During the creation of the Obi-Wan Kenobi TV series, James Earl Jones signed off on allowing Disney to replicate his vocal performance as Darth Vader in future projects using an AI voice-modeling tool called Respeecher, according to a Vanity Fair report published Friday. Jones, who is 91, has voiced the iconic Star Wars villain for 45 years, starting with Star Wars: Episode IV--A New Hope in 1977 and concluding with a brief line of dialog in 2019's The Rise of Skywalker. "He had mentioned he was looking into winding down this particular character," said Matthew Wood, a supervising sound editor at Lucasfilm, during an interview with Vanity Fair. "So how do we move forward?" The answer was Respeecher, a voice cloning product from a company in Ukraine that uses deep learning to model and replicate human voices in a way that is nearly indistinguishable from the real thing.

Netflix Inc. is creating its first in-house video games studio, in a push to be less reliable on third-party creators and to expand its gaming offerings. The new studio will be based in Helsinki and headed by Marko Lastikka, according to a statement from Netflix released Monday. Lastikka previously spent more than five years at Zynga, where he worked on FarmVille 3, and before that was the co-founder and executive producer at Electronic Arts' Tracktwenty studio in Helsinki, according to his LinkedIn page. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Assistant Professor/Associate Professor,/Professor, AI and Deep Learning for Sustainable in Electrical & Electronic Engineering, Academic Posts …

Being one of the most widely spoken language in the world, the use of Bangla has been increasing in the world of social media as well. Sarcasm is a positive statement or remark with an underlying negative motivation that is extensively employed in today's social media platforms. There has been a significant improvement in sarcasm detection in English over the previous many years, however the situation regarding Bangla sarcasm detection remains unchanged. As a result, it is still difficult to identify sarcasm in bangla, and a lack of high-quality data is a major contributing factor. This article proposes BanglaSarc, a dataset constructed specifically for bangla textual data sarcasm detection. This dataset contains of 5112 comments/status and contents collected from various online social platforms such as Facebook, YouTube, along with a few online blogs. Due to the limited amount of data collection of categorized comments in Bengali, this dataset will aid in the of study identifying sarcasm, recognizing people's emotion, detecting various types of Bengali expressions, and other domains. The dataset is publicly available at https://www.kaggle.com/datasets/sakibapon/banglasarc.

In recent years, with the large-scale deployment of space spacecraft entities and the increase of satellite onboard capabilities, delay/disruption tolerant network (DTN) emerged as a more robust communication protocol than TCP/IP in the case of excessive network dynamics. DTN node buffer management is still an active area of research, as the current implementation of the DTN core protocol still relies on the assumption that there is always enough memory available in different network nodes to store and forward bundles. In addition, the classical queuing theory does not apply to the dynamic management of DTN node buffers. Therefore, this paper proposes a centralized approach to automatically manage cognitive DTN nodes in low earth orbit (LEO) satellite constellation scenarios based on the advanced reinforcement learning (RL) strategy advantage actor-critic (A2C). The method aims to explore training a geosynchronous earth orbit intelligent agent to manage all DTN nodes in an LEO satellite constellation scenario. The goal of the A2C agent is to maximize delivery success rate and minimize network resource consumption cost while considering node memory utilization. The intelligent agent can dynamically adjust the radio data rate and perform drop operations based on bundle priority. In order to measure the effectiveness of applying A2C technology to DTN node management issues in LEO satellite constellation scenarios, this paper compares the trained intelligent agent strategy with the other two non-RL policies, including random and standard policies. Experiments show that the A2C strategy balances delivery success rate and cost, and provides the highest reward and the lowest node memory utilization.

Machine Translation System (MTS) serves as an effective tool for communication by translating text or speech from one language to another language. The need of an efficient translation system becomes obvious in a large multilingual environment like India, where English and a set of Indian Languages (ILs) are officially used. In contrast with English, ILs are still entreated as low-resource languages due to unavailability of corpora. In order to address such asymmetric nature, multilingual neural machine translation (MNMT) system evolves as an ideal approach in this direction. In this paper, we propose a MNMT system to address the issues related to low-resource language translation. Our model comprises of two MNMT systems i.e. for English-Indic (one-to-many) and the other for Indic-English (many-to-one) with a shared encoder-decoder containing 15 language pairs (30 translation directions). Since most of IL pairs have scanty amount of parallel corpora, not sufficient for training any machine translation model. We explore various augmentation strategies to improve overall translation quality through the proposed model. A state-of-the-art transformer architecture is used to realize the proposed model. Trials over a good amount of data reveal its superiority over the conventional models. In addition, the paper addresses the use of language relationships (in terms of dialect, script, etc.), particularly about the role of high-resource languages of the same family in boosting the performance of low-resource languages. Moreover, the experimental results also show the advantage of backtranslation and domain adaptation for ILs to enhance the translation quality of both source and target languages. Using all these key approaches, our proposed model emerges to be more efficient than the baseline model in terms of evaluation metrics i.e BLEU (BiLingual Evaluation Understudy) score for a set of ILs.

Netflix is slowly making good on its promise to develop a pipeline of 50 games by acquiring and hiring from established firms and in premiere locations,

The interwebs, as of late, have been filled with images created by artificial intelligence rendering bots such as DALL-E and Midjourney -- and the humans (I think they're humans) using them as tools. Brooklyn-based artist Zach Katz has used it to reimagine the urban design of cities. A reporter at SFGATE has undertaken a similar project, asking DALL-E 2 to retool some of the city's architecture and infrastructure. In July, the Guardian rounded up four artists to come up with unlikely prompts -- such as "biotech harpy in field at sunset" -- for DALL-E Mini (the free, public version of DALL-E). Naturally, the advent of bots that can create an image out of a simple text command is drawing the scrutiny of illustrators.

This episode was produced by Jennifer Strong with help from Anthony Green and Emma Cillekens. It was edited by Mat Honan and mixed by Garret Lang, with original music from Jacob Gorski. It's Jennifer Strong and I have something a little bit different this week. We're in the middle of making another miniseries… and this time I'm looking at how farming operations are using AI to more precisely grow food… with help from machine vision, satellites, and incredible quantities of data. So I've been spending a lot of time talking to farmers and agricultural tech workers who are building and experimenting with these tools…and in the weeks ahead?

Imagine producing your own film filled with big-budget scenery, but from a computer. A tech entrepreneur in Germany named Fabian Stelzer(Opens in a new window) is trying to do just that by using AI-powered programs to create the footage, sound effects, and voices for a 70s-inspired sci-fi film. The experimental project is called Salt(Opens in a new window), and it's built entirely with AI-generated art. To create the visuals, Stelzer has been tapping publicly available programs such as Midjourney, Stable Diffusion, and DALL-E 2, which can essentially draw anything you want by relying on a mere text description from the user. On Twitter, Stelzer has been releasing Salt in short clips, called "story seeds."