Using both conventional media and covert channels, the Kremlin relies on disinformation to create doubt, fear and discord in Europe and the United States.
Moscow’s targeting of the West with disinformation dates to a Cold War program the Soviets called “active measures.” … the ideological component has evaporated, but the goal of weakening adversaries remains.
Source: A Powerful Russian Weapon: The Spread of False Stories – The New York Times
Math Myth Conjecture: If one restricts one’s attention to the hardest cases, namely, graduates of top engineering schools such as MIT, RPI, Cal. Tech., Georgia Tech., etc., then the percent of such individuals holding engineering as opposed to management, financial or other positions, and using more than Excel and eighth grade level mathematics (arithmetic, a little bit of algebra, a little bit of statistics, and a little bit of programming) is less than 25% and possibly less than 10%.
Source: The Math Myth, Bryan Caplan | EconLog | Library of Economics and Liberty
One cybersecurity firm estimates that extortive attacks now cost small and medium companies at least $75 billion in expenses and lost productivity each year.
banks have started to keep tens of thousands of dollars in Bitcoin ready in case of an attack. “Buying bitcoin on any one of the U.S. exchanges is a three-to-five day wait time, so we’ve been forced into the position of having to stock bitcoin as if it were computer equipment and have it ready for our use,”
Source: How Ransomware Became a Billion-Dollar Nightmare for Businesses – The Atlantic
It’s not clear the organization can effectively confront—or even survive—today’s challenges.
Source: Australia’s Kevin Rudd Issues a Dire Warning About the United Nations – The Atlantic
“national political leaders are no longer, in substance, capable of delivering self-contained, national solutions to the problems faced by their people, as the policy levers available increasingly slip beyond their grasp”
— Kevin Rudd, 26th Prime Minister of Australia
PDF: UN 2030: Rebuilding Order in a Fragmenting World
Despite new biology-like tools, some insist interpretation is impossible.
Even if it were possible to impose this kind of interpretability, it may not always be desirable. The requirement for interpretability can be seen as another set of constraints, preventing a model from a “pure” solution that pays attention only to the input and output data it is given, and potentially reducing accuracy.
“What machines are picking up on are not facts about the world,” Batra says. “They’re facts about the dataset.” That the machines are so tightly tuned to the data they are fed makes it difficult to extract general rules about how they work. More importantly, he cautions, if you don’t know how it works, you don’t know how it will fail. And when they do they fail, in Batra’s experience, “they fail spectacularly disgracefully.”
They pick up on patterns invisible to their engineers; but can’t know which of those patterns exist nowhere else. Machine learning researchers go to great lengths to avoid this phenomenon, called “overfitting,” but as these algorithms are used in more and more dynamic situations, their brittleness will inevitably be exposed.
Source: Human and Artificial Intelligence May Be Equally Impossible to Understand