Avoiding Mirror World…
“One thing that every scientist gets taught is that the map isn’t the territory. This means that the models and theories we build are not the actual world, they are simplified versions of it. Illustrations. And like maps they can be very detailed or very imprecise, beautiful or horrible. But they are not the world. We all know that. But AI’s don’t. Because for them the map actually is the territory”…
Contemporary German Philosopher — Markus Gabriel — was recently interviewed on a podcast where he was asked for his perspectives on Artificial Intelligence (Ai).
In responding to the question he first began by providing a definition of what he believed was intelligence — Intelligence is the capacity to solve a given problem in a finite amount of time — efficiency — problem solving — he then provides a definition of artificial — Artificial can be defined has being produced by humans but not via biological reproduction.
He then goes on to make further observations, asking a range of questions and then sharing his current perspective on Ai.
If Intelligence is the measurable capacity of solving a problem in a finite amount of time then nothing that does not have a problem can solve a problem.
Can a house solve a problem or was it the architect ?
The house is not intelligent.
The house is as intelligent has a mobile phone, coffee machine or a table
There is no such thing as Ai …
He explains that Ai are thought models in a hardware that uses modern physics and electronics.
The technologies are fully dependent on human use.
They are not autonomous or intelligent.
“What we really have in this digital age are systems of incredibly intelligent human and machine interfaces created by software engineers in order to manipulate us to use this machinery” …
Through a philosophical lens of making sense of these new computational machines he draws on a rich tradition of philosophical enquiry.
As outlined in — Reflexivity — 20th C. American John Dewey’s ideas anchored in the philosophy of Pragmatism saw a major evolution in thinking from his predecessors Hegel and Kant.
It was a fundamental shift in the nature of philosophy and our understanding of Reality.
From — a model of inquiry and a view of the nature of knowledge where the mind is attempting to mirror the World via an internal abstract Model of the World — to — a view of the World where we are in the World ( Human Agency).
It’s through our practical actions and experiences that we navigate Reality.
Knowledge is practice.
At the core of this enquiry are questions relating to the nature of what is Intelligence, what is Knowledge, what is Learning, what are Algorithms and the nature of these Computational Machines and Systems?
“Algorithms are computational capsules for processing data through inference and abstraction to solve Material World problems.
Mental World rules that compress patterns and regularities in our complex & uncertain Material World.
They can assist in improving predictability and order through searching for Ground Truths.
However, without meaning, context, agency and a lived experience – Material World – are Algorithms knowledge?”…
-The Complexity Void
Senior Google Researcher and Founder of the Ai platform Keras — Francois Chollet — in his 28 November 2017 Medium article titled — The implausibility of intelligence explosion — provides his perspective on the nature of Intelligence.
It is a view that diverges from the Singularity, HAL9000 and abstract Computational Intelligence narrative that dominates our modern day public discourse.
His perspectives are grounded in a situational (contextual) and embodied form of Intelligence where it is the interplay between the Mind, Body and Environment to solve problems that shapes Intelligence.
An evolutionary dance that requires context, experience and our search for Meaning.
Drawing on important ideas emerging from domains such as Theoretical Evolutionary Biology, Information Theory, Neuroscience, Computation, Sociology, Philosophy, Education, and Cybernetics.
Apart from the absence of Agency in these physically anchored Deep Learning and Machine Learning machines — there is an omission of a core human trait relating to Reflexivity.
Second Order Cybernetics and an ability of these Automatic Control Systems to reflect on themselves — their lived experience of Reality.
A concept that we covered in Reflexivity and In search of Ground Truths — a capacity for Internal Inquiry, observation and the practice of navigating the world that Dewey had outlined over a century ago as central to the process of Learning.
Understanding that information which has causal powers come from Learning.
Learning being the process of creating Knowledge through practice.
Learning requires context, meaning and a lived experience.
In a recent August 2020 MIT Technology Review article titled — Too many AI researchers think real-world problems are not relevant — it highlights the increasing trend across the Ai Community to simply focus on techniques to build abstract digital Models of the World rather than exploring how we apply these techniques to solving problems in our World.
It ties back to the emergence of the Pragmatic philosophers of Charles Sanders Pierce, William James and John Dewey that came forward with fresh ideas in the mid 19th to the early parts of the 20th Century.
A Philosophy that challenged the separation of the Mind from Human Agency and Experience.
The Mind-Body Dualism of Aristotle, Plato and Descartes.
What if this abstraction did not represent the true nature of intelligence?
What if these Computational Systems were simply tools for Human Sensemaking and a way to enhance Human Intelligence ( Collective Intelligence) — a bicycle of the Mind?
What if these Models of the World are moving further and further away from the World — a Reality Gap?
What if this Mirror World is increasingly colliding with our World?
To avoid Mirror World it will require us to revisit our core Truths and Beliefs.
The anthropomorphism of Ai — a shift from Artificial Intelligence descriptions of Deep & Machine Learning to descriptive terms such as Computational Pattern Recognition tools.
A recognition of the limits of abstraction and algorithms.
A deeper understanding of the Human Condition.
A further exploration of the nature of Learning, Knowledge and Intelligence.
An appreciation of the Power of Patterns and these computational tools for Human Sensemaking and navigating an increasingly Complex World.
In 1980 Steve Jobs — the co-founder of Apple — gave a presentation where he made the following observation:
“When we invented the personal computer, we created a new kind of bicycle…a new man-machine partnership…a new generation of entrepreneurs”…
10 years later in 1990 he expanded on this perspective in an interview he gave to the United States Library of Congress where he outlined his vision of the emergent Digital Age — information, knowledge, libraries, networks and human learning.
An alternative to a Techno-Dystopia.
A quote from the 3:32 minute mark of the Interview:
“I think one of the things that really separates us from the high primates is that we’re tool builders.
I read a study that measured the efficiency of locomotion for various species on the planet.
The condor used the least energy to move a kilometre.
And, humans came in with a rather unimpressive showing, about a third of the way down the list.
It was not too proud a showing for the crown of creation.
So, that didn’t look so good.
But, then somebody at Scientific American had the insight to test the efficiency of locomotion for a man on a bicycle.
And, a man on a bicycle, a human on a bicycle, blew the condor away, completely off the top of the charts.
And that’s what a computer is to me.
What a computer is to me is it’s the most remarkable tool that we’ve ever come up with, and it’s the equivalent of a bicycle for our minds”…
— Steve Jobs
In the 21st Century we have the opportunity to avoid Mirror World, however it will require us to revisit our truths and beliefs as they relate to Intelligence, Knowledge and Learning.
Combining Abstraction with Experience — a transition to an Integral Consciousness.
The Algorithmic (abstraction) with the Geometric (perception) with the Semantic (experience) .
A shift to viewing Classical Computing, Deep & Machine Learning and Knowledge Graphs & the Semantic Web as simply potential computational tools for Human Sensemaking.
All references and footnotes to be added shortly