As large language models increasingly take on reasoning-intensive tasks in areas like math and science, their output lengths are getting significantly longer��sometimes spanning tens of thousands of tokens. This shift makes efficient throughput a critical bottleneck, especially when deploying models in real-world, latency-sensitive environments. To address these challenges and enable the��
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JSON is a widely adopted format for text-based information working interoperably between systems, most commonly in web applications and large language models (LLMs). While the JSON format is human-readable, it is complex to process with data science and data engineering tools. JSON data often takes the form of newline-delimited JSON Lines (also known as NDJSON) to represent multiple records��
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Parquet writers provide encoding and compression options that are turned off by default. Enabling these options may provide better lossless compression for your data, but understanding which options to use for your specific use case is critical to making sure they perform as intended. In this post, we explore which encoding and compression options work best for your string data.
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The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library for accelerating deep learning primitives with state-of-the-art performance. cuDNN is integrated with popular deep learning frameworks like PyTorch, TensorFlow, and XLA (Accelerated Linear Algebra). These frameworks abstract the complexities of direct GPU programming, enabling you to focus on designing and��
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NVIDIA SDKs have been instrumental in accelerating AI applications across a spectrum of use cases spanning smart cities, medical, and robotics. However, achieving a production-grade AI solution that can deployed at the edge to support human and machine collaboration safely and securely needs both high-quality hardware and software tailored for enterprise needs. NVIDIA is again accelerating��
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On March 5, 8am PT, learn how NVIDIA Metropolis microservices for Jetson Orin helps you modernize your app stack, streamline development and deployment, and future-proof your apps with the ability to bring the latest generative AI capabilities to any customer through simple API calls.
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NVIDIA AI Workbench is now in beta, bringing a wealth of new features to streamline how enterprise developers create, use, and share AI and machine learning (ML) projects. Announced at SIGGRAPH 2023, NVIDIA AI Workbench enables developers to create, collaborate, and migrate AI workloads on their GPU-enabled environment of choice. To learn more, see Develop and Deploy Scalable Generative AI Models��
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Following the introduction of ChatGPT, enterprises around the globe are realizing the benefits and capabilities of AI, and are racing to adopt it into their workflows. As this adoption accelerates, it becomes imperative for enterprises not only to keep pace with the rapid advancements in AI, but also address related challenges such as optimization, scalability, and security.
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The NVIDIA Maxine developer platform redefines video conferencing and editing by providing developers and businesses with a variety of low-code implementation options. These include GPU-accelerated AI microservices , SDKs, and NVIDIA-hosted API endpoints for AI enhancement of audio and video streams in real time. The latest Maxine developer platform release introduces early access to Voice��
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As we approach the end of another exciting year at NVIDIA, it��s time to look back at the most popular stories from the NVIDIA Technical Blog in 2023. Groundbreaking research and developments in fields such as generative AI, large language models (LLMs), high-performance computing (HPC), and robotics are leading the way in transformative AI solutions and capturing the interest of our readers.
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Nested data types are a convenient way to represent hierarchical relationships within columnar data. They are frequently used as part of extract, transform, load (ETL) workloads in business intelligence, recommender systems, cybersecurity, geospatial, and other applications. List types can be used to easily attach multiple transactions to a user without creating a new lookup table��
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With Internet-scale data, the computational demands of AI-generated content have grown significantly, with data centers running full steam for weeks or months to train a single model��not to mention the high inference costs in generation, often offered as a service. In this context, suboptimal algorithmic design that sacrifices performance is an expensive mistake. Much of the recent progress��
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AI is increasingly being used to improve medical imaging for health screenings and risk assessments. Medical image segmentation, for example, provides vital data for tumor detection and treatment planning. And yet the unique and varied nature of medical images makes achieving consistent and reliable results challenging. NVIDIA MONAI Cloud APIs help solve these challenges��
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Large language models (LLMs) provide a wide range of powerful enhancements to nearly any application that processes text. And yet they also introduce new risks, including: This post walks through these security vulnerabilities in detail and outlines best practices for designing or evaluating a secure LLM-enabled application. Prompt injection is the most common and well-known��
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Spark RAPIDS ML is an open-source Python package enabling NVIDIA GPU acceleration of PySpark MLlib. It offers PySpark MLlib DataFrame API compatibility and speedups when training with the supported algorithms. See New GPU Library Lowers Compute Costs for Apache Spark ML for more details. PySpark MLlib DataFrame API compatibility means easier incorporation into existing PySpark ML applications��
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Discover the power of integrating NVIDIA TAO and Edge Impulse to accelerate AI deployment at the edge.
