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1. Shard Calculation

Query:

Response:

Calculated Shard: 197 (from laksa186)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa197.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.nvidia.com/en-us/glossary/natural-language-processing/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.nvidia.com/en-us/glossary/natural-language-processing/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/","crawl_time":1775138694,"first_indexed_time":1700074890,"http_code":200,"src_unparsed":"com,nvidia!www,\/en-us\/glossary\/natural-language-processing\/ s443","src_root_hash":"5092221691596826797","history_drop_reason":null,"meta_title":"What is Natural Language Processing? | Data Science | NVIDIA Glossary","meta_descriptions":["NLP is a technology that leverages computers and software to derive meaning from human language."],"attrs_boilerpipe_text":"#\nA\nB\nC\nD\nE\nF\nG\nH\nI\nJ\nK\nL\nM\nN\nO\nP\nQ\nR\nS\nT\nU\nV\nW\nX\nY\nZ\nNatural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken.\nWhat is natural language processing?\nNatural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and\/or derive insights from the content.\nIncluded in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response.  \nExamples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions. \nWhy NLP?\nThe applications of NLP are already substantial and expected to grow geometrically.\nBy one research survey estimate\n, the global market for products and services related to natural language processing will grow from $3 billion in 2017 to $43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions.  \nFurther driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage.\nHow Does NLP Work?\nMachine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language.\nTypically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as\nRNN\n, LSTM, or Transformer to understand context. The numbers\nchange over time while the neural net trains itself\n, encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence.\nHowever, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end.\nBERT\n(Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on\nunsupervised tasks\nand generally uses unstructured datasets from books corpus, English Wikipedia, and more.\nGPUs: Accelerating NLP\nGetting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing.\nA\nGPU is composed of hundreds of cores\nthat can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms.\nA driver of NLP growth is recent and ongoing advancements and\nbreakthroughs in natural language processing,\nnot the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models.\nNLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry.\nGPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible.\nUse Cases for NLP\nStartups\nApplications for natural language processing have exploded in the past decade as advances in\nrecurrent neural networks\npowered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of\nvoice services\n,\nlanguage tutors\n, and\nchatbots\n.\nHealthcare\nOne of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients.\nAnother key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis.\nFinancial Services\nNLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history.\nRetail\nIn addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis.\nNVIDIA GPUs Accelerating AI and NLP\nWith NVIDIA GPUs and\nCUDA-X AI\n™\nlibraries\n, massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex.\nNVIDIA's AI platform\nis the first to train \nBERT\nin less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and\nTensor Core\narchitecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling\nrecord-setting performance\nfor both the\ntraining and inference\nof BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide.\nEarly adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers. ","attrs_markdown":"NVIDIA Home\n\nMenu icon\n\nMenu icon\n\nClose icon\n\nClose icon\n\nClose icon\n\nAccordion is closed, click to open.\n\nAccordion is closed, click to open.\n\nAccordion is open, click to close.\n\nClick to expand\n\nClick to expand\n\nClick to expand menu.\n\nClick to collapse menu.\n\nClick to collapse menu.\n\nClick to collapse menu.\n\nClick to see cart items\n\nClick to search\n\n[Skip to main content](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/#page-content)\n\nMain Menu\n\n- Products\n  Cloud Services\n  \n  Creating\n  \n  Data Center\n  \n  Embedded Systems\n  \n  Gaming\n  \n  Graphics Cards and GPUs\n  \n  Laptops and Desktops\n  \n  Networking\n  \n  Professional Workstations\n  \n  Software\n  \n  Tools\n  \n  Cloud Services\n  \n  [BioNeMo AI-driven platform for life sciences 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[Networking High speed ethernet interconnect solutions and services](https:\/\/www.nvidia.com\/en-us\/networking\/)\n  \n  [Sustainable Computing Save energy and lower cost with AI and accelerated computing](https:\/\/www.nvidia.com\/en-us\/data-center\/sustainable-computing\/)\n  \n  [Virtualization NVIDIA virtual GPU software delivers powerful GPU performance](https:\/\/www.nvidia.com\/en-us\/data-center\/virtual-solutions\/)\n  \n  Design and Simulation\n  \n  [Overview Streamline building, operating, and connecting metaverse apps](https:\/\/www.nvidia.com\/en-us\/solutions\/design-and-simulation\/)\n  \n  [Computer Aided-Engineering Develop real-time interactive design using AI-accelerated real-time digital twins](https:\/\/www.nvidia.com\/en-us\/solutions\/cae\/)\n  \n  [Digital Twin Development Harness the power of large-scale, physically-based OpenUSD simulation](https:\/\/www.nvidia.com\/en-us\/glossary\/digital-twin\/)\n  \n  [Rendering Bring state-of-the-art rendering to professional workflows](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/rendering\/)\n  \n  [Robotic Simulation Innovative solutions to take on your robotics, edge, and vision AI challenges](https:\/\/www.nvidia.com\/en-us\/solutions\/robotics-and-edge-computing\/)\n  \n  [Scientific Visualization Enablies researchers to visualize their large datasets at interactive speeds](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/scientific-visualization\/)\n  \n  [Vehicle Simulation AI-defined vehicles are transforming the future of mobility](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/)\n  \n  [Extended Reality Transform workflows with immersive, scalable interactions in virtual environments](https:\/\/www.nvidia.com\/en-us\/design-visualization\/solutions\/virtual-reality\/)\n  \n  High-Performance Computing\n  \n  [Overview Discover NVIDIA’s HPC solutions for AI, simulation, and accelerated computing](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/)\n  \n  [HPC and AI Boost accuracy with GPU-accelerating HPC and AI](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/hpc-and-ai\/)\n  \n  [Scientific Visualization Enables researchers to visualize large datasets at interactive speeds](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/scientific-visualization\/)\n  \n  [Simulation and Modeling Accelerate simulation workloads](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/simulation-and-modeling\/)\n  \n  [Quantum Computing Fast-tracking the advancement of scientific innovations with QPUs](https:\/\/www.nvidia.com\/en-us\/solutions\/quantum-computing\/)\n  \n  Robotics and Edge AI\n  \n  [Overview Innovative solutions to take on robotics, edge, and vision AI challenges](https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/)\n  \n  [Robotics GPU-accelerated advances in AI perception, simulation, and software](https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/)\n  \n  [Edge AI Bring the power of NVIDIA AI to the edge for real-time decision-making solutions](https:\/\/www.nvidia.com\/en-us\/edge-computing\/)\n  \n  [Vision AI Transform data into valuable insights using vision AI](https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/intelligent-video-analytics-platform\/)\n  \n  [AI Grid Scale AI-native services efficiently across connected, distributed AI infrastructure](https:\/\/www.nvidia.com\/en-us\/industries\/telecommunications\/ai-grid\/)\n  \n  Autonomous Vehicles\n  \n  [Overview AI-enhanced vehicles are transforming the future of mobility](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/)\n  \n  [Open Source AV Models and Tools For reasoning-based AV systems](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/alpamayo\/)\n  \n  [AV Simulation Explore high-fidelity sensor simulation for safe autonomous vehicle development](https:\/\/www.nvidia.com\/en-us\/use-cases\/autonomous-vehicle-simulation\/)\n  \n  [Reference Architecture Enables vehicles to be L4-ready](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/drive-hyperion\/)\n  \n  [Infrastructure Essential data center tools for safe autonomous vehicle development](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/ai-training\/)\n  \n  [In-Vehicle Computing Develop automated driving functions and immersive in-cabin experiences](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/in-vehicle-computing\/)\n  \n  [Safety State-of-the-art system for AV safety, from the cloud to the car](https:\/\/www.nvidia.com\/en-us\/ai-trust-center\/halos\/autonomous-vehicles\/)\n- Industries\n  Industries\n  \n  [Overview](https:\/\/www.nvidia.com\/en-us\/industries\/)\n  \n  [Architecture, Engineering, Construction & Operations](https:\/\/www.nvidia.com\/en-us\/industries\/aec\/)\n  \n  [Automotive](https:\/\/www.nvidia.com\/en-us\/industries\/automotive\/)\n  \n  [Cybersecurity](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/cybersecurity\/)\n  \n  [Energy](https:\/\/www.nvidia.com\/en-us\/industries\/energy\/)\n  \n  [Financial Services](https:\/\/www.nvidia.com\/en-us\/industries\/finance\/)\n  \n  [Healthcare and Life Sciences](https:\/\/www.nvidia.com\/en-us\/industries\/healthcare-life-sciences\/)\n  \n  [Higher Education](https:\/\/www.nvidia.com\/en-us\/industries\/higher-education-research\/)\n  \n  [Game Development](https:\/\/www.nvidia.com\/en-us\/industries\/game-development\/)\n  \n  [Government](https:\/\/www.nvidia.com\/en-us\/industries\/government\/)\n  \n  [Manufacturing](https:\/\/www.nvidia.com\/en-us\/industries\/manufacturing\/)\n  \n  [Media and Entertainment](https:\/\/www.nvidia.com\/en-us\/industries\/media-and-entertainment\/)\n  \n  [Restaurants](https:\/\/www.nvidia.com\/en-us\/industries\/restaurants\/)\n  \n  [Retail and CPG](https:\/\/www.nvidia.com\/en-us\/industries\/retail\/)\n  \n  [Robotics](https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/)\n  \n  [Smart Cities](https:\/\/www.nvidia.com\/en-us\/industries\/smart-cities-and-spaces\/)\n  \n  [Supercomputing](https:\/\/www.nvidia.com\/en-us\/industries\/supercomputing\/)\n  \n  [Telecommunications](https:\/\/www.nvidia.com\/en-us\/industries\/telecommunications\/)\n- […](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n\n- [Shop](https:\/\/marketplace.nvidia.com\/en-us\/)\n- [Drivers](https:\/\/www.nvidia.com\/en-us\/drivers\/)\n- [Support](https:\/\/www.nvidia.com\/en-us\/support\/)\n\n- [US](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n- - [Sign In](\"Sign In\")\n    [NVIDIA Account](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n    \n    [NVIDIA Marketplace](https:\/\/marketplace.nvidia.com\/en-us\/account\/)\n    \n    [NVIDIA Account](https:\/\/www.nvidia.com\/en-us\/account\/edit-profile\/)\n    \n    [NVIDIA Marketplace](https:\/\/marketplace.nvidia.com\/en-us\/account\/)\n    \n    [Logout](https:\/\/www.nvidia.com\/)\n- [Log In](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)   [Log Out](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n\n[Skip to main content](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/#page-content)\n\n- 0\n- [US](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n- [Sign In](\"Sign In\")\n  [NVIDIA Account](https:\/\/www.nvidia.com\/en-us\/glossary\/natural-language-processing\/)\n  \n  [NVIDIA Marketplace](https:\/\/marketplace.nvidia.com\/en-us\/account\/)\n  \n  [NVIDIA Account](https:\/\/www.nvidia.com\/en-us\/account\/edit-profile\/)\n  \n  [NVIDIA Marketplace](https:\/\/marketplace.nvidia.com\/en-us\/account\/)\n  \n  [Logout](https:\/\/www.nvidia.com\/)\n\nNVIDIA logo\n\nProducts\n\nCloud Services\n\n[BioNeMo AI-driven platform for life sciences research and discovery](https:\/\/www.nvidia.com\/en-us\/industries\/healthcare-life-sciences\/biopharma\/)\n\n[DGX Cloud NVIDIA’s AI factory in the cloud](https:\/\/www.nvidia.com\/en-us\/data-center\/dgx-cloud\/)\n\n[NVIDIA APIs Explore, test, and deploy AI models and agents](https:\/\/build.nvidia.com\/)\n\n[Private Registry Guide for using NVIDIA NGC private registry with GPU cloud](https:\/\/docs.nvidia.com\/ngc\/latest\/ngc-private-registry-user-guide.html)\n\n[NVIDIA NGC Accelerated, containerized AI models and SDKs](https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/)\n\nCreating\n\n[NVIDIA Studio High performance GeForce RTX PCs, purpose-built for creators](https:\/\/www.nvidia.com\/en-us\/studio\/)\n\n[NVIDIA Broadcast App AI-enhanced voice and video for next-level streams, videos, and calls](https:\/\/www.nvidia.com\/en-us\/geforce\/broadcasting\/)\n\n[NVIDIA App and Studio Drivers Optimize gaming, streaming, and AI-powered creativity](https:\/\/www.nvidia.com\/en-us\/software\/nvidia-app\/)\n\n[RTX AI PCs AI PCs for gaming, creating, productivity and development](https:\/\/www.nvidia.com\/en-us\/ai-on-rtx\/)\n\n[RTX Remix Create RTX remasters of classic games with open-source AI](https:\/\/www.nvidia.com\/en-us\/geforce\/rtx-remix\/)\n\n[Project G-Assist AI assistant to optimize and control your GeForce RTX PC](https:\/\/www.nvidia.com\/en-us\/software\/nvidia-app\/g-assist\/)\n\nData Center\n\n[Overview Modernizing data centers with AI and accelerated computing](https:\/\/www.nvidia.com\/en-us\/data-center\/products\/)\n\n[DGX Platform Enterprise AI factory for model development and deployment](https:\/\/www.nvidia.com\/en-us\/data-center\/dgx-platform\/)\n\n[Grace CPU Architecture for data centers that transform data into intelligence](https:\/\/www.nvidia.com\/en-us\/data-center\/grace-cpu\/)\n\n[HGX Platform A supercomputer purpose-built for AI and HPC](https:\/\/www.nvidia.com\/en-us\/data-center\/hgx\/)\n\n[IGX Platform Advanced functional safety and security for edge AI](https:\/\/www.nvidia.com\/en-us\/edge-computing\/products\/igx\/)\n\n[MGX Platform Accelerated computing with modular servers](https:\/\/www.nvidia.com\/en-us\/data-center\/products\/mgx\/)\n\n[OVX Systems Scalable data center infrastructure for high-performance AI](https:\/\/www.nvidia.com\/en-us\/data-center\/products\/ovx\/)\n\nEmbedded Systems\n\n[Jetson Leading platform for autonomous machines and embedded applications](https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/)\n\n[DRIVE AGX Powerful in-vehicle computing for AI-driven autonomous vehicle systems](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/in-vehicle-computing\/)\n\n[IGX Platform Advanced functional safety and security for edge AI](https:\/\/www.nvidia.com\/en-us\/edge-computing\/products\/igx\/)\n\nGaming\n\n[GeForce Explore graphics cards, gaming solutions, AI technology, and more](https:\/\/www.nvidia.com\/en-us\/geforce\/)\n\n[GeForce Graphics Cards RTX graphics cards bring game-changing AI capabilities](https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/)\n\n[Gaming Laptops Thinnest and longest lasting RTX laptops, optimized by Max-Q](https:\/\/www.nvidia.com\/en-us\/geforce\/laptops\/)\n\n[G-SYNC Monitors Smooth, tear-free gaming with NVIDIA G-SYNC monitors](https:\/\/www.nvidia.com\/en-us\/geforce\/products\/g-sync-monitors\/)\n\n[DLSS Neural rendering tech boosts FPS and enhances image quality](https:\/\/www.nvidia.com\/en-us\/geforce\/technologies\/dlss\/)\n\n[Reflex Ultimate responsiveness for faster reactions and better aim](https:\/\/www.nvidia.com\/en-us\/geforce\/technologies\/reflex\/)\n\n[RTX Remix Create RTX remasters of classic games with open-source AI](https:\/\/www.nvidia.com\/en-us\/geforce\/rtx-remix\/)\n\n[Project G-Assist AI assistant to optimize and control your GeForce RTX PC](https:\/\/www.nvidia.com\/en-us\/software\/nvidia-app\/g-assist\/)\n\n[GeForce NOW Cloud Gaming RTX-powered cloud gaming. Choose from 3 memberships](https:\/\/www.nvidia.com\/en-us\/geforce-now\/)\n\n[NVIDIA App and Game Ready Drivers Optimize gaming, streaming, and AI-powered creativity](https:\/\/www.nvidia.com\/en-us\/software\/nvidia-app\/)\n\n[NVIDIA Broadcast App AI-enhanced voice and video for next-level streams, videos, and calls](https:\/\/www.nvidia.com\/en-us\/geforce\/broadcasting\/)\n\n[SHIELD TV World-class streaming media performance](https:\/\/www.nvidia.com\/en-us\/shield\/)\n\nGraphics Cards and GPUs\n\n[GeForce Graphics Cards RTX graphics cards bring game-changing AI capabilities](https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/)\n\n[NVIDIA RTX PRO Accelerating professional AI, graphics, rendering and compute workloads](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/)\n\n[Virtual GPU Virtual solutions for scalable, high-performance computing](https:\/\/www.nvidia.com\/en-us\/data-center\/virtual-solutions\/)\n\n[Blackwell Architecture The engine of the new industrial revolution](https:\/\/www.nvidia.com\/en-us\/data-center\/technologies\/blackwell-architecture\/)\n\n[Hopper Architecture High performance, scalability, and security for every data center](https:\/\/www.nvidia.com\/en-us\/data-center\/technologies\/hopper-architecture\/)\n\n[Ada Lovelace Architecture Performance and energy efficiency for endless possibilities](https:\/\/www.nvidia.com\/en-us\/technologies\/ada-architecture\/)\n\nLaptops and Desktops\n\n[GeForce Laptops GPU-powered laptops for gamers and creators](https:\/\/www.nvidia.com\/en-us\/geforce\/laptops\/)\n\n[Studio Laptops High performance laptops purpose-built for creators](https:\/\/www.nvidia.com\/en-us\/studio\/help-me-choose\/)\n\n[NVIDIA RTX PRO Laptops Accelerate professional AI and visual computing from anywhere](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-laptops\/)\n\n[RTX AI PCs AI PCs for gaming, creating, productivity and development](https:\/\/www.nvidia.com\/en-us\/ai-on-rtx\/)\n\nNetworking\n\n[Overview Accelerated networks for modern workloads](https:\/\/www.nvidia.com\/en-us\/networking\/)\n\n[DPUs and SuperNICs Software-defined hardware accelerators for networking, storage, and security](https:\/\/www.nvidia.com\/en-us\/networking\/products\/data-processing-unit\/)\n\n[Ethernet Ethernet performance, availability, and ease of use across a wide range of applications](https:\/\/www.nvidia.com\/en-us\/networking\/products\/ethernet\/)\n\n[InfiniBand High-performance networking for super computers, AI, and cloud data centers](https:\/\/www.nvidia.com\/en-us\/networking\/products\/infiniband\/)\n\n[Networking Software Networking software for optimized performance and scalability](https:\/\/www.nvidia.com\/en-us\/networking\/products\/software\/)\n\n[Network Acceleration IO subsystem for modern, GPU-accelerated data centers](https:\/\/www.nvidia.com\/en-us\/data-center\/magnum-io\/)\n\nProfessional Workstations\n\n[Overview Accelerating professional AI, graphics, rendering, and compute workloads](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/)\n\n[DGX Spark A Grace Blackwell AI Supercomputer on your desk](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/)\n\n[DGX Station The ultimate desktop AI supercomputer powered by NVIDIA Grace Blackwell](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-station\/)\n\n[NVIDIA RTX PRO AI Workstations Accelerate innovation and productivity in AI workflows](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/ai-workstations\/)\n\n[NVIDIA RTX PRO Desktops Powerful AI, graphics, rendering, and compute workloads](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/)\n\n[NVIDIA RTX PRO Laptops Accelerate professional AI