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Two years ago, Barack Obama appointed a new Secretary of Defense, Ashton Carter—a technocrat physicist, an arms control veteran, and a professor at Stanford—to help close this divide. During his tenure, Carter set up a virtual outpost in Silicon Valley. He worked to make it easier for tech companies to sell things to the Pentagon, for their engineers to work there, and for their bosses to offer up advice. He even let WIRED tag along and write a profile of him. He also impressed the local royalty. “He’s been amazing,” Ben Horowitz, the co-founder of Andreessen Horowitz, told me in an interview.


The former Secretary of Defense built a bridge between tech and the Pentagon. Here, he talks about its importance in an uncertain time.

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Self-driving cars are pretty cool. Really, who wouldn’t want to spend their daily commute surfing social media, chatting with friends or finishing the Netflix series they were watching at 4 am the night before? It all sounds virtually utopian. But what if there is a dark side to self-driving cars? What if self-driving cars kill the jobs? ALL the jobs?

In this video series, the Galactic Public Archives takes bite-sized looks at a variety of terms, technologies, and ideas that are likely to be prominent in the future. Terms are regularly changing and being redefined with the passing of time. With constant breakthroughs and the development of new technology and other resources, we seek to define what these things are and how they will impact our future.

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The automotive industry is undergoing a period of rapid and radical transformation fueled by a range of technological innovations, digital advancements and wave after wave of new entrants and alternative business models; as a result, the entire sector is seeing major disruption.

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Teaches artificial intelligence superhuman relational reasoning.


A key challenge in developing artificial intelligence systems with the flexibility and efficiency of human cognition is giving them a similar ability — to reason about entities and their relations from unstructured data. Solving this would allow these systems to generalize to new combinations of entities, making infinite use of finite means.

Modern deep learning methods have made tremendous progress solving problems from unstructured data, but they tend to do so without explicitly considering the relations between objects.

In two new papers, we explore the ability for deep neural networks to perform complicated relational reasoning with unstructured data. In the first paper — A simple neural network module for relational reasoning — we describe a Relation Network (RN) and show that it can perform at superhuman levels on a challenging task. While in the second paper — Visual Interaction Networks — we describe a general purpose model that can predict the future state of a physical object based purely on visual observations.

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