Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI

This event will be webcast live from this page.

The Technology Policy Program invites you to the launch of our upcoming report, A National Machine Intelligence Strategy for the United States.

The United States is at the precipice of a defining moment in history. Over the past five years, progress in machine intelligence (MI) has greatly accelerated. From the defeat of Go champion Lee Sedol by DeepMind’s AlphaGo program to the first deployments of fully-autonomous vehicles on public roads, recent events are challenging us to re-evaluate what may soon be possible for computerized systems. MI systems have already begun to quietly pervade a growing share of businesses, governments, and individual lives around the world, and we are only just beginning to grasp the impacts that this technological revolution will have on our economy, our society, and our national security. In our paper, we outline they key elements of a comprehensive national strategy for the United States to promote the safe and responsible development of MI, and to maintain U.S. leadership in MI technology.

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The transition toward a new mobility ecosystem could have wide-reaching impacts that span a host of industries and players, including—but not limited to:

Global automotive OEMs face momentous and difficult decisions. OEMs will need to determine if they should evolve from a (relatively) fixed capital production, first-transaction, product-sale business into one centered on being an end-to-end mobility services provider. This would represent a profound business model change and the development of entirely new capabilities to be competitively and sustainably viable.

The traditional capabilities of vehicle manufacturers and suppliers will likely need to expand, collaborating with autonomous vehicle technology suppliers, software developers, and others to provide a much broader range of product choices.12 There are complex economics in being able to manufacture vehicles similar to today’s mass-produced driver-owned cars, highly customized personally owned autonomous vehicles, and utilitarian pods for urban environments. Manufacturers will likely require not only today’s traditional supply chains but new manufacturing capabilities that allow advanced, low-cost, efficient customization. They will need to determine if they should redesign their business model to compete in all four future states or to focus on one segment.

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Thousands of years ago, oracles read the future through divine inspiration. Today, we’ve still got Oracle making predictions (along with many other forward-thinking tech firms), but it uses something a little more grounded. Artificial intelligence and its capacity to assess approaching events are pretty awe-inspiring even without the supernatural flair.

Many industries are looking to artificially intelligent software to help make predictions on everything from a customer’s buying decisions to which medical treatments will be most effective for a sick patient. Though we live in a world that still depends on the educated guesses of experts, it is becoming increasingly clear that next generation of prognosticators will be more silicon-based than carbon-based.

AI is a prediction technology at its very essence. With the ability to evaluate data exponentially faster than any person, machine learning programs can assess patterns, make connections, and test hypotheses in less time than it takes their human equivalent to pour a cup of coffee. Thanks to its advanced capabilities, AI’s predictions are already taking shape, with strong implications for retail, health care, and the way we understand the world around us.

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Another milestone in the race to artificial superintelligence:

A study conducted by legal AI platform LawGeex in consultation with law professors from Stanford University, Duke University School of Law, and University of Southern California, pitted twenty experienced lawyers against an AI trained to evaluate legal contracts. Their 40 page report details how AI has overtaken top lawyers for the first time in accurately spotting risks in everyday business contracts.

Competitors were given four hours to review five non-disclosure agreements (NDAs) and identify 30 legal issues, including arbitration, confidentiality of relationship, and indemnification. They were scored by how accurately they identified each issue.

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The artificial intelligence craze isn’t just hitting Silicon Valley—the Justice Department wants to get in on the action, too.

The agency announced today that it will put $2 million towards research on AI, which it believes could be used to fight human trafficking, illegal border crossings, drug trafficking, and child pornography.

National Institute for Justice, the DoJ’s research wing, is funding the initiative in the hopes that it will help address the opioid crisis and fight crime by helping investigators sift through massive amounts of data.

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CIMON (Crew Interactive Mobile CompanioN) is a mobile and autonomous assistance system designed to aid astronauts with their everyday tasks on the ISS. This will be the first form of Artificial Intelligence (AI) on an ISS mission. CIMON is an experiment overseen by Space Administration at the German Aerospace Center (DLR) in cooperation with Airbus (Friedrichshafen/Bremen, Germany) as the prime contractor. CIMON is a free flyer fueled with Artificial Intelligence, enhancing human expertise. AI-based technology is about constantly understanding, reasoning and learning, so CIMON is designed to assist and to create a feeling of talking to a crew mate.

CIMON

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Finkbeiner’s success highlights how deep learning, one of the most promising branches of artificial intelligence (AI), is making inroads in biology. The algorithms are already infiltrating modern life in smartphones, smart speakers and self-driving cars. In biology, deep-learning algorithms dive into data in ways that humans can’t, detecting features that might otherwise be impossible to catch. Researchers are using the algorithms to classify cellular images, make genomic connections, advance drug discovery and even find links across different data types, from genomics and imaging to electronic medical records.

A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties.

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