Category: FAANG

Closed and exclusive ecosystems are a barrier to innovation in artificial intelligence (AI) and machine learning (ML), imposing incompatibilities across technologies and obscuring how to quickly and easily refine ML models. At Google, we believe open-source software (OSS) is essential to overcoming the challenges associated with inflexible strategies. And as the leading Cloud Native Computing …

Read more “Open source collaborations and key partnerships to help accelerate AI innovation”

When it comes to the adoption of artificial intelligence (AI), we have reached a tipping point. Technologies that were once accessible to only a few are now broadly available. This has led to an explosion in AI investment. However, according to research firm McKinsey, for AI to make a sizable contribution to a company’s bottom …

Read more “New AI Agents can drive business results faster: Translation Hub, Document AI, and Contact Center AI”

In this post, we present a solution that combines rich mobile device intelligence with customized machine learning (ML) modeling to help you catch fraudsters who exploit mobile apps. GrabDefence (GD), Grab’s proprietary fraud detection and prevention technology, and AWS have launched GDxAFD, a fraud detection solution tailored for mobile apps that integrates GD’s device intelligence …

Read more “Detect fraud in mobile-oriented businesses using GrabDefence device intelligence and Amazon Fraud Detector”

Back in 2018, BERT got people talking about how machine learning models were learning to read and speak. Today, large language models, or LLMs, are growing up fast, showing dexterity in all sorts of applications. They’re, for one, speeding drug discovery, thanks to research from the Rostlab at Technical University of Munich, as well as …

Read more “Beyond Words: Large Language Models Expand AI’s Horizon”

Machine learning (ML) is iterative in nature — model improvement is a necessity to drive the best business outcomes. Yet, with the proliferation of model artifacts, it can be difficult to ensure that only the best models make it into production. Data science teams may get access to new training data, expand the scope of …

Read more “Streamline your models to production with the Vertex AI Model Registry”

Introduction Personalized recommender systems are used widely for offering the right products or content to the right users. Some examples of such systems are video recommendations (“What to Watch Next”) on YouTube, Google Play Store app recommendations and similar services offered by other app stores and content services. In essence, recommendation systems filter products or …

Read more “Building Large Scale Recommenders using Cloud TPUs”