Interesting links and articles I read but didn’t make specific posts about.

Interesting links and articles I read but didn’t make specific posts about.

Big-name scientists worry that runaway artificial intelligence could pose a threat to humanity. Beyond the speculation is a simple question: Are we fully in control of our technology?

It is now clear that current Internet security measures and the cryptography behind them will not withstand the new computational capabilities that quantum computers will bring.

We are going to start recording and automatically transcribing most of what we say. Instead of evaporating into memory, words spoken aloud will calcify as text, into a Record that will be referenced, searched, and mined. It will happen by our standard combination of willing and allowing. It will happen because it can.

UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence.

The “real” challenge technology presents isn’t that it replaces workers, but rather displaces them.

Microelectromechanical systems (MEMS) are also called “smart dust,” and are now a reality.

How to maximize the benefits of AI while avoiding potential pitfalls. This article gives numerous examples of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

We can’t choose a world where the US gets to spy but China doesn’t, or even a world where governments get to spy and criminals don’t. We need to choose, as a matter of policy, communications systems that are secure for all users, or ones that are vulnerable to all attackers. It’s security or surveillance.

Using a bio-inspired system architecture, Deep Mind scientists have created a single algorithm (Deep Q-network: “deep convolutional network”) that is actually able to develop problem-solving skills and is able to understand spatial relationships between different objects in an image, such as distance from one another, in such a sophisticated way that it can actually re-envision the scene from a different viewpoint. This type of system was inspired by early work done on the visual cortex. And then they immediately put it to use learning a set of classic Atari video games.

Q&A with one of the people behind deep learning. Good details about machine learning.