InfoWorld

Databricks adds MemAlign to MLflow to cut cost and latency of LLM evaluation

By replacing repeated fine‑tuning with a dual‑memory system, MemAlign reduces the cost and instability of training LLM judges ...
No Jitter

Learning to Live With Your UCaaS LLM, Part 1

(Author’s note: this article in its entirety was written without the help of generative AI (Gen AI) in any way, nor was AI used to generate any graphics, either.) Leveraging the large language models ...
InfoWorld

16 open source projects transforming AI and machine learning

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...
Nature

Could machine learning help to build a unified theory of cognition?

Giosuè Baggio is in the Language Acquisition and Language Processing Lab, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway. Binz et al. explore horizontal integration ...
Diginomica

Want better LLM results? Then it's time for AI evaluation tools - learning from Galileo's RAG and agent metrics

A consistent media flood of sensational hallucinations from the big AI chatbots. Widespread fear of job loss, especially due to lack of proper communication from leadership - and relentless overhyping ...
CRN

The 10 Hottest Data Science And Machine Learning Tools Of 2025 (So Far)

The data science and machine learning technology space is undergoing rapid changes, fueled primarily by the wave of generative AI and—just in the last year—agentic AI systems and the large language ...

Agnik International is Developing New Scalable Distributed Machine Learning Architecture for Large Language Models and Physical AI Applications

Agnik International, a leading data science company with market-leading analytic products, today announced that they are developing a new distributed machine learning architecture based on decades of ...
WinBuzzer

AI: Memory Bottleneck Emerges as Main LLM Inference Challenge

Google researchers have revealed that memory and interconnect are the primary bottlenecks for LLM inference, not compute power, as memory bandwidth lags 4.7x behind.