Massive Foundation Model for Biomolecular Sciences Now Available via NVIDIA BioNeMo

Scientists everywhere can now access Evo 2, a powerful new
foundation model
that understands the genetic code for all domains of life. Unveiled today as the largest publicly available AI model for genomic data, it was built on the NVIDIA DGX Cloud platform in a collaboration led by nonprofit biomedical research organization Arc Institute and Stanford University.
Evo 2 is available to global developers on the
NVIDIA BioNeMo platform
, including as an NVIDIA NIM microservice for easy, secure AI deployment.
Trained on an enormous dataset of nearly 9 trillion nucleotides — the building blocks of DNA and RNA — Evo 2 can be applied to biomolecular research applications including predicting the form and function of proteins based on their genetic sequence, identifying novel molecules for healthcare and industrial applications, and evaluating how gene mutations affect their function.
“Evo 2 represents a major milestone for generative genomics,” said Patrick Hsu, Arc Institute cofounder and core investigator, and an assistant professor of bioengineering at the University of California, Berkeley. “By advancing our understanding of these fundamental building blocks of life, we can pursue solutions in healthcare and environmental science that are unimaginable today.”
The NVIDIA
NIM microservice for Evo 2
enables users to generate a variety of biological sequences, with settings to adjust model parameters. Developers interested in fine-tuning Evo 2 on their proprietary datasets can download the model through the open-source
NVIDIA BioNeMo Framework
, a collection of accelerated computing tools for biomolecular research.
“Designing new biology has traditionally been a laborious, unpredictable and artisanal process,” said Brian Hie, assistant professor of chemical engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow and an Arc Institute innovation investigator. “With Evo 2, we make biological design of complex systems more accessible to researchers, enabling the creation of new and beneficial advances in a fraction of the time it would previously have taken.”
Enabling Complex Scientific Research
Established in 2021 with $650 million from its founding donors, Arc Institute empowers researchers to tackle long-term scientific challenges by providing scientists with multiyear funding — letting scientists focus on innovative research instead of grant writing.
Its core investigators receive state-of-the-art lab space and funding for eight-year, renewable terms that can be held concurrently with faculty appointments with one of the institute’s university partners, which include Stanford University, the University of California, Berkeley, and the University of California, San Francisco.
By combining this unique research environment with accelerated computing expertise and resources from NVIDIA, Arc Institute’s researchers can pursue more complex projects, analyze larger datasets and more quickly achieve results. Its scientists are focused on disease areas including cancer, immune dysfunction and neurodegeneration.
NVIDIA accelerated the Evo 2 project by giving scientists access to 2,000 NVIDIA H100 GPUs via
NVIDIA DGX Cloud
on AWS. DGX Cloud provides short-term access to large compute clusters, giving researchers the flexibility to innovate. The fully managed AI platform includes
NVIDIA BioNeMo
, which features optimized software in the form of NVIDIA NIM microservices and NVIDIA BioNeMo Blueprints.
NVIDIA researchers and engineers also collaborated closely on AI scaling and optimization.
Applications Across Biomolecular Sciences
Evo 2 can provide insights into DNA, RNA and proteins. Trained on a wide array of species across domains of life — including plants, animals and bacteria — the model can be applied to scientific fields such as healthcare, agricultural biotechnology and materials science.
Evo 2 uses a novel model architecture that can process lengthy sequences of genetic information, up to 1 million tokens. This widened view into the genome could unlock scientists’ understanding of the connection between distant parts of an organism’s genetic code and the mechanics of cell function, gene expression and disease.
“A single human gene contains thousands of nucleotides — so for an AI model to analyze how such complex biological systems work, it needs to process the largest possible portion of a genetic sequence at once,” said Hsu.
In healthcare and drug discovery, Evo 2 could help researchers understand which gene variants are tied to a specific disease — and design novel molecules that precisely target those areas to treat the disease. For example, researchers from Stanford and the Arc Institute found that in tests with BRCA1, a gene associated with breast cancer, Evo 2 could predict with 90% accuracy whether previously unrecognized mutations would affect gene function.
In agriculture, the model could help tackle global food shortages by providing insights into plant biology and helping scientists develop varieties of crops that are more climate-resilient or more nutrient-dense. And in other scientific fields, Evo 2 could be applied to design biofuels or engineer proteins that break down oil or plastic.
“Deploying a model like Evo 2 is like sending a powerful new telescope out to the farthest reaches of the universe,” said Dave Burke, Arc’s chief technology officer. “We know there’s immense opportunity for exploration, but we don’t yet know what we’re going to discover.”
Read more about Evo 2 on the
NVIDIA Technical Blog
and in
Arc’s technical report
.
See
notice
regarding software product information.
Categories:
Deep Learning
|
Generative AI
Tags:
Arc Institute
|
Artificial Intelligence
|
Healthcare and Life Sciences
|
NVIDIA DGX Cloud
|
Open Source
|
Science
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Social Impact

用於生物分子科學的大型基礎模型現已透過 NVIDIA BioNeMo 提供

全球各地的科學家現在可以使用瞭解所有生命領域的遺傳密碼的強大全新
基礎模型
Evo 2。Evo 2 是由非營利生物醫學研究組織 Arc Institute 與史丹佛大學合作，在 NVIDIA DGX Cloud 平台上所開發，是目前規模最大的公開基因組資料人工智慧（AI）模型。
Evo 2 在
NVIDIA BioNeMo 平台
上供全球開發人員使用，包括以 NVIDIA NIM 微服務的方式進行簡易、安全的部署 AI。
Evo 2 模型使用近 9 兆個核苷酸（DNA 和 RNA 的組成部分）所組成的龐大資料集訓練出，可用於生物分子研究應用，包括根據基因序列預測蛋白質的形式和功能、識別用於醫療保健和工業應用的新型分子，以及評估基因突變如何影響其功能。
Arc Institute 共同創辦人暨核心研究員、加州大學柏克萊分校生物工程助理教授徐安祺（Patrick Hsu）表示：「Evo 2 代表著生成式基因組學的重要里程碑。透過推進我們對這些生命基本構成元素的了解，我們能在醫療保健和環境科學領域尋求目前難以想像的解決方案。」
適用於 Evo 2 的 NVIDIA NIM 微服務
可讓使用者產生各種生物序列，並能設定裡調整模型參數。對於想要使用自己專屬資料集來微調 Evo 2 的開發人員，可以透過開源的
NVIDIA BioNeMo 框架
下載模型，該框架是一系列用於生物分子研究的加速運算工具。
史丹佛大學化學工程助理教授、Dieter Schwarz 基金會史丹佛大學資料科學系研究員，同時也是 Arc Institute 創新研究員的 Brian Hie 表示：「設計新的生物學傳統上是一個費力、難以預測且需要用到大量人工的過程。有了 Evo 2，我們讓研究人員更容易進行複雜系統的生物設計，只要用到比以前短上不少的時間，就能創造出有益的新進展。」
推動複雜的科學研究
Arc Institute 在 6.5 億美元的捐助資金下於 2021 年成立，透過資助科學家多年期資金，讓科學家專注於創新研究，解決科學領域長期面對的難題，而不用忙於申請資金。
Arc Institute 的核心研究人員可以獲得最先進的實驗室，以及為期八年且可續約的資金，並可同時在與該單位合作的大學之一任教，包括史丹佛大學、加州大學柏克萊分校和加州大學舊金山分校。
透過結合這個獨特的研究環境與 NVIDIA 的加速運算專業技術與資源，Arc Institute 的研究人員可以進行更複雜的專案、分析更大的資料集，並且更快的取得成果。該單位的科學家專注於癌症、免疫功能障礙和神經退化性疾病等領域。
NVIDIA 透過 Amazon Web Services（AWS）上的
NVIDIA DGX Cloud
讓科學家們能夠使用 2,000 個 NVIDIA H100 GPU，加快進行 Evo 2 計畫。DGX Cloud 提供短期使用大型運算叢集的能力，使研究人員得以靈活進行創新。這個完全託管的 AI 平台包含
NVIDIA BioNeMo
，以 NVIDIA NIM 微服務和 NVIDIA BioNeMo Blueprints 的形式提供最佳化的軟體。
NVIDIA 研究人員與工程師同樣在 AI 擴展與最佳化方面密切合作。
應用於生物分子科學
Evo 2 可以提供對 DNA、RNA 和蛋白質的深入瞭解。該模型經過對植物、動物和細菌等生命領域各個物種的訓練，可以應用於醫療保健、農業生物技術和材料科學等科學領域。
Evo 2 採用新穎的模型架構，可以處理長序列的遺傳資訊，最多可達 100 萬個詞元（token）。這種對基因組的更深認識可以讓科學家明白生物遺傳密碼裡距離較遠的部分與細胞功能、基因表現和疾病機制之間的關係。
徐安祺表示：「一個人類基因裡有著數千個核苷酸，如果要讓 AI 模型分析這麼複雜的生物系統如何運作，就必須一次處理基因序列中的最大可能部分。」
在醫療保健和藥物探索方面，Evo 2 模型可以幫助研究人員了解哪些基因變異與特定疾病有關，並設計出新型分子，精確地針對這些區域來治療疾病。像是史丹佛大學與 Arc Institute 的研究人員發現，在測試與乳癌有關的基因 BRCA1 時，Evo 2 能以 90% 的準確率預測先前未識別的突變是否會影響基因功能。
在農業方面，這個模型可以提出對植物生物學的洞察，幫助科學家開發更能適應氣候或是更營養的農作物品種，從而幫助解決全球糧食短缺的問題。而在其他科學領域，Evo 2 可應用於設計生物燃料或工程蛋白質，以分解油脂或塑膠。
Arc Institute 技術長 Dave Burke 表示：「部署像 Evo 2 這樣的模型，就如同將一具強大的新望遠鏡送往宇宙最遙遠的地方。我們知道這裡有著無限的探索機會，但是我們還不知道會發現什麼。」
如欲獲得更多有關 Evo 2 的資訊，請參閱
NVIDIA 技術部落格
與
Arc 的技術報告
。
請參閱有關軟體產品資訊的
公告
。
Categories:
深度學習與人工智慧
|
生成式人工智慧
Tags:
Arc Institute
|
Artificial Intelligence
|
Healthcare and Life Sciences
|
NVIDIA DGX Cloud
|
Open Source
|
Science
|
Social Impact


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Telcos Dial Up AI: NVIDIA Survey Unveils Industry’s AI Trends

The telecom industry’s efforts to drive efficiencies with AI are beginning to show fruit.
An increasing focus on deploying AI into radio access networks (RANs) was among the key findings of NVIDIA’s third annual “
State of AI in Telecommunications
” survey, as more than a third of respondents indicated they’re investing or planning to invest in AI-RAN. The survey polled more than 450 telecommunications professionals worldwide, revealing continued momentum for AI adoption — including growth in generative AI use cases — and how the technology is helping optimize customer experiences and increase employee productivity.
Of the telecommunications professionals surveyed, almost all stated that their company is actively deploying or assessing AI projects. Here are some top insights on impact and use cases:
84% said AI is helping to increase their company’s annual revenue
77% said AI helped reduce annual operating costs
60% said increased employee productivity was their biggest benefit from AI
44% said they’re investing in AI for customer experience optimization, which is the No. 1 area of investment for AI in telecommunications
40% said they’re deploying AI into their network planning and operations, including RAN
Business Impact on AI in Telecommunications
Survey results highlight that use of AI in the telecom industry has helped increase revenue and reduce costs. 84% of respondents said that the technology is helping increase their company’s annual revenue, with 21% saying that AI had contributed to a more than 10% revenue increase in specific business areas. In addition, 77% agreed that AI helped reduce annual operating costs.
The wide array of AI use cases and impact on the bottom line has led to greater confidence in the future: 80% of respondents believe that AI is crucial for their company’s future success, while two-thirds plan to increase spending on AI infrastructure this year.
The telecommunications industry is at the forefront of AI adoption, with a clear focus on enhancing employee productivity, customer experience and network operations. By continuing to invest in AI infrastructure and training, telecom companies can stay ahead of the curve and capitalize on the numerous benefits that AI offers.
AI Finds Its Way Into the Network Stack
AI in the telecommunications network is gaining momentum, with 37% of respondents saying they’re investing in AI to improve network planning and operations. Similarly, 33% said they invested in using AI for field-operations optimization in the last year.
Of the respondents investing in AI for 5G monetization and/or 6G research and development, 66% are aiming to deploy AI services on RAN for operational and user needs, 53% are aiming to enhance spectral efficiency for the RAN, and 50% are aiming to colocate AI and RAN applications on the same infrastructure.
Generative AI Goes Mainstream
Generative AI
is gaining significant attention in telecoms. More than half of survey respondents who said they’re using generative AI have already deployed their first use case, while another third plan to do so this year.
Of those respondents adopting generative AI, 84% said that their companies plan to offer generative AI solutions externally to customers. 52% said they would offer generative AI as a software-as-a-service solution, while 35% will offer generative AI as a platform for developers, including for compute services.
There’s also a notable trend toward using multiple approaches for AI development, including a rise in in-house and open-source capabilities.
Download the “
State of AI in Telecommunications: 2025 Trends
” report for in-depth results and insights.
Explore NVIDIA’s
AI solutions and enterprise-level platforms
for telecommunications.
Categories:
Generative AI
Tags:
Artificial Intelligence
|
Telecommunications

