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Why Voice Assistants Get Fooled
Learn why voice assistants get fooled: wake-word misfires, noisy rooms, accents, intent confusion, training-data gaps, and how to reduce mistakes and misuse.
Sean William
Faster AI Video Recognition on Smartphones
Learn how to speed up on-device video recognition on smartphones by profiling bottlenecks, cutting input cost, picking mobile models, and using NNAPI/Core ML smartly.
Georgia Vincent
Control Robots with Human Muscle Signals
Learn how to control robots with EMG muscle signals: choose gestures or force, pick sensors, place electrodes, process features, map commands, and add safety.
Georgia Vincent
Lower AI Energy Use Without Sacrificing Performance
Learn how to lower AI energy use without losing performance using baselines, smaller models, quantization, token savings, caching, batching, and smarter training.
Madison Evans
AI Detects Hidden Signals
Learn what it means when AI detects hidden signals, how to separate signal from noise, avoid overfitting, and validate patterns before automation.
Kristina Cappetta
New Advances in AI Research
Learn what’s driving rapid AI research advances—multimodal models, agents, reasoning and retrieval, efficiency, and safety—and how to track progress vs hype.
Celia Kreitner
AI Evaluation Frameworks Simplify Model Selection
Learn how AI evaluation frameworks make model selection repeatable with scorecards, realistic eval sets, failure-mode metrics, fair bake-offs, and continuous monitoring.
Jennifer Redmond
Machine Learning Models Scale More Efficiently
Learn why scaling machine learning models efficiently beats raw accuracy, with practical guidance on compute, data, parallelism, serving costs, and bottlenecks.
Elva Flynn
Automated Network Design Improves AI Efficiency
Learn how automated network design reduces AI bottlenecks by optimizing topology, routing, and placement for faster training step time and lower p99 inference latency.
Elva Flynn
AI Decision Models Handle Uncertain Conditions
Learn how AI decision models handle uncertain conditions—noise, missing data, and distribution shift—using thresholds, deferrals, guardrails, and monitoring.
Maurice Oliver
Top R and Python Libraries for Creating Stunning Data Visualizations
The best data visualization tools in R and Python, including ggplot2, Matplotlib, and Plotly, to turn complex data into actionable insights.
Alison Perry
From DevOps to AIOps: 5 Key Challenges That Need Solving
Discover the 5 key challenges DevOps must solve to prepare for AIOps, from data quality to workforce readiness, and learn how to build a solid foundation
Alison Perry
Agentic Automation: The Path to a Seamlessly Orchestrated Enterprise
Know how agentic automation enables AI-driven orchestration, boosts efficiency, and prepares enterprises for future scalability
Tessa Rodriguez
Understanding the Differences Between ANN, CNN, and RNN Models
Understanding the strengths of ANN, CNN, and RNN can help you design smarter AI solutions. See how each neural network handles data in its own unique way
Alison Perry
Checking and Creating Palindrome Numbers Using Python
Ever noticed numbers that read the same backward? Learn how to check, create, and play with palindrome numbers using simple Python code
Tessa Rodriguez