It is the process of rapidly ever improving differentiation between noise and signal patterns and constant generalization of those that produces intelligence, not merely compression of data. [D]
Mirrored from r/MachineLearning for archival readability. Support the source by reading on the original site.
Until we can design a mathematical system with one unavoidable intrinsic goal that drives it with undeniable force and encode that to hardware, plug it into a simulator of raw data, and give it the initial faculties to form, store, manipulate and alter all patterns based on its own feedback with no restriction on developing new faculties; all this AI noise will only serve investors accumulating wealth.
The currently required data sanitization and filtration, and the missing intrinsic unavoidable goal, kill the very base requirement for intelligence to emerge as we see and value it in humans.
Of course if that happens, new questions arise: human safety from conflict with the system; not just the current concerns which are human misuse related; and what ideology to follow while deciding the goal. But those could be dealt with, given we have the base.
For the present situation of things: the current increasing productivity automation is ofcourse undeniable. But that should not be a bad thing if we look towards the long horizon of things. People enjoy cooking, and if doing the dishes and the prep and the shopping were to be automated, it should only make things better. Ofcourse if we can figure out a way to tackle the unemployment and resource access problem and thus wealth concentration, for people that were too specialized for the old system of labour.
Thoughts?
[link] [comments]
More from r/MachineLearning
-
A map of the latest 11 million papers split by semantic similarity and time slices [P]
Jun 30
-
Update on CVIL: the free CV interview prep checklist after landing my internship... just added Segmentation, OCR, and VLM sections [D]
Jun 30
-
EACL 2027: Author response and author-reviewer discussion are now two separate stages and allow more time [D]
Jun 30
-
Loss functions in Instance Representation Learning [R]
Jun 29
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.