電信業者增加 AI 使用：NVIDIA 調查揭示電信業 AI 趨勢

電信業使用人工智慧（AI）提高效率的努力已初見成效。
NVIDIA 第三次「
電信業 AI 現況（State of AI in Telecommunications）
」年度調查報告的其中一個主要發現，就是人們日漸重視將 AI 部署到無線存取網路技術（RAN）中，超過三分之一的受訪者表示正在投資或計劃投資 AI-RAN。這項調查訪問全球超過 450 位電信專業人士，結果顯示採用 AI 的趨勢持續成長，其中包括生成式 AI 用例的增加，以及這項技術如何協助最佳化客戶體驗和提高員工生產力。
在受訪的電信專業人士中，幾乎全員表示他們的公司正在積極部署或評估 AI 專案。以下是部分關於影響和用例的重要見解：
84% 表示 AI 有助於增加公司的年度營收
77% 表示 AI 有助於降低年度營運成本
60% 表示提高員工生產力是 AI 帶來的最大益處
44% 表示正在投資 AI 以最佳化客戶體驗，這是電信業在 AI 方面的首要投資領域
40% 表示正在將 AI 部署至網路規劃和營運，當中包括 RAN
AI
對電信業的商業影響
調查結果強調，電信業使用 AI 有助於增加收入和降低成本。84% 的受訪者表示，AI 技術有助於增加公司的年度營收，其中 21% 的受訪者表示，AI 對特定業務領域的營收增幅貢獻超過 10%。此外，77% 的受訪者同意 AI 有助於降低年度營運成本。
廣泛的 AI 用例以及對盈虧狀況的影響，讓受訪者對未來更具信心。80% 的受訪者認為 AI 對其公司未來的成功來說至關重要，而三分之二的受訪者則打算在今年增加 AI 基礎設施的支出。
電信業在採用 AI 方面領先，並明確專注於提高員工生產力、客戶體驗和網路營運上。透過持續投資 AI 基礎設施和培訓，電信業者可以維持領先地位，並且充分利用 AI 帶來的眾多優勢。
AI
進入網路堆疊
AI 在電信網路中的發展越來越蓬勃，37% 的受訪者表示正在投資 AI 以改善網路規劃和營運。同樣地，33% 的受訪者表示在去年投資使用 AI 來最佳化現場作業。
在為了 5G 變現及（或）6G 研發而投資 AI 的受訪者中，有 66% 的目標是在 RAN 上部署 AI 服務，以滿足營運及用戶需求，53% 的目標是在提升 RAN 的頻譜效率，有 50% 的受訪者目標是將 AI 及 RAN 應用部署在同一個基礎設施上。
生成式
AI
成為主流
生成式 AI
在電信領域正獲得顯著關注。半數以上表示正在使用生成式 AI 的受訪者已經部署了第一個用例，另有三分之一的受訪者計劃在今年這樣做。
在採用生成式 AI 的受訪者中，84% 表示他們的公司計劃對外提供生成式 AI 解決方案給客戶。52% 的受訪者表示，他們將以軟體即服務的方式提供生成式 AI 解決方案，而 35% 的受訪者則會以平台的方式提供生成式 AI 給開發人員，其中包括運算服務。
使用多種方法來開發 AI 也是一個顯著趨勢，包括提升內部和開源能力。
歡迎下載今年度的電信業 AI 現況調查報告「
State of AI in Telecommunications: 2025 Trends
」，以了解詳細調查結果與洞察。
探索 NVIDIA 適用於電信業的
AI
解決方案與企業級平台
Categories:
生成式人工智慧
Tags:
Artificial Intelligence
|
Telecommunications


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How Scaling Laws Drive Smarter, More Powerful AI

Just as there are widely understood empirical laws of nature — for example,
what goes up must come down
, or
every action has an equal and opposite reaction
— the field of AI was long defined by a single idea: that more compute, more training data and more parameters makes a better AI model.
However, AI has since grown to need three distinct laws that describe how applying compute resources in different ways impacts model performance. Together, these AI scaling laws — pretraining scaling, post-training scaling and test-time scaling, also called long thinking — reflect how the field has evolved with techniques to use additional compute in a wide variety of increasingly complex AI use cases.
The recent rise of
test-time scaling
— applying more compute at inference time to improve accuracy — has enabled AI reasoning models, a new class of large language models (
LLMs
) that perform multiple inference passes to work through complex problems, while describing the steps required to solve a task. Test-time scaling requires intensive amounts of computational resources to support AI reasoning, which will drive further demand for accelerated computing.
What Is Pretraining Scaling?
Pretraining scaling is the original law of AI development. It demonstrated that by increasing training dataset size, model parameter count and computational resources, developers could expect predictable improvements in model intelligence and accuracy.
Each of these three elements — data, model size, compute — is interrelated. Per the pretraining scaling law,
outlined in this research paper
, when larger models are fed with more data, the overall performance of the models improves. To make this feasible, developers must scale up their compute — creating the need for powerful accelerated computing resources to run those larger training workloads.
This principle of pretraining scaling led to large models that achieved groundbreaking capabilities. It also spurred major innovations in model architecture, including the rise of billion- and trillion-parameter
transformer models
,
mixture of experts
models and new distributed training techniques — all demanding significant compute.
And the relevance of the pretraining scaling law continues — as humans continue to produce growing amounts of multimodal data, this trove of text, images, audio, video and sensor information will be used to train powerful future AI models.
Pretraining scaling is the foundational principle of AI development, linking the size of models, datasets and compute to AI gains. Mixture of experts, depicted above, is a popular model architecture for AI training.
What Is Post-Training Scaling?
Pretraining a large
foundation model
isn’t for everyone — it takes significant investment, skilled experts and datasets. But once an organization pretrains and releases a model, they lower the barrier to AI adoption by enabling others to use their pretrained model as a foundation to adapt for their own applications.
This post-training process drives additional cumulative demand for accelerated computing across enterprises and the broader developer community. Popular open-source models can have hundreds or thousands of derivative models, trained across numerous domains.
Developing this ecosystem of derivative models for a variety of use cases could take around 30x more compute than pretraining the original foundation model.
Developing this ecosystem of derivative models for a variety of use cases could take around 30x more compute than pretraining the original foundation model.
Post-training techniques can further improve a model’s specificity and relevance for an organization’s desired use case. While pretraining is like sending an AI model to school to learn foundational skills, post-training enhances the model with skills applicable to its intended job. An LLM, for example, could be post-trained to tackle a task like sentiment analysis or translation — or understand the jargon of a specific domain, like healthcare or law.
The post-training scaling law posits that a pretrained model’s performance can further improve — in computational efficiency, accuracy or domain specificity — using techniques including fine-tuning, pruning, quantization, distillation, reinforcement learning and synthetic data augmentation.
Fine-tuning
uses additional training data to tailor an AI model for specific domains and applications. This can be done using an organization’s internal datasets, or with pairs of sample model input and outputs.
Distillation
requires a pair of AI models: a large, complex teacher model and a lightweight student model. In the most common distillation technique, called offline distillation, the student model learns to mimic the outputs of a pretrained teacher model.
Reinforcement learning
, or RL, is a machine learning technique that uses a reward model to train an agent to make decisions that align with a specific use case. The agent aims to make decisions that maximize cumulative rewards over time as it interacts with an environment — for example, a chatbot LLM that is positively reinforced by “thumbs up” reactions from users. This technique is known as reinforcement learning from human feedback (RLHF). Another, newer technique, reinforcement learning from AI feedback (RLAIF), instead uses feedback from AI models to guide the learning process, streamlining post-training efforts.
Best-of-n sampling
generates multiple outputs from a language model and selects the one with the highest reward score based on a reward model. It’s often used to improve an AI’s outputs without modifying model parameters, offering an alternative to fine-tuning with reinforcement learning.
Search methods
explore a range of potential decision paths before selecting a final output. This post-training technique can iteratively improve the model’s responses.
To support post-training, developers can use
synthetic data
to augment or complement their fine-tuning dataset. Supplementing real-world datasets with AI-generated data can help models improve their ability to handle edge cases that are underrepresented or missing in the original training data.
Post-training scaling refines pretrained models using techniques like fine-tuning, pruning and distillation to enhance efficiency and task relevance.
What Is Test-Time Scaling?
LLMs generate quick responses to input prompts. While this process is well suited for getting the right answers to simple questions, it may not work as well when a user poses complex queries. Answering complex questions — an essential capability for
agentic AI
workloads — requires the LLM to reason through the question before coming up with an answer.
It’s similar to the way most humans think — when asked to add two plus two, they provide an instant answer, without needing to talk through the fundamentals of addition or integers. But if asked on the spot to develop a business plan that could grow a company’s profits by 10%, a person will likely reason through various options and provide a multistep answer.
Test-time scaling, also known as long thinking, takes place during inference. Instead of traditional AI models that rapidly generate a one-shot answer to a user prompt, models using this technique allocate extra computational effort during inference, allowing them to reason through multiple potential responses before arriving at the best answer.
On tasks like generating complex, customized code for developers, this AI reasoning process can take multiple minutes, or even hours — and can easily require over 100x compute for challenging queries compared to a single inference pass on a traditional LLM, which would be highly unlikely to produce a correct answer in response to a complex problem on the first try.
This AI reasoning process can take multiple minutes, or even hours — and can easily require over 100x compute for challenging queries compared to a single inference pass on a traditional LLM.
This test-time compute capability enables AI models to explore different solutions to a problem and break down complex requests into multiple steps — in many cases, showing their work to the user as they reason. Studies have found that test-time scaling results in higher-quality responses when AI models are given open-ended prompts that require several reasoning and planning steps.
The test-time compute methodology has many approaches, including:
Chain-of-thought prompting
: Breaking down complex problems into a series of simpler steps.
Sampling with majority voting
: Generating multiple responses to the same prompt, then selecting the most frequently recurring answer as the final output.
Search
: Exploring and evaluating multiple paths present in a tree-like structure of responses.
Post-training methods like best-of-n sampling can also be used for long thinking during inference to optimize responses in alignment with human preferences or other objectives.
Test-time scaling enhances inference by allocating extra compute to improve AI reasoning, enabling models to tackle complex, multi-step problems effectively.
How Test-Time Scaling Enables AI Reasoning
The rise of test-time compute unlocks the ability for AI to offer well-reasoned, helpful and more accurate responses to complex, open-ended user queries. These capabilities will be critical for the detailed, multistep reasoning tasks expected of autonomous
agentic AI
and
physical AI
applications. Across industries, they could boost efficiency and productivity by providing users with highly capable assistants to accelerate their work.
In healthcare, models could use test-time scaling to analyze vast amounts of data and infer how a disease will progress, as well as predict potential complications that could stem from new treatments based on the chemical structure of a drug molecule. Or, it could comb through a database of clinical trials to suggest options that match an individual’s disease profile, sharing its reasoning process about the pros and cons of different studies.
In retail and supply chain logistics, long thinking can help with the complex decision-making required to address near-term operational challenges and long-term strategic goals. Reasoning techniques can help businesses reduce risk and address scalability challenges by predicting and evaluating multiple scenarios simultaneously — which could enable more accurate demand forecasting, streamlined supply chain travel routes, and sourcing decisions that align with an organization’s sustainability initiatives.
And for global enterprises, this technique could be applied to draft detailed business plans, generate complex code to debug software, or optimize travel routes for delivery trucks, warehouse robots and robotaxis.
AI reasoning models are rapidly evolving. OpenAI o1-mini and o3-mini,
DeepSeek R1
, and Google DeepMind’s Gemini 2.0 Flash Thinking were all introduced in the last few weeks, and additional new models are expected to follow soon.
Models like these require considerably more compute to reason during inference and generate correct answers to complex questions — which means that enterprises need to scale their accelerated computing resources to deliver the next generation of AI reasoning tools that can support complex problem-solving, coding and multistep planning.
Learn about the benefits of
NVIDIA AI for accelerated inference
.
Categories:
Explainer
|
Generative AI
Tags:
Artificial Intelligence
|
Inference

