Humanoid robots are proficient help robots made to imitate human movement and association. Like all help robots, they offer some benefits via mechanizing undertakings that prompt cost reserve funds and efficiency. Humanoid robots are a generally new type of expert help robots. While long-imagined, they're presently beginning to turn out to be economically feasible in a large number of utilizations. The humanoid robots market is ready for huge development.

Deep Voodoo, an artificial intelligence entertainment startup founded by "South Park" creators Trey Parker and Matt Stone, revealed today that it has raised $20 million in funding to accelerate its development of deepfake technology, cost-effective visual effects services and synthetic media projects. Connect Ventures and New Enterprise Associates led the round. If you've never heard of Deep Voodoo before, you're not alone, but Parker and Stone are well-known. "South Park" debuted on the cable television channel Comedy Central in 1997 and is still going some 25 years later. Deep Voodoo has developed a synthetic media technology platform that uses artificial intelligence to support productions in ways that are faster and more cost-effective than existing visual effects technology.

The future of AI robotics never seems to have a silver lining, and here's a new nightmarish vision of what could come to pass from Jung_E, the latest film from Yeon Sang-ho (Train to Busan). The Netflix release is slated to hit the streaming service exclusively on January 20, 2023. The official synopsis from Netflix reveals a tease of the plot: "In a post-apocalyptic 22nd century, a researcher at an AI lab leads the effort to end a civil war by cloning the brain of a heroic soldier--her mother." The sleek, dystopian shine of Kronoid Lab's "most advanced A.I. combat warrior" invokes a feeling of dread when the very human-looking creation is built. Even the full-on wired brain that looks like one of those steel wool sponges gave me the icks.

Current AI concerns revolve mostly around art and chatbots, but in the future of Jung_E, things are a little more intense. The first teaser for the sci-fi film features a company called Kronoid Lab introducing what it describes as the "most advanced AI combat warrior." We don't actually get to see it in action -- it's just a brief teaser, after all -- but it seems pretty clear that things aren't going to go as planned.

MSNBC guest Kurt Andersen on Tuesday attacked Mike Pence for not supporting the indictment of Donald Trump. He called the ex-VP a "p---y." An MSNBC host and his guest piled on Mike Pence on Tuesday, suggesting the former vice president was a "p---y" for not supporting the indictment of Donald Trump. Host John Heilemann mocked Pence as boring and dull, suggesting he had the personality of the "squarest person you knew growing up." But it was guest and author Kurt Andersen who made things personal.

In South Africa, it is a common practice for people to leave their vehicles beside the road when traveling long distances for a short comfort break. This practice might increase human encounters with wildlife, threatening their security and safety. Here we intend to create awareness about wildlife fencing, using drone technology and computer vision algorithms to recognize and detect wildlife fences and associated features. We collected data at Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa. We used wildlife electric fence data containing single and double fences for the classification task. Additionally, we used aerial and still annotated images extracted from the drone and still cameras for the segmentation and detection tasks. The model training results from the drone camera outperformed those from the still camera. Generally, poor model performance is attributed to (1) over-decompression of images and (2) the ability of drone cameras to capture more details on images for the machine learning model to learn as compared to still cameras that capture only the front view of the wildlife fence. We argue that our model can be deployed on client-edge devices to inform people about the presence and significance of wildlife fencing, which minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle collisions.

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs), with applications for real image editing. The state-of-the-art approach for real image editing with inversion uses denoising diffusion implicit models (DDIMs) to deterministically noise the image to the intermediate state along the path that the denoising would follow given the original conditioning. However, DDIM inversion for real images is unstable as it relies on local linearization assumptions, which result in the propagation of errors, leading to incorrect image reconstruction and loss of content. To alleviate these problems, we propose Exact Diffusion Inversion via Coupled Transformations (EDICT), an inversion method that draws inspiration from affine coupling layers. EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion. Using Stable Diffusion, a state-of-the-art latent diffusion model, we demonstrate that EDICT successfully reconstructs real images with high fidelity. On complex image datasets like MS-COCO, EDICT reconstruction significantly outperforms DDIM, improving the mean square error of reconstruction by a factor of two. Using noise vectors inverted from real images, EDICT enables a wide range of image edits--from local and global semantic edits to image stylization--while maintaining fidelity to the original image structure. EDICT requires no model training/finetuning, prompt tuning, or extra data and can be combined with any pretrained DDM. Code is available at https://github.com/salesforce/EDICT.

We investigate how humans perform the task of dubbing video content from one language into another, leveraging a novel corpus of 319.57 hours of video from 54 professionally produced titles. This is the first such large-scale study we are aware of. The results challenge a number of assumptions commonly made in both qualitative literature on human dubbing and machine-learning literature on automatic dubbing, arguing for the importance of vocal naturalness and translation quality over commonly emphasized isometric (character length) and lip-sync constraints, and for a more qualified view of the importance of isochronic (timing) constraints. We also find substantial influence of the source-side audio on human dubs through channels other than the words of the translation, pointing to the need for research on ways to preserve speech characteristics, as well as semantic transfer such as emphasis/emotion, in automatic dubbing systems.

Arthur C. Clarke once remarked, "Any sufficiently advanced technology is indistinguishable from magic." That ambient sense of magic has been missing from the past decade of internet history. Each new tablet and smartphone is only a modest improvement over its predecessor. The expected revolutions--the metaverse, blockchain, self-driving cars--have plodded along, always with promises that the real transformation is just a few years away. The one exception this year has been in the field of generative AI.