Applying for a frontline job can be a game of hurry-up-and-wait, and communication is not always the best when a company is trying to fill dozens of positions at the same time. Enter Emi, the latest company targeting technology to this portion of the workforce with a conversational artificial intelligence recruiting tool. The technology automates communication between global enterprises and candidates using a conversational interface. CEO Mateo Cavasotto says this reduces the time it takes to hire people, while also increasing candidate satisfaction, thus improving recruitment productivity. The idea for the company came a couple of years ago when Cavasotto and Andres Arslanian, CTO, worked as volunteers for a Microcredits NGO in Argentina. They were working to understand how problems among the poverty-stricken population could be solved with technology.

Urs Gasser (ugasser@cyber.harvard.edu) is the Dean of the new TUM School of Social Sciences and Technology at the Technical University of Munich, Germany, and a Faculty Director of the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA. Virgílio Almeida (virgilio@dcc.ufmg.br) is a Professor Emeritus of Computer Science at the Federal University of Minas Gerais (UFMG), Brazil, and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University, Cambridge, MA, USA.

Sci-fi films and TV shows have routinely depicted a brutal race of aliens visiting Earth in their spaceships and enslaving unfortunate Earthlings. But according to one expert, extraterrestrial life may actually be too scared of'dangerous' and'violent' humans to want to come here. Dr Gordon Gallup, a biopsychologist at the University of Albany, argues that humans are'dangerous, violent and ceaselessly engage in endless bloody conflicts and war'. For this reason, aliens with the technological capability of making a visit to Earth - if they exist - are likely inclined to stay away for fear of death and genocide, according to Dr Gallup. No life beyond Earth has ever been found and there is no evidence that alien life has ever visited our planet.

Autonomous driving safety is in the development stage. Whether on the highway or in a multi-story parking garage, highly automated driving features must perform safely and reliably in all situations. Parallel systems monitor the environment and determine what to do in key conditions. This is called redundancy and is one of the ways developers from Porsche Engineering ensure safety in autonomous driving applications. The cargo of a truck in front of us is lost.

This article is contributed by Hari Shetty, sector head and senior vice president of technology platforms & products at Wipro Limited. After a disruptive year, enterprises and startups are finding greater success together. Prior to 2020, the term "disruption" typically referred to startups and innovators that were doing things differently -- disrupting established industries like ecommerce, banking, and health services through a combination of new technologies and innovative business models. But the pandemic pushed companies over a technological tipping point. Now, even leaders who were reluctant to change with the times are embracing technology to keep pace.

We establish a general optimization framework for the design of automated bidding agent in dynamic online marketplaces. It optimizes solely for the buyer's interest and is agnostic to the auction mechanism imposed by the seller. As a result, the framework allows, for instance, the joint optimization of a group of ads across multiple platforms each running its own auction format. Bidding strategy derived from this framework automatically guarantees the optimality of budget allocation across ad units and platforms. Common constraints such as budget delivery schedule, return on investments and guaranteed results, directly translates to additional parameters in the bidding formula. We share practical learnings of the deployed bidding system in the LinkedIn ad marketplace based on this framework.

Naive Bayes(NB) is one of the essential algorithms in data mining. However, it is rarely used in reality because of the attribute independent assumption. Researchers have proposed many improved NB methods to alleviate this assumption. Among these methods, due to high efficiency and easy implementation, the filter attribute weighted NB methods receive great attentions. However, there still exists several challenges, such as the poor representation ability for single index and the fusion problem of two indexes. To overcome above challenges, we propose a general framework for Adaptive Two-index Fusion attribute weighted NB(ATFNB). Two types of data description category are used to represent the correlation between classes and attributes, intercorrelation between attributes and attributes, respectively. ATFNB can select any one index from each category. Then, we introduce a switching factor \{beta} to fuse two indexes, which can adaptively adjust the optimal ratio of the two index on various datasets. And a quick algorithm is proposed to infer the optimal interval of switching factor \{beta}. Finally, the weight of each attribute is calculated using the optimal value \{beta} and is integrated into NB classifier to improve the accuracy. The experimental results on 50 benchmark datasets and a Flavia dataset show that ATFNB outperforms the basic NB and state-of-the-art filter weighted NB models. In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive switching factor \{beta}. Auxiliary experimental results demonstrate the improved model significantly increases the accuracy compared to the original model without the adaptive switching factor \{beta}.

Large, black drones with the orange logo of EDGE, the UAE's arms consortium, were on display at this week's Unmanned Systems Exhibition (UMEX), along with remote-controlled machineguns and other "smart" weapons. The exhibition comes at a time of growing unmanned attacks around the region, including the January 17 drone-and-missile assault by Yemen rebels that killed three oil workers in Abu Dhabi, the first in a series of similar incidents. "Autonomous systems are becoming ever more prevalent around the world," Miles Chambers, EDGE's director of international business development, told AFP. "We are really heavily investing in developing our autonomous capability... as well as in electronic warfare and in our smart munitions. These are our three pillars." EDGE, an Abu Dhabi-based defence consortium that groups 25 Emirati firms, was formed three years ago but reached an estimated $4.8 billion in arms sales in 2020 -- nearly all of them to the UAE government.

There's a strand of thinking, from sci-fi films to Stephen Hawking that suggests artificial intelligence (AI) could spell doom for humans. But conservationists are increasingly turning to AI as an innovative tech solution to tackle the biodiversity crisis and mitigate climate change. From camera trap and satellite images to audio recordings, the report notes: "AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings – hugely reducing the manual labour required to collect vital conservation data." AI is helping to protect species as diverse as humpback whales, koalas and snow leopards, supporting the work of scientists, researchers and rangers in vital tasks, from anti-poaching patrols to monitoring species. With machine learning (ML) computer systems that use algorithms and models to learn, understand and adapt, AI is often able to do the job of hundreds of people, getting faster, cheaper and more effective results.

The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the tasks of the L3DAS21 edition. We generated a new dataset, which maintains the same general characteristics of L3DAS21 datasets, but with an extended number of data points and adding constrains that improve the baseline model's efficiency and overcome the major difficulties encountered by the participants of the previous challenge. We updated the baseline model of Task 1, using the architecture that ranked first in the previous challenge edition. We wrote a new supporting API, improving its clarity and ease-of-use. In the end, we present and discuss the results submitted by all participants. L3DAS22 Challenge website: www.l3das.com/icassp2022.