#shape

The Changing Shape of Doom: My AI Safety Explainer

Linch asked for more people to write basic AI safety explainers [1] . Here's mine. My main method is to ignore all the fictional evidence [2] and think about how achievable and how dangerous building AI seems just extrapolating from contemporary tech and how that has changed as the tech has advanced. My main thesis is that achievability has gone up with time, but the danger has varied and the present danger is serious but different from the classic fear. Serious attempts at building machine intelligence started seventy-five years ago [3] . No one had any idea how to do it though. We had comput

LessWrong · EN

Human-Centered Design: How does design shape the way we live, feel, and connect?

To say that we design "for people" seems like a fundamental premise of architecture. However, this statement presents a problem: people are not a homogeneous category. Human-Centered Design invites us to question whom we observe, listen to, and represent when making design decisions. Through an interdisciplinary conversation, we will explore how physical, sensory, and social needs influence architecture and interior design, and how these shape our way of living, feeling, and connecting. Drawing from the fields of health, air transport, education, urban planning, and architectural representatio

ArchDaily · EN

The Invisible Brief: How Laboratory Needs Shape Research Buildings

A floor that feels solid may still be moving. Vibrations travel through the ground and across buildings, often at levels too small for people to notice. Most have little effect on everyday activities, but in some research laboratories , instruments measuring matter at the nanoscale can register movements this small, and even traffic several miles away can interfere with an experiment.

ArchDaily · EN

Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning Research

What is the Role of Mathematics in Modern Machine Learning? The past decade has witnessed a shift in how progress is made in machine learning. Research involving carefully designed and mathematically principled architectures result in only marginal improvements while compute-intensive and engineering-first efforts that scale to ever larger training sets

The Gradient · EN