Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 2,211

Over 150 experts in AI, robotics, commerce, law, and ethics from 14 countries have signed an open letter denouncing the European Parliament’s proposal to grant personhood status to intelligent machines. The EU says the measure will make it easier to figure out who’s liable when robots screw up or go rogue, but critics say it’s too early to consider robots as persons—and that the law will let manufacturers off the liability hook.

This all started last year when the European Parliament proposed the creation of a specific legal status for robots:

so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons responsible for making good any damage they may cause, and possibly applying electronic personality to cases where robots make autonomous decisions or otherwise interact with third parties independently.

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Since the Cambridge Analytica scandal erupted in March, Facebook has been attempting to make a moral stand for your privacy, distancing itself from the unscrupulous practices of the U.K. political consultancy. “Protecting people’s information is at the heart of everything we do,” wrote Paul Grewal, Facebook’s deputy general counsel, just a few weeks before founder and CEO Mark Zuckerberg hit Capitol Hill to make similar reassurances, telling lawmakers, “Across the board, we have a responsibility to not just build tools, but to make sure those tools are used for good.” But in reality, a confidential Facebook document reviewed by The Intercept shows that the two companies are far more similar than the social network would like you to believe.

The recent document, described as “confidential,” outlines a new advertising service that expands how the social network sells corporations’ access to its users and their lives: Instead of merely offering advertisers the ability to target people based on demographics and consumer preferences, Facebook instead offers the ability to target them based on how they will behave, what they will buy, and what they will think. These capabilities are the fruits of a self-improving, artificial intelligence-powered prediction engine, first unveiled by Facebook in 2016 and dubbed “FBLearner Flow.”

One slide in the document touts Facebook’s ability to “predict future behavior,” allowing companies to target people on the basis of decisions they haven’t even made yet. This would, potentially, give third parties the opportunity to alter a consumer’s anticipated course. Here, Facebook explains how it can comb through its entire user base of over 2 billion individuals and produce millions of people who are “at risk” of jumping ship from one brand to a competitor. These individuals could then be targeted aggressively with advertising that could pre-empt and change their decision entirely — something Facebook calls “improved marketing efficiency.” This isn’t Facebook showing you Chevy ads because you’ve been reading about Ford all week — old hat in the online marketing world — rather Facebook using facts of your life to predict that in the near future, you’re going to get sick of your car. Facebook’s name for this service: “loyalty prediction.”

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Google today announced a pair of new artificial intelligence experiments from its research division that let web users dabble in semantics and natural language processing. For Google, a company that’s primary product is a search engine that traffics mostly in text, these advances in AI are integral to its business and to its goals of making software that can understand and parse elements of human language.

The website will now house any interactive AI language tools, and Google is calling the collection Semantic Experiences. The primary sub-field of AI it’s showcasing is known as word vectors, a type of natural language understanding that maps “semantically similar phrases to nearby points based on equivalence, similarity or relatedness of ideas and language.” It’s a way to “enable algorithms to learn about the relationships between words, based on examples of actual language usage,” says Ray Kurzweil, notable futurist and director of engineering at Google Research, and product manager Rachel Bernstein in a blog post. Google has published its work on the topic in a paper here, and it’s also made a pre-trained module available on its TensorFlow platform for other researchers to experiment with.

The first of the two publicly available experiments released today is called Talk to Books, and it quite literally lets you converse with a machine learning-trained algorithm that surfaces answers to questions with relevant passages from human-written text. As described by Kurzweil and Bernstein, Talk to Books lets you “make a statement or ask a question, and the tool finds sentences in books that respond, with no dependence on keyword matching.” The duo add that, “In a sense you are talking to the books, getting responses which can help you determine if you’re interested in reading them or not.”

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Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.

Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.

Good city governance enables and maintains smooth flow of things, data, and people. These include public services, traffic, and delivery services. Long queues in hospitals and banks imply poor management. Traffic congestion demonstrates that roads and traffic systems are inadequate. Goods that we increasingly order online don’t arrive fast enough. And the WiFi often fails our 24/7 digital needs. In sum, urban life, characterized by environmental pollution, speedy life, traffic congestion, connectivity and increased consumption, needs robotic solutions—or so we are led to believe.

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In the last year, the business and consumer markets alike have seen the release of advanced technologies that were once considered the stuff of science fiction. Smart gadgets that control every facet of your home, self-driving vehicles, facial and biometric identification systems and more have begun to emerge, giving us a glimpse of the high-tech reality we’re moving towards.

