The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. First, we break down attributions of blame and wrongness into more basic judgments about the epistemic and conative state of the agent, and the consequences of the agent's actions. In this way, we are able to examine any differences between the way participants treat artificial agents in terms of differences in these more basic judgments. our second strategy is to compare attributions of blame and wrongness across human, artificial, and group agents (corporations). Others have compared attributions of blame and wrongness between human and artificial agents, but the addition of group agents is significant because these agents seem to provide a clear middle-ground between human agents (for whom the notions of blame and wrongness were created) and artificial agents (for whom the question remains open).

Autoregressive sequence Generation models have achieved state-of-the-art performance in areas like machine translation and image captioning. These models are autoregressive in that they generate each word by conditioning on previously generated words, which leads to heavy latency during inference. Recently, non-autoregressive decoding has been proposed in machine translation to speed up the inference time by generating all words in parallel. Typically, these models use the word-level cross-entropy loss to optimize each word independently. However, such a learning process fails to consider the sentence-level consistency, thus resulting in inferior generation quality of these non-autoregressive models. In this paper, we propose a simple and efficient model for Non-Autoregressive sequence Generation (NAG) with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL). CMAL formulates NAG as a multi-agent reinforcement learning system where element positions in the target sequence are viewed as agents that learn to cooperatively maximize a sentence-level reward. On MSCOCO image captioning benchmark, our NAG method achieves a performance comparable to state-of-the-art autoregressive models, while brings 13.9x decoding speedup. On WMT14 EN-DE machine translation dataset, our method outperforms cross-entropy trained baseline by 6.0 BLEU points while achieves the greatest decoding speedup of 17.46x.

It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5G and deep learning is beginning to significantly transform the core communication infrastructure, network management and vertical applications. The paper first outlines the individual roadmaps of mobile communications and artificial intelligence in the early stage, with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge. With regard to telecommunications artificial intelligence, the paper further introduces in detail the progress of artificial intelligence in the ecosystem of mobile communications. The paper then summarizes the classifications of AI in telecom ecosystems along with its evolution paths specified by various international telecommunications standardization bodies. Towards the next decade, the paper forecasts the prospective roadmap of telecommunications artificial intelligence. In line with 3GPP and ITU-R timeline of 5G & 6G, the paper further explores the network intelligence following 3GPP and ORAN routes respectively, experience and intention driven network management and operation, network AI signalling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS and OSS convergence, evolution from SLA to ELA, and intelligent private network for verticals. The paper is concluded with the vision that AI will reshape the future B5G or 6G landscape and we need pivot our R&D, standardizations, and ecosystem to fully take the unprecedented opportunities.

Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game's rules, and constructing the neural network's architecture -- in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 500 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.

Google and Facebook have been in a long-running fight with Australian politicians, regulators and media companies over whether they should pay news organizations for showing their stories in search results. The battle reached a new level of intensity when a Google executive threatened to pull out of the country during testimony at the Australian Senate.

Google's testimony to an Australian Senate committee on Friday threatening to withdraw its search services from Australia is chilling to anyone who cares about democracy. It marks the latest escalation in the globally significant effort to regulate the way the big tech platforms use news content to drive their advertising businesses and the catastrophic impact on the news media across the world. The news bargaining code, which would require Google and Facebook to negotiate a fair price for the use of news content, is the product of an 18-month process driven by the competition regulator. That legislation is currently before the Australian parliament, where a Senate committee is taking final submissions from interested parties. The Google bombshell makes explicit what has been a slowly escalating threat that a binding code would not be tenable.

The tech giant says it will remove its main search function from Australia if it passes a new law.

Dynamic platforms that operate over manyunique terrain conditions typically require multiple controllers.To transition safely between controllers, there must be anoverlap of states between adjacent controllers. We developa novel method for training Setup Policies that bridge thetrajectories between pre-trained Deep Reinforcement Learning(DRL) policies. We demonstrate our method with a simulatedbiped traversing a difficult jump terrain, where a single policyfails to learn the task, and switching between pre-trainedpolicies without Setup Policies also fails. We perform anablation of key components of our system, and show thatour method outperforms others that learn transition policies.We demonstrate our method with several difficult and diverseterrain types, and show that we can use Setup Policies as partof a modular control suite to successfully traverse a sequence ofcomplex terrains. We show that using Setup Policies improvesthe success rate for traversing a single difficult jump terrain(from 1.5%success rate without Setup Policies to 82%), and asequence of various terrains (from 6.5%without Setup Policiesto 29.1%).

There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelity, robustness, or human-interpretability. However, explanations are seldom evaluated based on their true practical impact on decision-making tasks. Without that assessment, explanations might be chosen that, in fact, hurt the overall performance of the combined system of ML model + end-users. This study aims to bridge this gap by proposing XAI Test, an application-grounded evaluation methodology tailored to isolate the impact of providing the end-user with different levels of information. We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts. During the experiment, we gradually increased the information provided to the fraud analysts in three stages: Data Only, i.e., just transaction data without access to model score nor explanations, Data + ML Model Score, and Data + ML Model Score + Explanations. Using strong statistical analysis, we show that, in general, these popular explainers have a worse impact than desired. Some of the conclusion highlights include: i) showing Data Only results in the highest decision accuracy and the slowest decision time among all variants tested, ii) all the explainers improve accuracy over the Data + ML Model Score variant but still result in lower accuracy when compared with Data Only; iii) LIME was the least preferred by users, probably due to its substantially lower variability of explanations from case to case.

