We are the last.
The last generation to be unaugmented.
The last generation to be intellectually alone.
The last generation to be limited by our bodies.
We are the first.
The first generation to be augmented.
The first generation to be intellectually together.
The first generation to be limited only by our imaginations.
The current discourse around AI and computation seems
to be shifting from the singularity (a hypothetical
moment when AI surpasses human intelligence in all
areas) to breaking computational and conceptual
walls—addressing the limits and bottlenecks that
arise in computational and cognitive systems.
Herbert Simon’s work on bounded rationality
acknowledges that human decision-making is constrained
by cognitive limits. In AI, we're now grappling with
these conceptual walls—AI has its own limits based
on algorithms, models, and theoretical understanding
of computation.
Even with novel algorithms, some fundamental barriers
remain due to the intrinsic hardness of certain problems.
This could be because of lower bounds on algorithmic
complexity or because the problem requires exponential
time to solve, regardless of how you design
the algorithm.
How it started:
We are the last.
The last generation to be unaugmented.
The last generation to be intellectually alone.
The last generation to be limited by our bodies.
We are the first.
The first generation to be augmented.
The first generation to be intellectually together.
The first generation to be limited only by our imaginations.
How its going:
The current discourse around AI and computation seems
to be shifting from the singularity (a hypothetical
moment when AI surpasses human intelligence in all
areas) to breaking computational and conceptual
walls—addressing the limits and bottlenecks that
arise in computational and cognitive systems.
Herbert Simon’s work on bounded rationality
acknowledges that human decision-making is constrained
by cognitive limits. In AI, we're now grappling with
these conceptual walls—AI has its own limits based
on algorithms, models, and theoretical understanding
of computation.
Even with novel algorithms, some fundamental barriers
remain due to the intrinsic hardness of certain problems.
This could be because of lower bounds on algorithmic
complexity or because the problem requires exponential
time to solve, regardless of how you design
the algorithm.
Hi,
Besides interesting discussion of digital immortal
versus analog mortal brains by Geoffrey Hinton .
Also a nice piece of history concerning ChatGPT LLMs.
The key are feature vectors. According to Geoffrey
Hinton’s own statements, there was a prototype of
a Little Language Model (lLM) in 1985,
he mentions it in the middle of his talk here:
Will Digital Intelligence Replace Biological Intelligence?
Geoffrey Hinton - 2024
https://www.youtube.com/watch?v=Es6yuMlyfPw
He spends a few minutes in the talk to explain
how feature vectors can represent meaning of words.
And I suspect his ILM has been reflected in this paper,
probably the ChatGPT LLM ancestor:
Learning Distributed Representations of Concepts
Geoffrey Hinton - 1986
https://www.cs.toronto.edu/~hinton/absps/families.pdf
Prologers should be familier with the example he
uses, i.e. Family Trees. BTW: The family tree of Geoffrey
Hinton himself is also interesting, he is great-great-grandson
of the logician George Boole.
Bye
Mild Shock schrieb:
How it started:
We are the last.
The last generation to be unaugmented.
The last generation to be intellectually alone.
The last generation to be limited by our bodies.
;
We are the first.
The first generation to be augmented.
The first generation to be intellectually together.
The first generation to be limited only by our imaginations.
How its going:
The current discourse around AI and computation seems
to be shifting from the singularity (a hypothetical
moment when AI surpasses human intelligence in all
areas) to breaking computational and conceptual
walls—addressing the limits and bottlenecks that
arise in computational and cognitive systems.
;
Herbert Simon’s work on bounded rationality
acknowledges that human decision-making is constrained
by cognitive limits. In AI, we're now grappling with
these conceptual walls—AI has its own limits based
on algorithms, models, and theoretical understanding
of computation.
;
Even with novel algorithms, some fundamental barriers
remain due to the intrinsic hardness of certain problems.
This could be because of lower bounds on algorithmic
complexity or because the problem requires exponential
time to solve, regardless of how you design
the algorithm.
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