Israel Proves the Desalination Era is Here – Scientific American

 

Dailybugs 1

Warning: The postinst maintainerscript of the package skypeforlinux
Warning: seems to use apt-key (provided by apt) without depending on gnupg or gnupg2.
Warning: This will BREAK in the future and should be fixed by the package maintainer(s).
Note: Check first if apt-key functionality is needed at all – it probably isn’t!
OK

 

Introduction to Python Internals via CPython implementation

We discuss how Python works internally, what is PyObject and how types are being handled internally

Before we dive into the deep of Python language implementation, we need to get familiar with the main concept in Python. It’s quite simple – everything is an object. This is our first step in learning about Python internals and an entry point to our journey.

Main topic today is to understand how Python’s objects are being handled on a low-level. We’ll be talking about CPython implementation of Python 2.7.8.

I assume you download Python sources and unzip it, so all references to source code will be pointed relative to the root folder.

PyObject & PyVarObject

Everything in Python is an Object. Literally, anything you are working with in Python is a C‘s PyObject:

  • functions
  • slices
  • files
  • classes
  • iterators
  • descriptors
  • sequences
  • numeric types

Source: Introduction to Python Internals via CPython implementation

 

Why Experts Make Bad Teachers

We’d all agree that to teach a subject, you must know the subject. So you’d think that experts would be the best teachers, but they’re not. The question is why?

To understand why experts have trouble teaching well, you have to understand what makes experts different from the rest of us.

People who are truly experts in a subject have knowledge most of us don’t. But that does NOT make them a true expert.

What makes them a true expert is understanding.

And with real understanding comes Abstractions.

Source: Why Experts Make Bad Teachers — Medium

 

Special projects



Impactful scientific work requires working on the right problems
— problems which are not just interesting, but whose solutions
matter. In this post, we list several problem areas likely to be
important both for advancing AI and for its long-run impact on
society.
Special projects

 

Introduction to Scientific Python – Stanford

CME 193: Introduction to Scientific Python

Course description: This short course runs for the first three weeks of the quarter and is offered each quarter during the academic year. It is recommended for students who want to use Python in math, science, or engineering courses and for students who want to learn the basics of Python programming. The goal of the short course is to familiarize students with Python’s tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include control flow, basic data structures, File I/O, and an introduction to NumPy/SciPy.

Source: Introduction to Scientific Python