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Artificial Intelligence (AI) is the field developing computers and robots capable of parsing data contextually to provide requested information, supply analysis, or trigger events based on findings. Through techniques like machine learning and neural networks, companies globally are investing in teaching machines to ‘think’ more like humans. Below are some of the latest AI news from the industry:
NVIDIA CEO Jensen Huang Promotes AI in Washington, DC and China
This month, NVIDIA founder and CEO Jensen Huang promoted AI in both Washington, D.C. and Beijing — emphasizing the benefits that AI will bring to business and society worldwide. In the U.S. capital, Huang met with President Trump and U.S. policymakers, reaffirming NVIDIA’s support for the Administration’s effort to create jobs, strengthen domestic AI infras ...
Build AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock
Vehicle data is critical for original equipment manufacturers (OEMs) to drive continuous product innovation and performance improvements and to support new value-added services. Similarly, the increasing digitalization of vehicle architectures and adoption of software-configurable functions allow OEMs to add new features and capabilities efficiently. Sonatus ...
How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI
This post is cowritten with Jimmy Cancilla from Rapid7. Organizations are managing increasingly distributed systems, which span on-premises infrastructure, cloud services, and edge devices. As systems become interconnected and exchange data, the potential pathways for exploitation multiply, and vulnerability management becomes critical to managing risk. Vul ...
Build secure RAG applications with AWS serverless data lakes
Data is your generative AI differentiator, and successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Traditional data architectures often struggle to meet the unique demands of generative such as applications. An effective generative AI data strategy requires several key components l ...
Try featured notebooks on selected topics in NotebookLM
NotebookLM is adding featured notebooks, with works selected by partners like The Atlantic and The Economist.Read More
AI Testing and Evaluation: Learnings from cybersecurity
Generative AI presents a unique challenge and opportunity to reexamine governance practices for the responsible development, deployment, and use of AI. To advance thinking in this space, Microsoft has tapped into the experience and knowledge of experts across domains—from genome editing to cybersecurity—to investigate the role of testing and evaluatio ...
Overcoming Vocabulary Constraints with Pixel-level Fallback
Subword tokenization requires balancing computational efficiency and vocabulary coverage, which often leads to suboptimal performance on languages and scripts not prioritized during training. We propose to augment pretrained language models with a vocabulary-free encoder that generates input embeddings from text rendered as pixels. Through experiments on Eng ...
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to gradient heterogeneity acro ...