5 Ways Metaverse And Artificial Intelligence Influence Avatar 2


The newest technology is a hallmark of James Cameron’s films. No matter if it’s the Titanic, the Terminator, or the Magnum Opus Avatar 2. During the global transition to the internet. He made a significant step by creating the metaverse-based film avatar. Despite the audience’s lack of familiarity with the idea at the time, it was warmly accepted. The movie Avatar quickly gained popularity and rose to the top of the box office.

He understands what makes a story seem realistic as a storyteller, and cutting-edge technologies are a significant part of his toolkit. It takes a genius to design a planet that no one has ever seen before while making it understandable. He repeatedly emphasized the part artificial intelligence (AI) can play in improvised filmmaking. He used sophisticated artificial intelligence and machine learning algorithms in his film Avatar 2.

5 ways Metaverse and Artificial Intelligence influence Avatar 2

Virtual Reality

The viewer has a genuine underwater immersion experience. Evaluating the various facets of human perception and how they relate to one another is imperative. In order to explore and interact with the fictional Pandora in real-time, the production team and actors wore VR headsets.

Also Read: Year Ender: Top 3 Metaverse Trends To Watch Of 2022

Invent new scenes

Before the movie is finally released, new storylines are always possible. During the movie’s editing process, it is thought that dialogue or facial expressions need to be improved. It was previously accomplished by taking a wide shot or a headshot, which runs the risk of having an improper lipsync. To get around this, Cameron invented a brand-new technique for overlaying new dialogue or facial scans onto a scene that had already been performed.

Augmented Reality

A product of augmented reality(AR), the world of Pandora’s planet appears realistic in the film. With AR, elements from the real world are essentially superimposed, creating a sense of interaction and immersion. The presence of alien creatures with intricate anatomies makes the contribution AR made to this movie impossible to ignore.

Examine movie scripts

Recently, algorithms have developed the ability to comprehend story flow and adapt to various storytelling techniques. It makes sense that a VFX-heavy film like Avatar would use AI to analyze the plot. The script for Avatar 2 is written exquisitely, as can be seen by the audience.

Motion Capture

This technology, also known as performance capture, enables the recording of motions made by people or objects and the transfer of those motions to animated objects in a virtual environment. Usually, heavy equipment is needed for this process, but a machine-learning algorithm can easily take its place. It can increase the story’s pace, predictability, and analytics impact.

Avatar 2 earned more than $400 million at the global box office after debuting with $134 million in domestic ticket sales. Slowly and steadily gained popularity, earning more than $1 billion in its third weekend. After Top Gun 2, Avatar 2 is the second 2022 film to gross over a billion dollars. At the end of the New Year’s weekend, Avatar 2 brought in $1.379 billion at the global box office.

Also Read: Top 5 Technologies That Power The Metaverse

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NVIDIA DRIVE OS earns safety certification


NVIDIA drive

NVIDIA DRIVE OS is an operating system for in-vehicle accelerating computing power. | Source: NVIDIA

TÜV SÜD has determined that NVIDIA’s DRIVE OS 5.2 software meets the International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level (ASIL) B standard, which targets functional safety in road vehicles’ systems, hardware and software. 

NVIDIA DRIVE OS is an operating system for in-vehicle accelerating computing powered by the NVIDIA DRIVE platform. DRIVE OS is the foundation of NVIDIA’s DRIVE SDK, which includes NVIDIA’s CUDA libraries for efficient parallel computing, the NVIDIA Tensor RT SDK for real-time AI inferencing and the NvMedia library for sensor input processing, among other developer tools and modules. 

To meet the standard, NVIDIA’s software had to be able to detect failures during operation, and be developed in a process that handles potential systematic faults along the whole V-model, this includes everything from safety requirements definition to coding, analysis, verification and validation. Essentially, the software has to avoid failures whenever possible, and detect and respond to them if they can’t be avoided. 

TÜV SÜD’s team determined that DRIVE OS 5.2 complies with its strict testing criteria and is suitable for safety-related use in applications up to ASIL B. ISO 26262 identifies four ASILs, A, B, C and D, with A being the lowest degree and D being the highest degree of automotive hazard.

TÜV SÜD, based in Munich, Germany, assesses compliance to national and international standards for safety, durability and quality in various applications, including cars, factories, buildings, bridges and other infrastructure. 

NVIDIA DRIVE is an open platform, which means that experts from top car companies can build upon the company’s industrial-strength system. 

Earlier this year, NVIDIA filed a patent for a system that would help solve one of the biggest issues in autonomous driving: how self-driving cars identify and react to emergency vehicles.

Nvidia’s patent filing, which was published by the US Patent and Trademark Office in May 2022, seeks to help self-driving cars to avoid situations where an autonomous vehicle doesn’t how know to react to emergency vehicles, which could result in a slowed response time, meaning more property damage and personal injuries. 

The patent describes a system involving microphones attached to an autonomous or semi-autonomous car to capture the sounds of nearby emergency response vehicles’ sirens. The microphones will work with a Deep Neural Network (DNN) to create audio signals that correspond to the sirens detected.

NVIDIA won a 2022 RBR50 Robotics Innovation Award from our sister publication Robotics Business ReviewThe company won for its Omniverse Replicator, a data generation engine that produces synthetic data for training deep neural networks based on physical simulations in photorealistic, physically-accurate virtual environments.

The post NVIDIA DRIVE OS earns safety certification appeared first on The Robot Report.

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Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning


doi: 10.1093/bib/bbac586.

Online ahead of print.


Item in Clipboard

Zerun Lin et al.

Brief Bioinform.



The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.


contrastive learning; deep learning; network inference; single-cell RNA-Sequencing.