A restaurant in a rural Oregon city couldn't find enough servers to stay fully staffed. So the owner hired a robot named Plato. She had no idea how much pushback she'd get from the community. BACKLASH AT THE BAR: A struggling restaurant owner hired a robot to help her servers. Then the angry messages began.

In a world of shifting, new technologies, how do we stay connected with one another? That's one of the questions at the heart of "Rachels Don't Run," a short film directed by the French American filmmaker Joanny Causse. The film takes place entirely in an empty office, late at night, some time in the near future. Leah (Sera Barbieri), a customer-support agent for an A.I.-companionship company called Iris, sits alone at a sparingly lit desk, monitoring calls and dealing with frustrated clients. She helps them navigate the high-tech service and fields feedback about Svetlana, Siobhan, and Rachel--a few of the various personas of artificial-intelligence companions that Iris provides.

Since the days of Alan Turing, music created by computational models has been inextricably bound up with technological process. But now that tools like Voicify.AI are going viral on TikTok and the Recording Academy has updated their rules to allow music created with AI tools to be eligible for Grammy consideration, is it already too late to consider the musicians whose work will be made obsolete by AI? This podcast is produced by Se'era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' AI's latest product – Remini – allows users to upload photos of themselves and their partner to generate images of what their future child could look like. There are two sides to this. First, the app lets people envision themselves as parents – potentially encouraging people to pursue, rather than delay, parenthood. As one woman said, "I can actually see myself being [pregnant] at some point."

Former litigator Jacqueline Schafer, the CEO and founder of Clearbrief, said AI is frequently used in courtrooms, and she created Clearbrief to fact-check citations and court docs created by generative AI. Some researchers are increasingly convinced they will not be able to remove hallucinations from artificial intelligence (AI) models, which remain a considerable hurdle for large-scale public acceptance. "We currently do not understand a lot of the black box nature of how machine learning comes to its conclusions," Kevin Kane, CEO of quantum encryption company American Binary, told Fox News Digital. "Under the current approach to walking this path of AI, it's not clear how we would do that. We'd have to change how they work a lot."

'Gutfeld!' panelists discuss whether every generation has become less attractive. We'll get to the indictments and the Bidens in the next block. GUTFELD MUSIC VIDEO: So many stories too upsetting to look into. A bunch of news that just makes you wanna cry. Then suddenly everything gets less depressing.

Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers. More information and supplementary material can be found on our project webpage: https://motion-mode.github.io/

Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. However, the use of such systems raises significant ethical and safety concerns, including privacy violations, misinformation and intellectual property theft. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. To mitigate these risks, there is an urgent need of policies and regulations responsible and ethical development in the field of generative AI. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity, lack of interoperability across lines of jurisdictions, restricting innovation, and hindering free market competition. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative. However, they have clear deficiencies in terms of lack of adequacy of oversight and difficulty of enforcement of ethical and safety standards, and are thus not enough by themselves as a regulation mechanism. We propose a marriage of these two strategies via a framework we call Dual Governance. This framework proposes a cooperative synergy between centralized government regulations in a U.S. specific context and safety mechanisms developed by the community to protect stakeholders from the harms of generative AI. By implementing the Dual Governance framework, we posit that innovation and creativity can be promoted while ensuring safe and ethical deployment of generative AI.

Text-to-image diffusion models such as Stable Diffusion have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process and how to engineer prompts in order to obtain the desired images. To this end, we introduce the new task of predicting the text prompt given an image generated by a generative diffusion model. We combine a series of white-box and black-box models (with and without access to the weights of the diffusion network) to deal with the proposed task. We propose a novel learning framework comprising of a joint prompt regression and multi-label vocabulary classification objective that generates improved prompts. To further improve our method, we employ a curriculum learning procedure that promotes the learning of image-prompt pairs with lower labeling noise (i.e. that are better aligned), and an unsupervised domain-adaptive kernel learning method that uses the similarities between samples in the source and target domains as extra features. We conduct experiments on the DiffusionDB data set, predicting text prompts from images generated by Stable Diffusion. Our novel learning framework produces excellent results on the aforementioned task, yielding the highest gains when applied on the white-box model. In addition, we make an interesting discovery: training a diffusion model on the prompt generation task can make the model generate images that are much better aligned with the input prompts, when the model is directly reused for text-to-image generation.

Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection. Extrinsic camera calibration data is important for most analysis applications. However, security cameras are susceptible to environmental conditions and small camera movements, resulting in a need for an automated re-calibration method that can account for these varying conditions. In this paper, we present an automated camera-calibration process leveraging a dictionary-based approach that does not require prior knowledge on any camera settings. The method consists of a custom implementation of a Spatial Transformer Network (STN) and a novel topological loss function. Experiments reveal that the proposed method improves the IoU metric by up to 12% w.r.t. a state-of-the-art model across five synthetic datasets and the World Cup 2014 dataset.