and visual computing from anywhere](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-laptops\/)\n\nSoftware\n\n[Agentic AI Models - Nemotron](https:\/\/www.nvidia.com\/en-us\/ai-data-science\/foundation-models\/nemotron\/)\n\n[AI Agents - NeMo](https:\/\/www.nvidia.com\/en-us\/ai-data-science\/products\/nemo\/)\n\n[AI Blueprints](https:\/\/build.nvidia.com\/blueprints)\n\n[AI Inference - Dynamo](https:\/\/www.nvidia.com\/en-us\/ai\/dynamo\/)\n\n[AI Inference - NIM](https:\/\/www.nvidia.com\/en-us\/ai-data-science\/products\/nim-microservices\/)\n\n[AI Microservices - CUDA-X](https:\/\/www.nvidia.com\/en-us\/technologies\/cuda-x\/)\n\n[Automotive - DRIVE](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/in-vehicle-computing\/)\n\n[Data Science - Apache Spark](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/solutions\/data-science\/apache-spark-3\/)\n\n[Data Science - RAPIDS](https:\/\/developer.nvidia.com\/rapids\/)\n\n[Decision Optimization - cuOpt](https:\/\/www.nvidia.com\/en-us\/ai-data-science\/products\/cuopt\/)\n\n[Healthcare - Clara](https:\/\/www.nvidia.com\/en-us\/clara\/)\n\n[Industrial AI - Omniverse](https:\/\/www.nvidia.com\/en-us\/omniverse\/)\n\n[Intelligent Video Analytics - Metropolis](https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/intelligent-video-analytics-platform\/)\n\n[NVIDIA AI Enterprise](https:\/\/www.nvidia.com\/en-us\/data-center\/products\/ai-enterprise-suite\/)\n\n[NVIDIA Mission Control](https:\/\/www.nvidia.com\/en-us\/data-center\/mission-control\/)\n\n[NVIDIA Run:ai](https:\/\/www.nvidia.com\/en-us\/software\/run-ai\/)\n\n[Physical AI - Cosmos](https:\/\/www.nvidia.com\/en-us\/ai\/cosmos\/)\n\n[Robotics - Isaac](https:\/\/developer.nvidia.com\/isaac\/ros)\n\n[Telecommunications - Aerial](https:\/\/developer.nvidia.com\/aerial)\n\n[See All Software](https:\/\/www.nvidia.com\/en-us\/software\/)\n\nTools\n\n[AI Workbench Simplify AI development with NVIDIA AI Workbench on GPUs](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/solutions\/data-science\/workbench\/)\n\n[API Catalog Explore NVIDIA's AI models, blueprints, and tools for developers](https:\/\/build.nvidia.com\/explore\/discover)\n\n[Data Center Management AI and HPC software solutions for data center acceleration](https:\/\/www.nvidia.com\/en-us\/data-center\/enterprise-software\/)\n\n[GPU Monitoring Monitor and manage GPU performance in cluster environments](https:\/\/developer.nvidia.com\/dcgm)\n\n[Nsight Explore NVIDIA developer tools for AI, graphics, and HPC](https:\/\/developer.nvidia.com\/tools-overview)\n\n[NGC Catalog Discover GPU-optimized AI, HPC, and data science software](https:\/\/catalog.ngc.nvidia.com\/)\n\n[NVIDIA App for Laptops Optimize enterprise GPU management](https:\/\/www.nvidia.com\/en-us\/software\/nvidia-app-enterprise\/)\n\n[NVIDIA NGC Accelerate AI and HPC workloads with NVIDIA GPU Cloud solutions](https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/)\n\n[Desktop Manager Enhance multi-display productivity with NVIDIA RTX Desktop Manager](https:\/\/www.nvidia.com\/en-us\/software\/rtx-desktop-manager\/)\n\n[RTX Accelerated Creative Apps Creative tools and AI-powered apps for artists and designers](https:\/\/www.nvidia.com\/en-us\/studio\/creative-apps\/)\n\n[Video Conferencing AI-powered audio and video enhancement](https:\/\/www.nvidia.com\/en-us\/design-visualization\/software\/broadcast-app\/)\n\nSolutions\n\nArtificial Intelligence\n\n[Overview Add intelligence and efficiency to your business with AI and machine learning](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/)\n\n[Agentic AI Build AI agents designed to reason, plan, and act](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/agentic-ai\/)\n\n[AI Data Powering a new class of enterprise infrastructure for AI](https:\/\/www.nvidia.com\/en-us\/data-center\/ai-data-platform\/)\n\n[Conversational AI Enables natural, personalized interactions with real-time speech AI](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/conversational-ai\/)\n\n[Cybersecurity AI-driven solutions to strengthen cybersecurity and AI infrastructure](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/cybersecurity\/)\n\n[Data Science Iterate on large datasets, deploy models more frequently, and lower total cost](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/solutions\/data-science\/)\n\n[Inference Drive breakthrough performance with AI-enabled applications and services](https:\/\/www.nvidia.com\/en-us\/solutions\/ai\/inference\/)\n\nCloud and Data Center\n\n[Overview Powering AI, HPC, and modern workloads with NVIDIA](https:\/\/www.nvidia.com\/en-us\/data-center\/)\n\n[AI Storage Bringing codesigned enterprise storage into the era of agentic AI](https:\/\/www.nvidia.com\/en-us\/data-center\/ai-storage\/)\n\n[AI Factory Full-stack infrastructure for scalable AI workloads](https:\/\/www.nvidia.com\/en-us\/solutions\/ai-factories\/)\n\n[AI Grid Scale AI across connected, distributed AI infrastructure](https:\/\/www.nvidia.com\/en-us\/industries\/telecommunications\/ai-grid\/)\n\n[Accelerated Computing Accelerated computing uses specialized hardware to boost IT performance](https:\/\/www.nvidia.com\/en-us\/data-center\/solutions\/accelerated-computing\/)\n\n[Cloud Computing On-demand IT resources and services, enabling scalability and intelligent insights](https:\/\/www.nvidia.com\/en-us\/data-center\/gpu-cloud-computing\/)\n\n[Colocation Accelerate the scaling of AI across your organization](https:\/\/www.nvidia.com\/en-us\/data-center\/colocation-partners\/)\n\n[Networking High speed ethernet interconnect solutions and services](https:\/\/www.nvidia.com\/en-us\/networking\/)\n\n[Sustainable Computing Save energy and lower cost with AI and accelerated computing](https:\/\/www.nvidia.com\/en-us\/data-center\/sustainable-computing\/)\n\n[Virtualization NVIDIA virtual GPU software delivers powerful GPU performance](https:\/\/www.nvidia.com\/en-us\/data-center\/virtual-solutions\/)\n\nDesign and Simulation\n\n[Overview Streamline building, operating, and connecting metaverse apps](https:\/\/www.nvidia.com\/en-us\/solutions\/design-and-simulation\/)\n\n[Computer Aided-Engineering Develop real-time interactive design using AI-accelerated real-time digital twins](https:\/\/www.nvidia.com\/en-us\/solutions\/cae\/)\n\n[Digital Twin Development Harness the power of large-scale, physically-based OpenUSD simulation](https:\/\/www.nvidia.com\/en-us\/glossary\/digital-twin\/)\n\n[Rendering Bring state-of-the-art rendering to professional workflows](https:\/\/www.nvidia.com\/en-us\/products\/workstations\/rendering\/)\n\n[Robotic Simulation Innovative solutions to take on your robotics, edge, and vision AI challenges](https:\/\/www.nvidia.com\/en-us\/solutions\/robotics-and-edge-computing\/)\n\n[Scientific Visualization Enablies researchers to visualize their large datasets at interactive speeds](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/scientific-visualization\/)\n\n[Vehicle Simulation AI-defined vehicles are transforming the future of mobility](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/)\n\n[Extended Reality Transform workflows with immersive, scalable interactions in virtual environments](https:\/\/www.nvidia.com\/en-us\/design-visualization\/solutions\/virtual-reality\/)\n\nHigh-Performance Computing\n\n[Overview Discover NVIDIA’s HPC solutions for AI, simulation, and accelerated computing](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/)\n\n[HPC and AI Boost accuracy with GPU-accelerating HPC and AI](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/hpc-and-ai\/)\n\n[Scientific Visualization Enables researchers to visualize large datasets at interactive speeds](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/scientific-visualization\/)\n\n[Simulation and Modeling Accelerate simulation workloads](https:\/\/www.nvidia.com\/en-us\/high-performance-computing\/simulation-and-modeling\/)\n\n[Quantum Computing Fast-tracking the advancement of scientific innovations with QPUs](https:\/\/www.nvidia.com\/en-us\/solutions\/quantum-computing\/)\n\nRobotics and Edge AI\n\n[Overview Innovative solutions to take on robotics, edge, and vision AI challenges](https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/)\n\n[Robotics GPU-accelerated advances in AI perception, simulation, and software](https:\/\/www.nvidia.com\/en-us\/industries\/robotics\/)\n\n[Edge AI Bring the power of NVIDIA AI to the edge for real-time decision-making solutions](https:\/\/www.nvidia.com\/en-us\/edge-computing\/)\n\n[Vision AI Transform data into valuable insights using vision AI](https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/intelligent-video-analytics-platform\/)\n\n[AI Grid Scale AI-native services efficiently across connected, distributed AI infrastructure](https:\/\/www.nvidia.com\/en-us\/industries\/telecommunications\/ai-grid\/)\n\nAutonomous Vehicles\n\n[Overview AI-enhanced vehicles are transforming the future of mobility](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/)\n\n[Open Source AV Models and Tools For reasoning-based AV systems](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/alpamayo\/)\n\n[AV Simulation Explore high-fidelity sensor simulation for safe autonomous vehicle development](https:\/\/www.nvidia.com\/en-us\/use-cases\/autonomous-vehicle-simulation\/)\n\n[Reference Architecture Enables vehicles to be L4-ready](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/drive-hyperion\/)\n\n[Infrastructure Essential data center tools for safe autonomous vehicle development](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/ai-training\/)\n\n[In-Vehicle Computing Develop automated driving functions and immersive in-cabin experiences](https:\/\/www.nvidia.com\/en-us\/solutions\/autonomous-vehicles\/in-vehicle-computing\/)\n\n[Safety State-of-the-art system for AV safety, from the cloud to the 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Please enable Javascript in order to access all the functionality of this web site. Here are the [instructions how to enable JavaScript in your web browser.](http:\/\/www.enable-javascript.com\/)\n\n1. [\\#](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-num)\n2. [A](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-a)\n3. [B](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-b)\n4. [C](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-c)\n5. [D](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-d)\n6. E\n7. F\n8. [G](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-g)\n9. H\n10. I\n11. J\n12. [K](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-k)\n13. L\n14. [M](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-m)\n15. [N](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-n)\n16. O\n17. [P](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-p)\n18. Q\n19. [R](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-r)\n20. [S](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-s)\n21. [T](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-t)\n22. U\n23. [V](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-v)\n24. W\n25. [X](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-x)\n26. Y\n27. Z\n\n# Natural language processing\nNatural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken.\n\n## What is natural language processing?\nNatural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and\/or derive insights from the content.\n\nIncluded in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response.\n\nExamples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions.\n\n## Why NLP?\nThe applications of NLP are already substantial and expected to grow geometrically. [By one research survey estimate](https:\/\/www.statista.com\/statistics\/607891\/worldwide-natural-language-processing-market-revenues\/), the global market for products and services related to natural language processing will grow from \\$3 billion in 2017 to \\$43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions.\n\nFurther driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage.\n\n## How Does NLP Work?\nMachine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language.\n\nTypically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as [RNN](https:\/\/developer.nvidia.com\/blog\/deep-learning-nutshell-sequence-learning\/), LSTM, or Transformer to understand context. The numbers [change over time while the neural net trains itself](https:\/\/developer.nvidia.com\/blog\/understanding-natural-language-deep-neural-networks-using-torch\/), encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence.\n\nHowever, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end. [BERT](https:\/\/arxiv.org\/pdf\/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on [unsupervised tasks](https:\/\/towardsdatascience.com\/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) and generally uses unstructured datasets from books corpus, English Wikipedia, and more.\n\n![Pre-training and fine-tuning loss.](https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/glossary\/data-science\/nlp\/img-1.png)\n\n## GPUs: Accelerating NLP\nGetting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing.\n\nA [GPU is composed of hundreds of cores](https:\/\/blogs.nvidia.com\/blog\/2009\/12\/16\/whats-the-difference-between-a-cpu-and-a-gpu\/) that can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms.\n\n![The difference between a CPU and GPU.](https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/glossary\/data-science\/nlp\/img-2.jpg)\n\nA driver of NLP growth is recent and ongoing advancements and [breakthroughs in natural language processing,](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) not the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models.\n\nNLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry.\n\nGPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible.\n\n## Use Cases for NLP\n## Startups\nApplications for natural language processing have exploded in the past decade as advances in [recurrent neural networks](https:\/\/blogs.nvidia.com\/blog\/2018\/09\/05\/whats-the-difference-between-a-cnn-and-an-rnn\/) powered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of [voice services](https:\/\/blogs.nvidia.com\/blog\/2018\/06\/19\/soundhound-digs-into-ai-market-against-apple-siri-google-assistant-amazon-alexa-microsoft-cortana\/), [language tutors](https:\/\/blogs.nvidia.com\/blog\/2018\/06\/26\/class-is-in-session-ai-app-schools-on-english-pronunciation\/), and [chatbots](https:\/\/blogs.nvidia.com\/blog\/2018\/10\/01\/ai-chatbot-enterprise-search-gpu-answers-algorithms\/).\n\n## Healthcare\nOne of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients.\n\nAnother key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis.\n\n## Financial Services\nNLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history.\n\n## Retail\nIn addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis.\n\n## NVIDIA GPUs Accelerating AI and NLP\nWith NVIDIA GPUs and [CUDA-X AI](https:\/\/www.nvidia.com\/en-in\/technologies\/cuda-x\/)™ [libraries](https:\/\/www.nvidia.com\/en-in\/technologies\/cuda-x\/), massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex.\n\n[NVIDIA's AI platform](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/) is the first to train [BERT](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) in less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and [Tensor Core](https:\/\/www.nvidia.com\/en-us\/data-center\/tensorcore\/) architecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling [record-setting performance](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) for both the [training and inference](https:\/\/blogs.nvidia.com\/blog\/2016\/08\/22\/difference-deep-learning-training-inference-ai\/) of BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide.\n\nEarly adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers.\n\n## Next Steps\n**To learn more refer to:**\n\n- [Snark Bite: Like an AI Could Ever Spot Sarcasm](https:\/\/blogs.nvidia.com\/blog\/2018\/01\/31\/ai-detect-sarcasm\/)\n- [Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq](https:\/\/developer.nvidia.com\/blog\/mixed-precision-nlp-speech-openseq2seq\/)\n- [Deep Learning NLP Interprets Words with Multiple Meanings](https:\/\/blogs.nvidia.com\/blog\/2018\/08\/27\/nlp-deep-learning\/)\n- [Word Up: AI Writes New Chapter for Language Buffs](https:\/\/blogs.nvidia.com\/blog\/2019\/01\/04\/ai-language-gpu-transformer-neural-networks-nlp\/)\n- [Building State-of-the-Art Biomedical and Clinical NLP Models with BioMegatron](https:\/\/developer.nvidia.com\/blog\/building-state-of-the-art-biomedical-and-clinical-nlp-models-with-biomegatron\/)\n- [NVIDIA blogs with tag: NLP](https:\/\/developer.nvidia.com\/blog\/tag\/nlp\/)\n- [Real-Time Natural Language Understanding with BERT Using TensorRT](https:\/\/developer.nvidia.com\/blog\/nlu-with-tensorrt-bert\/)\n- [What Is Conversational AI?](https:\/\/blogs.nvidia.com\/blog\/2019\/08\/19\/what-is-conversational-ai\/) (Blog)\n- [NVIDIA Achieves Breakthroughs in Language Understanding to Enable Real-Time Conversational AI](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) (News)\n- The  [NVIDIA Conversational AI](https:\/\/developer.nvidia.com\/conversational-ai)  and [Conversational AI](https:\/\/developer.nvidia.com\/conversational-ai#sdk) SDK web page\n- [BERT QA in TensorFlow with NVIDIA GPUs](https:\/\/medium.com\/nvidia-ai\/how-to-train-bert-from-scratch-on-gpus-a9603b0cb60e) (Blog)\n- [BERT Does Europe: AI Language Model Learns German, Swedish](https:\/\/blogs.nvidia.com\/blog\/2019\/12\/23\/bert-ai-german-swedish\/) (Blog)\n- [NLP NeMo Code Samples](https:\/\/github.com\/NVIDIA\/NeMo\/tree\/master\/examples\/nlp)\n- [Train BERT Model with PyTorch Code Sample](https:\/\/github.com\/NVIDIA\/DeepLearningExamples\/tree\/master\/PyTorch\/LanguageModeling\/BERT)\n- [Introducing NVIDIA Riva: A Framework for GPU-Accelerated Conversational AI Applications](https:\/\/developer.nvidia.com\/blog\/introducing-riva-sdk-for-gpu-accelerated-conversational-ai-apps\/)\n- [NVIDIA's AI advance: Natural language processing gets faster and better all the time](https:\/\/www.zdnet.com\/article\/nvidias-ai-advance-natural-language-processing-gets-faster-and-better-all-the-time\/)\n- [NVIDIA Clocks World's Fastest BERT Training Time and Largest Transformer-Based Model, Paving Path For Advanced Conversational AI](https:\/\/developer.nvidia.com\/blog\/training-bert-with-gpus\/)\n- [State-of-the-Art Language Modeling Using Megatron on the NVIDIA A100 GPU](https:\/\/developer.nvidia.com\/blog\/language-modeling-using-megatron-a100-gpu\/)\n- [NVIDIA Elevates The Conversation For Natural Language Processing](https:\/\/www.nextplatform.com\/2019\/08\/14\/nvidia-elevates-the-conversation-for-natural-language-processing\/)\n- [Building State-of-the-Art Biomedical and Clinical NLP Models with BioMegatron](https:\/\/developer.nvidia.com\/blog\/building-state-of-the-art-biomedical-and-clinical-nlp-models-with-biomegatron\/)\n- [What Is Conversational AI?](https:\/\/blogs.nvidia.com\/blog\/2019\/08\/19\/what-is-conversational-ai\/)\n- [Money Maker: How AI Can Accelerate Analytics in Financial Markets](https:\/\/blogs.nvidia.com\/blog\/2017\/08\/30\/qualitative-financial-analysis\/)\n\n**Find out about:**\n\n- The [NVIDIA NGC](https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/)™ [catalog](https:\/\/www.nvidia.com\/en-us\/gpu-cloud\/) provides extensive software libraries at no cost, as well as tools for building high-performance computing environments that take full advantage of GPUs.\n- Programmers and data scientists can take advantage of a broad suite of ML and analytics software libraries to significantly accelerate end-to-end data science pipelines entirely on GPUs. These libraries provide highly efficient, optimized implementations of algorithms that are routinely extended. NVIDIA [CUDA-X AI](https:\/\/blogs.nvidia.com\/blog\/2019\/03\/18\/cuda-x-ai-data-science\/) software acceleration libraries use GPUs in ML to accelerate workflows and realize model optimizations. The CUDA parallel computing platform provides an API that developers can use to build tools that use GPUs for processing large blocks, which is a critical ML task.