To find out which futuristic technologies are on the horizon, we asked a panel of YEC members the following question:

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Researchers proposed implementing the residential energy scheduling algorithm by training three action dependent heuristic dynamic programming (ADHDP) networks, each one based on a weather type of sunny, partly cloudy, or cloudy. ADHDP networks are considered ‘smart,’ as their response can change based on different conditions.

“In the future, we expect to have various types of supplies to every household including the grid, windmills, and biogenerators. The issues here are the varying nature of these power sources, which do not generate electricity at a stable rate,” said Derong Liu, a professor with the School of Automation at the Guangdong University of Technology in China and an author on the paper. “For example, power generated from windmills and solar panels depends on the weather, and they vary a lot compared to the more stable power supplied by the grid. In order to improve these power sources, we need much smarter algorithms in managing/scheduling them.”

The details were published on the January 10th issue of IEEE/CAA Journal of Automatica Sinica, a joint bimonthly publication of the IEEE and the Chinese Association of Automation.

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People are remarkably good at focusing their attention on a particular person in a noisy environment, mentally “muting” all other voices and sounds. Known as the cocktail party effect, this capability comes natural to us humans. However, automatic speech separation — separating an audio signal into its individual speech sources — while a well-studied problem, remains a significant challenge for computers.

In “Looking to Listen at the Cocktail Party”, we present a deep learning audio-visual model for isolating a single speech signal from a mixture of sounds such as other voices and background noise. In this work, we are able to computationally produce videos in which speech of specific people is enhanced while all other sounds are suppressed. Our method works on ordinary videos with a single audio track, and all that is required from the user is to select the face of the person in the video they want to hear, or to have such a person be selected algorithmically based on context. We believe this capability can have a wide range of applications, from speech enhancement and recognition in videos, through video conferencing, to improved hearing aids, especially in situations where there are multiple people speaking.

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A fierce internal debate may undermine the company’s bid for the JEDI program.

Last August, U.S. Defense Secretary James Mattis made a journey to the West Coast and met with Google founder Sergey Brin and CEO Sundar Pichai. Over a half day of meetings, Google leaders described the company’s multi-year transition to cloud computing and how it was helping them develop into a powerhouse for research and development into artificial intelligence. Brin in particular was eager to showcase how much Google was learning every day about AI and cloud implementation, according to one current and one former senior Defense Department official who spoke on condition of anonymity.

It wasn’t an overt sales pitch, exactly, say the officials. But the effect of the trip, during which Mattis also met representatives from Amazon, was transformative. He went west with deep reservations about a department-wide move to the cloud and returned to Washington, D.C., convinced that the U.S. military had to move much of its data to a commercial cloud provider — not just to manage files, email, and paperwork but to push mission-critical information to front-line operators.

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We are now on the brink of a “third revolution in warfare,” heralded by killer robots — the fully autonomous weapons that could decide who to target and kill… without human input.

Over the weekend, experts on military artificial intelligence from more than 80 world governments converged on the U.N. offices in Geneva for the start of a week’s talks on autonomous weapons systems. Many of them fear that after gunpowder and nuclear weapons, we are now on the brink of a “third revolution in warfare,” heralded by killer robots — the fully autonomous weapons that could decide who to target and kill without human input. With autonomous technology already in development in several countries, the talks mark a crucial point for governments and activists who believe the U.N. should play a key role in regulating the technology.

The meeting comes at a critical juncture. In July, Kalashnikov, the main defense contractor of the Russian government, announced it was developing a weapon that uses neural networks to make “shoot-no shoot” decisions. In January 2017, the U.S. Department of Defense released a video showing an autonomous drone swarm of 103 individual robots successfully flying over California. Nobody was in control of the drones; their flight paths were choreographed in real-time by an advanced algorithm. The drones “are a collective organism, sharing one distributed brain for decision-making and adapting to each other like swarms in nature,” a spokesman said. The drones in the video were not weaponized — but the technology to do so is rapidly evolving.

This April also marks five years since the launch of the International Campaign to Stop Killer Robots, which called for “urgent action to preemptively ban the lethal robot weapons that would be able to select and attack targets without any human intervention.” The 2013 launch letter — signed by a Nobel Peace Laureate and the directors of several NGOs — noted that they could be deployed within the next 20 years and would “give machines the power to decide who lives or dies on the battlefield.”

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