Large language models (LLMs) have permeated every industry and changed the potential of technology. However, due to their massive size they are not practical for the current resource constraints that many companies have. The rise of small language models (SLMs) bridge quality and cost by creating models with a smaller resource footprint. SLMs are a subset of language models that tend to��
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NVIDIA has consistently developed automatic speech recognition (ASR) models that set the benchmark in the industry. Earlier versions of NVIDIA Riva, a collection of GPU-accelerated speech and translation AI microservices for ASR, TTS, and NMT, support English-Spanish and English-Japanese code-switching ASR models based on the Conformer architecture, along with a model supporting multiple��
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Translation plays an essential role in enabling companies to expand across borders, with requirements varying significantly in terms of tone, accuracy, and technical terminology handling. The emergence of sovereign AI has highlighted critical challenges in large language models (LLMs), particularly their struggle to capture nuanced cultural and linguistic contexts beyond English-dominant��
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The new model by Mistral excels at a variety of complex tasks including text summarization, multilingual translation and reasoning, programming, question and answering, and conversational AI.
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Scientists have enabled a stroke survivor, who is unable to speak, to communicate in both Spanish and English by training a neuroprosthesis implant to decode his bilingual brain activity. The research, published in Nature Biomedical Engineering, comes from the lab of University of California, San Francisco professor Dr. Edward Chang. It builds on his groundbreaking work from 2021 with the��
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Speech and translation AI models developed at NVIDIA are pushing the boundaries of performance and innovation. The NVIDIA Parakeet automatic speech recognition (ASR) family of models and the NVIDIA Canary multilingual, multitask ASR and translation model currently top the Hugging Face Open ASR Leaderboard. In addition, a multilingual P-Flow-based text-to-speech (TTS) model won the LIMMITS ��24��
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Large language models (LLMs) are a class of generative AI models built using transformer networks that can recognize, summarize, translate, predict, and generate language using very large datasets. LLMs have the promise of transforming society as we know it, yet training these foundation models is incredibly challenging. This blog articulates the basic principles behind LLMs��
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Generative AI is revolutionizing how organizations across all industries are leveraging data to increase productivity, advance personalized customer engagement, and foster innovation. Given its tremendous value, enterprises are looking for tools and expertise that help them integrate this new technology into their business operations and strategies effectively and reliably.
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In the realm of generative AI, building enterprise-grade large language models (LLMs) requires expertise collecting high-quality data, setting up the accelerated infrastructure, and optimizing the models. Developers can begin with pretrained models and fine-tune them for their use case, saving time and getting their solutions faster to market. Developers need an easy way to try out models��
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From start-ups to large enterprises, businesses use cloud marketplaces to find the new solutions needed to quickly transform their businesses. Cloud marketplaces are online storefronts where customers can purchase software and services with flexible billing models, including pay-as-you-go, subscriptions, and privately negotiated offers. Businesses further benefit from committed spending at��
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According to Gartner,? ��Nearly half of digital workers struggle to find the data they need to do their jobs, and close to one-third have made a wrong business decision due to lack of information awareness.��1 To address this challenge, more and more enterprises are deploying AI in customer service, as it helps to provide more efficient and information-based personalized services.
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Generative AI has captured the attention and imagination of the public over the past couple of years. From a given natural language prompt, these generative models are able to generate human-quality results, from well-articulated children��s stories to product prototype visualizations. Large language models (LLMs) are at the center of this revolution. LLMs are universal language comprehenders��
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Explore the latest advances in accurate and customizable automatic speech recognition, multi-language translation, and text-to-speech.
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As the global service economy grows, companies rely increasingly on contact centers to drive better customer experiences, increase customer satisfaction, and lower costs with increased efficiencies. Customer demand has increased far more rapidly than contact center employment ever could. Combined with the high agent churn rate, customer demand creates a need for more automated real-time customer��
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Speech AI is the ability of intelligent systems to communicate with users using a voice-based interface, which has become ubiquitous in everyday life. People regularly interact with smart home devices, in-car assistants, and phones through speech. Speech interface quality has improved leaps and bounds in recent years, making them a much more pleasant, practical, and natural experience than just a��
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If you��ve used a chatbot, predictive text to finish a thought in an email, or pressed ��0�� to speak to an operator, you��ve come across natural language processing (NLP). As more enterprises adopt NLP, the sub-field is developing beyond those popular use cases of machine-human communication to machines interpreting both human and non-human language. This creates an exciting opportunity for��
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Artificial intelligence (AI) has transformed synthesized speech from monotone robocalls and decades-old GPS navigation systems to the polished tone of virtual assistants in smartphones and smart speakers. It has never been so easy for organizations to use customized state-of-the-art speech AI technology for their specific industries and domains. Speech AI is being used to power virtual��
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To help localize subtitles from English to other languages, such as Russian, Spanish, or Portuguese, Netflix developed a proof-of-concept AI model that can automatically simplify and translate subtitles to multiple languages. The work is presented in a paper, Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation, published this month on the preprint platform��
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Neural machine translation exists across a wide variety consumer applications, including web sites, road signs, generating subtitles in foreign languages, and more. TensorRT, NVIDIA��s programmable inference accelerator, helps optimize and generate runtime engines for deploying deep learning inference apps to production environments. NVIDIA released TensorRT 4 with new features to accelerate��
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NVIDIA released TensorRT 4 with new features to accelerate inference of neural machine translation (NMT) applications on GPUs. Neural machine translation offers AI-based text translation for large number of consumer applications, including web sites, road signs, generating subtitles in foreign languages, and more. The new TensorRT 4 release brings support for new RNN layers such as Batch��
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NVIDIA has released TensorRT 4 at CVPR 2018. This new version of TensorRT, NVIDIA��s powerful inference optimizer and runtime engine provides: Additional features include the ability to execute custom neural network layers using FP16 precision and support for the Xavier SoC through NVIDIA DRIVE AI platforms. TensorRT 4 speeds up deep learning inference applications such as neural machine��
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Researchers from Microsoft recently announced they��ve created the first deep learning translation system capable of translating sentences of news articles from Chinese to English with the same level of accuracy as a person. Microsoft used NVIDIA Tesla GPUs and millions of sentences from various online newspapers to train their neural network. The team used a dual learning system where the AI��
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