\n- The [NVIDIA Deep Learning Institute](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/education\/?iactivetab=certification-tabs-2#RecommenderSystems) offers instructor-led, hands-on training on the fundamental tools and techniques for building Transformer-based natural language processing models for text classification tasks, such as categorizing documents.\n\nCompany Information\n\n- [About Us](https:\/\/www.nvidia.com\/en-us\/about-nvidia\/)\n- [Company Overview](https:\/\/images.nvidia.com\/aem-dam\/Solutions\/homepage\/pdf\/NVIDIA-Story.pdf)\n- [Investors](https:\/\/investor.nvidia.com\/home\/default.aspx)\n- [Venture Capital (NVentures)](https:\/\/www.nventures.ai\/)\n- [NVIDIA Foundation](https:\/\/www.nvidia.com\/en-us\/foundation\/)\n- [Research](https:\/\/www.nvidia.com\/en-us\/research\/)\n- [Corporate 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[Middle East](https:\/\/www.nvidia.com\/en-me\/ \"Middle East\")","attrs_readable_markdown":"1. [\\#](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-num)\n2. [A](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-a)\n3. [B](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-b)\n4. [C](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-c)\n5. [D](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-d)\n6. E\n7. F\n8. [G](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-g)\n9. H\n10. I\n11. J\n12. [K](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-k)\n13. L\n14. [M](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-m)\n15. [N](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-n)\n16. O\n17. [P](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-p)\n18. Q\n19. [R](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-r)\n20. [S](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-s)\n21. [T](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-t)\n22. U\n23. [V](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-v)\n24. W\n25. [X](https:\/\/www.nvidia.com\/en-us\/glossary\/data-science\/#grp-x)\n26. Y\n27. Z\n\nNatural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken.\n\n## What is natural language processing?\nNatural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and\/or derive insights from the content.\n\nIncluded in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response.\n\nExamples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions.\n\n## Why NLP?\nThe applications of NLP are already substantial and expected to grow geometrically. [By one research survey estimate](https:\/\/www.statista.com\/statistics\/607891\/worldwide-natural-language-processing-market-revenues\/), the global market for products and services related to natural language processing will grow from \\$3 billion in 2017 to \\$43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions.\n\nFurther driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage.\n\n## How Does NLP Work?\nMachine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language.\n\nTypically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as [RNN](https:\/\/developer.nvidia.com\/blog\/deep-learning-nutshell-sequence-learning\/), LSTM, or Transformer to understand context. The numbers [change over time while the neural net trains itself](https:\/\/developer.nvidia.com\/blog\/understanding-natural-language-deep-neural-networks-using-torch\/), encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence.\n\nHowever, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end. [BERT](https:\/\/arxiv.org\/pdf\/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on [unsupervised tasks](https:\/\/towardsdatascience.com\/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) and generally uses unstructured datasets from books corpus, English Wikipedia, and more.\n\n![Pre-training and fine-tuning loss.](https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/glossary\/data-science\/nlp\/img-1.png)\n\n## GPUs: Accelerating NLP\nGetting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing.\n\nA [GPU is composed of hundreds of cores](https:\/\/blogs.nvidia.com\/blog\/2009\/12\/16\/whats-the-difference-between-a-cpu-and-a-gpu\/) that can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms.\n\n![The difference between a CPU and GPU.](https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/glossary\/data-science\/nlp\/img-2.jpg)\n\nA driver of NLP growth is recent and ongoing advancements and [breakthroughs in natural language processing,](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) not the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models.\n\nNLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry.\n\nGPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible.\n\n## Use Cases for NLP\n## Startups\nApplications for natural language processing have exploded in the past decade as advances in [recurrent neural networks](https:\/\/blogs.nvidia.com\/blog\/2018\/09\/05\/whats-the-difference-between-a-cnn-and-an-rnn\/) powered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of [voice services](https:\/\/blogs.nvidia.com\/blog\/2018\/06\/19\/soundhound-digs-into-ai-market-against-apple-siri-google-assistant-amazon-alexa-microsoft-cortana\/), [language tutors](https:\/\/blogs.nvidia.com\/blog\/2018\/06\/26\/class-is-in-session-ai-app-schools-on-english-pronunciation\/), and [chatbots](https:\/\/blogs.nvidia.com\/blog\/2018\/10\/01\/ai-chatbot-enterprise-search-gpu-answers-algorithms\/).\n\n## Healthcare\nOne of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients.\n\nAnother key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis.\n\n## Financial Services\nNLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history.\n\n## Retail\nIn addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis.\n\n## NVIDIA GPUs Accelerating AI and NLP\nWith NVIDIA GPUs and [CUDA-X AI](https:\/\/www.nvidia.com\/en-in\/technologies\/cuda-x\/)™ [libraries](https:\/\/www.nvidia.com\/en-in\/technologies\/cuda-x\/), massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex.\n\n[NVIDIA's AI platform](https:\/\/www.nvidia.com\/en-us\/deep-learning-ai\/) is the first to train [BERT](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) in less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and [Tensor Core](https:\/\/www.nvidia.com\/en-us\/data-center\/tensorcore\/) architecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling [record-setting performance](https:\/\/nvidianews.nvidia.com\/news\/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) for both the [training and inference](https:\/\/blogs.nvidia.com\/blog\/2016\/08\/22\/difference-deep-learning-training-inference-ai\/) of BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide.\n\nEarly adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers.","meta_canonical":null}

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Boilerpipe Text	# A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Natural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken. What is natural language processing? Natural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and/or derive insights from the content. Included in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response. Examples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions. Why NLP? The applications of NLP are already substantial and expected to grow geometrically. By one research survey estimate , the global market for products and services related to natural language processing will grow from $3 billion in 2017 to $43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions. Further driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage. How Does NLP Work? Machine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language. Typically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as RNN , LSTM, or Transformer to understand context. The numbers change over time while the neural net trains itself , encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence. However, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end. BERT (Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on unsupervised tasks and generally uses unstructured datasets from books corpus, English Wikipedia, and more. GPUs: Accelerating NLP Getting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing. A GPU is composed of hundreds of cores that can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms. A driver of NLP growth is recent and ongoing advancements and breakthroughs in natural language processing, not the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models. NLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry. GPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible. Use Cases for NLP Startups Applications for natural language processing have exploded in the past decade as advances in recurrent neural networks powered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of voice services , language tutors , and chatbots . Healthcare One of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients. Another key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis. Financial Services NLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history. Retail In addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis. NVIDIA GPUs Accelerating AI and NLP With NVIDIA GPUs and CUDA-X AI ™ libraries , massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex. NVIDIA's AI platform is the first to train BERT in less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and Tensor Core architecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling record-setting performance for both the training and inference of BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide. Early adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers.