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Stable Diffusion is an open-source generative AI image-based model that enables users to generate images with simple text descriptions. Gaining traction among developers, it has powered popular applications like Wombo and Lensa. End users typically access the model through distributions that package it together with a user interface and a set of tools. The most popular distribution is the��
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Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. According to one study, by 2025 graph technologies will be used in 80% of data and analytics innovations, which will help facilitate rapid decision making across organizations. When working with graphs containing��
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Meta, NetworkX, Fast.ai, and other industry leaders share how to gain new insights from your data with emerging tools.
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The NVIDIA AI Red Team is focused on scaling secure development practices across the data, science, and AI ecosystems. We participate in open-source security initiatives, release tools, present at industry conferences, host educational competitions, and provide innovative training. Covering 3 years and totaling almost 140GB of source code, the recently released Meta Kaggle for Code dataset is��
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Performing an exhaustive exact k-nearest neighbor (kNN) search, also known as brute-force search, is expensive, and it doesn��t scale particularly well to larger datasets. During vector search, brute-force search requires the distance to be calculated between every query vector and database vector. For the frequently used Euclidean and cosine distances, the computation task becomes equivalent to a��
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In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in part 1 and give a brief explanation of how the algorithms work, along with a summary of important parameters to fine-tune their behavior. We then go through a simple end-to-end example to demonstrate cuVS�� Python APIs on a question-and-answer problem with a pretrained large language model and provide a��
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In the current AI landscape, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI. Semantic vector search enables a broad range of important tasks like detecting fraudulent transactions, recommending products to users, using contextual information to augment full-text searches, and finding actors that pose potential security risks.
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Caching is as fundamental to computing as arrays, symbols, or strings. Various layers of caching throughout the stack hold instructions from memory while pending on your CPU. They enable you to reload the page quickly and without re-authenticating, should you navigate away. They also dramatically decrease application workloads, and increase throughput by not re-running the same queries repeatedly.
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Developing custom generative AI models and applications is a journey, not a destination. It begins with selecting a pretrained model, such as a Large Language Model, for exploratory purposes��then developers often want to tune that model for their specific use case. This first step typically requires using accessible compute infrastructure, such as a PC or workstation. But as training jobs get��
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Prompt injection attacks are a hot topic in the new world of large language model (LLM) application security. These attacks are unique due to how ?malicious text is stored in the system. An LLM is provided with prompt text, and it responds based on all the data it has been trained on and has access to. To supplement the prompt with useful context, some AI applications capture the input from��
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Prompt injection is a new attack technique specific to large language models (LLMs) that enables attackers to manipulate the output of the LLM. This attack is made more dangerous by the way that LLMs are increasingly being equipped with ��plug-ins�� for better responding to user requests by accessing up-to-date information, performing complex calculations, and calling on external services through��
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AI is transforming industries, automating processes, and opening new opportunities for innovation in the rapidly evolving technological landscape. As more businesses recognize the value of incorporating AI into their operations, they face the challenge of implementing these technologies efficiently, effectively, and reliably. Enter NVIDIA AI Enterprise, a comprehensive software suite��
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Learn how AI is transforming financial services across use cases such as fraud detection, risk prediction models, contact centers, and more.
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In the high-frequency trading world, thousands of market participants interact daily. In fact, high-frequency trading accounts for more than half of the US equity trading volume, according to the paper High-Frequency Trading Synchronizes Prices in Financial Markets. Market makers are the big players on the sell side who provide liquidity in the market. Speculators are on the buy side��
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Real-time remote communication has become the new normal, yet many office workers still experience poor video and audio quality, which impacts collaboration and interpersonal engagement. NVIDIA Maxine was developed specifically to address these challenges through the use of state-of-the-art AI models that greatly improve the clarity of video conferencing calls. These capabilities have been largely��
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RAPIDS is a suite of accelerated libraries for data science and machine learning on GPUs: In many data analytics and machine learning algorithms, computational bottlenecks tend to come from a small subset of steps that dominate the end-to-end performance. Reusable solutions for these steps often require low-level primitives that are non-trivial and time-consuming to write well.
