Humanity has migrated to subaquatic domes to escape the lethal consequences of a vastly deteriorated ozone layer. Tremendous advances in solar power have made this shift possible, and an android underclass provides maintenance labor. Sentient but without rights, they are manufactured with organs that can be harvested by humans. Gradually, Momo grows enlightened to the oppression of androids, connecting the dots between a surgery she had as a child and the disappearance of her childhood best friend. There's an awful lot going on in this short work: new religions form in this future world, the Pacific Ocean territories are divided between countries like the United States and corporations like Toyota, and then there are the peculiar skin treatments at Momo's salon.

With current AI technologies, harm done by AIs is limited to power that we put directly in their control. As said in [59], "For Narrow AIs, safety failures are at the same level of importance as in general cybersecurity, but for AGI it is fundamentally different." Despite AGI (artificial general intelligence) still being well out of reach, the nature of AI catastrophes has already changed in the past two decades. Automated systems are now not only malfunctioning in isolation, they are interacting with humans and with each other in real time. This shift has made traditional systems analysis more difficult, as AI has more complexity and autonomy than software has before. In response to this, we analyze how risks associated with complex control systems have been managed historically and the patterns in contemporary AI failures to what kinds of risks are created from the operation of any AI system. We present a framework for analyzing AI systems before they fail to understand how they change the risk landscape of the systems they are embedded in, based on conventional system analysis and open systems theory as well as AI safety principles. Finally, we present suggested measures that should be taken based on an AI system's properties. Several case studies from different domains are given as examples of how to use the framework and interpret its results.

It's well established that deepfake images of people are problematic, but it's now clearer that bogus satellite imagery could also represent a threat. The Verge reports that University of Washington-led researchers have developed a way to generate deepfake satellite photography as part of an effort to detect manipulated images. The team used an AI algorithm to generate deepfakes by feeding the traits of learned satellite images into different base maps. They could use Tacoma's roads and building locations, for example (at top right in the picture below), but superimpose Beijing's taller buildings (bottom right) or Seattle's low-rises (bottom left). You can apply greenery, too. While the execution isn't flawless, it's close enough that scientists believe you might blame any oddities on low image quality.

If you have existing low-voltage wiring, you can take advantage of that power source--and your existing analog or digital chime--and never worry about replacing the Kami Doorbell Camera's batteries. If you don't have wiring in place, you can run this camera on battery power. Add in person detection in a camera that's currently selling on Amazon for $100 and you have a solid smart home value. Just don't buy one in anticipation of Kami delivering on its facial recognition promise, because that feature was highly unreliable in our experience. You'll also need to pay a subscription fee to unlock all of this camera's features.

Earthquakes are lethal and costly. This study aims at avoiding these catastrophic events by the application of injection policies retrieved through reinforcement learning. With the rapid growth of artificial intelligence, prediction-control problems are all the more tackled by function approximation models that learn how to control a specific task, even for systems with unmodeled/unknown dynamics and important uncertainties. Here, we show for the first time the possibility of controlling earthquake-like instabilities using state-of-the-art deep reinforcement learning techniques. The controller is trained using a reduced model of the physical system, i.e, the spring-slider model, which embodies the main dynamics of the physical problem for a given earthquake magnitude. Its robustness to unmodeled dynamics is explored through a parametric study. Our study is a first step towards minimizing seismicity in industrial projects (geothermal energy, hydrocarbons production, CO2 sequestration) while, in a second step for inspiring techniques for natural earthquakes control and prevention.

Deep Reinforcement Learning (DRL) underlies in a simulated environment and optimizes objective goals. By extending the conventional interaction scheme, this paper proffers gym-ds3, a scalable and reproducible open environment tailored for a high-fidelity Domain-Specific System-on-Chip (DSSoC) application. The simulation corroborates to schedule hierarchical jobs onto heterogeneous System-on-Chip (SoC) processors and bridges the system to reinforcement learning research. We systematically analyze the representative SoC simulator and discuss the primary challenging aspects that the system (1) continuously generates indefinite jobs at a rapid injection rate, (2) optimizes complex objectives, and (3) operates in steady-state scheduling. We provide exemplary snippets and experimentally demonstrate the run-time performances on different schedulers that successfully mimic results achieved from the standard DS3 framework and real-world embedded systems.

Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remains to be investigated. In this paper, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability: the ability to detect "actual" performance differences as opposed to those observed by chance; (2) fidelity: the ability to agree with ultimate user preference; and (3) intuitiveness: the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics varies significantly across different scenarios whereas consistent with prior studies, existing metrics only achieve a weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.

With the development of automatic diagnostics based on statistical predictive models, coming from any supervised machine learning (ML) algorithms, important issues about model validation have been raised. For example in the industrial nondestructive testing field (e.g. for aeronautic or nuclear industry), generalized automated inspection (that will allow large gain in terms of efficiency and economy) has to provide high guarantees in terms of performance. In this case, it is necessary to be able to select a validation data basis that will not be used for the training nor the selection of the ML model [3, 7]. This validation data basis (also referred as verification data in the literature) has not to be communicated to the ML developers because it will serve to realize an independent evaluation of the provided ML model (applying a cross validation method is then not possible). This validation sample is typically used to provide prediction residuals (which can be finely analyzed), as well as average ML model quality measures (as the mean square error in a regression problem or the misclassification rate in a classification problem). In this paper, we address the particular question about the way to select a "good" validation basis from a dataset useful to specify a ML model. We use indifferently the term "validation" and "test" for the basis (also called sample) because we restrict our problem to the distinction between a learning sample (which includes the ML fitting and selection phases) and a test sample. An important question is the number and the location of these test points.

Due to the fact that basic uncertain information provides a simple form for decision information with certainty degree, it has been developed to reflect the quality of observed or subjective assessments. In order to study the algebra structure and preference relation of basic uncertain information, we develop some algebra operations for basic uncertain information. The order relation of such type of information has also been considered. Finally, to apply the developed algebra operations and order relations, a generalized TODIM method for multi-attribute decision making with basic uncertain information is given. The numerical example shows that the developed decision procedure is valid.

Artificial intelligence (AI) and machine learning (ML) might be high in the hype cycle at the moment. But that doesn't mean organizations are not realizing tangible gains from deploying products that leverage the technologies. Here are three examples of how AI and ML are improving internal business processes and paying off for enterprises. Beacon Street Services needed to have a "single source of truth" for all its company's data, to ensure consistency and accuracy across its applications. The company is the services arm of Stansberry Holdings, which produces financial publications exclusively through purchased subscriptions.