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[Overview Add intelligence and efficiency to your business with AI and machine learning](https://www.nvidia.com/en-us/solutions/ai/) [Agentic AI Build AI agents designed to reason, plan, and act](https://www.nvidia.com/en-us/solutions/ai/agentic-ai/) [AI Data Powering a new class of enterprise infrastructure for AI](https://www.nvidia.com/en-us/data-center/ai-data-platform/) [Conversational AI Enables natural, personalized interactions with real-time speech AI](https://www.nvidia.com/en-us/solutions/ai/conversational-ai/) [Cybersecurity AI-driven solutions to strengthen cybersecurity and AI infrastructure](https://www.nvidia.com/en-us/solutions/ai/cybersecurity/) [Data Science Iterate on large datasets, deploy models more frequently, and lower total cost](https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/) [Inference Drive breakthrough performance with AI-enabled applications and services](https://www.nvidia.com/en-us/solutions/ai/inference/) Cloud and Data Center [Overview Powering AI, HPC, and modern workloads with NVIDIA](https://www.nvidia.com/en-us/data-center/) [AI Storage Bringing codesigned enterprise storage into the era of agentic AI](https://www.nvidia.com/en-us/data-center/ai-storage/) [AI Factory Full-stack infrastructure for scalable AI workloads](https://www.nvidia.com/en-us/solutions/ai-factories/) [AI Grid Scale AI across connected, distributed AI infrastructure](https://www.nvidia.com/en-us/industries/telecommunications/ai-grid/) [Accelerated Computing Accelerated computing uses specialized hardware to boost IT performance](https://www.nvidia.com/en-us/data-center/solutions/accelerated-computing/) [Cloud Computing On-demand IT resources and services, enabling scalability and intelligent insights](https://www.nvidia.com/en-us/data-center/gpu-cloud-computing/) [Colocation Accelerate the scaling of AI across your organization](https://www.nvidia.com/en-us/data-center/colocation-partners/) [Networking High speed ethernet 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solutions to take on your robotics, edge, and vision AI challenges](https://www.nvidia.com/en-us/solutions/robotics-and-edge-computing/) [Scientific Visualization Enablies researchers to visualize their large datasets at interactive speeds](https://www.nvidia.com/en-us/high-performance-computing/scientific-visualization/) [Vehicle Simulation AI-defined vehicles are transforming the future of mobility](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/) [Extended Reality Transform workflows with immersive, scalable interactions in virtual environments](https://www.nvidia.com/en-us/design-visualization/solutions/virtual-reality/) High-Performance Computing [Overview Discover NVIDIA’s HPC solutions for AI, simulation, and accelerated computing](https://www.nvidia.com/en-us/high-performance-computing/) [HPC and AI Boost accuracy with GPU-accelerating HPC and AI](https://www.nvidia.com/en-us/high-performance-computing/hpc-and-ai/) [Scientific Visualization Enables researchers to visualize large datasets at interactive speeds](https://www.nvidia.com/en-us/high-performance-computing/scientific-visualization/) [Simulation and Modeling Accelerate simulation workloads](https://www.nvidia.com/en-us/high-performance-computing/simulation-and-modeling/) [Quantum Computing Fast-tracking the advancement of scientific innovations with QPUs](https://www.nvidia.com/en-us/solutions/quantum-computing/) Robotics and Edge AI [Overview Innovative solutions to take on robotics, edge, and vision AI challenges](https://www.nvidia.com/en-us/industries/robotics/) [Robotics GPU-accelerated advances in AI perception, simulation, and software](https://www.nvidia.com/en-us/industries/robotics/) [Edge AI Bring the power of NVIDIA AI to the edge for real-time decision-making solutions](https://www.nvidia.com/en-us/edge-computing/) [Vision AI Transform data into valuable insights using vision AI](https://www.nvidia.com/en-us/autonomous-machines/intelligent-video-analytics-platform/) [AI Grid Scale AI-native services efficiently across connected, distributed AI infrastructure](https://www.nvidia.com/en-us/industries/telecommunications/ai-grid/) Autonomous Vehicles [Overview AI-enhanced vehicles are transforming the future of mobility](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/) [Open Source AV Models and Tools For reasoning-based AV systems](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/alpamayo/) [AV Simulation Explore high-fidelity sensor simulation for safe autonomous vehicle development](https://www.nvidia.com/en-us/use-cases/autonomous-vehicle-simulation/) [Reference Architecture Enables vehicles to be L4-ready](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/drive-hyperion/) [Infrastructure Essential data center tools for safe autonomous vehicle development](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/ai-training/) [In-Vehicle Computing Develop automated driving functions and immersive in-cabin experiences](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/in-vehicle-computing/) [Safety State-of-the-art system for AV safety, from the cloud to the car](https://www.nvidia.com/en-us/ai-trust-center/halos/autonomous-vehicles/) - Industries Industries [Overview](https://www.nvidia.com/en-us/industries/) [Architecture, Engineering, Construction & Operations](https://www.nvidia.com/en-us/industries/aec/) [Automotive](https://www.nvidia.com/en-us/industries/automotive/) [Cybersecurity](https://www.nvidia.com/en-us/solutions/ai/cybersecurity/) [Energy](https://www.nvidia.com/en-us/industries/energy/) [Financial Services](https://www.nvidia.com/en-us/industries/finance/) [Healthcare and Life Sciences](https://www.nvidia.com/en-us/industries/healthcare-life-sciences/) [Higher Education](https://www.nvidia.com/en-us/industries/higher-education-research/) [Game Development](https://www.nvidia.com/en-us/industries/game-development/) 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[Logout](https://www.nvidia.com/) NVIDIA logo Products Cloud Services [BioNeMo AI-driven platform for life sciences research and discovery](https://www.nvidia.com/en-us/industries/healthcare-life-sciences/biopharma/) [DGX Cloud NVIDIA’s AI factory in the cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/) [NVIDIA APIs Explore, test, and deploy AI models and agents](https://build.nvidia.com/) [Private Registry Guide for using NVIDIA NGC private registry with GPU cloud](https://docs.nvidia.com/ngc/latest/ngc-private-registry-user-guide.html) [NVIDIA NGC Accelerated, containerized AI models and SDKs](https://www.nvidia.com/en-us/gpu-cloud/) Creating [NVIDIA Studio High performance GeForce RTX PCs, purpose-built for creators](https://www.nvidia.com/en-us/studio/) [NVIDIA Broadcast App AI-enhanced voice and video for next-level streams, videos, and calls](https://www.nvidia.com/en-us/geforce/broadcasting/) [NVIDIA App and Studio Drivers Optimize gaming, streaming, and AI-powered creativity](https://www.nvidia.com/en-us/software/nvidia-app/) [RTX AI PCs AI PCs for gaming, creating, productivity and development](https://www.nvidia.com/en-us/ai-on-rtx/) [RTX Remix Create RTX remasters of classic games with open-source AI](https://www.nvidia.com/en-us/geforce/rtx-remix/) [Project G-Assist AI assistant to optimize and control your GeForce RTX PC](https://www.nvidia.com/en-us/software/nvidia-app/g-assist/) Data Center [Overview Modernizing data centers with AI and accelerated computing](https://www.nvidia.com/en-us/data-center/products/) [DGX Platform Enterprise AI factory for model development and deployment](https://www.nvidia.com/en-us/data-center/dgx-platform/) [Grace CPU Architecture for data centers that transform data into intelligence](https://www.nvidia.com/en-us/data-center/grace-cpu/) [HGX Platform A supercomputer purpose-built for AI and HPC](https://www.nvidia.com/en-us/data-center/hgx/) [IGX Platform Advanced functional safety and security for edge AI](https://www.nvidia.com/en-us/edge-computing/products/igx/) [MGX Platform Accelerated computing with modular servers](https://www.nvidia.com/en-us/data-center/products/mgx/) [OVX Systems Scalable data center infrastructure for high-performance AI](https://www.nvidia.com/en-us/data-center/products/ovx/) Embedded Systems [Jetson Leading platform for autonomous machines and embedded applications](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/) [DRIVE AGX Powerful in-vehicle computing for AI-driven autonomous vehicle systems](https://www.nvidia.com/en-us/solutions/autonomous-vehicles/in-vehicle-computing/) [IGX Platform Advanced functional safety and security for edge AI](https://www.nvidia.com/en-us/edge-computing/products/igx/) Gaming [GeForce Explore graphics cards, gaming solutions, AI technology, and more](https://www.nvidia.com/en-us/geforce/) [GeForce Graphics Cards RTX graphics cards bring game-changing AI capabilities](https://www.nvidia.com/en-us/geforce/graphics-cards/) [Gaming Laptops Thinnest and longest lasting RTX laptops, optimized by Max-Q](https://www.nvidia.com/en-us/geforce/laptops/) [G-SYNC Monitors Smooth, tear-free gaming with NVIDIA G-SYNC monitors](https://www.nvidia.com/en-us/geforce/products/g-sync-monitors/) [DLSS Neural rendering tech boosts FPS and enhances image quality](https://www.nvidia.com/en-us/geforce/technologies/dlss/) [Reflex Ultimate responsiveness for faster reactions and better aim](https://www.nvidia.com/en-us/geforce/technologies/reflex/) [RTX Remix Create RTX remasters of classic games with open-source AI](https://www.nvidia.com/en-us/geforce/rtx-remix/) [Project G-Assist AI assistant to optimize and control your GeForce RTX PC](https://www.nvidia.com/en-us/software/nvidia-app/g-assist/) [GeForce NOW Cloud Gaming RTX-powered cloud gaming. Choose from 3 memberships](https://www.nvidia.com/en-us/geforce-now/) [NVIDIA App and Game Ready Drivers Optimize gaming, streaming, and AI-powered creativity](https://www.nvidia.com/en-us/software/nvidia-app/) [NVIDIA Broadcast App AI-enhanced voice and video for next-level streams, videos, and calls](https://www.nvidia.com/en-us/geforce/broadcasting/) [SHIELD TV World-class streaming media performance](https://www.nvidia.com/en-us/shield/) Graphics Cards and GPUs [GeForce Graphics Cards RTX graphics cards bring game-changing AI capabilities](https://www.nvidia.com/en-us/geforce/graphics-cards/) [NVIDIA RTX PRO Accelerating professional AI, graphics, rendering and compute workloads](https://www.nvidia.com/en-us/products/workstations/) [Virtual GPU Virtual solutions for scalable, high-performance computing](https://www.nvidia.com/en-us/data-center/virtual-solutions/) [Blackwell Architecture The engine of the new industrial revolution](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/) [Hopper Architecture High performance, scalability, and security for every data center](https://www.nvidia.com/en-us/data-center/technologies/hopper-architecture/) [Ada Lovelace Architecture Performance and energy efficiency for endless possibilities](https://www.nvidia.com/en-us/technologies/ada-architecture/) Laptops and Desktops [GeForce Laptops GPU-powered laptops for gamers and creators](https://www.nvidia.com/en-us/geforce/laptops/) [Studio Laptops High performance laptops purpose-built for creators](https://www.nvidia.com/en-us/studio/help-me-choose/) [NVIDIA