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These Chrome features and tips for students can help you learn more easily while staying organized and focused, too.
In this post, we demonstrate how to prompt Amazon Nova understanding models to cite sources in responses. Further, we will also walk through how we can evaluate the responses (and citations) for accuracy.
Visualizing model performance is an essential piece of the machine learning workflow puzzle.
Claudia Ng reflects on real-world ML lessons, mentoring newcomers, and her journey from corporate ML to freelance AI.
The post “My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“ appeared first on Towards Data Science.
We’re entering a phase where openness equals power. The walls are coming down.
We’re introducing Flight Deals, a new, AI-powered search tool. Plus, there’s a new way to exclude basic economy on Google Flights.
Effectively increase your productivity with local LLMs using Ollama's new app.
The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA.
If you want to make the most of The Field We Now Call AI, look to trading. Specifically, the tech-driven sort. People who’ve read my other work, or who have had the misfortune of speaking with me one-on-one, have already heard this line. My long-running half-joke is that my AI consulting is based on best […]
Anytime you expect AI to be self-aware, you’re in for disappointment. That’s just not how it works.
In recent years, LLM Multi-Agent systems have garnered widespread attention for their collaborative approach to solving complex problems. However, it's a common scenario for these systems to fail at a task despite a flurry of activity.
The post Which Agent Causes Task Failures and When?Researchers from PSU and Duke explores automated failure attribution of LLM Multi-Agent Systems first appeared on Synced.
Jim Sanborn is auctioning off the elusive solution to K4, the outdoor sculpture that sits at CIA headquarters.
Brain meets AI, Musk vs Altman, Gemini memory, AGI 2028, Claude under watch, and more...
The new version of ChatGPT explains why it won’t generate rule-breaking outputs. WIRED’s initial analysis found that some guardrails were easy to circumvent.
In this post, we explore how Amazon Bedrock AgentCore Runtime simplifies the deployment and management of AI agents.
Google updated the Gemini app running of Gemini 2.5 Pro to reference all historical chats and offer new temporary chats.
Google is investing an additional $9 billion in Oklahoma within the next two years in cloud and AI infrastructure. This investment supports the development of a new data…
A beginner-friendly introduction to LLM-as-a-Judge
The post How to Use LLMs for Powerful Automatic Evaluations appeared first on Towards Data Science.
This post presents how AWS and PwC are developing new reasoning checks that combine deep industry expertise with Automated Reasoning checks in Amazon Bedrock Guardrails to support innovation.
Early-adopter realities gathered from real data mesh implementations
The post Data Mesh Diaries: Realities from Early Adopters appeared first on Towards Data Science.
We could soon find ourselves deferring to AI assistants that botsplain our every experience in real time. Is this empowerment or deferral?
Researchers studying the emotional impact of tools like ChatGPT propose a new kind of benchmark that measures a model’s emotional and social impact.
If you want your AI project to succeed, mastering expectation management comes first. When working with AI projets, uncertainty isn’t just a side effect, it can make or break the entire initiative. Most people impacted by AI projects don’t fully understand how AI works, or that errors are not only inevitable but actually a natural […]
The post Tips for Setting Expectations in AI Projects appeared first on Towards Data Science.
Since the way we manipulate high-dimensional vectors is primarily matrix multiplication, it isn’t a stretch to say it is the bedrock of the modern AI revolution.
The post A Bird’s-Eye View of Linear Algebra: Why Is Matrix Multiplication Like That? appeared first on Towards Data Science.
In this post, Amazon shares how they developed a multi-node inference solution for Rufus, their generative AI shopping assistant, using Amazon Trainium chips and vLLM to serve large language models at scale. The solution combines a leader/follower orchestration model, hybrid parallelism strategies, and a multi-node inference unit abstraction layer built on Amazon ECS to deploy models across multiple nodes while maintaining high performance and reliability.
This post describes an approach of combining three powerful technologies to illustrate an architecture that you can adapt and build upon for your specific financial analysis needs: LangGraph for workflow orchestration, Strands Agents for structured reasoning, and Model Context Protocol (MCP) for tool integration.
The Allen Institute of AI (Ai2)'s new physical AI model MolmoAct moves the needle for robots that can move freely in physical space.
In this post, we explore Amazon Bedrock AgentCore Memory, a fully managed service that enables AI agents to maintain both immediate and long-term knowledge, transforming one-off conversations into continuous, evolving relationships between users and AI agents. The service eliminates complex memory infrastructure management while providing full control over what AI agents remember, offering powerful capabilities for maintaining both short-term working memory and long-term intelligent memory across sessions.
In this post, we explore an innovative solution that uses Amazon Bedrock Agents, powered by Amazon Nova Lite, to create a conversational interface for Athena queries. We use AWS Cost and Usage Reports (AWS CUR) as an example, but this solution can be adapted for other databases you query using Athena. This approach democratizes data access while preserving the powerful analytical capabilities of Athena, so you can interact with your data using natural language.
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.