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As of March 18, 2025, NVIDIA Triton Inference Server is now part of the NVIDIA Dynamo Platform and has been renamed to NVIDIA Dynamo Triton, accordingly. In many production-level machine learning (ML) applications, inference is not limited to running a forward pass on a single ML model. Instead, a pipeline of ML models often needs to be executed. Take, for example��
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AI is impacting every industry, from improving customer service and streamlining supply chains to accelerating cancer research. As enterprises invest in AI to stay ahead of the competition, they often struggle with finding the strategy and infrastructure for success. Many AI projects are rapidly evolving, which makes production at scale especially challenging. We believe in developing��
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In the last few years, the roles of AI and machine learning (ML) in mainstream enterprises have changed. Once research or advanced-development activities, they now provide an important foundation for production systems. As more enterprises seek to transform their businesses with AI and ML, more and more people are talking about MLOps. If you have been listening to these conversations��
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Accurately annotated datasets are crucial for camera-based deep learning algorithms to perform autonomous vehicle perception. However, manually labeling data is a time-consuming and cost-intensive process. We have developed an automated labeling pipeline as a part of the Tata Consultancy Services (TCS) artificial intelligence (AI)-based autonomous vehicle platform. This pipeline uses NVIDIA��
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Explore the latest tools, optimizations, and best practices for deep learning training and inference.
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Get training, insights, and access to experts for the latest in recommender systems.
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Learn about the latest AI and data science breakthroughs from leading data science teams at NVIDIA GTC 2023.
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Many developers use tox as a solution to standardize and automate testing in Python. However, using the tool only for test automation severely limits its power and the full scope of what you could achieve. For example, tox is also a great solution for the ��it works on my machine�� problem. There are several reasons for this, such as: In addition, and most importantly��
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How can you tell if your Jupyter instance is secure? The NVIDIA AI Red Team has developed a JupyterLab extension to automatically assess the security of Jupyter environments. jupysec is a tool that evaluates the user��s environment against almost 100 rules that detect configurations and artifacts that have been identified by the AI Red Team as potential vulnerabilities, attack vectors��
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Leveraging image classification, object detection, automatic speech recognition (ASR), and other forms of AI can fuel massive transformation within companies and business sectors. However, building AI and deep learning models from scratch is a daunting task. A common prerequisite for building these models is having a large amount of high-quality training data and the right expertise to��
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AI computing is the work of machine learning systems and software, sifting through mountains of data to reveal useful insights and generate new capabilities.
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On February 15, at 8 am PST, learn how to use the AutoML feature in the NVIDIA TAO Toolkit for faster AI model tuning.
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Machine learning models are increasingly used to make important real-world decisions, from identifying fraudulent activity to applying automatic brakes in a car. The job of a machine learning practitioner is far from over once a model is deployed to production. You must monitor your models to ensure they continue to perform as expected in the face of real-world activity. However��
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Join this webinar on January 26 and learn how to integrate Isaac Sim into your ROS workflows to support robotics apps including navigation, manipulation, and more.
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Marking a year of new and evolving technologies, 2022 produced wide-ranging advancements and AI-powered solutions across industries. These include boosting HPC and AI workload power, research breakthroughs, and new capabilities in 3D graphics, gaming, simulation, robotics, and more. In a record-breaking year, the NVIDIA Technical Blog published nearly 550 posts and received over 2 million��
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A fundamental shift is currently taking place in how AI applications are built and deployed. AI applications are becoming more sophisticated and applied to broader use cases. This requires end-to-end AI lifecycle management��from data preparation, to model development and training, to deployment and management of AI apps. This approach can lower upfront costs, improve scalability��
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Retailers today have access to an abundance of video data provided by cameras and sensors installed in stores. Leveraging computer vision AI applications, retailers and software partners can develop AI applications faster while also delivering greater accuracy. These applications can help retailers: Building and deploying such highly efficient computer vision AI applications at scale��
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NVIDIA PhysicsNeMo is now available on NVIDIA LaunchPad. Sign-up for a free, hands-on lab that will teach you how to develop physics-informed machine-learning solutions.
