AI agents are revolutionizing the digital workforce by transforming business operations, automating complex tasks, and unlocking new efficiencies. With the ability to collaborate, these agents can now work together to tackle complex problems and drive even greater impact. The NVIDIA NeMo Agent toolkit is an open source library that simplifies the integration of agents…
]]>NeMo Retriever tops several visual document retrieval leaderboards, setting new standards for RAG apps.
]]>Enterprise data is exploding—petabytes of emails, reports, Slack messages, and databases pile up faster than anyone can read. Employees are left searching for answers in a sea of information, as “68% of available data in an organization goes unused,” according to market researcher Gartner1. That’s now possible with today’s availability of AI-Q, an open-source NVIDIA Blueprint that puts your…
]]>NVIDIA NeMo Agent toolkit is an open-source library for efficiently connecting and optimizing teams of AI agents. It focuses on enabling developers to quickly build, evaluate, profile, and accelerate complex agentic AI workflows?—?systems in which multiple AI agents collaborate to perform tasks. The Agent toolkit acts as a unifying framework that integrates existing agents, tools…
]]>As enterprises increasingly adopt AI technologies, they face a complex challenge of efficiently developing, securing, and continuously improving AI applications to leverage their data assets. They need a unified, end-to-end solution that simplifies AI development, enhances security, and enables continuous optimization, allowing organizations to harness the full potential of their data for AI…
]]>Trillions of PDF files are generated every year, each file likely consisting of multiple pages filled with various content types, including text, images, charts, and tables. This goldmine of data can only be used as quickly as humans can read and understand it. But with generative AI and retrieval-augmented generation (RAG), this untapped data can be used to uncover business insights that…
]]>Enterprises are sitting on a goldmine of data waiting to be used to improve efficiency, save money, and ultimately enable higher productivity. With generative AI, developers can build and deploy an agentic flow or a retrieval-augmented generation (RAG) chatbot, while ensuring the insights provided are based on the most accurate and up-to-date information. Building these solutions requires not…
]]>The latest embedding model from NVIDIA—NV-Embed—set a new record for embedding accuracy with a score of 69.32 on the Massive Text Embedding Benchmark (MTEB), which covers 56 embedding tasks. Highly accurate and effective models like NV-Embed are key to transforming vast amounts of data into actionable insights. NVIDIA provides top-performing models through the NVIDIA API catalog.
]]>The software development and deployment process is complex. Modern enterprise applications have complex software dependencies, forming an interconnected web that provides unprecedented functionality, but with the cost of exponentially increasing complexity. Patching software security issues is becoming progressively more challenging as the number of reported security flaws in the common…
]]>Generative AI is transforming computing, paving new avenues for humans to interact with computers in natural, intuitive ways. For enterprises, the prospect of generative AI is vast. Businesses can tap into their rich datasets to streamline time-consuming tasks—from text summarization and translation to insight prediction and content generation. But they must also navigate adoption challenges.
]]>Across every industry, and every job function, generative AI is activating the potential within organizations—turning data into knowledge and empowering employees to work more efficiently. Accurate, relevant information is critical for making data-backed decisions. For this reason, enterprises continue to invest in ways to improve how business data is stored, indexed, and accessed.
]]>Generative AI has the potential to transform every industry. Human workers are already using large language models (LLMs) to explain, reason about, and solve difficult cognitive tasks. Retrieval-augmented generation (RAG) connects LLMs to data, expanding the usefulness of LLMs by giving them access to up-to-date and accurate information. Many enterprises have already started to explore how…
]]>Learn how generative AI can help defend against spear phishing in this January 30 webinar.
]]>Identity-based attacks are on the rise, with phishing remaining the most common and second-most expensive attack vector. Some attackers are using AI to craft more convincing phishing messages and deploying bots to get around automated defenses designed to spot suspicious behavior. At the same time, a continued increase in enterprise applications introduces challenges for IT teams who must…
]]>NVIDIA showed how AI workflows can be leveraged to help you accelerate the development of AI solutions to address a range of use cases at NVIDIA GTC 2023. AI workflows are cloud-native, packaged reference examples showing how NVIDIA AI frameworks can be used to efficiently build AI solutions such as intelligent virtual assistants, digital fingerprinting for cybersecurity…
]]>Using generative AI and the NVIDIA Morpheus cybersecurity AI framework, developers can build solutions that detect spear phishing attempts more effectively and with extremely short training times. In fact, using NVIDIA Morpheus and a generative AI training technique, we were able to detect 90% of targeted spear phishing emails—a 20% improvement compared to a typical phishing detection solution…
]]>Learn how AI is improving your cybersecurity to detect threats faster.
]]>Learn how to use an NVIDIA AI workflow to uniquely fingerprint users and machines across your network in a new, free NVIDIA LaunchPad hands-on lab.
]]>Find out how federal agencies are adopting AI to improve cybersecurity in this November 16 webinar featuring Booz Allen Hamilton.
]]>Use of stolen or compromised credentials remains at the top of the list as the most common cause of a data breach. Because an attacker is using credentials or passwords to compromise an organization’s network, they can bypass traditional security measures designed to keep adversaries out. When they’re inside the network, attackers can move laterally and gain access to sensitive data…
]]>Network traffic continues to increase, with the number of Internet users across the globe reaching 5 billion in 2022. As the number of users expands, so does the number of connected devices, which is expected to grow into the trillions. The ever-increasing number of connected users and devices leads to an overwhelming amount of data generated across the network. According to IDC…
]]>Cybersecurity-related risk remains one of the top sources of risk in the enterprise. This has been exacerbated by the global pandemic, which has forced companies to accelerate digitization initiatives to better support a remote workforce. This includes not only the infrastructure to support a distributed workforce but also automation through robotics, data analytics, and new applications.
]]>Discover how to detect cyber threats using machine learning and NVIDIA Morpheus, an open-source AI framework.
]]>Cybercrime worldwide is costing as much as the gross domestic product of countries like Mexico or Spain, hitting more than $1 trillion annually. And global trends point to it only getting worse. Data centers face staggering increases in users, data, devices, and apps increasing the threat surface amid ever more sophisticated attack vectors. NVIDIA Morpheus enables cybersecurity…
]]>Traditional cybersecurity methods include creating barriers around your infrastructure to protect it from intruders with ill intentions. However, as enterprises continue along the path of digital transformation, faced with a proliferation of devices, more sophisticated cybersecurity attacks, and an incredibly vast network of data to protect, new cybersecurity methodologies must be explored.
]]>