This paper presents our research on leveraging social media Big Data and AI to support hurricane disaster emergency response. The current practice of hurricane emergency response for rescue highly relies on emergency call centres. The more recent Hurricane Harvey event reveals the limitations of the current systems. We use Hurricane Harvey and the associated Houston flooding as the motivating scenario to conduct research and develop a prototype as a proof-of-concept of using an intelligent agent as a complementary role to support emergency centres in hurricane emergency response. This intelligent agent is used to collect real-time streaming tweets during a natural disaster event, to identify tweets requesting rescue, to extract key information such as address and associated geocode, and to visualize the extracted information in an interactive map in decision supports. Our experiment shows promising outcomes and the potential application of the research in support of hurricane emergency response.

There is particular interest in Spike-based learning in plastic neuronal networks is deep learning, which is a central tool in modern machine playing increasingly key roles in both theoretical neuroscience learning. Deep learning relies on a layered, feedforward and neuromorphic computing. The brain learns network similar to the early layers of the visual cortex, in part by modifying the synaptic strengths between neurons with threshold nonlinearities at each layer that resemble and neuronal populations. While specific synaptic mean-field approximations of neuronal integrate-and-fire plasticity or neuromodulatory mechanisms may vary in models. While feedforward networks are readily translated different brain regions, it is becoming clear that a significant to neuromorphic hardware [6-8], the far more computationally level of dynamical coordination between disparate intensive training of these networks'on chip' neuronal populations must exist, even within an individual has proven elusive as the structure of backpropagation neural circuit [1]. Classically, backpropagation (BP, makes the algorithm notoriously difficult to implement and other learning algorithms) has been essential for supervised in a neural circuit [9, 10]. A feasible neural implementation learning in artificial neural networks (ANNs). of the backpropagation algorithm has gained renewed Although the question of whether or not BP operates in scrutiny with the rise of new neuromorphic computational the brain is still an outstanding issue [2], BP does solve architectures that feature local synaptic plasticity the problem of how a global objective function can be [5, 11-13]. Because of the well-known difficulties, neuromorphic related to local synaptic modification in a network.

Consistent alpha generation, i.e., maintaining an edge over the market, underpins the ability of asset traders to reliably generate profits. Technical indicators and trading strategies are commonly used tools to determine when to buy/hold/sell assets, yet these are limited by the fact that they operate on known values. Over the past decades, multiple studies have investigated the potential of artificial intelligence in stock trading in conventional markets, with some success. In this paper, we present RCURRENCY, an RNN-based trading engine to predict data in the highly volatile digital asset market which is able to successfully manage an asset portfolio in a live environment. By combining asset value prediction and conventional trading tools, RCURRENCY determines whether to buy, hold or sell digital currencies at a given point in time. Experimental results show that, given the data of an interval $t$, a prediction with an error of less than 0.5\% of the data at the subsequent interval $t+1$ can be obtained. Evaluation of the system through backtesting shows that RCURRENCY can be used to successfully not only maintain a stable portfolio of digital assets in a simulated live environment using real historical trading data but even increase the portfolio value over time.

Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey Corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-agnostic probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method -- multilingual contrastive pre-training (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks.

Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available. Modern Bayesian models, however, typically involve intractable posteriors that are approximated with, potentially crude, surrogates. This difficulty has engendered loss-calibrated techniques that aim to learn posterior approximations that favor high-utility decisions. In this paper, focusing on Bayesian neural networks, we develop methods for correcting approximate posterior predictive distributions encouraging them to prefer high-utility decisions. In contrast to previous work, our approach is agnostic to the choice of the approximate inference algorithm, allows for efficient test time decision making through amortization, and empirically produces higher quality decisions. We demonstrate the effectiveness of our approach through controlled experiments spanning a diversity of tasks and datasets.

When Assassin's Creed Valhalla new expansion comes out this summer, it will allow players to relieve the Siege of Paris, Ubisoft announced at its Forward E3 event on Saturday. Historically, the 845 CE event culminated with the Vikings occupying the city and doing what they did best, plundering it for all it was worth. How the event will unfold in Valhalla, we'll see, but Ubisoft promised the DLC will include new weapons, gear and abilities for players to discover. Additionally, The Siege of Paris will see the return of a fan favorite feature: black box infiltration missions. Leaning into the franchise's sandbox roots, these will give you an objective to complete, but how you go about accomplishing it will be up to you.

Incessant cicada shrill alarms a Georgia town. Fox News' Steve Harrigan has the details. Cicadas have taken over large swaths of the United States, interrupting sleep, causing car crashes and even bombarding President Biden on Wednesday as he prepared to board Air Force One. Trillions of the insects have emerged after 17 years underground in approximately 15 states, leaving nymph exoskeletons littered around city parks and backyards. The red-eyed bugs are especially active amid hot weather conditions that have swept the country in past weeks and residents of heavy cicada areas have taken note.

To help process 7.3 billion packages a year – 231 per second – the U.S. Postal Service is using artificial intelligence.

Cybersecurity failure could be among the greatest challenges confronting the world in the next decade, according to the World Economic Forum's Global Risks Report 2021. As artificial intelligence (AI) becomes increasingly embedded worldwide, fresh questions arise about how to safeguard countries and systems against attacks. To deal with the vulnerabilities of AI, engineers and developers need to evaluate existing security methods, develop new tools and strategies, and formulate technical guidelines and standards, said Arndt Von Twickel, Technical Officer at Germany's Federal Office for Information Security (BSI), at a recent AI for Good webinar. So-called "connectionist AI" systems support safety-critical applications like autonomous driving, which is set to be allowed on United Kingdom roads this year. Despite reaching "superhuman" performance levels in complex tasks like manoeuvring a vehicle, AI systems can still make critical mistakes based on misunderstood inputs.

Early in the 2020 presidential campaign, Democratic candidates Pete Buttigieg and Andrew Yang tried to build political momentum around the claim that the United States is losing ground in a new arms race with China -- not over nuclear missiles or conventional arms but artificial intelligence, or AI. Around the same time, former President Trump launched the American AI Initiative, which sought to marshal AI technologies against "adversarial nations for the security of our economy and our nation," as Trump's top technology adviser put it. Buttigieg, Yang and Trump may have agreed about little else, but they appeared to go along with the nonpartisan think tanks and public policy organizations –– many of them funded by weapons contractors –– that have worked to promote the supposedly alarming possibility that China and Russia may be "beating" the U.S. in defense applications for AI. Hawkish or "centrist" research organizations like the Center for New American Security (CNAS), the Brookings Institution and the Heritage Foundation, despite their policy and ideological differences in many areas, have argued that America must ratchet up spending on AI research and development, lest it lose its place as No. 1. Just last week, the National Security Commission on Artificial Intelligence (NSCAI) published a sweeping 756-page report, culminating two years of work following the 2019 National Defense Authorization Act, asking Congress to authorize a $40 billion federal investment in AI research and development, which the NSCAI calls "a modest down payment."