The NVIDIA PyG container, now generally available, packages PyTorch Geometric with accelerations for GNN models, dataloading, and pre-processing using cuGraph-Ops, cuGraph, and cuDF from NVIDIA RAPIDS, all with an effortless out-of-the-box experience.
Published work
AI security contributions
The financial services industry is undergoing a significant transformation with the adoption of AI technologies. NVIDIA’s fourth annual State of AI in Financial Services Report provides insights into the current landscape and emerging trends for 2024.
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.
Human analysts can no longer effectively defend against the increasing speed and complexity of cybersecurity attacks. The amount of data is simply too large to screen manually.
Large language models (LLMs) provide a wide range of powerful enhancements to nearly any application that processes text. And yet they also introduce new risks, including: This post walks through these security vulnerabilities in detail and outlines best practices for designing or evaluating a secure LLM-enabled application.
The world’s 5 billion internet users and nearly 54 billion devices generate 3.4 petabytes of data per second, according to IDC.
At Black Hat USA 2023, NVIDIA hosted a two-day training session that provided security professionals with a realistic environment and methodology to explore the unique risks presented by machine learning (ML) in today’s environments.
Graphs form the foundation of many modern data and analytics capabilities to find relationships between people, places, things, events, and locations across diverse data assets. According to one study, by 2025 graph technologies will be used in 80% of data and analytics innovations, which will help facilitate rapid decision making across organizations.
The NVIDIA AI Red Team is focused on scaling secure development practices across the data, science, and AI ecosystems. We participate in open-source security initiatives, release tools, present at industry conferences, host educational competitions, and provide innovative training.
Performing an exhaustive exact k-nearest neighbor (kNN) search, also known as brute-force search, is expensive, and it doesn’t scale particularly well to larger datasets. During vector search, brute-force search requires the distance to be calculated between every query vector and database vector.
More than 40 million people had their health data leaked in 2021, and the trend is not optimistic. The key goal of federated learning and analytics is to perform data analytics and machine learning without accessing the raw data of the remote sites.
In the wake of ChatGPT, every company is trying to figure out its AI strategy, work that quickly raises the question: What about security? Some may feel overwhelmed at the prospect of securing new technology.
In an event at the White House today, NVIDIA announced support for voluntary commitments that the Biden Administration developed to ensure advanced AI systems are safe, secure and trustworthy. The news came the same day NVIDIA’s chief scientist, Bill Dally, testified before a U.S. Senate subcommittee seeking input on potential legislation covering generative AI.
Spear phishing is the largest and most costly form of cyber threat, with an estimated 300,000 reported victims in 2021 representing $44 million in reported losses in the United States alone. Business e-mail compromises led to $2.4 billion in costs in 2021, according to the FBI Internet Crime Report.
In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in part 1 and give a brief explanation of how the algorithms work, along with a summary of important parameters to fine-tune their behavior.
In the current AI landscape, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI.
Ransomware attacks have become increasingly popular, more sophisticated, and harder to detect. For example, in 2022, a destructive ransomware attack took 233 days to identify and 91 days to contain, for a total lifecycle of 324 days.
The advent of cloud computing has ushered in a paradigm shift in our data storage and utilization practices. Businesses can bypass the complexities of managing their own computing infrastructure by tapping into remote, on-demand resources deftly managed by cloud service providers.
Explore how information retrieval systems may be used to perpetrate prompt injection attacks and how application developers can mitigate this risk.
Hardware virtualization is an effective way to isolate workloads in virtual machines (VMs) from the physical hardware and from each other. This offers improved security, particularly in a multi-tenant environment.
This post explains prompt injection and shows how the NVIDIA AI Red Team identified vulnerabilities where prompt injection can be used to exploit three plug-ins included in the LangChain library.
NVIDIA DOCA GPUNetIO library can be adopted in a wide range of applications from different contexts, providing huge improvements for latency, throughput, and system resource utilization.
Memory safety is a major security concern for unsafe programming languages, including C/C++ and CUDA/OpenACC.
Learn how financial firms can build automated, real-time fraud and threat detection solutions with NVIDIA Morpheus.
Machine learning has the promise to improve our world, and in many ways it already has. However, research and lived experiences continue to show this technology has risks.