NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models.…
SWARM_TOOLS = [ { “type”: “function”, “function”: { “name”: “task_update”, “description”: “Update the status of a task. Use ‘in_progress’ when starting, ‘completed’ when done.”, “parameters”: { “type”: “object”, “properties”: { “task_id”: {“type”: “string”, “description”: “The task ID”}, “status”: {“type”: “string”,…
Who benefits from artificial intelligence? This basic question, which has been especially salient during the AI surge of the last few years, was front and center at a conference at MIT on Wednesday, as speakers and audience members grappled with…
The following is a joint announcement from the MIT School of Architecture and Planning, MIT Schwarzman College of Computing, Hasso Plattner Institute, and Hasso Plattner Foundation. The MIT Morningside Academy for Design (MAD), MIT Schwarzman College of Computing, Hasso Plattner…
In the current landscape of Retrieval-Augmented Generation (RAG), the primary bottleneck for developers is no longer the large language model (LLM) itself, but the data ingestion pipeline. For software developers, converting complex PDFs into a format that an LLM can…
Google has officially released the Colab MCP Server, an implementation of the Model Context Protocol (MCP) that enables AI agents to interact directly with the Google Colab environment. This integration moves beyond simple code generation by providing agents with programmatic…
import os, sys, subprocess, importlib, pathlib SENTINEL = “/tmp/diffrax_colab_ready_v3” def _run(cmd): subprocess.check_call(cmd) def _need_install(): try: import numpy import jax import diffrax import equinox import optax import matplotlib return False except Exception: return True if not os.path.exists(SENTINEL) or _need_install(): _run([sys.executable, “-m”,…
The scaling of inference-time compute has become a primary driver for Large Language Model (LLM) performance, shifting architectural focus toward inference efficiency alongside model quality. While Transformer-based architectures remain the standard, their quadratic computational complexity and linear memory requirements create…
MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items. Now, the researchers are leveraging generative…
Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the…
