Time Series and Trend Analysis Challenge Inspired by Real World Datasets
See how different time series methods reveal the shifts, surges, and stabilization in inflation expectations.
See how different time series methods reveal the shifts, surges, and stabilization in inflation expectations.
Author(s): Utkarsh Mittal Originally published on Towards AI. Introduction XGBoost (Extreme Gradient Boosting) has become the go-to algorithm for winning machine learning competitions and solving real-world prediction problems. But what makes it so powerful? In this comprehensive tutorial, we’ll unpack the mathematical foundations and practical mechanisms that make XGBoost superior to traditional gradient boosting methods. This tutorial assumes you have basic knowledge of decision trees and machine learning concepts. We’ll walk through the algorithm step-by-step with visual examples […]
Both Google and Apple are cramming new AI features into their phones and other devices, and neither company has offered clear ways to control which apps those AI systems can access. Recent issues around WhatsApp on both Android and iPhone demonstrate how these interactions can go sideways, risking revealing chat conversations beyond what you intend. Users deserve better controls and clearer documentation around what these AI features can access. After diving into how Google Gemini and Apple Intelligence […]
Learning science consistently shows us that true learning requires active engagement. This is fundamental to how Gemini helps you learn. Going beyond simple text and sta…
An overview, summary, and position of cutting-edge research conducted on the emergent topic of LLM introspection on self internal states
The CEO of the AI data center provider, which has Nvidia as an investor and a supplier, described the environment as a “violent change” in demand.
Port offers a proprietary alternative to Spotify’s popular devtool catalog Backstage, but with a valuable addition: it can be used to manage AI agents, too.
In this tutorial, we build an advanced meta-cognitive control agent that learns how to regulate its own depth of thinking. We treat reasoning as a spectrum, ranging from fast heuristics to deep chain-of-thought to precise tool-like solving, and we train a neural meta-controller to decide which mode to use for each task. By optimizing the trade-off between accuracy, computation cost, and a limited reasoning budget, we explore how an agent can monitor its internal state and adapt its […]