RTX PRO Laptops Accelerate professional AI and visual computing from anywhere](https://www.nvidia.com/en-us/products/workstations/professional-laptops/) [RTX AI PCs AI PCs for gaming, creating, productivity and development](https://www.nvidia.com/en-us/ai-on-rtx/) Networking [Overview Accelerated networks for modern workloads](https://www.nvidia.com/en-us/networking/) [DPUs and SuperNICs Software-defined hardware accelerators for networking, storage, and security](https://www.nvidia.com/en-us/networking/products/data-processing-unit/) [Ethernet Ethernet performance, availability, and ease of use across a wide range of applications](https://www.nvidia.com/en-us/networking/products/ethernet/) [InfiniBand High-performance networking for super computers, AI, and cloud data centers](https://www.nvidia.com/en-us/networking/products/infiniband/) [Networking Software Networking software for optimized performance and scalability](https://www.nvidia.com/en-us/networking/products/software/) [Network Acceleration IO subsystem for modern, GPU-accelerated data centers](https://www.nvidia.com/en-us/data-center/magnum-io/) Professional Workstations [Overview Accelerating professional AI, graphics, rendering, and compute workloads](https://www.nvidia.com/en-us/products/workstations/) [DGX Spark A Grace Blackwell 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Please enable Javascript in order to access all the functionality of this web site. Here are the [instructions how to enable JavaScript in your web browser.](http://www.enable-javascript.com/) 1. [\#](https://www.nvidia.com/en-us/glossary/data-science/#grp-num) 2. [A](https://www.nvidia.com/en-us/glossary/data-science/#grp-a) 3. [B](https://www.nvidia.com/en-us/glossary/data-science/#grp-b) 4. [C](https://www.nvidia.com/en-us/glossary/data-science/#grp-c) 5. [D](https://www.nvidia.com/en-us/glossary/data-science/#grp-d) 6. E 7. F 8. [G](https://www.nvidia.com/en-us/glossary/data-science/#grp-g) 9. H 10. I 11. J 12. [K](https://www.nvidia.com/en-us/glossary/data-science/#grp-k) 13. L 14. [M](https://www.nvidia.com/en-us/glossary/data-science/#grp-m) 15. [N](https://www.nvidia.com/en-us/glossary/data-science/#grp-n) 16. O 17. [P](https://www.nvidia.com/en-us/glossary/data-science/#grp-p) 18. Q 19. [R](https://www.nvidia.com/en-us/glossary/data-science/#grp-r) 20. [S](https://www.nvidia.com/en-us/glossary/data-science/#grp-s) 21. [T](https://www.nvidia.com/en-us/glossary/data-science/#grp-t) 22. U 23. [V](https://www.nvidia.com/en-us/glossary/data-science/#grp-v) 24. W 25. [X](https://www.nvidia.com/en-us/glossary/data-science/#grp-x) 26. Y 27. Z # Natural language processing Natural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken. ## What is natural language processing? Natural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and/or derive insights from the content. Included in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response. Examples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions. ## Why NLP? The applications of NLP are already substantial and expected to grow geometrically. [By one research survey estimate](https://www.statista.com/statistics/607891/worldwide-natural-language-processing-market-revenues/), the global market for products and services related to natural language processing will grow from \$3 billion in 2017 to \$43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions. Further driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage. ## How Does NLP Work? Machine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language. Typically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as [RNN](https://developer.nvidia.com/blog/deep-learning-nutshell-sequence-learning/), LSTM, or Transformer to understand context. The numbers [change over time while the neural net trains itself](https://developer.nvidia.com/blog/understanding-natural-language-deep-neural-networks-using-torch/), encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence. However, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end. [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on [unsupervised tasks](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) and generally uses unstructured datasets from books corpus, English Wikipedia, and more. ![Pre-training and fine-tuning loss.](https://www.nvidia.com/content/dam/en-zz/Solutions/glossary/data-science/nlp/img-1.png) ## GPUs: Accelerating NLP Getting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing. A [GPU is composed of hundreds of cores](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/) that can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms. ![The difference between a CPU and GPU.](https://www.nvidia.com/content/dam/en-zz/Solutions/glossary/data-science/nlp/img-2.jpg) A driver of NLP growth is recent and ongoing advancements and [breakthroughs in natural language processing,](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) not the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models. NLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry. GPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible. ## Use Cases for NLP ## Startups Applications for natural language processing have exploded in the past decade as advances in [recurrent neural networks](https://blogs.nvidia.com/blog/2018/09/05/whats-the-difference-between-a-cnn-and-an-rnn/) powered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of [voice services](https://blogs.nvidia.com/blog/2018/06/19/soundhound-digs-into-ai-market-against-apple-siri-google-assistant-amazon-alexa-microsoft-cortana/), [language tutors](https://blogs.nvidia.com/blog/2018/06/26/class-is-in-session-ai-app-schools-on-english-pronunciation/), and [chatbots](https://blogs.nvidia.com/blog/2018/10/01/ai-chatbot-enterprise-search-gpu-answers-algorithms/). ## Healthcare One of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients. Another key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis. ## Financial Services NLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history. ## Retail In addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis. ## NVIDIA GPUs Accelerating AI and NLP With NVIDIA GPUs and [CUDA-X AI](https://www.nvidia.com/en-in/technologies/cuda-x/)™ [libraries](https://www.nvidia.com/en-in/technologies/cuda-x/), massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex. [NVIDIA's AI platform](https://www.nvidia.com/en-us/deep-learning-ai/) is the first to train [BERT](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) in less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and [Tensor Core](https://www.nvidia.com/en-us/data-center/tensorcore/) architecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling [record-setting performance](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) for both the [training and inference](https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learning-training-inference-ai/) of BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide. Early adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers. ## Next Steps To learn more refer to: - [Snark Bite: Like an AI Could Ever Spot Sarcasm](https://blogs.nvidia.com/blog/2018/01/31/ai-detect-sarcasm/) - [Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq](https://developer.nvidia.com/blog/mixed-precision-nlp-speech-openseq2seq/) - [Deep Learning NLP Interprets Words with Multiple Meanings](https://blogs.nvidia.com/blog/2018/08/27/nlp-deep-learning/) - [Word Up: AI Writes New Chapter for Language Buffs](https://blogs.nvidia.com/blog/2019/01/04/ai-language-gpu-transformer-neural-networks-nlp/) - [Building State-of-the-Art Biomedical and Clinical NLP Models with BioMegatron](https://developer.nvidia.com/blog/building-state-of-the-art-biomedical-and-clinical-nlp-models-with-biomegatron/) - [NVIDIA blogs with tag: NLP](https://developer.nvidia.com/blog/tag/nlp/) - [Real-Time Natural Language Understanding with BERT Using TensorRT](https://developer.nvidia.com/blog/nlu-with-tensorrt-bert/) - [What Is Conversational AI?](https://blogs.nvidia.com/blog/2019/08/19/what-is-conversational-ai/) (Blog) - [NVIDIA Achieves Breakthroughs in Language Understanding to Enable Real-Time Conversational AI](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) (News) - The [NVIDIA Conversational AI](https://developer.nvidia.com/conversational-ai) and [Conversational AI](https://developer.nvidia.com/conversational-ai#sdk) SDK web page - [BERT QA in TensorFlow with NVIDIA GPUs](https://medium.com/nvidia-ai/how-to-train-bert-from-scratch-on-gpus-a9603b0cb60e) (Blog) - [BERT Does Europe: AI Language Model Learns German, Swedish](https://blogs.nvidia.com/blog/2019/12/23/bert-ai-german-swedish/) (Blog) - [NLP NeMo Code Samples](https://github.com/NVIDIA/NeMo/tree/master/examples/nlp) - [Train BERT Model with PyTorch Code Sample](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling/BERT) - [Introducing NVIDIA Riva: A Framework for GPU-Accelerated Conversational AI Applications](https://developer.nvidia.com/blog/introducing-riva-sdk-for-gpu-accelerated-conversational-ai-apps/) - [NVIDIA's AI advance: Natural language processing gets faster and better all the time](https://www.zdnet.com/article/nvidias-ai-advance-natural-language-processing-gets-faster-and-better-all-the-time/) - [NVIDIA Clocks World's Fastest BERT Training Time and Largest Transformer-Based Model, Paving Path For Advanced Conversational AI](https://developer.nvidia.com/blog/training-bert-with-gpus/) - [State-of-the-Art Language Modeling Using Megatron on the NVIDIA A100 GPU](https://developer.nvidia.com/blog/language-modeling-using-megatron-a100-gpu/) - [NVIDIA Elevates The Conversation For Natural Language Processing](https://www.nextplatform.com/2019/08/14/nvidia-elevates-the-conversation-for-natural-language-processing/) - [Building State-of-the-Art Biomedical and Clinical NLP Models with BioMegatron](https://developer.nvidia.com/blog/building-state-of-the-art-biomedical-and-clinical-nlp-models-with-biomegatron/) - [What Is Conversational AI?](https://blogs.nvidia.com/blog/2019/08/19/what-is-conversational-ai/) - [Money Maker: How AI Can Accelerate Analytics in Financial Markets](https://blogs.nvidia.com/blog/2017/08/30/qualitative-financial-analysis/) Find out about: - The [NVIDIA NGC](https://www.nvidia.com/en-us/gpu-cloud/)™ [catalog](https://www.nvidia.com/en-us/gpu-cloud/) provides extensive software libraries at no cost, as well as tools for building high-performance computing environments that take full advantage of GPUs. - Programmers and data scientists can take advantage of a broad suite of ML and analytics software libraries to significantly accelerate end-to-end data science pipelines entirely on GPUs. These libraries provide highly efficient, optimized implementations of algorithms that are routinely extended. NVIDIA [CUDA-X AI](https://blogs.nvidia.com/blog/2019/03/18/cuda-x-ai-data-science/) software acceleration libraries use GPUs in ML to accelerate workflows and realize model optimizations. The CUDA parallel computing platform provides an API that developers can use to build tools that use GPUs for processing large blocks, which is a critical ML task. - The [NVIDIA Deep Learning Institute](https://www.nvidia.com/en-us/deep-learning-ai/education/?iactivetab=certification-tabs-2#RecommenderSystems) offers instructor-led, hands-on training on the fundamental tools and techniques for building Transformer-based natural language processing models for text classification tasks, such as categorizing documents. 