Persistent AI memory: what to keep, trust, and forget
Separate context, retrieval, and model adaptation, then test persistent AI memory for provenance, corrections, permissions, deletion, and useful continuity.
From the archive
carousel
Articles
LLM observability: tracing failures from request to outcome
A practical guide to AI telemetry, evaluation, privacy and incident response, with a worked investigation and checks for selecting an observability platform.
Liquid Neural Networks & Edge‑Optimized Foundation Models: Sustainable On-Device AI for the Future
Liquid Neural Networks (LNNs) are transforming the landscape of edge AI, offering lightweight, adaptive alternatives to traditional deep learning models. Inspired by biological neural dynamics, LNNs operate with continuous-time updates,…
AI Hardware Innovations: GPUs, TPUs, and Emerging Neuromorphic and Photonic Chips Driving Machine Learning
AI hardware choices depend on the model, memory, interconnect, software, and operating conditions—not a chip label alone. This guide compares GPUs and specialized accelerators with edge, neuromorphic, photonic, and quantum…
Test Time Compute (TTC): Enhancing Real-Time AI Inference and Adaptive Reasoning
Test Time Compute (TTC) represents a transformative shift in how AI systems process information, moving beyond traditional static inference to enable real-time adaptive reasoning. OpenAI's groundbreaking o1 model showcases this…
Benchmarking Large Language Models: A Comprehensive Evaluation Guide
Compare LLM benchmarks, scoring methods and limits, then design workflow tests for answer quality, evidence, permissions, latency and cost.
Explainable AI: techniques, practical uses and the limits of an explanation
An explanation can help someone inspect a model’s output, but it does not establish that the decision is correct or compliant. This guide compares LIME, SHAP, counterfactuals, and interpretable models,…
Neuromorphic Computing: How Brain-Inspired Technology Works, Where It Fits, and What It Still Cannot Promise
Neuromorphic computing takes design cues from nervous systems, including event-based signals and distributed state. This guide explains representative architectures and hardware, then considers candidate workloads and the evidence needed to…
Guiding the Next Generation: Ethical AI Use in Education
AI can support explanation, practice, and feedback, but students still need to understand and verify their work. This guide outlines useful learning applications, risks such as submitting generated work as…
You must be logged in to post a comment.