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.

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.

Machine learning impact on the economy will be increased productivity and abundance, but far less human labour and judgement. We may need a system focused on more than just efficient production that places greater prioritization on the less desirable side effects of capitalism.

An incredible, in-depth overview of the thinking around the development and rise of ASI (Artificial Superintelligece).

Daily news about AI and robotics.

Do you worry about your vacuum? Not in the way where you’re concerned about breaking a valuable object - but in an emotional, empathetic concern for its being? If so, then you’re primed for today’s discussion about the wellbeing of robots! We can personify objects, assigning them human qualities, but will technology get to a point where it DESERVES these moral concerns? Can you imagine a day when you will empathize with your computer or care if it lives or dies?

If time is money, then the endlessly recycled quotes from Musk and Hawking are a goldmine for harried reporters and editors. What more context do you need, than a pair of geniuses publicly fretting about the fall of humankind?

This study compares the accuracy of personality judgment—a ubiquitous and important social-cognitive activity—between computer models and humans. Using several criteria, we show that computers’ judgments of people’s personalities based on their digital footprints are more accurate and valid than judgments made by their close others or acquaintances (friends, family, spouse, colleagues, etc.). Our findings highlight that people’s personalities can be predicted automatically and without involving human social-cognitive skills.

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.