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HDBSCAN is a state-of-the-art, density-based clustering algorithm that has become popular in domains as varied as topic modeling, genomics, and geospatial analytics. RAPIDS cuML has provided accelerated HDBSCAN since the 21.10 release in October 2021, as detailed in GPU-Accelerated Hierarchical DBSCAN with RAPIDS cuML �C Let��s Get Back To The Future. However, support for soft clustering (also��
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Machine learning (ML) security is a new discipline focused on the security of machine learning systems and the data they are built upon. It exists at the intersection of the information security and data science domains. While the state-of-the-art moves forward, there is no clear onboarding and learning path for securing and testing machine learning systems. How, then��
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Join NVIDIA on December 1 at 3 pm GMT to learn the fundamentals of accelerated data analytics, high-level use cases, and problem-solving methods.
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Our trust in AI will largely depend on how well we understand it �� explainable AI, or XAI, helps shine a flashlight into the ��black box�� of complexity in AI models.
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Since its inception, artificial intelligence (AI) has transformed every aspect of the global economy through the ability to solve problems of all sizes in every industry. NVIDIA has spent the last decade empowering companies to solve the world��s toughest problems such as improving sustainability, stopping poachers, and bettering cancer detection and care. What many don��t know is that behind��
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Career-related questions are common during NVIDIA cybersecurity webinars and GTC sessions. How do you break into the profession? What experience do you need? And how do AI skills intersect with cybersecurity skills? The truth is, that while the barrier to entry may seem high, there is no single path into a career that focuses on or incorporates cybersecurity and AI. With many disciplines in��
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A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence.
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Machine learning (ML) is increasingly used across industries. Fraud detection, demand sensing, and credit underwriting are a few examples of specific use cases. These machine learning models make decisions that affect everyday lives. Therefore, it��s imperative that model predictions are fair, unbiased, and nondiscriminatory. Accurate predictions become vital in high-risk applications where��
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Zero trust is a cybersecurity strategy for verifying every user, device, application, and transaction in the belief that no user or process should be trusted.
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An AI model card is a document that details how machine learning (ML) models work. Model cards provide detailed information about the ML model��s metadata including the datasets that it is based on, performance measures that it was trained on, and the deep learning training methodology itself. This post walks you through the current practice for AI model cards and how NVIDIA is planning to advance��
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An exaflop is a measure of performance for a supercomputer that can calculate at least one quintillion floating point operations per second.
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A QPU, aka a quantum processor, is the brain of a quantum computer that uses the behavior of particles like electrons or photons to make certain kinds of calculations much faster than processors in today��s computers.
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Sophia Abraham always thought she would become a medical doctor. She is now pursuing a Ph.D. in computer science and computer engineering at the University of Notre Dame. How did this aspiring medical doctor end up programming AI to recognize invasive grass species in Australia and designing drones to help with search and rescue efforts? Many of Sophia��s aspirations originally stemmed��
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Learn about the latest AI and data science breakthroughs from the world��s leading data science teams at GTC 2022.
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Ken Jee is a data scientist and YouTube content creator who has quickly become known for creating engaging and easy-to-follow videos. Jee has helped countless people learn about data science, machine learning, and AI and is the initiator of the popular #66daysofdata movement. Currently, Jee works as the Head of Data Science at Scouts Consulting Group. In this post, he discusses his work as a��
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Any business or industry, from retail and healthcare to financial services, is subject to fraud. The cost of fraud can be staggering. Every $1 of fraud loss costs financial firms about $4 to mitigate. Online sellers will lose $130B to online payment fraud between 2018 and 2023. By using AI and big data analytics, enterprises can efficiently prevent fraud attempts in real time.
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Cybersecurity software is getting more sophisticated these days, thanks to AI and ML capabilities. It��s now possible to automate security measures without direct human intervention. The value in these powerful solutions is real��in stopping breaches, providing highly detailed alerts, and protecting attack surfaces. Still, it pays to be a skeptic. This interview with NVIDIA experts Bartley��
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Sign up for Edge AI News to stay up to date with the latest trends, customers use cases, and technical walkthroughs. Cloud-native is one of the most important concepts associated with edge AI. That��s because cloud-native delivers massive scale for application deployments. It also delivers performance, resilience, and ease of management, all critical capabilities for edge AI.
