Category robots

The buzz around Nintendo’s next-generation console, popularly referred to as the Nintendo Switch 2, has reached a fever pitch. With leaks emerging from accessory makers, YouTubers, and even case manufacturers, the gaming world is abuzz with speculation and excitement. As the official reveal looms—rumored to be in January 2025—let’s dive into the most credible leaks...

The post Nintendo Switch 2 Leaks: A Glimpse into the Future of Handheld Gaming appeared first on 1redDrop.

Scientists have developed swarms of tiny magnetic robots that work together like ants to achieve Herculean feats, including traversing and picking up objects many times their size. The findings suggest that these microrobot swarms -- operating under a rotating magnetic field -- could be used to take on difficult tasks in challenging environments that individual robots would struggle to handle, such as offering a minimally invasive treatment for clogged arteries and precisely guiding organisms.

The recent spate of unidentified drone sightings in the U.S., including some near sensitive locations such as airports and military installations, has caused significant public concern.

Startups T-Robotics and Mbodi selected to collaborate on conversational AI and adaptive learning robotics solutions . Both winners selected from over 100 global pioneers of next-generation automation technology.

Reinforcement Learning, an artificial intelligence approach, has the potential to guide physicians in designing sequential treatment strategies for better patient outcomes but requires significant improvements before it can be applied in clinical settings, finds a new study.

Infertility affects an estimated 186 million people worldwide, with fallopian tube obstruction contributing to 11%-67% of female infertility cases. Researchers have developed an innovative solution using a magnetically driven robotic microscrew to treat fallopian tube blockages. The microrobot is made from nonmagnetic photosensitive resin, coated with a thin iron layer to give it magnetic properties. By applying an external magnetic field, the robot rotates, generating translational motion that enables it to navigate through a glass channel simulating a fallopian tube.

Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations

Unveiling the Strategy The assassination of Lieutenant-General Igor Kirillov, Russia’s head of nuclear, biological, and chemical defense forces, marks a dramatic escalation in Ukraine’s unconventional warfare against Moscow. By targeting a top military figure accused of orchestrating chemical attacks, Ukraine signals its readiness to strike at the heart of Russian leadership. This development raises significant...

The post The Death of Russia’s Chemical Weapons Chief: A Turning Point in Ukraine’s Unconventional Warfare appeared first on 1redDrop.

Ensuring safety and accuracy of correct packaging is a high priority in the food industry. In addition to the packaging quality, the high variation of different packaging and labeling is also a challenge.

The serene city of Madison, Wisconsin, was struck by tragedy on December 16, 2024, when an active shooter incident at the Abundant Life Christian School shattered its peace. Three lives were lost, including the juvenile suspect, and nine others were injured, with injuries ranging from minor to life-threatening. This incident, part of a grim pattern...

The post Tragedy in Madison: The Abundant Life Christian School Shooting appeared first on 1redDrop.

A study is the first-of-its-kind to recognize American Sign Language (ASL) alphabet gestures using computer vision. Researchers developed a custom dataset of 29,820 static images of ASL hand gestures. Each image was annotated with 21 key landmarks on the hand, providing detailed spatial information about its structure and position. Combining MediaPipe and YOLOv8, a deep learning method they trained, with fine-tuning hyperparameters for the best accuracy, represents a groundbreaking and innovative approach that hasn't been explored in previous research.

We’re rolling out a new, state-of-the-art video model, Veo 2, and updates to Imagen 3. Plus, check out our new experiment, Whisk.

A month after mysterious drones were first reported over New Jersey, authorities are still trying to determine where they are coming from as sightings increase in frequency and spread into neighboring states.

Two roboticists from the University of Leeds and University College London have developed a framework that enables robots to traverse complex terrain without extra sensors or prior rough terrain training. Joseph Humphreys and Chengxu Zhou outlined the details of their framework in a paper posted to the arXiv preprint server.

Faster, smarter, more responsive AI applications – that’s what your users expect. But when large language models (LLMs) are slow to respond, user experience suffers. Every millisecond counts. 

With Cerebras’ high-speed inference endpoints, you can reduce latency, speed up model responses, and maintain quality at scale with models like Llama 3.1-70B. By following a few simple steps, you’ll be able to customize and deploy your own LLMs, giving you the control to optimize for both speed and quality.

