TL;DR We asked o1 to share its thoughts on our recent LNM/LMM post. https://www.artificial-intelligence.show/the-ai-podcast/o1s-thoughts-on-lnms-and-lmms What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“? Thought about large numerical and mathematics models for a few seconds.Confirming Additional BreakthroughsOK, I’m confirming if LNMs/LMMs need more than Transformer models to match …
Palantir and Grafana Labs’ Strategic Partnership Introduction In today’s rapidly evolving technological landscape, government agencies face the pressing challenge of managing increasingly complex IT infrastructures. Effective software observability and monitoring are critical for ensuring operational efficiency and security. Recognizing this need, Palantir and Grafana Labs have formed a strategic partnership through the FedStart program aimed …
Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. This allows scientists and model developers to focus on model development and rapid experimentation rather than infrastructure management Pipelines offers the ability to orchestrate complex ML workflows with a simple Python SDK with the ability to visualize those workflows …
Read more “How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines”
When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs and TPUs) across multiple compute hosts to train their models efficiently. This can lead to orchestration, resource …
Read more “Orchestrating GPU-based distributed training workloads on AI Hypercomputer”
Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting …
In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy …
Dun & Bradstreet, a leading global provider of business data and analytics, is committed to maintaining its position at the forefront of innovation. For the past two years, this commitment has included the company’s deliberate approach to improving its software development lifecycle by infusing AI solutions. While development velocity and security were the company’s most …
Read more “How Dun & Bradstreet is transforming software development with Gemini Code Assist”
Today, we’re announcing Gemini 2.0, our most capable multimodal AI model yet.
*Equal Contributors Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limited effect on the …
Read more “Evaluating Gender Bias Transfer between Pre-trained and Prompt-Adapted Language Models”
Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. In addition to planning considerations when building an AI application from the ground up, it focused on our Account Summaries use case, which allows account teams to quickly understand the …
Read more “How AWS sales uses Amazon Q Business for customer engagement”