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Marine biologists have a new AI tool for monitoring and protecting coral reefs. The project��a collaboration between Google and Australia��s Commonwealth Scientific and Industrial Research Organization (CSIRO)��employs computer vision detection models to pinpoint damaging outbreaks of crown-of-thorns starfish (COTS) through a live camera feed. Keeping a closer eye on reefs helps scientists address��
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Utilities are challenged to integrate distributed clean energy resources��such as wind farms, rooftop solar, home batteries, and electric vehicles��onto legacy electric grid infrastructure. Existing systems were built to manage a one-way flow of power from a small number of industrial-scale generation plants, often run using coal, natural gas, or nuclear. Sign up for Edge AI News to stay up��
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Inference is an important part of the machine learning lifecycle and occurs after you have trained your model. It is when a business realizes value from their AI investment. Common applications of AI include image classification (��this is an image of a tumor��), recommendation (��here is a movie you will like��), transcription of speech audio into text, and decision (��turn the car to the left��).
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This is part of a series on how NVIDIA researchers have developed methods to improve and accelerate sampling from diffusion models, a novel and powerful class of generative models. Part 2 covers three new techniques for overcoming the slow sampling challenge in diffusion models. Generative models are a class of machine learning methods that learn a representation of the data they are trained��
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The pace for development and deployment of AI-powered robots and other autonomous machines continues to grow rapidly. The next generation of applications require large increases in AI compute performance to handle multimodal AI applications running concurrently in real time. Human-robot interactions are increasing in retail spaces, food delivery, hospitals, warehouses, factory floors��
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A major contributor to CO2 emissions in cities is traffic. City planners are always looking to reduce their carbon footprint and design efficient and sustainable infrastructure. NVIDIA Metropolis partner, MarshallAI, is helping cities improve their traffic management and reduce CO2 emissions with vision AI applications. MarshallAI��s computer vision and AI solution helps cities get closer to��
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Even while 5G wireless networks are being installed and used worldwide, researchers in academia and industry have already started defining visions and critical technologies for 6G. Although nobody knows what 6G will be, a recurring vision is that 6G must enable the creation of digital twins and distributed machine learning (ML) applications at an unprecedented scale. 6G research requires new tools.
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The following post provides a deep dive into some of the accomplishments and current focus of drug discovery and genomics work by NVIDIA. A leader in innovations within healthcare and life sciences, NVIDIA is looking to add AI, deep learning, simulation, and drug discovery researchers and engineers to the team. If what you read aligns with your career goals please review the current job postings.
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The nature of edge deployments means that they are always on, sometimes running 24/7 in a different time zone than the IT administrators. So when a system experiences a bug or major issue, IT is required to travel to the edge site and debug the system. Sometimes this happens in the middle of the night. Even when teams have the foresight to set up tools in place to allow for remote access��
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Each day, energy flows throughout our lives �C from the fuel that powers cars and planes, to the gas used for stove top cooking, to the electricity that keeps the lights on in homes and businesses. Oil, gas, and electricity are mature commodity markets, but AI is transforming the processes used to produce, transport, and deliver these resources. Enter AI deployed at the edge: on oil rigs��
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Whether your organization is new to data science or has a mature strategy in place, many come to a similar realization: Most data does not originate at the core. Scientists often want access to amounts of data that are unreasonable to securely stream to the data center in real time. Whether the distance is 10 miles or thousands of miles, the bounds of traditional IT infrastructure are simply��
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New weather-forecasting research using AI is fast-tracking global weather predictions. The study, recently published in the Journal of Advances in Modeling Earth Systems, could help identify potential extreme weather 2�C6 weeks into the future. Accurate predictions of extreme weather with a longer lead time give communities and critical sectors such as public health, water management, energy��
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Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. A lot of love goes into building a machine-learning model. Challenges range from identifying the variables to predict to experimentation finding the best model architecture to sampling the correct training data. But, what good is the model if��
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Spotting painting forgeries just became a bit easier with a newly developed AI tool that picks up style differences with precision down to a single paintbrush bristle. The research, from a team at Case Western Reserve University (CWRU), trained convolutional neural networks to learn and identify a painter based on the 3D topography of a painting. This work could help historians and art experts��
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Is it necessary for data scientists or machine-learning experts to read research papers? The short answer is yes. And don��t worry if you lack a formal academic background or have only obtained an undergraduate degree in the field of machine learning. Reading academic research papers may be intimidating for individuals without an extensive educational background. However��
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A team of researchers from Princeton and the University of Washington created a new camera that captures stunning images and measures in at only a half-millimeter��the size of a coarse grain of salt. The new study, published in Nature Communications, outlines the use of optical metasurfaces with machine learning to produce high-quality color imagery, with a wide field of view.
