Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model

by Sean Moran — How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Towards Data Science.

Towards Data Science·2d ago

From idea to AI app: Creating intelligent research assistants with Strands

Building an AI app shouldn’t require a PhD in machine learning (ML) or months of wrestling with complex architectures. Yet that’s exactly what happens when you try to orchestrate multiple API calls,...

AWS ML Blog·3d ago

Get Real-Time Visibility into GPU Usage Across Kubernetes Clusters

Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with... Maximizing the value of AI...

NVIDIA Developer·May 21

NVIDIA and Google Cloud Empower the Next Wave of AI Builders

At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning...

NVIDIA·May 19

Drafted, Not Sent: How I Built The Second Half of an AI Outbound Agent

by Anand Bhaskaran — A few weeks ago, I wrote about the AI outbound agent I built in two weeks, a deep research on the account and the person, delivered as an 80-word brief, inside Outreach, exactly where the rep already...

Towards AI·5h ago

Evaluating Deep Agents using LangSmith on AWS

This post combines learnings from LangChain’s work on evaluating deep agents and Anthropic’s guide to demystifying evals for AI agents into a practical guide. In this post, you will learn how to: 1)...

AWS ML Blog·21h ago

AI Proves Language Evolves for Learnability

A new study uses deep linear networks to prove that language undergoes iterated learning to become structured and learnable.

Neuroscience News·2d ago

Google I/O showed how the path for AI-driven science is shifting

During Tuesday’s Google I/O keynote, Demis Hassabis, the CEO of Google DeepMind, proclaimed that we are currently “standing in the foothills of the singularity.” It was a striking statement—the...

MIT Tech Review·May 22

Add a Specialized Deep Research Skill to Agent Harnesses

Agent harnesses like Claude Code, Codex, and LangChain Deep Agents are excellent orchestrators. They manage sessions, chain tools, execute code, and respond to... Agent harnesses like Claude Code,...

NVIDIA Developer·May 20

Strengthening Singapore’s AI Future: A New National Partnership

Google DeepMind and Singapore partner to apply frontier AI to address complex challenges across health, education, and sustainability and more.

Google DeepMind·May 16

NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code

NVIDIA researchers have introduced Polar, a rollout framework that trains language agents using reinforcement learning without modifying their agent harnesses. Polar places a model API proxy between...

Marktechpost·2d ago

Technical deep dive: AgentCore payments and innovation in agentic commerce

Amazon Bedrock AgentCore payments is now available in preview, it provides instant payments to paid external services with no manual billing setup per provider, stablecoin support for cost-effective...

AWS ML Blog·3d ago

We’re launching the Google DeepMind Accelerator program in Asia Pacific to tackle environmental risks

Google DeepMind·May 21

The next phase of OpenAI’s Education for Countries

OpenAI advances Education for Countries, expanding AI adoption in schools with new partnerships, teacher training, and tools to improve global learning outcomes.

OpenAI·May 20

NVIDIA, Ineffable Intelligence Team Up to Build the Future of Reinforcement Learning Infrastructure

Reinforcement-learning agents — AI systems that learn by trial and error — can convert computation into new knowledge. That’s the focus of a new engineering-level collaboration between NVIDIA and...

NVIDIA·May 13