The next big moment in AI is in sight — literally.
Today, more than 1.5 billion enterprise level cameras deployed worldwide are generating roughly 7 trillion hours of video per year. Yet, only a fraction of it gets analyzed.
It’s estimated that less than 1% of video from industrial cameras is watched live by humans, meaning critical operational incidents can go largely unnoticed.
This comes at a high cost. For example, manufacturers are losing trillions of dollars annually to poor product quality or defects that they could’ve spotted earlier, or even predicted, by using AI agents that can perceive, analyze and help humans take action.
Interactive AI agents with built-in visual perception capabilities can serve as always-on video analysts, helping factories run more efficiently, bolster worker safety, keep traffic running smoothly and even up an athlete’s game.
To accelerate the creation of such agents, NVIDIA today announced early access to a new version of the
NVIDIA AI Blueprint
for
video search and summarization
. Built on top of the
NVIDIA Metropolis
platform — and now supercharged by
NVIDIA Cosmos Nemotron
vision language models (VLMs),
NVIDIA Llama Nemotron
large language models (LLMs) and
NVIDIA NeMo Retriever
— the blueprint provides developers with the tools to build and deploy AI agents that can analyze large quantities of video and image content.
The blueprint integrates the
NVIDIA AI Enterprise
software platform — which includes
NVIDIA NIM
microservices for VLMs, LLMs and advanced AI frameworks for
retrieval-augmented generation
— to enable batch video processing that’s 30x faster than watching it in real time.
The blueprint contains several agentic AI features — such as chain-of-thought reasoning, task planning and tool calling — that can help developers streamline the creation of powerful and diverse visual agents to solve a range of problems.
AI agents with video analysis abilities can be combined with other agents with different skill sets to enable even more sophisticated agentic AI services. Enterprises have the flexibility to build and deploy their AI agents from the edge to the cloud.
How Video Analyst AI Agents Can Help Industrial Businesses
AI agents with visual perception and analysis skills can be fine-tuned to help businesses with industrial operations by:
Increasing productivity and reducing waste:
Agents can help ensure standard operating procedures are followed during complex industrial processes like product assembly. They can also be fine-tuned to carefully watch and understand nuanced actions, and the sequence in which they’re implemented.
Boosting asset management efficiency through better space utilization:
Agents can help optimize inventory storage in warehouses by performing 3D volume estimation and centralizing understanding across various camera streams.
Improving safety through auto-generation of incident reports and summaries:
Agents can process huge volumes of video and summarize it into contextually informative reports of accidents. They can also help ensure personal protective equipment compliance in factories, improving worker safety in industrial settings.
Preventing accidents and production problems:
AI agents can identify atypical activity to quickly mitigate operational and safety risks, whether in a warehouse, factory or airport, or at a traffic intersection or other municipal setting.
Learning from the past:
Agents can search through operations video archives, find relevant information from the past and use it to solve problems or create new processes.
Video Analysts for Sports, Entertainment and More
Another industry where video analysis AI agents stand to make a mark is sports — a $500 billion market worldwide, with hundreds of billions in projected growth over the next several years.
Coaches, teams and leagues — whether professional or amateur — rely on video analytics to evaluate and enhance player performance, prioritize safety and boost fan engagement through player analytics platforms and data visualization. With visually perceptive AI agents, athletes now have unprecedented access to deeper insights and opportunities for improvement.
During his CES opening keynote, NVIDIA founder and CEO Jensen Huang demonstrated an AI video analytics agent that assessed the fastball pitching skills of an amateur baseball player compared with a professional’s. Using video captured from the ceremonial first pitch that Huang threw for the San Francisco Giants baseball team, the video analytics AI agent was able to suggest areas for improvement.
https://blogs.nvidia.com/wp-content/uploads/2025/01/3568870_CES25_Agentic_AI_MetropolisSection_720p_10mb.mp4
The $3 trillion media and entertainment industry is also poised to benefit from video analyst AI agents. Through the
NVIDIA Media2 initiative
, these agents will help drive the creation of smarter, more tailored and more impactful content that can adapt to individual viewer preferences.
Worldwide Adoption and Availability
Partners from around the world are integrating the blueprint for building AI agents for video analysis into their own developer workflows, including Accenture,
Centific
, Deloitte, EY,
Infosys
,
Linker Vision
,
Pegatron
,
TATA Consultancy Services (TCS)
,
Telit Cinterion
and
VAST
.
Apply for early access
to the NVIDIA Blueprint for video search and summarization.
See
notice
regarding software product information.
Editor’s note:
Omdia
is the source for 1.5 billion enterprise-level cameras deployed.
