MarktechPost·
We build an end-to-end forecasting workflow with TimeCopilot on a panel of real airline passenger data and a synthetic seasonal series with injected anomalies. We evaluate statistical, foundation, and optional GPU-based models using rolling cross-validation and multiple error metrics. We generate probabilistic forecasts with prediction intervals, visualize future trends, and flag unusual observations. We then explore TimeCopilot's optional LLM agent, which selects a model and explains its predictions. The post How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection appeared first on MarkTechPost.
Read full articleGeneral Intuition, the New York-based AI startup building foundation models that teach agents to reason through space and time, is in talks to raise approximately $300 million, according to sources familiar with the matter. The round would value the company at just over $2 billion and follows a $134 million seed raised eight months ago […]
Insider Brief Alibaba has introduced a new suite of robotics foundation models designed to help robots navigate, manipulate objects and predict how the physical world will respond to their actions. “The Qwen family of foundation models already gives strong perception and reasoning about the physical world,” the company wrote in a blog post. “But seeing is […]
Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we found out how foundation models are being used for conservation efforts, how AI can help with scarce resource allocation, and how color metaphors and LLMs […]
The data observability market has evolved rapidly over the past five years. What began as a niche category focused primarily on monitoring modern data pipelines has expanded into a broad ecosystem encompassing anomaly detection, data quality, lineage, schema monitoring, business observability, and increasingly, AI-driven analytics. As organizations continue investing in... Read more » The post The 2026 Data Observability Vendor Database: 20+ Platforms by Founding Year, Funding, Hosting, and Pricing appeared first on Big Data Analytics News.
Apple will open the doors to developers at its Worldwide Developer Conference (WWDC) next week. Beyond a big push on AI and new OSes focused on stability and performance, what should developers expect? Mostly it’s about new APIs, Foundation Models, and App Intents; here’s what I’ve been able to figure out so far. Foundation Models Apple has been building new Apple Intelligence APIs. One way it is achieving this is to take models made with Google Gemini, then distill and shrink them to fit inside (and run on) its devices. The progression will be to introduce these as a new crop of Foundation models developers can use in their apps. There’s more: New APIs mean developers will be able to run Apple Intelligence tools such as summarization directly on the customer device, all offline, all private. Developers that use Apple’s standard text editing/entry views will gain access to improved Apple-developed tools inside their apps without custom-coding. Because intelligence takes place on the us
Led by ORNL, the DataHub supports AI-driven workflows for lifetime prediction, anomaly detection and digital twin development using curated energy storage data May 28, 2026 — The Department of Energy’s Rapid […] The post ROVI DataHub Unifies Energy Storage Data to Accelerate Long Duration Battery Innovation appeared first on AIwire.
Insider Brief PRESS RELEASE — Graphon AI emerged from stealth with $8.3 million in seed funding to build a new class of AI infrastructure: a pre-model intelligence layer that captures how data connects — making foundation models more accurate and capable of reasoning over unlimited multimodal data. The round was led by Arvind Gupta of Novera Ventures, with participation from […]
Humanoid robots are crossing the gap from lab demos to real warehouses, kitchens, and factory floors — but most teams discover the hard part isn’t the model. It’s the data behind it. Foundation models can recognize a cup; deploying a humanoid that picks one up, hands it to an elderly person, and adapts when the […]
