Posts in category: nVidia AI

The NVIDIA AI Summit India, taking place Oct. 23–25 at the Jio World Convention Centre in Mumbai, will bring together the brightest minds to explore how India is tackling the world’s grand challenges.

A major highlight: a fireside chat with NVIDIA founder and CEO Jensen Huang on October 24. He’ll share his insights on AI’s pivotal role in reshaping industries and how India is emerging as a global AI leader, and be joined by the chairman and managing director of Reliance Industries, Mukesh Ambani.

Passes for the event are sold out. But don’t worry — audiences can tune in via livestream or watch on-demand sessions at NVIDIA AI Summit.

With over 50 sessions, live demos and hands-on workshops, the event will showcase AI’s transformative impact across industries like robotics, supercomputing and industrial digitalization.

It will explore opportunities both globally and locally in India. Over 70% of the use cases discussed will focus on how AI can address India’s most pressing challenges.

India’s AI Journey

India’s rise to become a global AI leader is powered by its focus on building AI infrastructure and foundational models.

NVIDIA’s accelerated computing platform, now 100,000x more energy-efficient for processing large language models than a decade ago, is driving this progress.

If car efficiency had improved at the same rate, vehicles today would get 280,000 miles per gallon — enough to drive to the moon with a single gallon of gas.

As India solidifies its place in AI leadership, the summit will tackle key topics.

These include building AI infrastructure with NVIDIA’s advanced GPUs, harnessing foundational models for Indian languages, fueling innovation in India’s startup ecosystem and upskilling developers to take the country’s workforce to the AI front office. The momentum is undeniable.

India’s AI Summit: Driving Innovation, Solving Grand Challenges

NVIDIA is at the heart of India’s rise as an AI powerhouse.

With six locations across the country hosting over 4,000 employees, NVIDIA plays a central role in the country’s rapid progress in AI.

The company works with enterprises, cloud providers and startups to build AI infrastructure powered by NVIDIA’s accelerated computing stack comprising tens of thousands of its most advanced GPUs, high-performance networking, and AI software platforms and tools.

The summit will feature sessions on how this infrastructure empowers sectors like healthcare, agriculture, education and manufacturing.

Jensen Huang’s Fireside Chat

The fireside chat with Huang on October 24 is a must-watch.

He’ll discuss how AI is revolutionizing industries worldwide and India’s increasingly important role as a global AI leader.

To hear his thoughts firsthand, tune in to the livestream or catch the session on demand for insights from one of the most influential figures in AI.

Key Sessions and Speakers

Top industry experts like Niki Parmar (Essential AI), Deepu Talla (NVIDIA) and Abhinav Aggarwal (Fluid AI) will dive into a range of game-changing topics, including:

• Generative AI and large language models (LLMs): Discover innovations in video synthesis and high-quality data models for large-scale inference.
• Robotics and industrial efficiency: See how AI-powered robotics tackle automation challenges in manufacturing and warehouse operations.
• AI in healthcare: Learn how AI transforms diagnostics and treatments, improving outcomes across India’s healthcare system.

These sessions will also introduce cutting-edge NVIDIA AI networking technologies, essential for building next-gen AI data centers.

Workshops and Startup Innovation

India’s vibrant startup ecosystem will be in the spotlight at the summit.

Nearly 2,000 companies in India are part of NVIDIA Inception, a program that supports startups driving innovation in AI and other fields.

Onsite workshops at the AI Summit will offer hands-on experiences with NVIDIA’s advanced AI tools, giving developers and startups practical skills to push the boundaries of innovation.

Meanwhile, Reverse VC Pitches will provide startups with unique insights as venture capital firms pitch their visions for the future, sparking fresh ideas and collaborations.

Industrial AI and Manufacturing Innovation

NVIDIA is also backing India’s industrial expansion by deploying AI technologies like Omniverse and Isaac.

These tools are enhancing everything from factory planning to manufacturing and construction, helping build greenfield factories that are more efficient and sustainable.