擴展定律如何推動更有智慧又更強大的 AI 發展

就像是人們普遍理解的自然經驗定律一樣，例如有上必有下，或者每個動作都有相等和相反的反應，人工智慧（AI）領域長期以來都是由單一想法所定義：更多的運算、更多的訓練資料和更多的參數，就可以產生更好的 AI 模型。
然而，AI 發展至今，需要三個不同的定律來描述不同方式利用運算資源如何影響模型效能。這些 AI 擴展定律合在一起，包含預訓練擴展（pretraining scaling）、訓練後擴展（post-training scaling），以及又稱為長思考（long thinking）的測試階段擴展（test-time scaling），反映出 AI 領域如何在各種日益複雜的 AI 用例中運用額外的運算技術演進發展。
近期興起的測試階段擴展，也就是在推論階段應用更多運算來提高準確度，已經實現 AI 推理模型這類新式的大型語言模型（
LLM
），以執行多次推論來處理複雜的問題，同時描述解決任務所需的步驟。測試階段擴展需要用到大量運算資源來支援 AI 推理，這將進一步推動對加速運算的需求。
什麼是預訓練擴展？
預訓練擴展是 AI 發展的原始定律。它證明透過增加訓練資料集大小、模型參數數量和運算資源，開發人員可以期望模型智慧和準確度會出現可預期的改善。
資料、模型大小、運算這三個要素中的每一個都息息相關。根據
本篇研究論文所概述
的預訓練擴展定律，當大型模型獲得更多資料時，模型的整體效能就會提高。為了實現這個目標，開發人員必須擴大運算規模，這就需要強大的加速運算資源來運行那些較大的訓練工作負載。
這種預訓練擴展原則使得大型模型達到突破性的能力。它還激發了模型架構的重大創新，包括有著數十億個和上兆個參數的
transformer 模型
、
混合專家
模型和新式分散式訓練技術的興起，而這一切都需要大量的運算。
而預訓練擴展定律的相關性仍在不斷發展，隨著人類持續產生越來越多的多模態資料，這些文字、影像、音訊、影片和感測器資訊的寶藏庫將會被用來訓練未來強大的 AI 模型。
預訓練擴展是 AI 發展的基本原則，它將模型、資料集和運算的大小與 AI 的效益連結起來。如上圖所示的混合專家模型，是訓練 AI 時常用的模型架構
什麼是訓練後擴展？
預先訓練大型
基礎模型
並非人人適用，這需要大量投資、熟練的專家和資料集。然而，一旦組織預先訓練好並發布模型，就能讓其他人使用其預先訓練的模型當成基礎，以配合自己的應用，從而降低採用 AI 的門檻。
這種訓練後的流程會推動企業及更廣泛的開發人員社群對加速運算的額外累積需求。受歡迎的開源模型可能有著上百個或上千個在多個領域裡訓練出的衍生模型。
針對各種用例開發衍生模型的生態系，可能需要比預先訓練原始基礎模型多出約 30 倍的運算時間。
訓練後技術可以進一步提升模型的特異性，以及與組織所需用例的相關性。預訓練擴展就像是將 AI 模型送去學校學習基本技能，而訓練後擴展則是增強模型適用於其預期工作的技能。比如一個大型語言模型可以經過訓練後擴展來處理情感分析或翻譯等任務，或是理解醫療保健或法律等特定領域的術語。
訓練後擴展定律假設使用微調、剪枝、量化、蒸餾、強化學習和合成資料增強等技術，可以進一步改善預訓練模型在運算效率、準確性或領域特異性方面的效能。
微調
（fine-tuning）使用額外的訓練資料，針對特定領域和應用量身打造 AI 模型。這可以使用組織的內部資料集，或是成對的樣本模型輸入和輸出內容來完成。
蒸餾
（distillation）需要使用一對 AI 模型：一個大型複雜的教師模型和一個輕量級的學生模型。在離線蒸餾這個最常見的蒸餾技術中，學生模型學習模仿預先訓練的教師模型的輸出。
強化學習
（reinforcement learning，RL）是一種機器學習技術，它使用獎勵模型來訓練代理做出符合特定用例的決定。代理的目標是在與環境互動的過程中，隨著時間的推移做出累積獎勵最大化的決策，例如聊天機器人大型語言模型會受到使用者做出「按讚」反應的正向強化。這種技術稱為基於人類回饋的強化學習（RLHF）。另一種較新的技術是基於 AI 回饋強化學習（RLAIF），它使用 AI 模型的回饋來引導學習過程，簡化訓練後的工作。
最佳解搜尋採樣
（Best-of-n sampling）會從語言模型產生多個輸出，並根據獎勵模型選擇獎勵分數最高的一個。它通常用來提高 AI 的輸出，而不需要修改模型參數，提供一種使用強化學習進行微調的替代方法。
搜尋方法
會在選擇最終輸出之前探索一系列潛在的決策路徑。這種訓練後擴展技術可以反覆改善模型的反應。
為了支援訓練後擴展，開發人員可以使用
合成資料
來增強或補充微調資料集。使用 AI 產生的資料來補充現實世界的資料集，有助於模型改善處理原始訓練資料中代表性不足或遺漏的邊緣案例的能力。
訓練後擴展使用微調、修剪和蒸餾等技術來完善預訓練模型，以提高效率和任務相關性
什麼是測試階段擴展？
大型語言模型會對輸入提示做出快速回應。這個過程非常適合用來獲得簡單問題的正確答案，但當使用者提出複雜的詢問，這個流程可能就沒那麼好使用。要回答複雜的問題，大型語言模型必須先對問題進行推理，才能給出答案，而回答複雜的問題是
代理型 AI
工作負載的基本能力。
這跟大多數人的思考方式類似，在被問到二加二的答案時，他們會馬上脫口而出，而不需要講解加法或整數的基本原理。可是萬一當場被要求制定一個可以讓公司利潤成長 10% 的商業計畫時，人們可能會透過各種選項進行推理，並且提供一個多步驟的答案。
測試階段擴展也稱為長思考，發生在推論過程中。傳統的 AI 模型會快速針對使用者的提示產生一次性答案，而使用這項技術的模型則會在推論過程中分配額外的運算工作，讓模型在得出最佳答案前先推理出多個可能的回應。
在為開發人員生成複雜的客製化程式碼等工作上，這個 AI 推理過程可能需要幾分鐘，甚至幾小時的時間，而且相較於傳統大型語言模型的單次推論，高難度的查詢可能需要超過 100 倍的運算量，因為傳統大型語言模型不太可能在第一次嘗試時，就能對複雜的問題產生正確的答案。
這種測試階段運算能力可以讓 AI 模型探索問題的不同解決方案，並將複雜的要求拆解成多個步驟，在許多情況下，在推理過程中向使用者展示其工作。研究發現，當給予 AI 模型需要多個推理與規劃步驟的開放式提示時，測試階段擴展可以獲得更高品質的回應。
測試階段運算方法有多種方法，包括：
思維鏈（chain-of-thought）提示：把複雜的問題分解成一系列更簡單的步驟。
多數決抽樣：針對同一個提示產生多個回應，然後選擇最常出現的答案作為最終輸出。
搜尋：探索與評估回覆樹狀結構裡的多個路徑。
類似最佳解搜尋採樣的訓練後擴展方法也可用於推論過程中的長思考，以最佳化符合人類喜好或其他目標的回應。
測試階段擴展技術透過分配額外的運算來增強 AI推理能力，使得模型能夠有效解決複雜的多步驟問題
測試階段擴展如何進行
AI
推理
測試階段運算技術的興起，讓 AI 有能力對使用者所提出複雜、開放式的查詢項目，提供有理有據、有幫助且更加準確的回應。這些能力對於自主
代理型 AI
及
實體 AI
應用所期待的詳細、多重推理任務來說至關重要。它們可以為各產業的使用者提供能力強大的助理來加速工作，從而提高效率和生產力。
在醫療保健領域，模型可以使用測試階段擴展技術來分析大量資料，推斷疾病的發展情況，以及根據藥物分子的化學結構，預測新療法可能產生的潛在併發症。或者，它可以梳理臨床試驗資料庫，建議符合個人病況的方案，分享其對不同研究利弊的推理過程。
在零售和供應鏈物流領域，長思考有助於解決近期營運挑戰和長期策略目標所需的複雜決策。推理技術可以同時預測與評估多種情境，協助企業降低風險，並因應在擴充方面的難題。這可以實現更精準的需求預測、簡化供應鏈行程路線，以及做出符合組織永續發展計畫的採購決策。
對於全球企業而言，這項技術可應用於草擬詳細的商業計畫、產生複雜的程式碼以對軟體進行除錯，或是最佳化貨車、倉儲機器人和無人駕駛計程車的行駛路線。
AI 推理模型發展迅速。OpenAI o1-mini 和 o3-mini、
DeepSeek R1
以及 Google DeepMind 的 Gemini 2.0 Flash Thinking 都是在過去幾週推出，預計不久後還會有更多新的模型問世。
這些模型在推理過程中需要使用大量運算，才能對複雜問題進行推理與產生正確答案，這表示企業需要擴充加速運算資源，以提供能夠解決複雜問題、編寫程式碼和規劃多步驟的下一代AI推理工具。
了解
NVIDIA AI
在加速推論
方面的優勢。
Categories:
生成式人工智慧
|
解釋達人
Tags:
Artificial Intelligence
|
Inference


Safety First: Leading Partners Adopt NVIDIA Cybersecurity AI to Safeguard Critical Infrastructure

