Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI

When history’s pilgrims and pioneers arrived in a new territory, they used the land’s natural resources to build their settlements. Space colonists, on the other hand, will have to bring materials from Earth and assemble them on Mars. Andrew Rush, president and CEO of space-based manufacturing firm Made In Space, believes the process of creating off-world infrastructure will be similar to building IKEA furniture. Only the parts will be made with an advanced 3D printer and put together by an autonomous robot.

“We think the future of in-space operation is one of manufacturing and assembly, just like how you built the table you’re sitting on right now,” Rush says. “That table is a multi-material object, and its pieces were all manufactured in different ways. I don’t think space colonies are going to take a different approach.”

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France isn’t alone. Last month, the European Union’s executive branch recommended its member states increase their public and private sector investment in AIt also pledged billions in direct research spending. Meanwhile, China laid out its AI plan for global dominance last year, a plan that has also been backed up with massive investment. China’s goal is to lead the world in AI technology by 2030. Around the world, our global economic competitors are taking action on artificial intelligence.

Opinion: Rep. John K. Delaney argues that if the United States wants a prosperous economy, it needs a national plan for artificial intelligence.

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Microsoft has purchased startup company Semantic Machines in an effort to make artificial intelligence bots sound more human. The Berkeley, California-based business focuses on contextual understanding of conversation.

Previously, the firm has worked with Apple on speech recognition technology for Siri. Semanitc Machines is lead by professor Dan Klein of UC Berkeley and professor Percy Liang of Standford University in addition to Apple’s former chief speech scientist Larry Gillick.

Microsoft has been working on speech recognition and natural language processing for nearly two decades now. As Cortana has gained a more prominent role in recent years, Redmond is aiming to improve the accuracy and fluency of its assistant.

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The chairs were filled not with Gerard’s fellow Google employees but, instead, more than 100 engineers from about a dozen big privately held companies that Google’s Alphabet had invested in.

As it battles to stand out in late-stage investing, Alphabet’s CapitalG is throwing a new machine learning marathon for its portfolio companies.

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Machine-learning technology is growing ever more accessible. Let’s not have a 9/11-style ‘failure of imagination’ about it.

There is a general tendency among counterterrorism analysts to understate rather than hyperbolize terrorists’ technological adaptations. In 2011 and 2012, most believed that the “Arab Spring” revolutions would marginalize jihadist movements. But within four years, jihadists had attracted a record number of foreign fighters to the Syrian battlefield, in part by using the same social media mobilization techniques that protesters had employed to challenge dictators like Zine El Abidine Ben Ali, Hosni Mubarak, and Muammar Qaddafi.

Militant groups later combined easy accessibility to operatives via social media with new advances in encryption to create the “virtual planner” model of terrorism. This model allows online operatives to provide the same offerings that were once the domain of physical networks, including recruitment, coordinating the target and timing of attacks, and even providing technical assistance on topics like bomb-making.

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“Within five years, I have no doubt there will be robots in every Army formation.”

From the spears hurled by Romans to the missiles launched by fighter pilots, the weapons humans use to kill each other have always been subject to improvement. Militaries seek to make each one ever-more lethal and, in doing so, better protect the soldier who wields it. But in the next evolution of combat, the U.S. Army is heading down a path that may lead humans off the battlefield entirely.

Over the next few years, the Pentagon is poised to spend almost $1 billion for a range of robots designed to complement combat troops. Beyond scouting and explosives disposal, these new machines will sniff out hazardous chemicals or other agents, perform complex reconnaissance and even carry a soldier’s gear.

More from Bloomberg.com: China Casts Doubt on Report of $200 Billion Trade Deficit Offer.

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https://youtube.com/watch?v=vfuHNHLJzoM

Aurora Flight Services’ Autonomous Aerial Cargo Utility System (AACUS) took another step forward as an AACUS-enabled UH-1H helicopter autonomously delivered 520 lb (236 kg) of water, gasoline, MREs, communications gear, and a cooler capable of carrying urgent supplies such as blood to US Marines in the field.

Last week’s demonstration at the Marine Corps Air Ground Combat Center Twentynine Palms in California was the first ever autonomous point-to-point cargo resupply mission to Marines and was carried out as part of an Integrated Training Exercise. The completion of what has been billed as the system’s first closed-loop mission involved the modified helicopter carrying out a full cargo resupply operation that included takeoff and landing with minimal human intervention.

Developed as part of a US$98-million project by the US Office of Naval Research (ONR), AACUS is an autonomous flight system that can be retrofitted to existing helicopters to make them pilot optional. The purpose of AACUS is to provide the US armed forces with logistical support in the field with a minimum of hazard to human crews.

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We propose a method that can generate soft segments, i.e. layers that represent the semantically meaningful regions as well as the soft transitions between them, automatically by fusing high-level and low-level image features in a single graph structure. The semantic soft segments, visualized by assigning each segment a solid color, can be used as masks for targeted image editing tasks, or selected layers can be used for compositing after layer color estimation.

Abstract

Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce semantic soft segments, a set of layers that correspond to semantically meaningful regions in an image with accurate soft transitions between different objects. We approach this problem from a spectral segmentation angle and propose a graph structure that embeds texture and color features from the image as well as higher-level semantic information generated by a neural network. The soft segments are generated via eigendecomposition of the carefully constructed Laplacian matrix fully automatically. We demonstrate that otherwise complex image editing tasks can be done with little effort using semantic soft segments.

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