These technologies integrate advanced AI capabilities into factory operations, cutting costs while boosting sustainability.

Through hands-on workshops and deep industry insights, participants will see how India is positioning itself to lead the world in AI innovation.

Join the livestream or explore sessions on demand at NVIDIA AI Summit.

Read More

The NVIDIA AI Summit India, taking place Oct. 23–25 at the Jio World Convention Centre in Mumbai, will bring together the brightest minds to explore how India is tackling the world’s grand challenges.

A major highlight: a fireside chat with NVIDIA founder and CEO Jensen Huang on October 24. He’ll share his insights on AI’s pivotal role in reshaping industries and how India is emerging as a global AI leader, and be joined by the chairman and managing director of Reliance Industries, Mukesh Ambani.

Passes for the event are sold out. But don’t worry — audiences can tune in via livestream or watch on-demand sessions at NVIDIA AI Summit.

With over 50 sessions, live demos and hands-on workshops, the event will showcase AI’s transformative impact across industries like robotics, supercomputing and industrial digitalization.

It will explore opportunities both globally and locally in India. Over 70% of the use cases discussed will focus on how AI can address India’s most pressing challenges.

India’s AI Journey

India’s rise to become a global AI leader is powered by its focus on building AI infrastructure and foundational models.

NVIDIA’s accelerated computing platform, now 100,000x more energy-efficient for processing large language models than a decade ago, is driving this progress.

If car efficiency had improved at the same rate, vehicles today would get 280,000 miles per gallon — enough to drive to the moon with a single gallon of gas.

As India solidifies its place in AI leadership, the summit will tackle key topics.

These include building AI infrastructure with NVIDIA’s advanced GPUs, harnessing foundational models for Indian languages, fueling innovation in India’s startup ecosystem and upskilling developers to take the country’s workforce to the AI front office. The momentum is undeniable.

India’s AI Summit: Driving Innovation, Solving Grand Challenges

NVIDIA is at the heart of India’s rise as an AI powerhouse.

With six locations across the country hosting over 4,000 employees, NVIDIA plays a central role in the country’s rapid progress in AI.

The company works with enterprises, cloud providers and startups to build AI infrastructure powered by NVIDIA’s accelerated computing stack comprising tens of thousands of its most advanced GPUs, high-performance networking, and AI software platforms and tools.

The summit will feature sessions on how this infrastructure empowers sectors like healthcare, agriculture, education and manufacturing.

Jensen Huang’s Fireside Chat

The fireside chat with Huang on October 24 is a must-watch.

He’ll discuss how AI is revolutionizing industries worldwide and India’s increasingly important role as a global AI leader.

To hear his thoughts firsthand, tune in to the livestream or catch the session on demand for insights from one of the most influential figures in AI.

Key Sessions and Speakers

Top industry experts like Niki Parmar (Essential AI), Deepu Talla (NVIDIA) and Abhinav Aggarwal (Fluid AI) will dive into a range of game-changing topics, including:

• Generative AI and large language models (LLMs): Discover innovations in video synthesis and high-quality data models for large-scale inference.
• Robotics and industrial efficiency: See how AI-powered robotics tackle automation challenges in manufacturing and warehouse operations.
• AI in healthcare: Learn how AI transforms diagnostics and treatments, improving outcomes across India’s healthcare system.

These sessions will also introduce cutting-edge NVIDIA AI networking technologies, essential for building next-gen AI data centers.

Workshops and Startup Innovation

India’s vibrant startup ecosystem will be in the spotlight at the summit.

Nearly 2,000 companies in India are part of NVIDIA Inception, a program that supports startups driving innovation in AI and other fields.

Onsite workshops at the AI Summit will offer hands-on experiences with NVIDIA’s advanced AI tools, giving developers and startups practical skills to push the boundaries of innovation.

Meanwhile, Reverse VC Pitches will provide startups with unique insights as venture capital firms pitch their visions for the future, sparking fresh ideas and collaborations.

Industrial AI and Manufacturing Innovation

NVIDIA is also backing India’s industrial expansion by deploying AI technologies like Omniverse and Isaac.