The rapid evolution of generative AI has created countless opportunities for innovation across industry and research. As is often the case with state-of-the-art technology, this evolution has also shifted the landscape of cybersecurity threats, creating new security requirements. Critical infrastructure cybersecurity is advancing to thwart the next wave of emerging threats in the AI era.
Leading operational technology (OT) providers today showcased at the S4 conference for industrial control systems (ICS) and OT cybersecurity how they’re adopting the NVIDIA cybersecurity AI platform to deliver real-time threat detection and critical infrastructure protection.
Armis, Check Point, CrowdStrike, Deloitte and World Wide Technology (WWT) are integrating the platform to help customers bolster critical infrastructure, such as energy, utilities and manufacturing facilities, against cyber threats.
Critical infrastructure operates in highly complex environments, where the convergence of IT and OT, often accelerated by digital transformation, creates a perfect storm of vulnerabilities. Traditional cybersecurity measures are no longer sufficient to address these emerging threats.
By harnessing
NVIDIA’s cybersecurity AI platform
, these partners can provide exceptional visibility into critical infrastructure environments, achieving robust and adaptive security while delivering operational continuity.
The platform integrates NVIDIA’s accelerated computing and AI, featuring
NVIDIA BlueField-3 DPUs
,
NVIDIA DOCA
and the
NVIDIA Morpheus AI cybersecurity framework
, part of the
NVIDIA AI Enterprise
. This combination enables real-time threat detection, empowering cybersecurity professionals to respond swiftly at the edge and across networks.
Unlike conventional solutions that depend on intrusive methods or software agents, BlueField-3 DPUs function as a virtual security overlay. They inspect network traffic and safeguard host integrity without disrupting operations. Acting as embedded sensors within each server, they stream telemetry data to NVIDIA Morpheus, enabling detailed monitoring of host activities, network traffic and application behaviors — seamlessly and without operational impact.
Driving Cybersecurity Innovation Across Industries
Integrating Armis Centrix, Armis’ AI-powered cyber exposure management platform, with NVIDIA cybersecurity AI helps secure critical infrastructure like energy, manufacturing, healthcare and transportation.
“OT environments are increasingly targeted by sophisticated cyber threats, requiring robust solutions that ensure both security and operational continuity,” said Nadir Izrael, chief technology officer and cofounder of Armis. “Combining Armis’ unmatched platform for OT security and cyber exposure management with NVIDIA BlueField-3 DPUs enables organizations to comprehensively protect cyber-physical systems without disrupting operations.”
CrowdStrike is helping secure critical infrastructure such as ICS and OT by deploying its CrowdStrike Falcon security agent on BlueField-3 DPUs to boost real-time AI-powered threat detection and response.
“OT environments are under increasing threat, demanding AI-powered security that adapts in real time,” said Raj Rajamani, head of products at CrowdStrike. “By integrating NVIDIA BlueField-3 DPUs with the CrowdStrike Falcon platform, we’re extending industry-leading protection to critical infrastructure without disrupting operations — delivering unified protection at the edge and helping organizations stay ahead of modern threats.”
Deloitte is driving customers’ digital transformation, enabled by NVIDIA’s cybersecurity AI platform, to help meet the demands of breakthrough technologies that require real-time, granular visibility into data center networks to defend against increasingly sophisticated threats.
“Protecting OT and ICS systems is becoming increasingly challenging as organizations embrace digital transformation and interconnected technologies,” said Dmitry Dudorov, an AI security leader at Deloitte U.K. “Harnessing NVIDIA’s cybersecurity AI platform can enable organizations to determine threat detection, enhance resilience and safeguard their infrastructure to accelerate their efforts.”
A Safer Future, Powered by AI
NVIDIA’s cybersecurity AI platform, combined with the expertise of ecosystem partners, offers a powerful and scalable solution to protect critical infrastructure environments against evolving threats. Bringing NVIDIA AI and accelerated computing to the forefront of OT security can help organizations protect what matters most — now and in the future.
Learn more by attending the
NVIDIA GTC
global AI conference, running March 17-21, where Armis, Check Point and CrowdStrike  cybersecurity leaders will host
sessions
about their collaborations with NVIDIA.
Categories:
Generative AI
|
Networking
|
Software
Tags:
Artificial Intelligence
|
Cybersecurity
|
NVIDIA AI Enterprise
|
NVIDIA BlueField

安全至上：領先合作夥伴採用 NVIDIA 網路安全 AI 保護關鍵基礎設施

生成式人工智慧（AI）的快速發展，為產業與研究領域的創新帶來無數機會。正如最先進的技術常見的情況，這種演進同樣改變了網路安全威脅的格局，產生出全新的安全需求。關鍵基礎設施的網路安全正在不斷進步，以嚇阻 AI 時代的下一波新興威脅。
領先的營運技術（OT）供應商今日在專注於工業控制系統（ICS）與 OT 網路安全的S4大會上，展示他們如何採用 NVIDIA 網路安全 AI 平台來提供即時偵測威脅與關鍵基礎設施保護。
Armis、Check Point、CrowdStrike、德勤（Deloitte）與 World Wide Technology（WWT）正在整合該平台，以協助客戶強化能源、公用事業和製造設施等關鍵基礎設施對抗網路威脅。
關鍵基礎設施在高度複雜的環境中運作，常常因數位轉型而加速整合 IT 與 OT，產生出資安漏洞的完美風暴。傳統的網路安全措施已經不足以應對這些新興威脅。
利用
NVIDIA 的網路安全 AI 平台
，這些合作夥伴能夠為關鍵基礎設施環境提供極佳的可視性，並在維持設施持續運作的同時，實現強大且適應性高的安全功能。
該平台整合了 NVIDIA 的加速運算與 AI，採用
NVIDIA BlueField-3 DPU
、
NVIDIA DOCA
及作為
NVIDIA AI Enterprise
一部分的
NVIDIA Morpheus AI 網路安全框架
。這個組合能夠實現即時偵測威脅，讓網路安全專業人員能夠在邊緣和整個網路迅速回應。
與傳統依賴侵入性方法或軟體代理的解決方案不同，BlueField-3 DPU 有著虛擬安全覆蓋的功能，可以在不中斷運作的情況下檢查網路流量與保護主機完整性。作為嵌入每一台伺服器裡的感測器，它們將遙測資料傳輸至 NVIDIA Morpheus，以流暢且不影響運作的方式，實現主機活動、網路流量和應用程式行為的詳細監控。
推動各產業的網路安全創新
整合 Armis Centrix 的 AI 驅動 Armis 網路暴露管理平台搭配 NVIDIA 網路安全 AI，協助確保能源、製造、醫療保健與運輸等關鍵基礎設施的安全。
Armis 技術長暨共同創辦人 Nadir Izrael 表示：「OT 環境日益成為複雜網路威脅的目標，需要強大的解決方案來確保安全性與營運的連續性。將 Armis 無與倫比的 OT 安全與網路暴露管理平台與 NVIDIA BlueField-3 DPU 相結合，可以讓企業在不中斷營運的情況下，全面保護虛實整合系統。」
CrowdStrike 透過在 BlueField-3 DPU 上部署 CrowdStrike Falcon 安全代理程式，以提升即時 AI 驅動的威脅偵測與回應能力，幫助保護 ICS 與 OT 等關鍵基礎設施的安全。
CrowdStrike 產品負責人 Raj Rajamani 表示：「OT 環境面臨越來越多威脅，需要可即時適應各種情況以 AI 驅動的安全。透過將 NVIDIA BlueField-3 DPUs 與 CrowdStrike Falcon 平台整合，我們在不中斷營運的情況下，將領先業界的防護功能擴展至關鍵基礎設施，在邊緣提供統一的防護，協助企業在現代威脅下保持領先。」
德勤使用 NVIDIA 網路安全 AI 平台推動客戶的數位轉型，以協助滿足突破性技術的需求，這些技術需要為資料中心網路提供即時且精細的可視性，以抵禦日益複雜的威脅。
德勤英國分公司 AI 安全主管 Dmitry Dudorov 表示：「隨著企業擁抱數位轉型與互聯技術，保護 OT 與 ICS 系統的難度與日俱增。利用 NVIDIA 的網路安全 AI 平台，可讓組織確定威脅偵測、增強復原能力，並保障基礎設施的安全，以加快執行各項工作。」
AI
助力開創更安全的未來
NVIDIA 的網路安全 AI 平台結合生態系合作夥伴的專業知識，提供強大且可擴充的解決方案，保護關鍵基礎設施環境免受不斷演進的威脅。將 NVIDIA AI 與加速運算帶入 OT 安全的最前線，可協助組織保護現在和未來最重要的事物。
歡迎參加 3 月 17 至 21 日舉辦的
NVIDIA GTC
全球 AI 大會了解更多資訊，屆時 Armis、Check Point 與 CrowdStrike 等網路安全領導廠商將主持多場
會議
，介紹他們與 NVIDIA 的合作項目。
Categories:
互聯網路
|
生成式人工智慧
|
軟體
Tags:
Artificial Intelligence
|
cybersecurity
|
NVIDIA AI Enterprise
|
NVIDIA BlueField


AI Pays Off: Survey Reveals Financial Industry’s Latest Technological Trends

The financial services industry is reaching an important milestone with AI, as organizations move beyond testing and experimentation to successful AI implementation, driving business results.
NVIDIA’s fifth annual
State of AI in Financial Services report
shows how financial institutions have consolidated their AI efforts to focus on core applications, signaling a significant increase in AI capability and proficiency.
AI Helps Drive Revenue and Save Costs
Companies investing in AI are seeing tangible benefits, including increased revenue and cost savings.
Nearly 70% of respondents report that AI has driven a revenue increase of 5% or more, with a dramatic rise in those seeing a 10-20% revenue boost. In addition, more than 60% of respondents say AI has helped reduce annual costs by 5% or more. Nearly a quarter of respondents are planning to use AI to create new business opportunities and revenue streams.
The top
generative AI
use cases in terms of return on investment (ROI) are trading and portfolio optimization, which account for 25% of responses, followed by customer experience and engagement at 21%. These figures highlight the practical, measurable benefits of AI as it transforms key business areas and drives financial gains.
Overcoming Barriers to AI Success
Half of management respondents said they’ve deployed their first generative AI service or application, with an additional 28% planning to do so within the next six months. A 50% decline in the number of respondents reporting a lack of AI budget suggests increasing dedication to AI development and resource allocation.
The challenges associated with early AI exploration are also diminishing. The survey revealed fewer companies reporting data issues and privacy concerns, as well as reduced concern over insufficient data for model training. These improvements reflect growing expertise and better data management practices within the industry.
As financial services firms allocate budget and grow more savvy at data management, they can better position themselves to harness AI for enhanced operational efficiency, security and innovation across business functions.
Generative AI Powers More Use Cases
After data analytics, generative AI has emerged as the second-most-used AI workload in the financial services industry. The applications of the technology have expanded significantly, from enhancing customer experience to optimizing trading and portfolio management.
Notably, the use of generative AI for customer experience, particularly via chatbots and virtual assistants, has more than doubled, rising from 25% to 60%. This surge is driven by the increasing availability, cost efficiency and scalability of generative AI technologies for powering more sophisticated and accurate digital assistants that can enhance customer interactions.
More than half of the financial professionals surveyed are now using generative AI to enhance the speed and accuracy of critical tasks like document processing and report generation.
Financial institutions are also poised to benefit from
agentic AI
— systems that harness vast amounts of data from various sources and use sophisticated reasoning to autonomously solve complex, multistep problems. Banks and asset managers can use agentic AI systems to enhance risk management, automate compliance processes, optimize investment strategies and personalize customer services.
Advanced AI Drives Innovation
Recognizing the transformative potential of AI, companies are taking proactive steps to build AI factories — specially built accelerated computing platforms equipped with full-stack AI software — through cloud providers or on premises. This strategic focus on implementing high-value AI use cases is crucial to enhancing customer service, boosting revenue and reducing costs.
By tapping into advanced infrastructure and software, companies can streamline the development and deployment of AI models and position themselves to harness the power of agentic AI.
With industry leaders predicting at least 2x ROI on AI investments, financial institutions remain highly motivated to implement their highest-value AI use cases to drive efficiency and innovation.
Download the full report
to learn more about how financial services companies are using accelerated computing and AI to transform services and business operations.
Categories:
Generative AI
Tags:
Artificial Intelligence
|
Financial Services

AI 帶來亮眼報酬：調查結果揭示金融業最新技術趨勢

金融服務業在使用人工智慧（AI）方面正邁入一個重要的里程碑，各大組織開始邁出測試與實驗的範疇，成功使用 AI 推動業務成果。
NVIDIA 的第五份
《金融服務業 AI 現況（State of AI in Financial Services）》年度調查報告
顯示，金融機構已經整合自身在 AI 方面的各項作為，以專注在核心應用項目上，這標誌著 AI 能力與熟練程度大幅提升。
AI
有助於增加營收與節省成本
投資於 AI 的公司正在看到實質效益，包括增加營收和節省成本等。
近七成的受訪者表示，AI 已經帶來 5% 或以上的營收成長，其中營收成長幅度達 10% 至 20% 的受訪者比例更是大幅增加。此外，超過六成的受訪者表示 AI 已協助減少 5% 或以上的年度成本。近四分之一的受訪者正計劃使用 AI 創造新的商機和收入來源。
交易與投資組合最佳化是投資報酬率（ROI）最高的
生成式 AI
使用案例，佔回應數量的 25%，其次是客戶體驗與參與度，佔 21%。這些數字突顯 AI在改變關鍵業務領域和推動財務收益時，所帶來可衡量的實際效益。
克服
AI
成功的關卡
半數管理層的受訪者表示，他們已經部署了第一個生成式 AI 服務或應用，另有 28% 的受訪者計劃在未來六個月內部署。回覆缺乏 AI 預算的受訪者人數減少了五成，這顯示對於 AI 開發與資源分配的投入程度日益增加。
與早期探索 AI 相關的挑戰同樣在減少。調查顯示，回答有資料問題和隱私疑慮的公司數量減少，對於模型訓練資料不足的疑慮也降低。這些改善反映出業界的專業知識與資料管理實務正在不斷增加。
隨著金融服務公司分配預算並更加擅長管理資料，他們可以更好地利用 AI 來提高跨業務單位的營運效率、安全性和進行創新。
生成式
AI
驅動更多使用案例
繼資料分析之後，生成式 AI 已經成為金融服務業裡第二大宗的 AI 工作負載。這項技術的應用範圍已大幅擴展，從提升客戶體驗到最佳化交易和投資組合管理。
值得注意的是，生成式 AI 在客戶體驗方面的應用，特別是透過聊天機器人和虛擬助理，數量增加了一倍以上，從 25% 上升到 60%。這樣大幅成長的趨勢是基於生成式 AI 技術的可用性、成本效率和可擴展性不斷提高，能夠驅動更複雜、更精準的數位助理，從而提升客戶互動情況。
半數以上受訪的金融專業人員現正使用生成式 AI 技術，以提高處理文件和產生報告等重要工作的速度和準確性。
金融機構也準備好從
代理型 AI
中受惠，代理型 AI 系統是指利用各種來源的大量資料，並使用複雜的推理流程自主解決複雜的多步驟問題。銀行和資產管理公司可以使用代理型 AI 系統來加強管理風險、自動化合規流程、最佳化投資策略，還有提供個人化的客戶服務。
先進的
AI
推動創新
在意識到 AI 的轉型潛力後，企業正積極採取措施，透過與雲端服務供應商合作或是在地端建立 AI 工廠，這些 AI 工廠是專門打造的加速運算平台，配備全端的 AI 軟體。企業在策略上特別鎖定實施高價值的 AI 使用案例，這對於提升客戶服務、增加收入與降低成本來說至關重要。
企業利用先進的基礎設施和軟體，可以簡化 AI 模型的開發和部署，並在善加發揮代理型 AI 力量方面站穩腳步。
由於業界領導業者預測 AI 投資的投資報酬率至少為兩倍，因此金融機構仍有很大動力去實現其最高價值的 AI 使用案例，以推動效率和創新。
下載完整報告
，進一步瞭解金融服務公司如何利用加速運算和 AI 來改變服務和業務運作。
Categories:
生成式人工智慧
Tags:
Artificial Intelligence
|
Financial Services


NVIDIA Releases NIM Microservices to Safeguard Applications for Agentic AI

AI agents are poised to transform productivity for the world’s billion knowledge workers with “knowledge robots” that can accomplish a variety of tasks. To develop AI agents, enterprises need to address critical concerns like trust, safety, security and compliance.
New
NVIDIA NIM
microservices for AI guardrails — part of the
NVIDIA NeMo Guardrails
collection of software tools — are portable, optimized inference microservices that help companies improve the safety, precision and scalability of their generative AI applications.
Central to the orchestration of the microservices is NeMo Guardrails, part of the
NVIDIA NeMo
platform for curating, customizing and guardrailing AI. NeMo Guardrails helps developers integrate and manage AI guardrails in large language model (LLM) applications. Industry leaders Amdocs, Cerence AI and Lowe’s are among those using NeMo Guardrails to safeguard AI applications.
Developers can use the NIM microservices to build more secure, trustworthy AI agents that provide safe, appropriate responses within context-specific guidelines and are bolstered against jailbreak attempts. Deployed in customer service across industries like automotive, finance, healthcare, manufacturing and retail, the agents can boost customer satisfaction and trust.
One of the new microservices, built for moderating content safety, was trained using the Aegis Content Safety Dataset — one of the highest-quality, human-annotated data sources in its category. Curated and owned by NVIDIA, the dataset
is publicly available
on Hugging Face and includes over 35,000 human-annotated data samples flagged for AI safety and jailbreak attempts to bypass system restrictions.
NVIDIA NeMo Guardrails Keeps AI Agents on Track
AI is rapidly boosting productivity for a broad range of business processes. In customer service, it’s helping resolve customer issues up to
40% faster
. However, scaling AI for customer service and other AI agents requires secure models that prevent harmful or inappropriate outputs and ensure the AI application behaves within defined parameters.
NVIDIA has introduced three new NIM microservices for NeMo Guardrails that help AI agents operate at scale while maintaining controlled behavior:
Content safety NIM microservice
that safeguards AI against generating biased or harmful outputs, ensuring responses align with ethical standards.
Topic control NIM microservice
that keeps conversations focused on approved topics, avoiding digression or inappropriate content.
Jailbreak detection NIM microservice
that adds protection against jailbreak attempts, helping maintain AI integrity in adversarial scenarios.
By applying multiple lightweight, specialized models as guardrails, developers can cover gaps that may occur when only more general global policies and protections exist — as a one-size-fits-all approach doesn’t properly secure and control complex
agentic AI
workflows.
Small language models, like those in the NeMo Guardrails collection, offer lower latency and are designed to run efficiently, even in resource-constrained or distributed environments. This makes them ideal for scaling AI applications in industries such as healthcare, automotive and manufacturing, in locations like hospitals or warehouses.
Industry Leaders and Partners Safeguard AI With NeMo Guardrails
NeMo Guardrails, available to the open-source community, helps developers orchestrate multiple AI software policies — called rails — to enhance LLM application security and control. It works with NVIDIA NIM microservices to offer a robust framework for building AI systems that can be deployed at scale without compromising on safety or performance.
Amdocs, a leading global provider of software and services to communications and media companies, is harnessing NeMo Guardrails to enhance AI-driven customer interactions by delivering safer, more accurate and contextually appropriate responses.
“Technologies like NeMo Guardrails are essential for safeguarding generative AI applications, helping make sure they operate securely and ethically,” said Anthony Goonetilleke, group president of technology and head of strategy at Amdocs. “By integrating NVIDIA NeMo Guardrails into our amAIz platform, we are enhancing the platform’s ‘Trusted AI’ capabilities to deliver agentic experiences that are safe, reliable and scalable. This empowers service providers to deploy AI solutions safely and with confidence, setting new standards for AI innovation and operational excellence.”
Cerence AI, a company specializing in AI solutions for the automotive industry, is using NVIDIA NeMo Guardrails to help ensure its in-car assistants deliver contextually appropriate, safe interactions powered by its CaLLM family of large and small language models.
“Cerence AI relies on high-performing, secure solutions from NVIDIA to power our in-car assistant technologies,” said Nils Schanz, executive vice president of product and technology at Cerence AI. “Using NeMo Guardrails helps us deliver trusted, context-aware solutions to our automaker customers and provide sensible, mindful and hallucination-free responses. In addition, NeMo Guardrails is customizable for our automaker customers and helps us filter harmful or unpleasant requests, securing our CaLLM family of language models from unintended or inappropriate content delivery to end users.”
Lowe’s, a leading home improvement retailer, is leveraging generative AI to build on the deep expertise of its store associates. By providing enhanced access to comprehensive product knowledge, these tools empower associates to answer customer questions, helping them find the right products to complete their projects and setting a new standard for retail innovation and customer satisfaction.
“We’re always looking for ways to help associates go above and beyond for our customers,” said Chandhu Nair, senior vice president of data, AI and innovation at Lowe’s. “With our recent deployments of NVIDIA NeMo Guardrails, we ensure AI-generated responses are safe, secure and reliable, enforcing conversational boundaries to deliver only relevant and appropriate content.”
To further accelerate AI safeguards adoption in AI application development and deployment in retail, NVIDIA recently announced at the NRF show that its
NVIDIA AI Blueprint for retail shopping assistants
incorporates NeMo Guardrails microservices for creating more reliable and controlled customer interactions during digital shopping experiences.
Consulting leaders Taskus, Tech Mahindra and Wipro are also integrating NeMo Guardrails into their solutions to provide their enterprise clients safer, more reliable and controlled generative AI applications.
NeMo Guardrails is open and extensible, offering integration with a robust ecosystem of leading AI safety model and guardrail providers, as well as AI observability and development tools. It supports integration with
ActiveFence’s ActiveScore
, which filters harmful or inappropriate content in conversational AI applications, and provides visibility, analytics and monitoring.
Hive, which provides its
AI-generated content detection models
for images, video and audio content as NIM microservices, can be easily integrated and orchestrated in AI applications using NeMo Guardrails.
The Fiddler AI Observability platform easily integrates with NeMo Guardrails to enhance AI guardrail monitoring capabilities. And Weights & Biases, an end-to-end AI developer platform, is expanding the capabilities of W&B Weave by adding integrations with NeMo Guardrails microservices. This enhancement builds on Weights & Biases’ existing portfolio of NIM integrations for optimized AI inferencing in production.
NeMo Guardrails Offers Open-Source Tools for AI Safety Testing
Developers ready to test the effectiveness of applying safeguard models and other rails can use
NVIDIA Garak
— an open-source toolkit for LLM and application vulnerability scanning developed by the NVIDIA Research team.
With Garak, developers can
identify vulnerabilities
in systems using LLMs by assessing them for issues such as data leaks, prompt injections, code hallucination and jailbreak scenarios. By generating test cases involving inappropriate or incorrect outputs, Garak helps developers detect and address potential weaknesses in AI models to enhance their robustness and safety.
Availability
NVIDIA NeMo Guardrails microservices, as well as
NeMo Guardrails
for rail orchestration and the
NVIDIA Garak
toolkit, are now available for developers and enterprises. Developers can get started building AI safeguards into AI agents for customer service using NeMo Guardrails with
this tutorial
.
See
notice
regarding software product information.
Categories:
Generative AI
Tags:
Artificial Intelligence
|
Cybersecurity
|
NVIDIA Blueprints
|
NVIDIA NeMo
|
NVIDIA NIM