Housebound by a pandemic, humanity slowed its emissions of greenhouse gases in 2020. But Earth paid little heed: Temperatures last year tied the modern record, climate scientists reported last week. Overall, the planet was about 1.25°C warmer than in preindustrial times, a trend that puts climate targets in jeopardy, according to jointly reported assessments from NASA, Berkeley Earth, the U.K. Met Office, and the National Oceanic and Atmospheric Administration. The annual update of global surface temperatures—an average of readings from thousands of weather stations and ocean probes—shows 2020 essentially tied records set in 2016. But the years were nothing alike. Temperatures in 2016 were boosted by a strong El Niño, a weather pattern that warms the globe by blocking the rise of cold deep waters in the eastern Pacific Ocean. Last year, however, the Pacific entered La Niña, which has a cooling effect. That La Niña didn't provide more relief is an unwelcome surprise, says Nerilie Abram, a climate scientist at Australian National University. “It makes me worried about how quickly the global warming trend is growing.” The past 6 years are the six warmest on record, but the warming of the atmosphere is unsteady because of its chaotic nature. The ocean, which absorbs more than 90% of the heat from global warming, displays a steadier trend, and here, too, 2020 was a record year. The upper levels of the ocean contained 20 zettajoules (1021 joules) more heat than in 2019, and the rise was double the typical annual increase, scientists reported last week in Advances in Atmospheric Sciences . The subtropical Atlantic Ocean was particularly hot, fueling a record outbreak of hurricanes, says Lijing Cheng, a climate scientist at the Chinese Academy of Sciences's Institute of Atmospheric Physics who led the work. This heat, monitored down to 2000 meters by a fleet of 4000 robotic probes, is spreading deeper into the ocean while also migrating toward the poles. An extreme heat wave struck the northern Pacific, killing marine life. For the first time, warm Atlantic waters were seen penetrating into the Arctic Ocean, melting sea ice from below and reducing its extent nearly to a record low ( Science , 28 August 2020, p. [1043][1]). The warming ocean and melting ice sheets are raising sea levels by 4.8 millimeters per year, and the rate is accelerating ( Science , 20 November 2020, p. [901][2]). On land, 2020 was even more relentless, with temperatures rising 1.96°C above preindustrial levels, a clear record, Berkeley Earth reported. It was the warmest year ever in Asia and Europe and tied for the warmest in South America. Russia was particularly hot, breaking its previous record by 1.2°C, while swaths of Siberia were 7°C warmer than in preindustrial times, leading to large-scale fires and thawing permafrost that caused buildings to founder and set off oil spills ( Science , 7 August 2020, p. [612][3]). “Siberia was crazy,” says Zeke Hausfather, a climate scientist at the Breakthrough Institute and co-author of the Berkeley Earth analysis. “That heat would effectively be impossible without the warming we've seen.” In Australia, record-setting heat and drought fueled catastrophic bushfires at the start of 2020. Fires torched nearly one-quarter of southeastern Australia's forests and destroyed 3000 homes. Climate change was to blame for the country's “Black Summer,” Abram and co-authors concluded in a study published this month in Communications Earth & Environment . Meanwhile, in the United States, unprecedented heat came to the desert Southwest, which is already warming faster than the rest of the country. Phoenix wilted under its hottest summer ever, averaging 36°C. Arizona's Maricopa county, home to Phoenix, is a leader in addressing heat exposure, yet its heat deaths have hit a new record each year since 2016. In 2020, the number approached 300, a jump of some 50% over the previous year, says David Hondula, a climatologist who studies heat mortality at Arizona State University, Tempe. “It was just off the charts in terms of heat.” ![Figure][4] Turning up the heatCREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) MET OFFICE; NASA; BERKELEY EARTH; NOAA Although the global economic slowdown of the COVID-19 pandemic cut carbon dioxide (CO2) emissions by some 7%, atmospheric CO2 is long-lived, and warming from previous emissions is preordained. In any case, the drop in emissions is unlikely to last. Later this year, in May, before photosynthesis in the Northern Hemisphere draws down CO2, the U.K. Met Office predicts that levels of atmospheric CO2 will pass 417 parts per million for several weeks, 50% higher than preindustrial levels. Only dramatic action by the world's countries, far beyond existing efforts, can begin to halt this build up, Cheng says. Should the current rate of warming continue, the world will breach the targets set in the Paris climate agreement—limiting warming to 1.5°C or 2°C—by 2035 and 2065, respectively. But Hausfather says it's quite possible that warming, which has largely held steady for the past few decades at 0.19°C per decade, will actually speed up. The rate of warming over the past 14 years is well above the long-term trend. The debate now, he says, is whether that is an omen of an even darker future. [1]: https://www.sciencemag.org/content/369/6507/1043.full [2]: https://www.sciencemag.org/content/370/6519/901.full [3]: https://www.sciencemag.org/content/369/6504/612.full [4]: pending:yes