In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming […]
The post A Coding Implementation on Microsoft’s Phi-4-Mini for Quantized Inference Reasoning Tool Use RAG and LoRA Fine-Tuning appeared first on MarkTechPost.
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer.
Not obviously wrong — just outdated enough to mislead.
That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw.
The fix wasn’t in the retriever or the model. It was in the gap between them.
I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches.
The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.
OpenAI CEO Sam Altman and Microsoft CTO Kevin Scott. | Image: Getty Images
When OpenAI was busy experimenting with AI-powered gaming bots, Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman were in the early days of forming an AI partnership. Court documents from the ongoing Musk v. Altman trial have provided a rare look at the communications between Microsoft's top executives about investing in OpenAI and fears the AI startup could "storm off to Amazon" and "shit-talk" Microsoft.
Just days after OpenAI showed a bot beating a Dota 2 professional in the summer of 2017, Altman responded to Nadella's congratulations email with a proposal for a much bigger partnership with OpenAI to fund its next phase of AI resear …
Read the full story at The Verge.
RAG is a model that connects large language models to live agency knowledge bases — enabling grounded, mission-specific responses, rather than generic outputs.
MRC (Multipath Reliable Connection) is a new open networking protocol developed by OpenAI in partnership with AMD, Broadcom, Intel, Microsoft, and NVIDIA that improves GPU networking performance and resilience in large-scale AI training clusters by spreading packets across hundreds of paths simultaneously, recovering from network failures in microseconds, and enabling supercomputers with over 100,000 GPUs to be built using only two tiers of Ethernet switches.
The post OpenAI Introduces MRC (Multipath Reliable Connection): A New Open Networking Protocol for Large-Scale AI Supercomputer Training Clusters appeared first on MarkTechPost.
The Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce, has signed agreements with Google DeepMind, Microsoft, and xAI that would give the agency the ability to vet AI models from these organizations and others prior to their being made publicly available.
According to a release from CAISI, which is part of the department’s National Institute of Standards and Technology (NIST), it will “conduct pre-deployment evaluations and targeted research to better assess frontier AI capabilities and advance the state of AI security.”
The three join Anthropic and OpenAI, which signed similar agreements almost two years ago during the Biden administration, when CAISI was known as the US Artificial Intelligence Safety Institute.
An August 2024 release about those agreements indicated that the institute planned to provide feedback to both companies on “potential safety improvements to their models, in close collaboration with its partners at the UK AI Safety In