NVIDIA 發表為代理型 AI 應用提供安全防護的 NIM 微服務

AI 代理為全球數十億名知識工作者提供可完成各種任務的「知識機器人」，改變他們的生產力。而為了開發 AI 代理，企業需要解決信任、安全、保全及法遵等關鍵問題。
作為
NVIDIA NeMo Guardrails
軟體工具集的一部分，全新用於人工智慧（AI）防護工作的
NVIDIA NIM
微服務是一款可攜式且經過最佳化的推論微服務，可以協助企業提高其生成式 AI 應用的安全性、精確性與可擴充性。
NeMo Guardrails 是這些微服務的協調核心，也是用於彙整、客製化和為 AI 提供保護的
NVIDIA NeMo
平台一部分。NeMo Guardrails 可協助開發人員在大型語言模型（LLM）應用中整合與管理 AI 防護工作。Amdocs、Cerence AI 和 Lowe’s 等業界領導廠商均使用 NeMo Guardrails 來保護 AI 應用。
開發人員可以使用 NIM 微服務來建立更安全、更值得信賴的 AI 代理，在特定情境的指引下提供安全且適當的回應，並且加強防禦嘗試越獄的行為。這些代理可以部署在汽車、金融、醫療保健、製造和零售等產業的客戶服務中，以提升客戶滿意度和信任度。
其中一個新的微服務是為了控制內容安全而建立，使用 Aegis 內容安全資料集（Aegis Content Safety Dataset）進行訓練，該資料集是同類型中品質最高、經人工註解的資料來源之一。Aegis 內容安全資料集由 NVIDIA 編輯和擁有，並在 Hugging Face 上
公開提供
，其中包括超過 35,000 個經人工註解的資料樣本，標示為 AI 安全和試圖繞過系統限制的越獄行為。
NVIDIA NeMo Guardrails
讓
AI
代理保持正常運作
AI 正在快速提升各種業務流程的工作效率。在客戶服務方面，AI 協助解決客戶問題的速度
加快了 40%
。然而，為客戶服務及其他 AI 代理擴大 AI 規模需要輔以安全的模型，以避免輸出有害或不當內容，並且確保 AI 應用的按照訂定的參數運作。
NVIDIA 為 NeMo Guardrails 推出三款全新的 NIM 微服務，可協助 AI 代理大規模運作，同時確保行為受到控制：
內容安全
NIM
微服務
可避免 AI 產生偏見或有害的輸出內容，確保回應內容符合道德標準。
主題控制
NIM
微服務
使得對話專注於經核准的主題上，避免離題或出現不當內容。
越獄偵測
NIM
微服務
可增加對越獄嘗試的防護，協助在對抗性情境中維持 AI 的完整性。
透過應用多種輕量、專用的模型作為防護措施，開發人員可以補足只有適用於一般情況的全面性政策與保護措施時可能出現的缺口，因為一體適用的做法無法妥善保護與控制複雜的
AI 代理
工作流程。
小型的語言模型，如 NeMo Guardrails 系列中的模型，可提供較低的延遲，即使在資源有限或分散式環境中也能高效率地執行。這使得它們成為醫療保健、汽車和製造業等產業在醫院或倉庫等地點擴大 AI 應用範圍的理想選擇。
業界領導廠商與合作夥伴利用
NeMo Guardrails
保護
AI
開放給開源社群使用的 NeMo Guardrails，可協助開發人員協調多種稱為 rails的 AI 軟體原則，以增強大型語言模型應用的安全性與控制能力。它可以與 NVIDIA NIM 微服務搭配使用，提供建置 AI 系統的強大框架，並在不影響安全性或效能的情況下進行大規模部署。
Amdocs 是全球領先的通訊與媒體公司軟體及服務供應商，該公司正使用 NeMo Guardrails 提供更安全、準確且符合情境的回應內容，以強化 AI 驅動的客戶互動。
Amdocs 科技事業群總裁暨策略部門主管 Anthony Goonetilleke 表示：「像 NeMo Guardrails 這樣的技術對於保護生成式 AI 應用的安全來說是非常重要的，能夠確保它們能安全且符合道德標準地進行運作。透過將 NVIDIA NeMo Guardrails 整合至 amAIz 平台，我們強化了平台的『可信任 AI』功能，以提供安全、可靠且具擴充能力的代理體驗。這讓服務供應商能夠安全放心地部署 AI 解決方案，為 AI 創新和卓越營運樹立新標準。」
專為汽車產業提供 AI 解決方案的 Cerence AI 正在使用 NVIDIA NeMo Guardrails 來協助確保其車載助理能夠在該公司 CaLLM 系列大小語言模型的支援下，提供符合情境的安全互動。
Cerence AI 產品與技術部門執行副總裁 Nils Schanz 表示：「Cerence AI 仰賴 NVIDIA 的高效能、安全解決方案來支援我們的車載助理技術。使用 NeMo Guardrails 能夠幫助我們為汽車製造商客戶提供可信賴的情境感知解決方案，並且提供合理、貼心且無幻覺的回應。NeMo Guardrails 還能配合汽車製造商客戶的需求進行客製化，同時協助我們過濾有害或令人不愉快的請求，確保我們的 CaLLM 語言模型系列不會向終端使用者傳送非預期或不當的內容。」
領先的家居裝修零售商 Lowe’s 正在使用生成式 AI 來培養店員具備深厚的專業知識。這些工具能夠讓店員取得更全面的產品知識，協助他們回答客戶的問題，並幫助找到完成裝修案所需的合適產品，同時為零售創新和客戶滿意度立下新標準。
Lowe’s 資料、AI 與創新部門資深副總裁 Chandhu Nair 表示：「我們一直在尋找方法幫助員工為客戶提供超乎期望的服務。透過最近部署的 NVIDIA NeMo Guardrails，我們可以確保 AI 產生出安全、穩妥且可靠的回應，為對話內容設下邊界，只提供相關且適當的內容。」
為了進一步加快在零售業 AI 應用開發和部署的過程中採用 AI 防護措施，NVIDIA 最近在 NRF 大會上宣布，其
適用於零售購物助理的 NVIDIA AI Blueprint
整合 NeMo Guardrails 微服務，以在數位購物體驗中創造更可靠、控制程度更高的客戶互動。
顧問業領導廠商 Taskus、Tech Mahindra 與 Wipro 也將 NeMo Guardrails 與該公司的解決方案進行整合，為企業客戶提供更安全、可靠且可控的生成式 AI 應用。
NeMo Guardrails 具有開放性和可擴展性，可與領先的 AI 安全模型和防護解決方案供應商，以及 AI 可觀察性和開發工具組成的強大的生態系進行整合。它支援與
ActiveFence 的 ActiveScore
整合，可以過濾對話式 AI 應用中的有害或不當內容，並且提供可視性、分析與監控等功能。
Hive 以 NIM 微服務的方式提供該公司針對圖片、影片和聲音內容的
AI 生成內容偵測模型
，可輕鬆整合至使用 NeMo Guardrails 的 AI 應用中並進行協調。
Fiddler AI Observability 平台能輕鬆與 NeMo Guardrails 整合，強化 AI 防護功能的監控能力。而端對端的 AI 開發者平台 Weights & Biases，則是透過加入與 NeMo Guardrails 微服務的整合，來擴充 W&B Weave 的功能。這項增強功能建立在 Weights & Biases 現有的 NIM 整合產品組合上，能夠在生產環境裡最佳化 AI 推論結果。
NeMo Guardrails
提供
AI
安全測試開源工具
準備測試應用安全防護模型和其他 rails 效果的開發人員，能夠使用 NVIDIA Research 團隊開發用於掃描大型語言模型及應用程式漏洞的開源工具包
NVIDIA Garak
。
透過使用 Garak，開發人員可以評估使用大型語言模型的系統是否存在資料外洩、提示注入、程式碼幻覺和越獄情境等問題，從而
找出系統中的漏洞
。Garak 可以藉由產生涉及不適當或不正確輸出內容的測試案例，協助開發人員偵測及解決 AI 模型中的潛在漏洞，以提升其穩健性與安全性。
上市時程
NVIDIA NeMo Guardrails 微服務，以及用於協調 rail 的
NeMo Guardrails
和
NVIDIA Garak
工具包，現已提供給開發人員和企業使用。開發人員可以利用
此教學內容
開始使用 NeMo Guardrails，為用於客戶服務的 AI 代理建置 AI 防護措施。
軟體產品資訊請參見
公告
。
Categories:
生成式人工智慧
Tags:
Artificial Intelligence
|
cybersecurity
|
NVIDIA Blueprints
|
NVIDIA NeMo
|
NVIDIA NIM


NVIDIA Announces Isaac GR00T Blueprint to Accelerate Humanoid Robotics Development

Over the next two decades, the market for humanoid robots is expected to reach $38 billion. To address this significant demand, particularly in industrial and manufacturing sectors, NVIDIA is releasing a collection of robot foundation models, data pipelines and simulation frameworks to accelerate next-generation
humanoid robot
development efforts.
Announced by NVIDIA founder and CEO Jensen Huang today at the
CES
trade show, the
NVIDIA Isaac GR00T
Blueprint for synthetic motion generation helps developers generate exponentially large synthetic motion data to train their humanoids using imitation learning.
Imitation learning — a subset of
robot learning
— enables
humanoids
to acquire new skills by observing and mimicking expert human demonstrations. Collecting these extensive, high-quality datasets in the real world is tedious, time-consuming and often prohibitively expensive. Implementing the
Isaac GR00T blueprint
for synthetic motion generation allows developers to easily generate exponentially large synthetic datasets from just a small number of human demonstrations.
Starting with the GR00T-Teleop workflow, users can tap into the Apple Vision Pro to capture human actions in a
digital twin
. These human actions are mimicked by a robot in  simulation and recorded for use as ground truth.
The GR00T-Mimic workflow then multiplies the captured human demonstration into a larger synthetic motion dataset. Finally, the GR00T-Gen workflow, built on the
NVIDIA Omniverse
and
NVIDIA Cosmos
platforms, exponentially expands this dataset through domain randomization and 3D upscaling.
The dataset can then be used as an input to the robot policy, which teaches robots how to move and interact with their environment effectively and safely in
NVIDIA Isaac Lab
, an open-source and modular framework for robot learning.
World Foundation Models Narrow the Sim-to-Real Gap
NVIDIA also
announced Cosmos
at CES, a platform featuring a family of open, pretrained world foundation models purpose-built for generating physics-aware videos and world states for
physical AI
development. It includes autoregressive and diffusion models in a variety of sizes and input data formats. The models were trained on 18 quadrillion tokens, including 2 million hours of autonomous driving, robotics, drone footage and
synthetic data
.
In addition to helping generate large datasets, Cosmos can reduce the simulation-to-real gap by upscaling images from 3D to real. Combining Omniverse — a developer platform of application programming interfaces and microservices for building 3D applications and services — with Cosmos is critical, because it helps minimize potential hallucinations commonly associated with world models by providing crucial safeguards through its highly controllable, physically accurate simulations.
An Expanding Ecosystem
Collectively,
NVIDIA Isaac GR00T
,
Omniverse
and
Cosmos
are helping physical AI and humanoid innovation take a giant leap forward. Major robotics companies have started adopting and demonstrated results with Isaac GR00T, including Boston Dynamics and Figure.
Humanoid software, hardware and robot manufacturers can
apply for early access
to NVIDIA’s humanoid robot developer program.
Watch the
CES opening keynote
from NVIDIA founder and CEO Jensen Huang, and stay up to date by subscribing to the
newsletter
and following NVIDIA Robotics on
LinkedIn
,
Instagram
,
X
and
Facebook
.
See
notice
regarding software product information.
Categories:
Robotics
Tags:
Artificial Intelligence
|
CES 2025
|
Cosmos
|
Digital Twin
|
Isaac
|
Omniverse
|
Robotics
|
Synthetic Data Generation

NVIDIA 宣布推出 Isaac GR00T 藍圖以加速開發人型機器人

人型機器人的市場規模在未來二十年內，有望達到 380 億美元之譜。為滿足如此龐大的需求，尤其是來自工業和製造業的需求，NVIDIA 發表了一系列機器人基礎模型、資料管道和模擬框架，以加速開發下一代
人型機器人
。
NVIDIA 創辦人暨執行長黃仁勳今日在
CES
大會宣布，用於產生合成動作的
NVIDIA Isaac GR00T
藍圖（blueprint）可以協助開發人員產生出極為大量的合成動作資料，以利用模仿學習的方式訓練人型機器人。

