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MIT affiliates named 2025 Schmidt Sciences AI2050 Fellows

Two current MIT affiliates and seven additional alumni are among those named to the 2025 cohort of AI2050 Fellows.  Zongyi Li, a postdoc in the MIT Computer Science and Artificial Intelligence Lab, and Tess Smidt ’12, an associate professor of electrical engineering and computer science (EECS), were both named as AI2050 Early Career Fellows. Seven additional MIT […]

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Interview: From CUDA to Tile-Based Programming: NVIDIA’s Stephen Jones on Building the Future of AI

As AI models grow in complexity and hardware evolves to meet the demand, the software layer connecting the two must also adapt. We recently sat down with Stephen Jones, a Distinguished Engineer at NVIDIA and one of the original architects of CUDA. Jones, whose background spans from fluid mechanics to aerospace engineering, offered deep insights

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From Transformers to Associative Memory, How Titans and MIRAS Rethink Long Context Modeling

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 backbone. MIRAS is a general framework that views

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Cisco Released Cisco Time Series Model: Their First Open-Weights Foundation Model based on Decoder-only Transformer Architecture

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 task specific fine tuning. The model extends TimesFM 2.0

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Google Colab Integrates KaggleHub for One Click Access to Kaggle Datasets, Models and Competitions

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 announced the feature recently where they describe

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A Coding Implementation of a Complete Hierarchical Bayesian Regression Workflow in NumPyro Using JAX-Powered Inference and Posterior Predictive Analysis

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 set up inference using NUTS, analyze posterior distributions, and perform posterior

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Microsoft AI Releases VibeVoice-Realtime: A Lightweight Real‑Time Text-to-Speech Model Supporting Streaming Text Input and Robust Long-Form Speech Generation

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 language model is still generating the rest of

Microsoft AI Releases VibeVoice-Realtime: A Lightweight Real‑Time Text-to-Speech Model Supporting Streaming Text Input and Robust Long-Form Speech Generation Read More »

How to Build an Adaptive Meta-Reasoning Agent That Dynamically Chooses Between Fast, Deep, and Tool-Based Thinking Strategies

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 computation, and then adapts its behaviour in real time. By examining each

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Kernel Principal Component Analysis (PCA): Explained with an Example

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 limitation by mapping the data into a higher-dimensional feature space where nonlinear

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How to Design a Fully Local Multi-Agent Orchestration System Using TinyLlama for Intelligent Task Decomposition and Autonomous Collaboration

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 any external APIs. By running everything directly through the transformers library, we create

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