DJI's Mavic 3 is a useful cinematic drone, but its steep price ($1,899 as we write this) can be off-putting compared to the $1,449 of the older Mavic 2 Pro. The company thinks it has a simple solution, though: offer a trimmed-back version. DJI has introduced the Mavic 3 Classic, a new variant that drops the telephoto lens in exchange for a better $1,469 starting price (more on that later). The Classic otherwise includes the features that might draw you to the Mavic 3 in the first place. The centerpiece remains a 20-megapixel, 24mm-equivalent Hasselblad camera that can shoot 5.1K video up to 50 frames per second (4K at 60FPS) and capture 12-bit RAW photos.

In particular, it said weaker economic conditions are weighing on brands such as Plenty of Fish, which cater to people with lower incomes, while people were starting to make fewer in-app purchases on its platforms.

Our mission is to build entertainment for millions of people around the world and connect people through high-quality games. Since we were founded, we've drawn 500 of the world's most talented people into our ranks. Our team has worked on international AAA titles like Transformers, Star Wars: The Old Republic, Real Steel, Rio, Mech Conquest, and Dueling Blades. Our designers have worked on some of Hollywood's biggest hits including the movie Avatar. Junglee is not just a gaming business - it is a blend of data science, innovation, cutting-edge technology and, most importantly, a values-driven culture that is creating the next set of conscious leaders.

A wave of AI music tools has washed ashore in recent years, and they have quietly been getting better and better. From music mastering services to AI-assisted reverb, they're helping to make audio production more accessible to beginners while enhancing the creative workflow for pro users. If you're curious about what AI music tools are worth checking out, here is our list of some of the most impressive services, plugins, and software out there. Another area that AI has proved to be a fantastic solution is in stem separation. If you need to remove the vocals from a song and just keep the instrumental backing track, then LALAL.AI is one of the most capable tools.

Mankind's technological advancements have been developing at such breakneck speeds that we often take for granted how they have made our lives easier. One of the most interesting results of human brainpower is the machine-like replication of itself: artificial intelligence. AI is already present in our daily tasks, from search engines, algorithms, virtual assistant technology, and the like. However, there seems to be budding movements in applying AI technologies to the cultural productions, with some already venturing into anime and art. Should this prove successful both commercially and critically, it is only inevitable that this AI movement would permeate the cultural zeitgeist of other media, and one that is of great interest is the medium of film.

The results of this research have recently been published in the open-source journal, PLoS One, in a paper co-authored by Dr. Souptick Chanda and his research scholar, Mr. Pratik Nag.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Before diving into actual websites, there is PART 1 of this series.

Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multi-score-SIQA

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles of different ideological leanings. We present a novel generative framework to allow the generation of canonical names for entities as well as stances among them. We further enhance the model with a graph encoder to summarize entity activities and external knowledge surrounding the entities. Experiments show that our model outperforms strong comparisons by large margins. Further analyses demonstrate the usefulness of E2E stance detection for understanding media quotation and stance landscape, as well as inferring entity ideology.

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.