Dec 15, 2024
Current AI practice is not engineering, even when it aims for
practical applications, because it is not based on scientific
understanding. Enforcing engineering norms on the field could lead
to considerably safer systems.
This episode has a lot of links! Here they...
Oct 16, 2024
Few AI experiments constitute meaningful tests of hypotheses.
As a branch of machine learning research, AI science has
concentrated on black box investigation of training time phenomena.
The best of this work is has been scientifically excellent.
However, the hypotheses tested are mainly irrelevant to user and
societal...
Sep 8, 2024
Do AI As Science And Engineering Instead - We’ve seen that
current AI practice leads to technologies that are expensive,
difficult to apply in real-world situations, and inherently unsafe.
Neglected scientific and engineering investigations can bring
better understanding of the risks of current AI technology,
and...
Aug 25, 2024
Current AI results from experimental variation of mechanisms,
unguided by theoretical principles. That has produced systems that
can do amazing things. On the other hand, they are extremely
error-prone and therefore unsafe. Backpropaganda, a collection of
misleading ways of talking about “neural networks,”...
Aug 11, 2024
The conclusion of this chapter.
So-called “neural networks” are extremely expensive, poorly
understood, unfixably unreliable, deceptive, data hungry, and
inherently limited in capabilities. In short: they are
bad.