模仿學習是
機器人學習
裡的一個子集合，可以讓
人型機器人
用觀察和模仿專家真人示範的方式來學習新技能。想要收集這些廣泛又高品質的現實世界資料集，非常無聊且要花費許多時間，成本往往更高得令人卻步。使用適用於產生合成動作的
Isaac GR00T 藍圖
，開發人員只要少數的真人示範，就能輕鬆產生出龐大的大型合成資料集。
使用者從使用 GR00T-Teleop 工作流程開始，利用 Apple Vision Pro 在
數位孿生
模型裡捕捉真人的動作。模擬環境裡的機器人會模仿這些動作，並且記錄下來作為基本事實資料。
GR00T-Mimic 工作流程會將擷取到的真人示範內容乘以更大的合成動作資料集。最後，建構在
NVIDIA Omniverse
及
NVIDIA Cosmos
平台上的 GR00T-Gen 工作流程，會透過域隨機化與 3D 畫質提升技術，以倍數成長的方式擴充這個資料集。
隨後可以將這個資料集當成機器人策略的輸入項目，在
NVIDIA Isaac Lab
這個開源模組化的機器人學習框架中教導機器人如何有效安全地移動，且與周遭環境進行互動。
世界基礎模型縮小模擬與真實的差距
NVIDIA 也在 CES 上
宣布推出 Cosmos
，在這個平台上提供一系列預先訓練好的開放式世界基礎模型，專門用於產生物理感知影片內容與世界狀態，以協助開發
實體 AI
。它包括各種大小和輸入資料格式的自回歸和擴散模型。使用 18 千兆個詞元來訓練這些模型，這些詞元包括 200 萬小時的自動駕駛、機器人、無人機影片和
合成資料
。
Cosmos 平台除了有助於產生大型資料集，還能使用圖像畫質提升技術，將 3D 圖像變得更真實，以縮小模擬與真實之間的差距。把 Omniverse（用於開發 3D 應用程式和服務的應用程式介面和微服務開發平台）搭配 Cosmos 使用非常重要，因為 Cosmos 提供一個具有高度可控性、精準基於物理的模擬環境，能夠有效確保將世界模型常見可能造成幻覺的情況降至最低。
不斷成長茁壯的生態系
NVIDIA Isaac GR00T
、
Omniverse
及
Cosmos
這三個平台加起來協助實體 AI 及人型機器人的創新發展向前邁進一大步。各大機器人開發業者已經開始採用 Isaac GR00T 與展示其成果，包括 Boston Dynamics 和 Figure。
人型機器人軟體、硬體與機器人製造商可以
申請搶先體驗
NVIDIA 的人型機器人開發者計畫。
歡迎觀看 NVIDIA 創辦人暨執行長黃仁勳精彩的
CES 大會開幕主題演講
，並且訂閱
電子報
，也別忘了在
LinkedIn
、
Instagram
、
X
及
Facebook
追蹤 NVIDIA Robotics，隨時掌握最新資訊。
請見有關軟體產品資訊的
通知
。
Categories:
自主機器
Tags:
Artificial Intelligence
|
CES 2025
|
Cosmos
|
Digital Twin
|
Isaac
|
Omniverse
|
Robotics
|
Synthetic Data Generation


Building Smarter Autonomous Machines: NVIDIA Announces Early Access for Omniverse Sensor RTX

Generative AI and
foundation models
let autonomous machines generalize beyond the operational design domains on which they’ve been trained. Using new AI techniques such as
tokenization
and
large language and diffusion models
, developers and researchers can now address longstanding hurdles to autonomy.
These larger models require massive amounts of diverse data for training, fine-tuning and validation. But collecting such data — including from rare edge cases and potentially hazardous scenarios, like a pedestrian crossing in front of an autonomous vehicle (AV) at night or a human entering a welding robot work cell — can be incredibly difficult and resource-intensive.
To help developers fill this gap,
NVIDIA Omniverse Cloud Sensor RTX APIs
enable physically accurate
sensor simulation
for generating datasets at scale. The application programming interfaces (APIs) are designed to support sensors commonly used for autonomy — including cameras, radar and lidar — and can integrate seamlessly into existing workflows to accelerate the development of autonomous vehicles and robots of every kind.
Omniverse Sensor RTX APIs are now available to select developers in
early access
. Organizations such as Accenture, Foretellix, MITRE and Mcity are integrating these APIs via domain-specific blueprints to provide end customers with the tools they need to deploy the next generation of industrial manufacturing robots and self-driving cars.
Powering Industrial AI With Omniverse Blueprints
In complex environments like factories and warehouses, robots must be orchestrated to safely and efficiently work alongside machinery and human workers. All those moving parts present a massive challenge when designing, testing or validating operations while avoiding disruptions.
Mega
is an Omniverse Blueprint that offers enterprises a reference architecture of NVIDIA accelerated computing, AI,
NVIDIA Isaac
and
NVIDIA Omniverse
technologies. Enterprises can use it to develop
digital twins
and test AI-powered robot brains that drive robots, cameras, equipment and more to handle enormous complexity and scale.
Integrating Omniverse Sensor RTX, the blueprint lets robotics developers simultaneously render sensor data from any type of intelligent machine in a factory for high-fidelity, large-scale sensor simulation.
With the ability to test operations and workflows in simulation, manufacturers can save considerable time and investment, and improve efficiency in entirely new ways.
International supply chain solutions company KION Group and Accenture are using the Mega blueprint to build Omniverse digital twins that serve as virtual training and testing environments for industrial AI’s robot brains, tapping into data from smart cameras, forklifts, robotic equipment and digital humans.
The robot brains perceive the simulated environment with physically accurate sensor data rendered by the Omniverse Sensor RTX APIs. They use this data to plan and act, with each action precisely tracked with Mega, alongside the state and position of all the assets in the
digital twin
. With these capabilities, developers can continuously build and test new layouts before they’re implemented in the physical world.
Driving AV Development and Validation
Autonomous vehicles have been under development for over a decade, but barriers in acquiring the right training and validation data and slow iteration cycles have hindered large-scale deployment.
To address this need for sensor data, companies are harnessing the
NVIDIA Omniverse Blueprint for AV simulation
, a reference workflow that enables physically accurate sensor simulation. The workflow uses Omniverse Sensor RTX APIs to render the camera, radar and lidar data necessary for AV development and validation.
AV toolchain provider Foretellix has integrated the blueprint into its
Foretify AV development toolchain
to transform object-level simulation into physically accurate sensor simulation.
The Foretify toolchain can generate any number of testing scenarios simultaneously. By adding sensor simulation capabilities to these scenarios, Foretify can now enable  developers to evaluate the completeness of their AV development, as well as train and test at the levels of fidelity and scale needed to achieve large-scale and safe deployment. In addition, Foretellix will use the newly announced
NVIDIA Cosmos platform
to generate an even greater diversity of scenarios for verification and validation.
Nuro, an autonomous driving technology provider with one of the largest level 4 deployments in the U.S., is using the Foretify toolchain to train, test and validate its self-driving vehicles before deployment.
In addition, research organization MITRE is collaborating with the University of Michigan’s Mcity testing facility to build a digital AV validation framework for regulatory use, including a digital twin of Mcity’s 32-acre proving ground for autonomous vehicles. The project uses the AV simulation blueprint to render physically accurate sensor data at scale in the virtual environment, boosting training effectiveness.
The future of robotics and autonomy is coming into sharp focus, thanks to the power of high-fidelity sensor simulation. Learn more about these solutions at CES by visiting Accenture at Ballroom F at the Venetian and Foretellix booth 4016 in the West Hall of Las Vegas Convention Center.
Learn more about the latest in automotive and generative AI technologies by joining
NVIDIA at CES
.
See
notice
regarding software product information.
Categories:
Robotics
Tags:
Artificial Intelligence
|
CES 2025
|
Cosmos
|
Digital Twin
|
Industrial and Manufacturing
|
Isaac
|
NVIDIA Blueprints
|
Omniverse
|
Robotics
|
Simulation and Design
|
Transportation

建造更聰明的自主機器：NVIDIA 宣布 Omniverse Sensor RTX 推出搶先體驗活動

生成式人工智慧（AI）和
基礎模型
讓自主機器能夠超越它們所接受訓練的操作設計領域。開發人員和研究人員使用
標記化
（tokenization）及
大型語言和擴散模型
等嶄新 AI 技術，現在可以解決一直以來在自主領域方面的各項障礙。
需要使用大量相異的資料來訓練、微調與驗證這些大型模型。不過收集這些資料（包括從罕見的邊緣情況和潛在危險情境中收集資料，例如行人在夜間橫越自動駕駛車前方，或是人類進入焊接機器人工作單元）可能非常困難，又得耗費不少資源。
為了協助開發人員填補這個缺口，
NVIDIA Omniverse Cloud Sensor RTX API
提供了物理精確的感測器模擬，用於大規模生成資料集。這些應用程式介面（API）用於支援常用於自主機器上的感測器，包括攝影機、雷達與光達，且能完美與現有的工作流程進行整合，以加快開發各種自動駕駛車輛與機器人。
現已開放部分開發人員
搶先體驗
Omniverse Sensor RTX API。埃森哲（Accenture）、Foretellix、MITRE 和 Mcity等企業正透過特定領域藍圖整合這些 API，為終端客戶提供部署下一代工業製造機器人和自動駕駛車所需的工具。
使用
Omniverse Blueprints
為工業
AI
提供動力
在工廠和倉庫等複雜環境中，機器人必須被精心協調，才能安全高效率地與機器和人類工作者並肩作業。在設計、測試或驗證操作，又要避免中斷作業時，所有這些移動部件都會帶來巨大的挑戰。
Mega
是一個 Omniverse Blueprint ，可為企業提供 NVIDIA 加速運算、AI、
NVIDIA Isaac
及
NVIDIA Omniverse
技術的參考架構。企業可以用它開發
數位孿生
模型，測試由 AI 驅動的機器人大腦，而這些大腦驅動著機器人、攝影機、設備等項目，以處理極為複雜又大量的作業。
這個整合了 Omniverse Sensor RTX 的藍圖可以讓機器人開發人員同時渲染工廠內任何類型智慧機器的感測器資料，實現高保真、大規模的感測器模擬。
隨著能夠在模擬環境裡測試操作和工作流程，製造商可以省下大量時間和投資，以全新方式提高作業效率。
國際供應鏈解決方案公司凱傲集團（KION Group）與埃森哲利用來自智慧攝影機、堆高機、機器人設備和數位人類的資料，使用 Mega 藍圖建立 Omniverse 數位孿生，作為工業AI機器人大腦的虛擬訓練和測試環境。
機器人大腦透過 Omniverse Sensor RTX API 渲染的物理精確感測器資料來感知模擬環境。機器人使用這些資料來計劃和採取行動，並透過 Mega 精準追蹤每一個動作，以及
數位孿生
中所有資產的狀態和位置。借助這些功能，開發人員可以在真正部署至實體環境裡之前，不斷建立和測試新配置。
推動開發與驗證自動駕駛車
自動駕駛車輛已開發超過十多年，但在取得正確的訓練與驗證資料方面所遇到的阻礙，還有緩慢的迭代週期，都阻礙了大規模部署。
為了滿足對感測器資料的這種需求，各家公司利用
NVIDIA Omniverse Blueprint for AV simulation
，這是一個實現物理精確感測器模擬的參考工作流程。這個工作流程使用 Omniverse Sensor RTX API 來渲染出開發與驗證自動駕駛汽車所需的攝影機、雷達與光達資料。
自動駕駛汽車工具鏈供應商 Foretellix 已經把這個藍圖納入該公司的
Foretify 自動駕駛車開發工作鏈
，將物件級模擬轉換為物理精準感測器模擬。
Foretify 工具鏈可以同時產生任意數量的測試情境。Foretify 在這些情境中加入感測器模擬功能，開發人員便能評估自己在開發自動駕駛車方面的完整性，並以實現大規模安全部署所需的保真度和規模水平進行訓練和測試。。Foretellix 還將使用最新發表的
NVIDIA Cosmos 平台
，產生更多樣化的情境進行確認與驗證。

自動駕駛技術提供商 Nuro 是美國規模最大的 level 4 部署業者之一，使用 Foretify 工具鏈在部署前對其自動駕駛車輛進行訓練、測試和驗證。
再者，研究機構 MITRE 與密西根大學的 Mcity 測試設施合作，建立供主管機關使用的數位自動駕駛車驗證框架，包括 Mcity 32 英畝自動駕駛車試驗場的數位孿生模型。這項合作案使用 自動駕駛車 模擬藍圖，在虛擬環境中大規模渲染出物理精確的感測器資料，以提升訓練成效。
得益於高保真感測器模擬技術，機器人與自動化的未來正逐漸成為人們關注的焦點。如需更深入瞭解 CES 大會上這些解決方案的資訊，請造訪埃森哲位於拉斯維加斯威尼斯人F展廳的攤位，以及 Foretellix 位於拉斯維加斯展覽中心西館 4016 號的展位。
欲了解最新的汽車與生成式 AI 技術，參加
NVIDIA 在 CES 大會的各項活動
。
請見有關軟體產品資訊的
通知
。
Categories:
自主機器
Tags:
Artificial Intelligence
|
CES 2025
|
Cosmos
|
Digital Twin
|
Industrial and Manufacturing
|
Isaac
|
NVIDIA Blueprints
|
Omniverse
|
Robotics
|
Simulation and Design
|
Transportation


