×
![]()
To reflect democratic principles, AI must be built in the open. If the U.S. wants to lead the AI race, it must lead the open-source AI race.
On today’s episode of Uncanny Valley, our senior business editor joins us to talk Meta, brain aging, and ChatGPT’s recent dark turn.
In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.
A new study from Anthropic suggests that traits such as sycophancy or evilness are associated with specific patterns of activity in large language models—and turning on those patterns during training can, paradoxically, prevent the model from adopting the related traits. Large language models have recently acquired a reputation for behaving badly. In April, ChatGPT suddenly…
The Gemini 2.5 Deep Think released to users is not that same competition model, rather, a lower performing but apparently faster version.
Long before ChatGPT, a group of AI luminaries gathered on an island to discuss the future of artificial intelligence. Their funder ultimately cast a shadow on all who attended.
In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.
In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).
Generative Molecular Design (Part 1): common molecular representations in data science.
The post How Computers “See” Molecules appeared first on Towards Data Science.
Mariya Mansurova explains how hands-on learning, agentic AI, and engineering habits shape her writing and work.
The post “I think of analysts as data wizards who help their product teams solve problems” appeared first on Towards Data Science.
Debugging LLMs is important because their workflows are complex and involve multiple parts like chains, prompts, APIs, tools, retrievers, and more.
Models don't just fail with noise; they fail in silence, by narrowing their attention to the point of fragility.
The post When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems appeared first on Towards Data Science.
This tutorial explores ten practical and surprising applications of the Python time module.
Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.
Deep Think utilizes extended, parallel thinking and novel reinforcement learning techniques for significantly improved problem-solving.
A “gooner” tells WIRED he became hooked on the cartoonish nature of AI porn. Several addiction experts say the genre could pose a problem for people prone to compulsive sexual behavior.
AI-to-AI habits, AI sci-fi shorts, Apple’s AI pay scale, Grok AI video gen, 100+ models via API, and more...
AI-to-AI habits, AI sci-fi shorts, Apple’s AI pay scale, Grok AI video gen, 100+ models via API, and more...
OpenAI abruptly removed a ChatGPT feature that made conversations searchable on Google, sparking privacy concerns and industry-wide scrutiny of AI data handling.
The AI Frontiers article (reproduced below) builds on a previous Asimov Addendum article written by Tim O’Reilly, entitled: “Disclosures. I do not think that word means what you think it means.” I (Ilan) think it’s important to first very briefly go through parts of Tim’s original piece to help recap why we—at the AI Disclosures Project—care about protocols […]
Arora explains this as a difference between searching for a path versus already knowing roughly where the destination lies.
Intuit Mailchimp's experience with vibe coding reveals governance frameworks and tool selection strategies that enterprises can apply to avoid common AI coding pitfalls.
AWS continues to expand its serverless database offerings, aiming to help improve cost and lower operational complexity.
AWS Batch now seamlessly integrates with Amazon SageMaker Training jobs. In this post, we discuss the benefits of managing and prioritizing ML training jobs to use hardware efficiently for your business. We also walk you through how to get started using this new capability and share suggested best practices, including the use of SageMaker training plans.
The implication seems to be that running all these agents in parallel is faster and will result in a better and more varied set of products.
Whether you’re looking for simple point-and-click solutions or hardcore APIs for scraping the entire web, this list offers something for everyone.
We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%. In this post, we explore how you can use Amazon Nova FMs for structured output use cases.
Learn how to enhance Amazon Q with custom plugins to combine semantic search capabilities with precise analytics for AWS Support data. This solution enables more accurate answers to analytical questions by integrating structured data querying with RAG architecture, allowing teams to transform raw support cases and health events into actionable insights. Discover how this enhanced architecture delivers exact numerical analysis while maintaining natural language interactions for improved operational decision-making.
In regression models , failure occurs when the model produces inaccurate predictions — that is, when error metrics like MAE or RMSE are high — or when the model, once deployed, fails to generalize well to new data that differs from the examples it was trained or tested on.
In this post, we first introduce the Strands Agents SDK and its core features. Then we explore how it integrates with AWS environments for secure, scalable deployments, and how it provides rich observability for production use. Finally, we discuss practical use cases, and present a step-by-step example to illustrate Strands in action.