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The holidays should be a time for relaxation, and with NVIDIA Jetson technology, you can do just that. The latest Jetson Project of the Month comes from a developer who has created ways to simplify home automation projects, using a combination of DeepStack, Home Assistant, and NVIDIA Jetson. Robin Cole, a senior data scientist at Satellite Vu with a background in physics��
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At the forefront of AI innovation, NVIDIA continues to push the boundaries of technology in machine learning, self-driving cars, robotics, graphics, and more. NVIDIA researchers will present 20 papers at the 35th annual conference on Neural Information Processing Systems (NeurIPS) from December 6 to December 14, 2021. Here are some of the featured papers: Alias-Free Generative��
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Researchers at Los Alamos National Laboratory in New Mexico are working toward earthquake detection with a new machine learning algorithm capable of global monitoring. The study uses Interferometric Synthetic Aperture Radar (InSAR) satellite data to detect slow-slip earthquakes. The work will help scientists gain a deeper understanding of the interplay between slow and fast earthquakes��
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Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. Today NVIDIA released TensorRT 8.2, with optimizations for billion parameter NLU models. These include T5 and GPT-2, used for translation and text generation, making it possible to run NLU apps in real time. TensorRT is a high-performance��
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See the latest innovations spanning from the cloud to the edge at AWS re:Invent. Plus, learn more about the NVIDIA NGC catalog��a comprehensive collection of GPU-optimized software. Working closely together, NVIDIA and AWS developed a session and workshop focused on learning more about NVIDIA GPUs and providing hands-on training on NVIDIA Jetson modules. Register now for the virtual AWS��
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NVIDIA continues to enhance CUTLASS to provide extensive support for mixed-precision computations, providing specialized data-movement, and multiply-accumulate abstractions. Today, NVIDIA is announcing the availability of CUTLASS version 2.8. Download the free CUTLASS v2.8 software. See the CUTLASS Release Notes for more information. CUTLASS is a collection of CUDA��
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At NVIDIA GTC last week, Jensen Huang laid out the vision for realizing multi-Million-X speedups in computational performance. The breakthrough could solve the challenge of computational requirements faced in data-intensive research, helping scientists further their work. Million-X unlocks new worlds of potential and the applications are vast. Current examples from NVIDIA include��
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Today, NVIDIA is announcing the availability of cuSPARSELt, version 0.2.0, which increases performance on activation functions, bias vectors, and Batched Sparse GEMM. This software can be downloaded now free of charge. Download the cuSPARSELt software. For more technical information, see the cuSPARSELt Release Notes. NVIDIA cuSPARSELt is a high-performance CUDA��
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Molecular simulation communities have faced the accuracy-versus-efficiency dilemma in modeling the potential energy surface and interatomic forces for decades. Deep Potential, the artificial neural network force field, solves this problem by combining the speed of classical molecular dynamics (MD) simulation with the accuracy of density functional theory (DFT) calculation.1 This is achieved by��
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Data scientists wrestle with many challenges that slow development. There are operational tasks, including software stack management, installation, and updates that impact productivity. Reproducing state-of-the-art assets can be difficult as modern workflows include many tedious and complex tasks. Access to the tools you need is not always fast or convenient. Also, the use of multiple tools and��
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AI pioneer Andrew Ng is calling for a broad shift to a more data-centric approach to machine learning (ML). He recently held the first data-centric AI competition on data quality, which many claim represents 80% of the work in AI. ��I��m optimistic that the AI community before long will take as much interest in systematically improving data as architecting models,�� Ng wrote in his newsletter��
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Building training and testing playgrounds to help advance sport analytics AI solutions out of the lab and into the real world is exceedingly challenging. In team-based sports, building a correct playing strategy before the championship season is a key to success for any professional coach and club owner. While coaches strive at providing best tips and point out mistakes during the game��
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