Categories:
Generative AI
Tags:
Artificial Intelligence
|
CES 2025
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Industrial and Manufacturing
|
Media and Entertainment
|
Metropolis
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NVIDIA AI Enterprise
|
NVIDIA Blueprints
|
NVIDIA NIM
人工智慧（AI） 的下一個重要時刻就在我們眼前。
目前全球部署企業級攝影機的數量已經超過 15 億具，每年產生約 7 兆小時的影片內容。不過我們只分析了其中一小部分。
經估算，人類只有即時看了不到 1% 工業級攝影機所產生出的影片內容，代表重要作業事故多被忽略。
這將帶來高昂代價。像是製造商每年因產品品質不佳或瑕疵而損失數兆美元，不過若能使用能夠感知、分析及協助人類採取行動的 AI 代理，他們本可提早發現或甚至預測這些問題。
內建視覺感知能力的互動式 AI 代理可充當隨時待命的影片分析人員，協助工廠提高運作效率、加強工人安全、保持交通暢順，甚至提升運動員的比賽表現。
NVIDIA 今日宣布開放搶先體驗用於
影片搜尋與摘要
的新版
NVIDIA AI Blueprint
，以加快開發這一類的代理。這個藍圖（blueprint）建構在
NVIDIA Metropolis
平台之上，由
NVIDIA Cosmos Nemotron
視覺語言模型（VLM）、
NVIDIA Llama Nemotron
大型語言模型（LLM）及
NVIDIA NeMo Retriever
支援，為開發人員提供建置與部署可分析大量影片與圖像內容的 AI 代理的工具。
這個藍圖整合了
NVIDIA AI Enterprise
軟體平台能夠批量處理影片，其處理速度是即時觀看的 30 倍。NVIDIA AI Enterprise包括用於 VLM、LLM 的
NVIDIA NIM
微服務，以及用於
檢索增強生成
的先進 AI 框架。
在這個藍圖裡有數種代理型 AI 功能，例如思維鏈推理、任務規畫和工具呼叫，可協助開發人員輕鬆建立強大且多樣化的視覺代理，以解決一系列問題。
具備影片分析能力的 AI 代理可以搭配其他有著不同技能組合的代理使用，以提供更複雜的代理型 AI 服務。 企業可以靈活地從邊緣到雲端建立和部署自己的 AI 代理。
影片分析
AI
代理如何協助工業領域裡的企業
具備視覺感知與分析技能的 AI 代理可以透過下列方式微調後，協助企業進行工業運作：
提高生產力與減少浪費：
代理可以協助確保在產品組裝等複雜的工業流程中遵循標準作業程序。並能微調仔細觀察和理解細微的動作，還有執行順序。
提高空間利用率來提升資產管理效率：
代理可以估算 3D 體積，集中瞭解多個攝影機串流內容，協助改善倉庫庫存。
自動產生事故報告和摘要以提高安全性：
代理可以處理大量視訊與總結成情節內容豐富的事故報告。並能協助確保工廠內的個人防護裝備符合規定，改善工業環境中的工人作業安全。
預防意外與生產問題：
無論是在倉庫、工廠或機場，或是在交通路口或其他市政環境，AI 代理都能發現異常活動，以快速降低作業與安全風險。
從過去學習：
AI 代理可以搜尋作業影片檔案，從過去找到相關資訊，利用這些資訊來解決問題或建立新的流程。
分析體育娛樂等產業的影片內容
另一個影片分析 AI 代理可以大展拳腳的產業便是體育，在全球市值高達 5,000 億美元，預計在未來幾年內將有數千億美元的成長。
教練、球隊，以及無論職業或業餘聯盟都仰賴影片分析功能，以評估和提升球員表現、優先考量安全性，並且透過球員分析平台和資料可視化技術來提高球迷參與度。有了視覺感知的 AI 代理，運動員現在可以獲得更深入的看法和改進機會，這是過去做不到的。
NVIDIA 創辦人暨執行長黃仁勳在 CES 大會的開幕主題演講中，展示了一個 AI 影片分析代理，這款代理能夠評估業餘棒球選手與職業棒球選手的快速投球技巧。利用黃仁勳為舊金山巨人隊開球儀式所擷取的影片，視訊分析 AI 代理能夠提出需要改進的地方。
https://blogs.nvidia.com.tw/wp-content/uploads/sites/19/2025/01/JHH-pitch-metropolis-trim-final.mp4
影片分析 AI 代理也將嘉惠市價三兆美元的媒體與娛樂產業。這些代理透過
NVIDIA Media2 計畫
將有助於推動建立更聰明、更符合個人需求且更有影響力的內容，以適應個別觀眾的喜好。
全球合作夥伴正在將建立影片分析 AI 代理的藍圖整合到他們自己的開發人員工作流程中，包括埃森哲（Accenture）、Centific、德勤、Infosys、
Linker Vision
、和碩、TATA Consultancy Services（TCS）、Telit Cinterion 和
VAST
。
請在
此申請搶先體驗 NVIDIA 影片搜尋與摘要用藍圖
。
請見有關軟體產品資訊的
通知
。
Categories:
生成式人工智慧
Tags:
Artificial Intelligence
|
CES 2025
|
Industrial and Manufacturing
|
Media and Entertainment
|
Metropolis
|
NVIDIA AI Enterprise
|
NVIDIA Blueprints
|
NVIDIA NIM