NVIDIA Advances Physical AI With Accelerated Robotics Simulation on AWS

Field AI is building robot brains that enable robots to autonomously manage a wide range of industrial processes. Vention creates pretrained skills to ease development of robotic tasks. And Cobot offers Proxie, an AI-powered cobot designed to handle material movement and adapt to dynamic environments, working seamlessly alongside humans.
These leading robotics startups are all making advances using
NVIDIA Isaac Sim
on Amazon Web Services. Isaac Sim is a reference application built on
NVIDIA Omniverse
for developers to simulate and test AI-driven robots in physically based virtual environments.
NVIDIA announced at AWS re:Invent today that Isaac Sim now runs on Amazon Elastic Cloud Computing (EC2) G6e instances accelerated by
NVIDIA L40S GPUs
. And with
NVIDIA OSMO
, a cloud-native orchestration platform, developers can easily manage their complex robotics workflows across their AWS computing infrastructure.
This combination of NVIDIA-accelerated hardware and software — available on the cloud — allows teams of any size to scale their physical AI workflows.
Physical AI
describes AI models that can understand and interact with the physical world. It embodies the next wave of
autonomous machines and robots
, such as self-driving cars, industrial manipulators, mobile robots, humanoids and even robot-run infrastructure like factories and warehouses.
With physical AI, developers are embracing a
three computer solution
for training, simulation and inference to make breakthroughs.
Yet physical AI for robotics systems requires robust training datasets to achieve precision inference in deployment. Developing such datasets, however, and testing them in real situations can be impractical and costly.
Simulation offers an answer, as it can significantly accelerate the training, testing and deployment of AI-driven robots.
Harnessing L40S GPUs in the Cloud to Scale Robotics Simulation and Training
Simulation is used to verify, validate and optimize robot designs as well as the systems and their algorithms before deployment. Simulation can also optimize facility and system designs before construction or remodeling starts for maximum efficiencies, reducing costly manufacturing change orders.
Amazon EC2 G6e instances accelerated by NVIDIA L40S GPUs provide a 2x performance gain over the prior architecture, while allowing the flexibility to scale as scene and simulation complexity grows. The instances are used to train many computer vision models that power AI-driven robots. This means the same instances can be extended for various tasks, from data generation to simulation to model training.
Using
NVIDIA OSMO
in the cloud allows teams to orchestrate and scale complex robotics development workflows across distributed computing resources, whether on premises or in the AWS cloud.
Isaac Sim provides access to the latest
robotics simulation capabilities
and the cloud, fostering collaboration. One of the critical workflows is generating synthetic data for perception model training.
Using a
reference workflow
that combines
NVIDIA Omniverse Replicator
, a framework for building custom synthetic data generation (SDG) pipelines and a core extension of Isaac Sim, with
NVIDIA NIM microservices
, developers can build generative AI-enabled SDG pipelines.
These include the USD Code NIM microservice for generating Python USD code and answering OpenUSD queries, and the USD Search NIM microservice for exploring OpenUSD assets using natural language or image inputs. The Edify 360 HDRi NIM microservice generates 360-degree environment maps, while the Edify 3D NIM microservice creates ready-to-edit 3D assets from text or image prompts. This eases the synthetic data generation process by reducing many tedious and manual steps, from asset creation to image augmentation, using the power of generative AI.
Rendered.ai’s
synthetic data engineering platform integrated with Omniverse Replicator enables companies to generate synthetic data for computer vision models used in industries from security and intelligence to manufacturing and agriculture.
SoftServe
, an IT consulting and digital services provider, uses Isaac Sim to generate synthetic data and validate robots used in vertical farming with Pfeifer & Langen, a leading European food producer.
Tata Consultancy Services
is building custom synthetic data generation pipelines to power its Mobility AI suite to address automotive and autonomous use cases by simulating real-world scenarios. Its applications include defect detection, end-of-line quality inspection and hazard avoidance.
Learning to Be Robots in Simulation
While Isaac Sim enables developers to test and validate robots in physically accurate simulation,
Isaac Lab
, an open-source robot learning framework built on Isaac Sim, provides a virtual playground for building robot policies that can run on
AWS Batch
.
Because these simulations are repeatable, developers can easily troubleshoot and reduce the number of cycles required for validation and testing.
Several robotics developers are embracing
NVIDIA Isaac
on AWS to develop physical AI, such as:
Aescape’s robots
are able to provide precision-tailored massages by accurately modeling and tuning onboard sensors in Isaac Sim.
Cobot
has used Isaac Sim with its AI-powered cobot, Proxie, to optimize logistics in warehouses, hospitals, manufacturing sites, and more.
Cohesive Robotics
has integrated Isaac Sim into its software framework called Argus OS for developing and deploying robotic workcells used in high-mix manufacturing environments.
Field AI, a builder of robot foundation models, uses Isaac Sim and Isaac Lab to evaluate the performance of its models in complex, unstructured environments across industries such as construction, manufacturing, oil and gas, mining and more.
Standard Bots
is simulating and validating the performance of its R01 robot used in manufacturing and machining setup.
Swiss Mile
is using Isaac Sim and Isaac Lab for robot learning so that wheeled quadruped robots can perform tasks autonomously with new levels of efficiency in factories and warehouses.
Vention
, which offers a full-stack cloud-based automation platform, is harnessing Isaac Sim for developing and testing new capabilities for robot cells used by small to medium-size manufacturers.
Learn more about Isaac Sim 4.2, now available on Amazon EC2 G6e instances powered by NVIDIA L40S GPUs on
AWS Marketplace
.
Categories:
Robotics
Tags:
NVIDIA Isaac Sim
|
Omniverse Enterprise
|
Physical AI

NVIDIA 運用 AWS 上的加速機器人模擬技術推進實體 AI

Field AI 正在建構機器人大腦，讓機器人得以自主管理各項工業流程。Vention 創造預先訓練好的技能，以簡化機器人任務的開發。而 Cobot 則提供一個由人工智慧（AI）驅動的協作機器人 Proxie，可處理材料移動並適應動態環境，與人類一起無縫合作。
這些領先的機器人新創公司皆使用 Amazon Web Services（AWS）上的
NVIDIA Isaac Sim
來取得進展。Isaac Sim 是建置在
NVIDIA Omniverse
上的參考應用程式，供開發人員在以物理原則為基礎的虛擬環境中模擬與測試 AI 驅動的機器人。
NVIDIA 今日在 AWS re:Invent 大會中宣布，現在可以在由 NVIDIA L40S GPU 加速的 Amazon Elastic Cloud Computing（EC2）G6e 執行個體上執行 Isaac Sim。開發人員還能透過雲端原生的協調平台
NVIDIA OSMO
，在 AWS 運算基礎架構中輕鬆管理複雜的機器人工作流程。
可在雲端使用的 NVIDIA 加速硬體與軟體組合，可讓任何規模的團隊擴充其實體 AI 工作流程。
實體 AI
描述了能夠理解實體世界並與其進行互動的 AI 模型。它體現了
自主機器和機器人
的下一波發展浪潮，如自駕車、工業機械手、移動機器人、人形機器人，甚至是機器人管理的基礎設施，如工廠和倉庫。
有了實體 AI，開發人員正採用
三電腦解決方案（three computer solution）
進行訓練、模擬和推論，以求突破。
然而，機器人系統的實體 AI 需要強大的訓練資料集，才能在部署環境裡取得精確的推論結果。不過想要開發這樣的資料集，並在實際環境裡進行測試，既不實際且成本又高。
模擬提供了答案，因為這項技術可以顯著加快 AI 驅動機器人的訓練、測試和部署。
在雲端運用
L40S GPU
來擴大模擬與訓練機器人的規模
模擬可在部署前用於確認、驗證和最佳化機器人設計，以及相關系統及其演算法。模擬還能在施工或改造開始前最佳化設施和系統設計，以達到最高效率，避免在製造過程中因變更訂單而產生的高昂成本。
由 NVIDIA L40S GPU 加速的 Amazon EC2 G6e 執行個體，提供比先前架構高出兩倍的效能提升，同時還能隨著場景及模擬複雜度增加而擴充的彈性。這些執行個體用於訓練許多為 AI驅動機器人提供動力的電腦視覺模型。這意味著相同的執行個體可以擴充來執行各種任務，從資料生成到模擬，再到模型訓練。
在雲端使用
NVIDIA OSMO
，可以讓團隊對分散各處的運算資源，無論是在本地或 AWS 雲端，都能協調與擴充複雜的機器人開發工作流程。
Isaac Sim 讓使用者可以獲得最新的
機器人模擬功能
及雲端資源，以促進合作。其中一個關鍵的工作流程是產生訓練感知模型所需的合成資料。
開發人員使用結合
NVIDIA
Omniverse
Replicator
與
NVIDIA NIM 微服務
的
參考工作流程
，便能建立支援生成式 AI 的 SDG 管道。NVIDIA Omniverse Replicator 是用於建立自訂合成資料生成（SDG）管道的框架，以及 Isaac Sim 的核心擴充功能。
其中包括用於產生 Python USD 程式碼和回答 OpenUSD 查詢的 USD Code NIM 微服務，以及用於使用自然語言或圖像輸入探索 OpenUSD 資產的 USD Search NIM 微服務。Edify 360 HDRi NIM 微服務可產生 360 度環境地圖，而 Edify 3D NIM 微服務則可根據文字或影像提示，建立可立即編輯的 3D 資產。如此一來便能利用生成式 AI 的力量，減少從建立資產到增強影像等許多繁瑣的手動步驟，簡化生成合成資料的流程。
Rendered.ai
的合成資料工程平台與 Omniverse Replicator 整合後，可讓企業為安全、情報、製造到農業等產業所使用的電腦視覺模型產生合成資料。
IT 諮詢與數位服務供應商
SoftServe
使用 Isaac Sim 來產生合成資料，且與歐洲領先的食品生產商 Pfeifer & Langen 合作驗證垂直農業中使用的機器人。
塔塔顧問服務（Tata Consultancy Services）建立客製化的合成資料生成管道，驅動其 Mobility AI 套件，藉由模擬真實世界的情境來解決汽車與自動化使用個案。其應用包括瑕疵偵測、生產線末端品質檢查及避免危險情況。
在模擬環境中學習成為機器人
Isaac Sim 可讓開發人員在精準符合物理原則的模擬環境中測試及驗證機器人，而建立在 Isaac Sim 上的開源機器人學習框架
Isaac Lab
則為建立可在 AWS Batch 上執行的機器人政策提供虛擬空間。
由於這些模擬是可以重複的，因此開發人員可以輕鬆排除故障，減少驗證和測試所需的週期。
多家機器人開發業者在 AWS 上採用
NVIDIA Isaac
來開發實體 AI。
Aescape
的機器人能夠透過 Isaac Sim 中對機器人身上的感應器進行準確的建模及調整，提供精準且量身打造的按摩服務。
Cobot 已將 Isaac Sim 與其 AI 驅動的協作機器人 Proxie 搭配使用，以最佳化倉庫、醫院、製造場所等地的物流作業化。
Cohesive Robotics 已將 Isaac Sim 整合至其名為 Argus OS™ 的軟體框架，用於開發和部署在高混合製造環境裡使用的機器人工作單元。
機器人基礎模型建造商的 Field AI 使用 Isaac Sim 和 Isaac Lab 來評估其模型在複雜、非結構性環境下的效能表現，這些環境涵蓋建築、製造、石油和天然氣、採礦等產業。
Standard Bots
正在模擬和驗證其用於製造和加工設置之的R01 機器人效能。
Swiss Mile
正在使用 Isaac Sim 和 Isaac Lab 進行機器人學習，使輪型四足機器人能夠在工廠和倉庫裡，以更高的效率自主執行各項任務。
Vention
提供基於雲端的全端自動化平台，正在使用 Isaac Sim 開發和測試中小型製造商使用的機器人單元新功能。
了解更多關於 Issac Sim 4.2 資訊，Issac Sim 4.2 現已在
AWS Marketplace
上由 NVIDIA L40S GPU 驅動的 Amazon EC2 G6e 執行個體上提供。
Categories:
自主機器
Tags:
NVIDIA Isaac Sim
|
Omniverse Enterprise


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