AI Worth Knowing — What the technology actually is
How It Works
Learning, in a neural network, means adjusting numbers until the errors shrink
The word was borrowed from human experience and it flatters the mechanism; what actually happens is a very large curve being fitted through a very large number of points.
By Manish Trivedi · · 4 min read
In focus: In The World
All of In The World →Where these systems are already deployed, and what changed when they were.
In The World
Machine translation cleared a threshold and left its hardest problems behind
· 4 min read
In The World
When a first draft costs almost nothing the bottleneck moves to checking it
· 3 min read
In The World
Open weights are not open source, and the difference decides who can check the work
· 3 min read
Elsewhere
Everything →
In The World
Machine translation cleared a threshold and left its hardest problems behind
· 4 min read
Also worth reading
A model trained on past decisions will reproduce the reasoning behind them
Recognition systems perform unevenly and the unevenness is not random
When a first draft costs almost nothing the bottleneck moves to checking it
Being confidently wrong is the default behaviour, not a malfunction
What a benchmark score measures and what it quietly does not
Correlation carries these systems a long way and then stops at the edges
A system’s account of its own reasoning is a reconstruction, not a record