10 posts
Apple's sensor-fusion research hints at a privacy-sensitive future where models learn from multimodal context without simply grabbing more cloud data.
Musk's idea of using idle Teslas for inference turns a car fleet into a provocative vision of distributed AI infrastructure.
Apple's image-editing research suggests smarter creative tools may learn from failed edits instead of hiding them.
A decade after Her, the post asks how close today's AI companions really are to Samantha, technically and emotionally.
AI classroom companions echo William Gibson's fictional guides, raising questions about education, intimacy, and dependence.
Neural texture compression promises richer game graphics with lower memory costs, changing the pipeline for artists and developers.
SEAL points toward language models that rewrite their own training material, hinting at AI systems that learn after deployment.
A machine-learning Christmas poem turns training runs, GPUs, and convergence into a festive technical fable.
A year-end inventory of ten unresolved AI problems that still define the frontier despite rapid progress.
Multimodal LLMs are explained as a key step toward systems that can reason across text, images, and other signals.