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Spatial Web and the Era of AI - Part 1 | KB #10 - Spatial Web AI Podcast
Spatial Web and the Era of AI - Part 1 #futureofai #artificialintelligence by Denise Holt
Deep Learning Language Models vs. Cognitive Science
The
pioneering goal of Artificial Intelligence has been to understand how humans
think. The original idea was to merge intellectual and computer contributions
to learn about cognition.
In the
1990’s, a shift took place from a knowledge-driven AI approach to a data-driven
AI approach, replacing the original objectives with a type of Machine Learning
called Deep Learning, capable of analyzing large amounts of data, drawing
conclusions from the results.
Deep
Learning is a predictive machine model that operates off of pattern
recognition. Some people believe that if you simply feed the model more and
more data, then the AI will begin to evolve on its own, eventually reaching AGI
(Artificial General Intelligence), the ‘Holy Grail’ of AI.
This
theory, however, is viewed as being deeply flawed because these AI machines are
not capable of “awareness” or the ability to “reason.” With Machine
Learning/Deep Learning AI, there is no “thinking taking place.”
These
predictive machines are void of any actual intelligence.
Scaling
into bigger models by adding more and more parameters until these models
consume the entire internet, will only prove useful to a point.
A
larger data bank will not be able to solve for recognizing toxicity within the
data structures, nor will it enable the ability to navigate sensitive data,
permissioned information, protected identities, or intellectual property. A
larger data bank does not enable reasoning or abstract thinking.
For AI
to achieve the ultimate goal of AGI we need to be able to construct cognitive
models of the world and map ‘meaning’ onto the data. We need a large database
of abstract knowledge that can be interpreted by a machine imparting a level of
‘awareness’.
Newton
vs. Einstein
Model
Based AI for Active Inference is an Artificial Intelligence methodology that
possesses all the ingredients required to achieve the breakthrough to AGI by
surpassing all of the fundamental limitations of current Machine Learning/Deep
Learning AI.
The
difference between Machine Learning AI and Active Inference AI is as stark as
the jump from Newton’s Laws of Universal Gravitation to Einstein’s Theory of
Relativity.
In the
late 1800’s, physicists believed that we had already discovered the laws that
govern motion and gravity within our physical universe. Little did they know
how naïve Isaac Newton’s ideas were, until Albert Einstein opened mankind’s
eyes to spacetime and the totality of existence and reality.
This
is what is happening with AI right now.
It’s
simply not possible to get to AGI (Artificial General Intelligence) with a
machine learning model, but AGI is inevitable with Active Inference.
______________________
Special thanks to Dan Mapes, President and Co-Founder, VERSES
AI and Director of The Spatial Web Foundation. If you’d like to know more about
The Spatial Web, I highly recommend a helpful introductory book written by Dan
and his VERSES Co-Founder, Gabriel Rene, titled, “The Spatial Web,” with a
dedication “to all future generations.”
Listen to more episodes in my Knowledge Bank Playlist to
learn everything you need to know to stay ahead of this rapidly accelerating
technology.
Check out more at, SpatialWebAI and Spatial Web Foundation
#futureofai
#artificialintelligence
#spatialweb
#intelligentagents
#aitools
View all episodes
By Denise HoltSpatial Web and the Era of AI - Part 1 #futureofai #artificialintelligence by Denise Holt
Deep Learning Language Models vs. Cognitive Science
The
pioneering goal of Artificial Intelligence has been to understand how humans
think. The original idea was to merge intellectual and computer contributions
to learn about cognition.
In the
1990’s, a shift took place from a knowledge-driven AI approach to a data-driven
AI approach, replacing the original objectives with a type of Machine Learning
called Deep Learning, capable of analyzing large amounts of data, drawing
conclusions from the results.
Deep
Learning is a predictive machine model that operates off of pattern
recognition. Some people believe that if you simply feed the model more and
more data, then the AI will begin to evolve on its own, eventually reaching AGI
(Artificial General Intelligence), the ‘Holy Grail’ of AI.
This
theory, however, is viewed as being deeply flawed because these AI machines are
not capable of “awareness” or the ability to “reason.” With Machine
Learning/Deep Learning AI, there is no “thinking taking place.”
These
predictive machines are void of any actual intelligence.
Scaling
into bigger models by adding more and more parameters until these models
consume the entire internet, will only prove useful to a point.
A
larger data bank will not be able to solve for recognizing toxicity within the
data structures, nor will it enable the ability to navigate sensitive data,
permissioned information, protected identities, or intellectual property. A
larger data bank does not enable reasoning or abstract thinking.
For AI
to achieve the ultimate goal of AGI we need to be able to construct cognitive
models of the world and map ‘meaning’ onto the data. We need a large database
of abstract knowledge that can be interpreted by a machine imparting a level of
‘awareness’.
Newton
vs. Einstein
Model
Based AI for Active Inference is an Artificial Intelligence methodology that
possesses all the ingredients required to achieve the breakthrough to AGI by
surpassing all of the fundamental limitations of current Machine Learning/Deep
Learning AI.
The
difference between Machine Learning AI and Active Inference AI is as stark as
the jump from Newton’s Laws of Universal Gravitation to Einstein’s Theory of
Relativity.
In the
late 1800’s, physicists believed that we had already discovered the laws that
govern motion and gravity within our physical universe. Little did they know
how naïve Isaac Newton’s ideas were, until Albert Einstein opened mankind’s
eyes to spacetime and the totality of existence and reality.
This
is what is happening with AI right now.
It’s
simply not possible to get to AGI (Artificial General Intelligence) with a
machine learning model, but AGI is inevitable with Active Inference.
______________________
Special thanks to Dan Mapes, President and Co-Founder, VERSES
AI and Director of The Spatial Web Foundation. If you’d like to know more about
The Spatial Web, I highly recommend a helpful introductory book written by Dan
and his VERSES Co-Founder, Gabriel Rene, titled, “The Spatial Web,” with a
dedication “to all future generations.”
Listen to more episodes in my Knowledge Bank Playlist to
learn everything you need to know to stay ahead of this rapidly accelerating
technology.
Check out more at, SpatialWebAI and Spatial Web Foundation
#futureofai
#artificialintelligence
#spatialweb
#intelligentagents
#aitools