Tag: Google DeepMind

  • Google DeepMind Genie 2: Real-Time 3D World Generator

    Google DeepMind Genie 2: Real-Time 3D World Generator

    Google DeepMind, a research branch of Google focused on AI, has introduced Genie 2, a foundational world model capable of creating "action-controllable, playable 3D environments" for fast prototyping and training AI agents.

    Advanced Capabilities

    According to the company, Genie 2 enhances the abilities of its earlier version and can produce "a vast diversity of rich 3D worlds." It’s capable of simulating interactions between objects, animations for characters, physics, and Non-Playable Characters (NPCs) along with their animations and interactions. This model can take both text and visual cues as input.

    Memory and Perspective

    Genie 2 is designed to remember elements of the world that aren’t visible to the player and can render them when they become visible again. This is akin to the Level of Detail (LOD) technique used in gaming, which adjusts the complexity of the objects and environments based on the player’s Field Of View (FOV).

    The model can create new content in real-time and keep a stable world "for up to a minute." It also offers the ability to render environments from various viewpoints, such as first-person, third-person, or isometric perspectives.

    Realistic Effects

    Additionally, it can produce sophisticated effects, including smoke, object interactions, fluid dynamics, gravity, and advanced lighting and reflections. DeepMind claims this model can facilitate the quick prototyping of fresh concepts and ideas. Users can also create and manage AI agents with straightforward prompts.

    Numerous companies are developing foundational world models that can simulate and build representations of environments. For instance, Decart’s Oasis allows users to engage with a real-time AI-generated version of Minecraft, while AI leader Fei Fei Li’s start-up, World Labs, also features a 3D generator.

    Google DeepMind’s contributions are setting a new standard in the realm of AI and simulated environments.

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  • Google DeepMind AI Creates Music and Sound for Silent Videos

    Google DeepMind AI Creates Music and Sound for Silent Videos

    Google's DeepMind has unveiled a new AI tool capable of generating background music and sound effects for silent videos. This "video-to-audio" system aims to simplify the video editing process, especially for content creators.

    Currently under development, this technology offers some intriguing capabilities. Here’s an overview of the process:

    User Input

    Creators start by uploading their silent video and can include keywords or phrases to guide the AI in producing the appropriate soundscape. For instance, a silent video featuring someone walking in the dark might benefit from prompts such as “movies, horror films, music, tension, footsteps on concrete” to help the AI grasp the mood and context.

    AI in Action

    DeepMind’s AI model begins by breaking down the video to analyze its visuals. This visual data is then paired with the user-provided text prompts. Through a diffusion model, the AI processes this combined information iteratively, eventually creating background sounds that match the video content.

    Tailoring the Soundscape

    The model can generate different audio options for a single video, allowing creators to select the best match for their project. DeepMind’s system can also take into account the emotional tone of the prompt words. For example, prompts that emphasize “tension” might produce suspenseful background music, whereas prompts like “joyful celebration” could result in more upbeat sounds.

    Looking forward, DeepMind is continuously refining this technology. Future plans include enabling the AI to generate sounds automatically based solely on the video content, eliminating the need for user prompts. Additionally, they aim to enhance the system’s ability to synchronize generated dialogue with the characters’ lip movements in the video.

    This "video-to-audio" technology has the potential to transform video editing, particularly for creators who do not have access to professional audio tools or expertise.

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  • Google DeepMind’s SIMA: Your New In-Game Teammate Training Guide

    Google DeepMind’s SIMA: Your New In-Game Teammate Training Guide

    Get ready for a novel gaming companion! Google DeepMind has unveiled SIMA, a sophisticated language model undergoing training to serve as your in-game partner. Is this the true purpose of AI? It seems like a fitting role.

    SIMA, short for “Scalable, Instructable, Multiworld Agent,” is currently in the developmental stages but holds the promise of transforming our gaming experiences. Unlike conventional AI companions, SIMA transcends the typical NPC character archetype. This model is crafted to be a collaborative teammate, capable of comprehending your actions and adjusting its own responses accordingly. Envision having a cooperative partner in Borderlands who allows you to loot first before claiming items themselves. The prospect is undeniably exciting.

    AI Gaming Evolution

    To achieve this level of interaction, SIMA leverages a blend of natural language processing and image recognition. This fusion enables it to perceive the 3D gaming environment and react to your commands and movements. Google has collaborated with eight game developers, including studios responsible for popular titles like No Man’s Sky and Valheim, to train this AI teammate.

    Training and Future Prospects

    Through these partnerships, SIMA is mastering the basics of gameplay—from simple tasks like turning or climbing to navigating menus and maps. While more intricate activities such as resource collection and constructing camps currently lie beyond its capabilities, Google anticipates a significant expansion of SIMA’s skill set in the near future. Gamers may soon find themselves with a Google AI companion ready to fill that elusive third slot in their Apex Legends Lobby.

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  • DeepMind AI Surpasses Boundaries: Unveils 2 Million Unimaginable New Materials

    DeepMind AI Surpasses Boundaries: Unveils 2 Million Unimaginable New Materials

    In a groundbreaking achievement, Google DeepMind, a subsidiary of Alphabet (GOOGL), has harnessed the power of artificial intelligence (AI) to predict the structures of over 2 million new materials. This milestone, published in the prestigious science journal Nature, holds the potential to reshape various industries by significantly improving the production of batteries, solar panels, and computer chips.

    Revolutionizing Material Discovery with AI

    Traditionally, the discovery and synthesis of new materials have been both costly and time-consuming, often spanning a decade or more. However, DeepMind’s innovative AI, trained on data from the Materials Project, an international research group founded in 2011, has managed to predict the structure of nearly 400,000 hypothetical material designs. This breakthrough is expected to shorten the typically lengthy 10 to 20-year timeline for material development.

    The Power of GNoME Deep Learning Tool

    The GNoME Deep Learning Tool, a crucial component of DeepMind’s arsenal, identified an astounding 2.2 million new inorganic crystals, with 380,000 identified as the most stable for experimental research. This predictive accuracy extends to the stability of crystal structures, exemplified by the discovery of 52,000 new layered compounds similar to graphene and 528 potential lithium-ion conductors, a remarkable 25 times more than previous studies.

    Sharing Data for Further Advancements

    To facilitate further advancements, DeepMind is taking a collaborative approach by sharing its vast dataset with the research community through the Next Gen Materials Project. The data encompasses all of GNoME’s discoveries and predictions, providing researchers with free access to explore and experiment with the newfound treasure trove of material structures.

    Transforming Material Synthesis with AI

    A significant leap towards efficient material synthesis has been made possible through DeepMind’s collaboration with the Berkeley Lab, resulting in the creation of a robotic laboratory. This autonomous lab successfully synthesized 41 of the newly discovered materials, demonstrating the transformative potential of AI in experimental synthesis.

    Ekin Dogus Cubuk, a research scientist at DeepMind, expressed hope for substantial improvements in experimentation, autonomous synthesis, and machine learning models. The shared optimism extends to Kristin Persson, director of the Materials Project, who emphasized the need to shrink timelines for material development. Despite industry tendencies to be cautious about cost increases, the collaborative breakthroughs driven by AI may reshape dynamics, potentially reducing the time it takes for new materials to become cost-effective.

    The Future of AI and Material Science

    The convergence of AI and material science showcased by DeepMind’s achievement represents a radical acceleration in technological development. The impact on industries such as energy storage, electronics, and beyond is poised to unlock untold paths of innovation.

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