These tools are enhancing everything from factory planning to manufacturing and construction, helping build greenfield factories that are more efficient and sustainable.

These technologies integrate advanced AI capabilities into factory operations, cutting costs while boosting sustainability.

Through hands-on workshops and deep industry insights, participants will see how India is positioning itself to lead the world in AI innovation.

Join the livestream or explore sessions on demand at NVIDIA AI Summit.

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture and Salesforce are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

How Does Agentic AI Work?

Agentic AI uses a four-step process for problem-solving:

1. Perceive: AI agents gather and process data from various sources, such as sensors, databases and digital interfaces. This involves extracting meaningful features, recognizing objects or identifying relevant entities in the environment.
2. Reason: A large language model acts as the orchestrator, or reasoning engine, that understands tasks, generates solutions and coordinates specialized models for specific functions like content creation, vision processing or recommendation systems. This step uses techniques like retrieval-augmented generation (RAG) to access proprietary data sources and deliver accurate, relevant outputs.
3. Act: By integrating with external tools and software via application programming interfaces, agentic AI can quickly execute tasks based on the plans it has formulated. Guardrails can be built into AI agents to help ensure they execute tasks correctly. For example, a customer service AI agent may be able to process claims up to a certain amount, while claims above the amount would have to be approved by a human.
4. Learn: Agentic AI continuously improves through a feedback loop, or
   “data flywheel,” where the data generated from its interactions is fed into the system to enhance models. This ability to adapt and become more effective over time offers businesses a powerful tool for driving better decision-making and operational efficiency.

Fueling Agentic AI With Enterprise Data

Across industries and job functions, generative AI is transforming organizations by turning vast amounts of data into actionable knowledge, helping employees work more efficiently.

AI agents build on this potential by accessing diverse data through accelerated AI query engines, which process, store and retrieve information to enhance generative AI models. A key technique for achieving this is RAG, which allows AI to tap into a broader range of data sources.

Over time, AI agents learn and improve by creating a data flywheel, where data generated through interactions is fed back into the system, refining models and increasing their effectiveness.

The end-to-end NVIDIA AI platform, including NVIDIA NeMo microservices, provides the ability to manage and access data efficiently, which is crucial for building responsive agentic AI applications.

Agentic AI in Action

The potential applications of agentic AI are vast, limited only by creativity and expertise. From simple tasks like generating and distributing content to more complex use cases such as orchestrating enterprise software, AI agents are transforming industries.

Customer Service: AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction.

There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation: Agentic AI can help quickly create high-quality, personalized marketing content. Generative AI agents can save marketers an average of three hours per content piece, allowing them to focus on strategy and innovation. By streamlining content creation, businesses can stay competitive while improving customer engagement.

Software Engineering: AI agents are boosting developer productivity by automating repetitive coding tasks. It’s projected that by 2030 AI could automate up to 30% of work hours, freeing developers to focus on more complex challenges and drive innovation.

Healthcare: For doctors analyzing vast amounts of medical and patient data, AI agents can distill critical information to help them make better-informed care decisions. Automating administrative tasks and capturing clinical notes in patient appointments reduces the burden of time-consuming tasks, allowing doctors to focus on developing a doctor-patient connection.

AI agents can also provide 24/7 support, offering information on prescribed medication usage, appointment scheduling and reminders, and more to help patients adhere to treatment plans.

How to Get Started

With its ability to plan and interact with a wide variety of tools and software, agentic AI marks the next chapter of artificial intelligence, offering the potential to enhance productivity and revolutionize the way organizations operate.

To accelerate the adoption of generative AI-powered applications and agents, NVIDIA NIM Agent Blueprints provide sample applications, reference code, sample data, tools and comprehensive documentation.

NVIDIA partners including Accenture are helping enterprises use agentic AI with solutions built with NIM Agent Blueprints.

Visit ai.nvidia.com to learn more about the tools and software NVIDIA offers to help enterprises build their own AI agents. 

Read More