Building an AI solution for human problems Editor’s Note: This is the first in a three-part blog series about Palantir’s AI-enabled Customer Service Engine. Part 1: Introduction and Case Study The opportunity to provide exceptional customer service is greater than ever, leveraging the latest capabilities of artificial intelligence. AI can streamline and automate aspects of customer service operations, …
In the generative AI era, agents that simulate human actions and behaviors are emerging as a powerful tool for enterprises to create production-ready applications. Agents can interact with users, perform tasks, and exhibit decision-making abilities, mimicking humanlike intelligence. By combining agents with foundation models (FMs) from the Amazon Titan in Amazon Bedrock family, customers can …
Organizations use artificial intelligence (AI) and high-performance computing (HPC) applications to process massive datasets, run complex simulations, and train generative models with billions of parameters for diverse use cases such as LLMs, genomic analysis, quantitative analysis, or real-time sports analytics. These workloads place big performance demands on their storage systems, requiring high throughput and I/O …
Read more “Parallelstore is now GA, fueling the next generation of AI and HPC workloads”
This post is co-written with Dean Steel and Simon Gatie from Aviva. With a presence in 16 countries and serving over 33 million customers, Aviva is a leading insurance company headquartered in London, UK. With a history dating back to 1696, Aviva is one of the oldest and most established financial services organizations in the …
Read more “How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker”
Palantir and Edgescale AI Join Forces This post contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These statements may relate to, but are not limited to, Palantir’s expectations regarding the amount and the terms of …
This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Adrian Boeh, Senior Data Scientist – NAM AI, from Schneider Electric. Schneider Electric is a global leader in the digital transformation of energy management and automation. The company specializes in providing integrated solutions that make energy …
Retrieval Augmented Generation (RAG) is a powerful technique for enhancing large language models (LLMs) by grounding them in external knowledge sources. This blog post looks into a common challenge in RAG implementations: achieving high-quality semantic search. We’ll explore why traditional similarity search often falls short, and how new “task type” embeddings in Vertex AI offer …
Read more “Enhancing your gen AI use case with Vertex AI embeddings and task types”
Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than the other major cloud providers combined. This accelerated innovation is enabling organizations of all sizes, from disruptive AI startups like Hugging Face, AI21 Labs, and Articul8 AI to industry …
Google Cloud Storage is at the core of many customers’ cloud deployment because of its simplicity, affordability and near-infinite scale. But managing millions or billions of objects across numerous projects and with hundreds of developers can be complex, often requiring a team of analysts manually analyzing data for insights. Earlier this year, we introduced the …
Read more “Understand your Cloud Storage footprint with AI-powered queries and insights”
Machine learning (ML) and artificial intelligence (AI) systems rely heavily on human-annotated data for training and evaluation. A major challenge in this context is the occurrence of annotation errors, as their effects can degrade model performance. This paper presents a predictive error model trained to detect potential errors in search relevance annotation tasks for three …