In this blog, we’ll walk you through you how to:

• et up Llama 3.1-70B in the DataRobot LLM Playground.
• Generate and apply an API key to leverage Cerebras for inference.
• Customize and deploy smarter, faster applications.

By the end, you’ll be ready to deploy LLMs that deliver speed, precision, and real-time responsiveness.

Prototype, customize, and test LLMs in one place

Prototyping and testing generative AI models often require a patchwork of disconnected tools. But with a unified, integrated environment for LLMs, retrieval techniques, and evaluation metrics, you can move from idea to working prototype faster and with fewer roadblocks.

This streamlined process means you can focus on building effective, high-impact AI applications without the hassle of piecing together tools from different platforms.

Let’s walk through a use case to see how you can leverage these capabilities to develop smarter, faster AI applications. 

Use case: Speeding up LLM interference without sacrificing quality

Low latency is essential for building fast, responsive AI applications. But accelerated responses don’t have to come at the cost of quality. 

The speed of Cerebras Inference outperforms other platforms, enabling developers to build applications that feel smooth, responsive, and intelligent.

When combined with an intuitive development experience, you can:

• Reduce LLM latency for faster user interactions.
• Experiment more efficiently with new models and workflows.
• Deploy applications that respond instantly to user actions.

The diagrams below show Cerebras’ performance on Llama 3.1-70B, illustrating faster response times and lower latency than other platforms. This enables rapid iteration during development and real-time performance in production.

How model size impacts LLM speed and performance

As LLMs grow larger and more complex, their outputs become more relevant and comprehensive — but this comes at a cost: increased latency. Cerebras tackles this challenge with optimized computations, streamlined data transfer, and intelligent decoding designed for speed.

These speed improvements are already transforming AI applications in industries like pharmaceuticals and voice AI. For example:

• GlaxoSmithKline (GSK) uses Cerebras Inference to accelerate drug discovery, driving higher productivity.
• LiveKit has boosted the performance of ChatGPT’s voice mode pipeline, achieving faster response times than traditional inference solutions.

The results are measurable. On Llama 3.1-70B, Cerebras delivers 70x faster inference than vanilla GPUs, enabling smoother, real-time interactions and faster experimentation cycles.

This performance is powered by  Cerebras’ third-generation Wafer-Scale Engine (WSE-3), a custom processor designed to optimize the tensor-based, sparse linear algebra operations that drive LLM inference.

By prioritizing performance, efficiency, and flexibility, the WSE-3 ensures faster, more consistent results during model performance.

Cerebras Inference’s speed reduces the latency of AI applications powered by their models, enabling deeper reasoning and more responsive user experiences. Accessing these optimized models is simple — they’re hosted on Cerebras and accessible via a single endpoint, so you can start leveraging them with minimal setup.

Step-by-step: How to customize and deploy Llama 3.1-70B for low-latency AI

Integrating LLMs like Llama 3.1-70B from Cerebras into DataRobot allows you to customize, test, and deploy AI models in just a few steps.  This process supports faster development, interactive testing, and greater control over LLM customization.

1. Generate an API key for Llama 3.1-70B in the Cerebras platform.

2. In DataRobot, create a custom model in the Model Workshop that calls out to the Cerebras endpoint where Llama 3.1 70B is hosted.

3. Within the custom model, place the Cerebras API key within the custom.py file.

4. Deploy the custom model to an endpoint in the DataRobot Console, enabling  LLM blueprints to leverage it for inference.

5. Add your deployed Cerebras LLM to the LLM blueprint in the DataRobot LLM Playground to start chatting with Llama 3.1 -70B.

6. Once the LLM is added to the blueprint, test responses by adjusting prompting and retrieval parameters, and compare outputs with other LLMs directly in the DataRobot GUI.

Expand the limits of LLM inference for your AI applications

Deploying LLMs like Llama 3.1-70B with low latency and real-time responsiveness is no small task. But with the right tools and workflows, you can achieve both.

By integrating LLMs into DataRobot’s LLM Playground and leveraging Cerebras’ optimized inference, you can simplify customization, speed up testing, and reduce complexity – all while maintaining the performance your users expect. 

As LLMs grow larger and more powerful, having a streamlined process for testing, customization, and integration, will be essential for teams looking to stay ahead. 

Explore it yourself. Access Cerebras Inference, generate your API key, and start building AI applications in DataRobot.

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