US firm Nvidia is scrapping its $40 billion bid to buy UK mobile chip technology powerhouse Arm from SoftBank after persistent objections from regulators, the Financial Times reported Tuesday. Nvidia and SoftBank Group both declined to comment on the report, which cited three unnamed sources with direct knowledge of the deal. But the collapse would be no surprise, after recent speculation that the deal was on the verge of failure following pressure from US, UK and EU regulators concerned it would undermine competition. In December, US regulators filed a lawsuit seeking to block the merger, while British and European regulators had ordered probes into the deal. Japan's SoftBank Group announced in 2020 that it was selling Arm for up to $40 billion in a deal it hoped to complete in early 2022, subject to regulatory approvals. The value of the cash-and-shares deal has risen since as stock markets have rallied, with Nvidia's shares soaring.

The Mojave Desert in the southwestern United States is a vast landscape combining mountains and dried lake beds, forests, and wildflower fields. It has a rich cultural history, with 12,000-year-old archaeological sites, and it harbors protected species such golden eagles and desert tortoises, as well as its famous Joshua trees, some of which are 900-years old and 30-feet tall. But according to Lukas Agnew, senior consultant at Capgemini's insights and data practice, this unique environment is under threat. "Humans are increasingly using the desert for recreation, such as off-road dirt biking," he says. "While one bike might not cause a problem, over time, real damage is done."

Editor's note: Rostam J. Neuwirth is a professor of the Faculty of Law, University of Macau. The article reflects the author's opinions, and not necessarily the views of CGTN. At the end of last year, the General Conference of the United Nations Educational, Scientific and Cultural Organization adopted the Recommendation on the Ethics of Artificial Intelligence as the first global standard-setting instrument responding to the ethical concerns related to artificial intelligence (AI). This recommendation must strongly be welcome, because – albeit legally non-binding – it reflects an emerging global consensus on the ethical concerns raised and serious risks caused by AI. Most of all, it recognizes the need to work on global solutions to this problem of fundamental importance for present and future generations.

Publisher Activision Blizzard, responsible for the game this article refers to, is currently embroiled in ongoing litigation in regards to claims reporting a workplace culture that allegedly enabled acts of sexual harassment, abuse and discrimination. Diablo 4 is currently in development but it looks like its release is still a long way off. That hasn't stopped us from searching out the best rumors and the latest news about Blizzard's upcoming hack'n slash adventure. The Diablo series certainly is undergoing something of a resurgence right now. First announced at Blizzcon 2019, Diablo 4 development has supposedly been progressing since. Diablo 2 Resurrected, a remaster of the PC classic, has already been released and Diablo Immortal is expected to arrive on Android and iOS devices in 2022. Naturally, though, we're most excited about the release of Diablo 4 and thanks to Blizzard's quarterly development updates, we're learning more about it all the time. With the recent announcement that Microsoft has agreed to acquire Activision Blizzard, the landscape around Diablo 4's development is changing and it currently remains unclear what the acquisition could mean for the game if it goes through, especially as Diablo 4's release is so far down the line--we're not expecting it until at least 2023. While we wait, though, here's all the news, updates and rumors we've collated about Diablo 4 so far. What could this mean for Diablo 4's release? Read on to find out more.] Bad news here: Diablo 4 probably won't be released anytime soon. At a Blizzcon 2019 deep dive on the game, the game's director said that he doesn't expect the game to be finished anytime soon, "even by Blizzard's standards of soon." Fast-forward to the end of 2021, and that comment still stands after the announcement of an indefinite delay. During Activision Blizzard's Q3 earnings call in November 2021, it made the following statement: "While we are still planning to deliver a substantial amount of content from Blizzard next year, we are now planning for a later launch for Overwatch 2 and Diablo IV than originally envisaged".

Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average treatment effect (ATE) and conditional ATE (CATE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ATE and CATE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P$^3$A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P$^3$A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P$^3$A. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ATE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P$^3$A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.

Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using human annotators to hand-write test cases. However, human annotation is expensive, limiting the number and diversity of test cases. In this work, we automatically find cases where a target LM behaves in a harmful way, by generating test cases ("red teaming") using another LM. We evaluate the target LM's replies to generated test questions using a classifier trained to detect offensive content, uncovering tens of thousands of offensive replies in a 280B parameter LM chatbot. We explore several methods, from zero-shot generation to reinforcement learning, for generating test cases with varying levels of diversity and difficulty. Furthermore, we use prompt engineering to control LM-generated test cases to uncover a variety of other harms, automatically finding groups of people that the chatbot discusses in offensive ways, personal and hospital phone numbers generated as the chatbot's own contact info, leakage of private training data in generated text, and harms that occur over the course of a conversation. Overall, LM-based red teaming is one promising tool (among many needed) for finding and fixing diverse, undesirable LM behaviors before impacting users.

In this chapter, we provide a review of conversational agents (CAs), discussing chatbots, intended for casual conversation with a user, as well as task-oriented agents that generally engage in discussions intended to reach one or several specific goals, often (but not always) within a specific domain. We also consider the concept of embodied conversational agents, briefly reviewing aspects such as character animation and speech processing. The many different approaches for representing dialogue in CAs are discussed in some detail, along with methods for evaluating such agents, emphasizing the important topics of accountability and interpretability. A brief historical overview is given, followed by an extensive overview of various applications, especially in the fields of health and education. We end the chapter by discussing benefits and potential risks regarding the societal impact of current and future CA technology.

Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, different groups in society may have irreconcilable disagreements about ground truth labels. Supervised ML today resolves these label disagreements implicitly using majority vote, which overrides minority groups' labels. We introduce jury learning, a supervised ML approach that resolves these disagreements explicitly through the metaphor of a jury: defining which people or groups, in what proportion, determine the classifier's prediction. For example, a jury learning model for online toxicity might centrally feature women and Black jurors, who are commonly targets of online harassment. To enable jury learning, we contribute a deep learning architecture that models every annotator in a dataset, samples from annotators' models to populate the jury, then runs inference to classify. Our architecture enables juries that dynamically adapt their composition, explore counterfactuals, and visualize dissent.

With geopolitical instability during an omicron surge, the AI of military factions are under the microscope. With Russia/Ukraine, China/India and China/Taiwan borders under pressure, there's a greater danger of A.I being misused in geographical tensions. Terminators were once just a movie. Engineers in Korea have developed a highly dexterous robotic hand that's capable of crushing beer cans or gently clutching an egg. It looks nearly exactly like those old movies.

Given the growing use of artificial intelligence (AI) and automated decision-making tools in consumer-facing decisions, we expect federal regulators in 2022 to continue their recent track record of interest in potential discrimination and unfairness, as well as data accuracy and transparency. Significant technological developments in these areas and the increasing use of data analytics to make automated decisions will likely result in further regulatory action this year in three key areas: (1) assessing whether AI and algorithms are excluding particular consumer groups in an unfair and discriminatory manner, whether intentionally or not; (2) evaluating whether collected data accurately reflects real-world facts and whether companies are giving consumers an opportunity to correct mistakes; and (3) assessing whether automated decisionmaking tools are being used in a transparent manner. Over the last year, federal regulators with enforcement authority in the consumer space--the Federal Trade Commission (FTC) and the Consumer Financial Protection Bureau (CFPB)--have expressed their intention to continue enforcement efforts. The FTC has identified "technology companies and digital platforms," "bias in algorithms and biometrics," and "deceptive and manipulative conduct on the Internet" as among its top enforcement priorities for the coming years, and directed staff to use compulsory processes to demand documents and testimony to investigate potential abuses in these areas. The FTC and the CFPB have each initiated or continued investigations into practices involving the collection of consumer data and the use of data analytics in consumer decisions, including the use of AI and algorithms by financial institutions, digital payment platforms, and social media, and video streaming firms.