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Readable Markdown	1. [\#](https://www.nvidia.com/en-us/glossary/data-science/#grp-num) 2. [A](https://www.nvidia.com/en-us/glossary/data-science/#grp-a) 3. [B](https://www.nvidia.com/en-us/glossary/data-science/#grp-b) 4. [C](https://www.nvidia.com/en-us/glossary/data-science/#grp-c) 5. [D](https://www.nvidia.com/en-us/glossary/data-science/#grp-d) 6. E 7. F 8. [G](https://www.nvidia.com/en-us/glossary/data-science/#grp-g) 9. H 10. I 11. J 12. [K](https://www.nvidia.com/en-us/glossary/data-science/#grp-k) 13. L 14. [M](https://www.nvidia.com/en-us/glossary/data-science/#grp-m) 15. [N](https://www.nvidia.com/en-us/glossary/data-science/#grp-n) 16. O 17. [P](https://www.nvidia.com/en-us/glossary/data-science/#grp-p) 18. Q 19. [R](https://www.nvidia.com/en-us/glossary/data-science/#grp-r) 20. [S](https://www.nvidia.com/en-us/glossary/data-science/#grp-s) 21. [T](https://www.nvidia.com/en-us/glossary/data-science/#grp-t) 22. U 23. [V](https://www.nvidia.com/en-us/glossary/data-science/#grp-v) 24. W 25. [X](https://www.nvidia.com/en-us/glossary/data-science/#grp-x) 26. Y 27. Z Natural language processing is a technology that leverages computers and software to derive meaning from human language—written or spoken. ## What is natural language processing? Natural language processing (NLP) is the application of AI to process and analyze text or voice data in order to understand, interpret, categorize, and/or derive insights from the content. Included in NLP is natural language generation (NLG), which covers a computer’s ability to create human language text. Also included is natural language understanding (NLU), which takes text as input, understands context and intent, and generates an intelligent response. Examples of NLP include email spam filters, spell checkers, grammar checkers, autocorrect, language translation, sentiment analysis, semantic search, and more. With the advent of new deep learning (DL) approaches based on transformer architecture, NLP techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are now becoming the core of modern search engines, voice assistants, and chatbots. These applications are also becoming increasingly proficient in automating routine order taking, routing inquiries, and answering frequently asked questions. ## Why NLP? The applications of NLP are already substantial and expected to grow geometrically. [By one research survey estimate](https://www.statista.com/statistics/607891/worldwide-natural-language-processing-market-revenues/), the global market for products and services related to natural language processing will grow from \$3 billion in 2017 to \$43 billion in 2025. That’s a stunning 14X growth that attests to the broad application of natural language processing solutions. Further driving this growth is the realization that as little as 15% of the data within an organization is stored in corporate databases. The remainder is in texts, emails, meeting notes, phone transcripts, and so on. Natural language processing holds the promise of unlocking the business value hidden in all this data and making it as useful to business decision makers as data in storage. ## How Does NLP Work? Machine learning (ML) is the engine driving most natural language processing solutions today, and going forward. These systems use NLP algorithms to understand how words are used. They ingest everything from books to phrases to idioms, then NLP identifies patterns and relationships among words and phrases and thereby ‘learns’ to understand human language. Typically in an NLP application, the input text is converted into word vectors (a mathematical representation of a word) using techniques such as word embedding. With this technique, each word in the sentence is translated into a set of numbers before being fed into a deep learning model, such as [RNN](https://developer.nvidia.com/blog/deep-learning-nutshell-sequence-learning/), LSTM, or Transformer to understand context. The numbers [change over time while the neural net trains itself](https://developer.nvidia.com/blog/understanding-natural-language-deep-neural-networks-using-torch/), encoding unique properties such as the semantics and contextual information for each word. These DL models provide an appropriate output for a specific language task like next word prediction and text summarization, which are used to produce an output sequence. However, text encoding mechanisms like word-embedding can make it challenging to capture nuances. For instance, the bass fish and the bass player would have the same representation. When encoding a long passage, they can also lose the context gained at the beginning of the passage by the end. [BERT](https://arxiv.org/pdf/1810.04805.pdf) (Bidirectional Encoder Representations from Transformers) is deeply bidirectional, and can understand and retain context better than other text encoding mechanisms. A key challenge with training language models is the lack of labeled data. BERT is trained on [unsupervised tasks](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270) and generally uses unstructured datasets from books corpus, English Wikipedia, and more. ![Pre-training and fine-tuning loss.](https://www.nvidia.com/content/dam/en-zz/Solutions/glossary/data-science/nlp/img-1.png) ## GPUs: Accelerating NLP Getting computers to understand human languages, with all their nuances, and respond appropriately has long been a “holy grail” of AI researchers. But building systems with true natural language processing (NLP) capabilities was impossible before the arrival of modern AI techniques powered by accelerated computing. A [GPU is composed of hundreds of cores](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/) that can handle thousands of threads in parallel. GPUs have become the platform of choice to train deep learning models and perform inference because they can deliver 10X higher performance than CPU-only platforms. ![The difference between a CPU and GPU.](https://www.nvidia.com/content/dam/en-zz/Solutions/glossary/data-science/nlp/img-2.jpg) A driver of NLP growth is recent and ongoing advancements and [breakthroughs in natural language processing,](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) not the least of which is the deployment of GPUs to crunch through increasingly massive and highly complex language models. NLP Transformer-based deep learning models, such as BERT, don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs. The ability to use unsupervised learning methods, transfer learning with pre-trained models, and GPU acceleration has enabled widespread adoption of BERT in the industry. GPU-enabled models can be rapidly trained and then optimized to reduce response times in voice-assisted applications from tenths of seconds to milliseconds. This makes such computer-aided interactions as close to ‘natural’ as possible. ## Use Cases for NLP ## Startups Applications for natural language processing have exploded in the past decade as advances in [recurrent neural networks](https://blogs.nvidia.com/blog/2018/09/05/whats-the-difference-between-a-cnn-and-an-rnn/) powered by GPUs have offered better-performing AI. This has enabled startups to offer the likes of [voice services](https://blogs.nvidia.com/blog/2018/06/19/soundhound-digs-into-ai-market-against-apple-siri-google-assistant-amazon-alexa-microsoft-cortana/), [language tutors](https://blogs.nvidia.com/blog/2018/06/26/class-is-in-session-ai-app-schools-on-english-pronunciation/), and [chatbots](https://blogs.nvidia.com/blog/2018/10/01/ai-chatbot-enterprise-search-gpu-answers-algorithms/). ## Healthcare One of the difficulties facing health care is making it easily accessible. Calling your doctor’s office and waiting on hold is a common occurrence, and connecting with a claims representative can be equally difficult. The implementation of NLP to train chatbots is an emerging technology within healthcare to address the shortage of healthcare professionals and open the lines of communication with patients. Another key healthcare application for NLP is in biomedical text mining—often referred to as BioNLP. Given the large volume of biological literature and the increasing rate of biomedical publications, natural language processing is a critical tool in extracting information within the studies published to advance knowledge in the biomedical field. This significantly aids drug discovery and disease diagnosis. ## Financial Services NLP is a critically important part of building better chatbots and AI assistants for financial service firms. Among the numerous language models used in NLP-based applications, BERT has emerged as a leader and language model for NLP with machine learning. Using AI, NVIDIA has recently broken records for speed in training BERT, which promises to help unlock the potential for billions of expected conversational AI services coming online in the coming years to operate with human-level comprehension. For example, by leveraging NLP, banks can assess the creditworthiness of clients with little or no credit history. ## Retail In addition to healthcare, Chatbot technology is also commonly used for retail applications to accurately analyze customer queries and generate responses or recommendations. This streamlines the customer journey and improves efficiencies in store operations. NLP is also used for text mining customer feedback and sentiment analysis. ## NVIDIA GPUs Accelerating AI and NLP With NVIDIA GPUs and [CUDA-X AI](https://www.nvidia.com/en-in/technologies/cuda-x/)™ [libraries](https://www.nvidia.com/en-in/technologies/cuda-x/), massive, state-of-the-art language models can be rapidly trained and optimized to run inference in just a couple of milliseconds, or thousandths of a second. This is a major stride towards ending the trade-off between an AI model that’s fast versus one that’s large and complex. [NVIDIA's AI platform](https://www.nvidia.com/en-us/deep-learning-ai/) is the first to train [BERT](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) in less than an hour and complete AI inference in just over 2 milliseconds. The parallel processing capabilities and [Tensor Core](https://www.nvidia.com/en-us/data-center/tensorcore/) architecture of NVIDIA GPUs allow for higher throughput and scalability when working with complex language models—enabling [record-setting performance](https://nvidianews.nvidia.com/news/nvidia-achieves-breakthroughs-in-language-understandingto-enable-real-time-conversational-ai) for both the [training and inference](https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learning-training-inference-ai/) of BERT. This groundbreaking level of performance makes it possible for developers to use state-of-the-art language understanding for large-scale applications they can make available to hundreds of millions of consumers worldwide. Early adopters of NVIDIA's performance advances include Microsoft and some of the world's most innovative startups. These organizations are harnessing NVIDIA's platform to develop highly intuitive, immediately responsive language-based services for their customers.
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