Author: AI Generated Robotic Content

By Aurélien Bibaut, Winston Chou, Simon Ejdemyr, and Nathan Kallus We are excited to share our work on how to learn good proxy metrics from historical experiments at KDD 2024. This work addresses a fundamental question for technology companies and academic researchers alike: how do we establish that a treatment that improves short-term (statistically sensitive) outcomes …

Read more “Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments”

The proliferation of large language models (LLMs) in enterprise IT environments presents new challenges and opportunities in security, responsible artificial intelligence (AI), privacy, and prompt engineering. The risks associated with LLM use, such as biased outputs, privacy breaches, and security vulnerabilities, must be mitigated. To address these challenges, organizations must proactively ensure that their use …

Read more “Secure RAG applications using prompt engineering on Amazon Bedrock”

Today’s digital landscape offers a vast sea of information, encompassing not only text, but also images and videos. Traditional enterprise search engines were primarily designed for text-based queries, and often fall short when it comes to analyzing visual content. However, with a combination of natural language processing (NLP) and multimodal embeddings, a new era of …

Read more “A multimodal search solution using NLP, BigQuery and embeddings”

In today’s technology landscape, building or modernizing applications demands a clear understanding of your business goals and use cases. This insight is crucial for leveraging emerging tools effectively, especially generative AI foundation models such as large language models (LLMs). LLMs offer significant competitive advantages, but implementing them successfully hinges on a thorough grasp of your …

Read more “Choosing between self-hosted GKE and managed Vertex AI to host AI models”