Dario Amodei, CEO of Anthropic, envisions a future where AI systems are not only powerful but also aligned with human values. After leaving OpenAI, Amodei co-founded Anthropic to tackle the safety challenges of AI, aiming to create systems that are both intelligent and ethical. One of the key methods Anthropic employs is “Constitutional AI,” a training approach that instills AI models with a set of core principles derived from universally accepted documents like the United Nations Declaration of Human Rights.
GaiaNet is a decentralized computing infrastructure that enables everyone to create, deploy, scale, and monetize their own AI agents that reflect their styles, values, knowledge, and expertise. It allows individuals and businesses to create AI agents. Each GaiaNet node provides
a web-based chatbot UI.
an OpenAI compatible API. See how to use a GaiaNet node as a drop-in OpenAI replacement in your favorite AI agent app.
This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or “hallucinations.” This technique is commonly known as “Retrieval Augmented Generation”, or RAG.
LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.
An open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
The new material provides an energy density—the amount that can be squeezed into a given space—of 1,000 watt-hours per liter, which is about 100 times greater than TDK’s current battery in mass production.
TDK has 50 to 60 percent global market share in the small-capacity batteries that power smartphones and is targeting leadership in the medium-capacity market, which includes energy storage devices and larger electronics such as drones.
Blender 3 updated Intel® Open Image Denoise to version 1.4.2 which improved many artifacts in render, even separating into passes, but still loses a lot of definition when used in standard mode, DENOISER COMP separates passes and applies denoiser only in the selected passes and generates the final pass (beauty) keeping much more definition as can be seen in the videos.
Gen-3 Alpha is the first of an upcoming series of models trained by Runway on a new infrastructure built for large-scale multimodal training. It is a major improvement in fidelity, consistency, and motion over Gen-2, and a step towards building General World Models.
This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or “hallucinations.” This technique is commonly known as “Retrieval Augmented Generation”, or RAG.
LARS aims to be the ultimate open-source RAG-centric LLM application. Towards this end, LARS takes the concept of RAG much further by adding detailed citations to every response, supplying you with specific document names, page numbers, text-highlighting, and images relevant to your question, and even presenting a document reader right within the response window. While all the citations are not always present for every response, the idea is to have at least some combination of citations brought up for every RAG response and that’s generally found to be the case.
🔸 Gaussian Splats: imagine throwing thousands of tiny ellipsoidal paint drops. They overlap, blend, and create a smooth, photorealistic look. Fast, great for visualization, but less structured for measurements.
🔸 Point Clouds: every dot is a measured hit. LiDAR or photogrammetry gives us millions of them forming a constellation of reality. Amazing for accuracy, but they don’t connect the dots out of the box.
🔸 Meshes: take those points, connect them into triangles, and you get very realistic surfaces. Strong for 3D analysis, simulation as continues watertight models.
The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.
For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.
The Dynamic Range of real-world scenes can be quite high — ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel. Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera.
For TVs HDR is great, but it’s not the only new TV feature worth discussing.
