Morning Overview on MSN

AI might not need huge training sets, and that changes everything

For a decade, the story of artificial intelligence has been told in ever larger numbers: more parameters, more GPUs, more ...
Tech Xplore on MSN

Decoding black box AI with human-readable data descriptions and influence

Artificial intelligence (AI), particularly deep learning models, are often considered black boxes because their ...

AI Models Are Starting to Learn by Asking Themselves Questions

An AI model that learns without human input—by posing interesting queries for itself—might point the way to superintelligence ...

Researchers poison their own data when stolen by an AI to ruin results

RAG, on the other hand, exists because many real questions depend on current, specific, or proprietary data (such as company ...
Opinion

New Platform Challenges AI Industry Hype, Advocates for Embodied Intelligence Over Language Models

Emerging Voice in Tech Analysis Questions Trillion-Dollar AI Valuations and Points to Robotics as True Future of Artificial Intelligence The real value of AI will come from the systems we build around ...
The Emory Wheel

Best Web Search APIs for LLM Training Data Aggregation

Performance. Top-level APIs allow LLMs to achieve higher response speed and accuracy. They can be used for training purposes, as they empower LLMs to provide better replies in real-world situations.
Security Boulevard

Securing the Knowledge Layer: Enterprise Security Architecture Frameworks for Proprietary Data Integration With Large Language Models

A practical overview of security architectures, threat models, and controls for protecting proprietary enterprise data in retrieval-augmented generation (RAG) systems.
Defense One

For DOD, the future of large language models is smaller

The U.S. military is working on ways to get the power of cloud-based, big-data AI in tools that can run on local computers, draw upon more focused data sets, and remain safe from spying eyes, ...
Semiconductor Engineering

Small Vs. Large Language Models

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal ...