If you’re a longtime reader, you probably know that our newsroom selects 10 breakthroughs every year that we think will define the future. This group exercise is mostly fun and always engrossing, but at times it can also be quite difficult.  We collectively pitch dozens of ideas, and the editors ...

What comes after Transformers? Google Research is proposing a new way to give sequence models usable long term memory with Titans and MIRAS, while keeping training parallel and inference close to linear. Titans is a concrete architecture that adds a deep neural memory to a Transformer style ...

Google is closing an old gap between Kaggle and Colab. Colab now has a built in Data Explorer that lets you search Kaggle datasets, models and competitions directly inside a notebook, then pull them in through KaggleHub without leaving the editor. What Colab Data Explorer actually ships? Kaggle ...

Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face under an Apache 2.0 license, and it targets forecasting workloads without ...

Microsoft has released VibeVoice-Realtime-0.5B, a real time text to speech model that works with streaming text input and long form speech output, aimed at agent style applications and live data narration. The model can start producing audible speech in about 300 ms, which is critical when a ...

In this tutorial, we explore hierarchical Bayesian regression with NumPyro and walk through the entire workflow in a structured manner. We start by generating synthetic data, then we define a probabilistic model that captures both global patterns and group-level variations. Through each snippet, we ...

We begin this tutorial by building a meta-reasoning agent that decides how to think before it thinks. Instead of applying the same reasoning process for every query, we design a system that evaluates complexity, chooses between fast heuristics, deep chain-of-thought reasoning, or tool-based ...

How do you turn slow, manual click work across browsers and desktops into a reliable, automated system that can actually use a computer for you at scale? Lux is the latest example of computer use agents moving from research demo to infrastructure. OpenAGI Foundation team has released Lux, a ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets such as two moons: PCA flattens the structure and mixes the classes together.  Kernel PCA fixes this ...

In this tutorial, we explore how we can orchestrate a team of specialized AI agents locally using an efficient manager-agent architecture powered by TinyLlama. We walk through how we build structured task decomposition, inter-agent collaboration, and autonomous reasoning loops without relying on ...

How do you keep RAG systems accurate and efficient when every query tries to stuff thousands of tokens into the context window and the retriever and generator are still optimized as 2 separate, disconnected systems? A team of researchers from Apple and University of Edinburgh released CLaRa, ...

Clustering models in machine learning must be assessed by how well they separate data into meaningful groups with distinctive characteristics.