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If You Think "Don't Look Up" Is Just an Allegory About Climate Change, You're Missing Something

Mother Jones

This story was originally published by Slate and is reproduced here as part of the Climate Desk collaboration. It also contains spoilers for the film Don't Look Up. Streaming just in time for Christmas, Adam McKay's decidedly uncheery Netflix comedy, Don't Look Up, finds Jennifer Lawrence and Leonardo DiCaprio playing a pair of intrepid astronomers as they try (and mostly fail) to warn the world about a planet-killing comet that's hurtling toward Earth. From the beginning, the scientists' efforts are marked by futility, encapsulated in an early scene in which Kate Dibiasky (Lawrence) and Randall Mindy (DiCaprio) are brought to the White House to debrief President Janie Orlean (Meryl Streep) on the impending extinction-level event. Predictably, the meeting goes disastrously.


em Don't Look Up /em Is About Much More Than Climate Change

Slate

This article contains spoilers for the film Don't Look Up. Streaming just in time for Christmas, Adam McKay's decidedly uncheery Netflix comedy, Don't Look Up, finds Jennifer Lawrence and Leonardo DiCaprio playing a pair of intrepid astronomers as they try (and mostly fail) to warn the world about a planet-killing comet that's hurtling toward Earth. From the beginning, the scientists' efforts are marked by futility, encapsulated in an early scene in which Kate Dibiasky (Lawrence) and Randall Mindy (DiCaprio) are brought to the White House to debrief President Janie Orlean (Meryl Streep) on the impending extinction-level event. Predictably, the meeting goes disastrously. The president's son and chief of staff (played by Jonah Hill) lounges on the couch, nurses a bad case of coke sniffles, and proclaims to be "so bored" by all the world-ending comet talk.


Your CEO Isn't Real: How to Deal With Deep Fakes

#artificialintelligence

The history of deep fake technology is surprisingly long. Researchers at academic institutions have been developing deep fake tech since the early 1990s. The idea is even older, as popular science fiction--like the 1987 film The Running Man--can attest. But deep fakes are no longer relegated to the realm of sci-fi; they are, in fact, more present in our daily lives than you might realize. It's easy to think of deep fakes as some sort of advanced CGI used to create highly realistic animated films or to replace established actors in a film or television series, especially in cases where actors pass away unexpectedly before filming is complete.


When Creators Meet the Metaverse: A Survey on Computational Arts

arXiv.org Artificial Intelligence

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace.


'Gutfeld!' on CNN, Olympic Games

FOX News

'Gutfeld!' panel debates whether CNN will change their coverage This is a rush transcript from "Gutfeld!," This copy may not be in its final form and may be updated. I want to protect free speech. No, we want people to be protected from disinformation, to be protected from dying in this country, to be protected from people like Donald Trump who spread this information for -- who love to make sure that the division and the death continues. That was a rough weekend, and not just for Kat. But at least she kept her clothes on unlike our other guests, Jimmy Failla. But it was a far worse weekend for CNN. First let's go to our roly-poly guacamole gossip goalie. See how bad it got unreliable fart noises. Here's Michael Wolff delivering that smack to the hack. You know, you become part of -- one of the parts of the problem of the media. You know, you come on here and you -- and you have a, you know, a monopoly on truth. You know, you know exactly how things are supposed to be done. You know, you are why one of the reasons people can't stand the media. You should see the rest of the world, buddy. Can I hear that chuckle again? But if that was a heavyweight fight, and it is because, you know, Stelter, it would have been stopped in the first 25 seconds. It got worse, meaning better, lots better. STELTER: It's -- how -- so what should I do differently, Michael? WOLFF: You know, don't talk so much. Listen more, you know, people have genuine problems with the media. The media doesn't get the story right.


MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

arXiv.org Artificial Intelligence

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. MultiBench provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, MultiBench offers a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. To accompany this benchmark, we also provide a standardized implementation of 20 core approaches in multimodal learning. Simply applying methods proposed in different research areas can improve the state-of-the-art performance on 9/15 datasets. Therefore, MultiBench presents a milestone in unifying disjoint efforts in multimodal research and paves the way towards a better understanding of the capabilities and limitations of multimodal models, all the while ensuring ease of use, accessibility, and reproducibility. MultiBench, our standardized code, and leaderboards are publicly available, will be regularly updated, and welcomes inputs from the community.


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


A Differential Privacy Mechanism that Accounts for Network Effects for Crowdsourcing Systems

Journal of Artificial Intelligence Research

In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore this network effect, we formulate the interaction among users regarding their participation decisions as a population game, because a user's welfare from the interaction depends not only on his own participation decision but also the distribution of others' decisions. We show that the Nash equilibrium of this game consists of a threshold strategy, where all users whose privacy sensitivity is below a certain threshold will participate and the remaining users will not. We characterize the existence and uniqueness of this equilibrium, which depends on the privacy guarantee, the reward provided by the initiator and the population size. Based on this equilibria analysis, we design the PINE (Privacy Incentivization with Network Effects) mechanism and prove that it maximizes the initiator's payoff while providing participating users with a guaranteed degree of privacy protection. Numerical simulations, on both real and synthetic data, show that (i) PINE improves the initiator's expected payoff by up to 75%, compared to state of the art mechanisms that do not consider this effect; (ii) the performance gain by exploiting the network effect is particularly good when the majority of users are flexible over their privacy attitudes and when there are a large number of low quality task performers.


Stealth (film) - Wikipedia

#artificialintelligence

Stealth is a 2005 American military science fiction action film directed by Rob Cohen and written by W. D. Richter, and starring Josh Lucas, Jessica Biel, Jamie Foxx, Sam Shepard, Joe Morton and Richard Roxburgh. The film follows three top fighter pilots as they join a project to develop an automated robotic stealth aircraft. Released on July 29, 2005 by Columbia Pictures, the film was a box office bomb, grossing $79 million worldwide against a budget of $135 million. It was one of the worst losses in cinematic history.[2][3] In the near future, the U.S. Navy develops the F/A-37 Talon, a single-seat fighter-bomber with advanced payload, range, speed, and stealth capabilities.


Life Imitates Orwell...

#artificialintelligence

And I am talking Season 3. Or Amazon's hit, The Handmaid's Tale? Do you just binge and veg out or are you like me, and see how easily we could, and are, slipping into these worlds? After watching shows like this I often find myself reflecting back on George Orwell's 1984. It proves more eerily prophetic with each passing year. This Season, I fear, the writers of Westworld are almost scripting our future lives. You may not have caught it, but it is all in there.