While
I was looking at another episode of Through the Wormhole (clip a bit further
down), on quantifying consciousness (or the math of consciousness), an
interesting similarity between the discourse on connected learning or networked
learning, and consciousness arose. When I also added the hive mind, or swarm theory to it… all of a sudden I thought: this is a fun parallel if you look at
the evolution of learning and plug it into an evolutionary, physics/math
perspective.
Community of experts parallel specialized brain regions
I
am part of online educators group, and I frequently reflect on what that means.
In a way it means that my direct family does not always know what I am doing, I
talk, but to them it is often gibberish as they do not have similar backgrounds
and interests. On the other hand, because I am a firm believer in educational
freedom (and Star Trek Society), I am also only part of that type of online
learners. Although I can enter into conversation with people who are more of
the powerful
This
also means my endeavors and experiments are on the outskirts of the
educational powerhouses. Yet, I do find that my research has been picked up by
some of these powerhouses (I can see it the data stream, and sometimes in some
of my reappearing content which is either attributed, or sometimes is not).
If
you take the brain and zoom out, you can see areas of expertise. And within
these areas you have very strong connected neurons (like the group of online
educators I feel I belong too), and lesser connected neurons (eg. other areas
of expertise). In between the brain regions, there are bridges and
communication often moves from one region to the other, even on specialized
tasks. The same happens if you look at interdisciplinary research, the field
experts come together, build bridges, but at the end reinforce the new
interdisciplinary knowledge that is assimilated into their own more specialized
discipline.
So,
looking from outer space, and visualizing the inter-connectivity of field
experts, with an overlay of interdisciplinary researchers… what might you get? I
would imagine a new type of consciousness will arise. The next evolutionary
step. Admittedly, sometimes I feel this could be scary: if we humans are put in
isolated spaces because of this (or become fertile fields that grow stem-cells
for artificial beings who harvest us…. Mmm, should probably stop reading SciFi),
or it feels comfortable, if we humans would be kept as ‘fun organic life’ and
we humans were provided with endless leisure time in which we could learn
whatever and from whoever (yes, my ideal world there).
We learn at increasing speed
Each
of us who loves learning has the potential to learn at bigger speed than ever
before (Internet, MOOC, the shoulders of giants and peers). This results in
stronger and more paths to more knowledge. Each one of us that has an interest
and a cognitive capacity to use and add to the area of robotics can now do this
(mentioned in a previous blogpost) which means the chances of someone in that group of practitioners being able to
lift that field into a much higher level of expertise also becomes a reality.
Then
at what level does the next spark of consciousness appear? What level of
information must be distributed across a network before it leaps out of the
network to become the next level of consciousness?
Calculating Consciousness
When
Integrated Information Theory came along (Integrated Information theory
https://en.wikipedia.org/wiki/Integrated_information_theory), all of a sudden the
mechanisms of consciousness were being quantified (article From the
Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory
3.0
),
Phi (consciousness) became a formula, and all life on earth could be calculated
for its amount of consciousness. A thrilling bit of research. The University of
Wisconsin has done some pioneering work in that area (to that extend that I had
a look at their job applications). In the series of Through the Wormhole,Season 5 Episode 8, they look into making consciousness quantifiable
Moving beyond the human brain
The
way each of us evolves throughout life feels natural to us. We know we start
out as babies, we then learn the basic human actions throughout our childhood,
and eventually – if all goes well – we become adult with a place in society. In
a way we know the path of raised consciousness each one of us passes throughout
life. But this feeling of knowing how consciousness evolves is of course – up
to now – not been reproduced in an artificial setting. We do make impressive
progress, but none of us humans knows when the next leap in consciousness, the
next leap in cognition will happen with artificial intelligence. We just move
forward, and once it does happen we will observe this birth of autonomous
artificial intelligence.
Referring to A network of artificial
neurons learns to use human language
An
interesting step along this way towards autonomous artificial intelligence was
recently described in research from the University of Sassari (Italy) and the University of Plymouth (UK) who have developed a cognitive model, made up of two million interconnected artificial neurons, able to learn to communicate using human language starting from a state of 'tabula rasa', only through communication with a human interlocutor. Taking some info from an article in the NeuroscientistNews: The ANNABELL (Artificial Neural Network with Adaptive Behaviour Exploited for Language Learning) and it is described in an article published in PLOS ONE and described in this article.
ANNABELL does not have pre-coded language knowledge; it learns only through communication with a human interlocutor, thanks to two fundamental mechanisms, which are also present in the biological brain: synaptic plasticity and neural gating. Synaptic plasticity is the ability of the connection between two neurons to increase its efficiency when the two neurons are often active simultaneously, or nearly simultaneously. This mechanism is essential for learning and for long-term memory. Neural gating mechanisms are based on the properties of certain neurons (called bistable neurons) to behave as switches that can be turned 'on' or 'off' by a control signal coming from other neurons. When turned on, the bistable neurons transmit the signal from a part of the brain to another, otherwise they block it. The model is able to learn, due to synaptic plasticity, to control the signals that open and close the neural gates, so as to control the flow of information among different areas
ANNABELL does not have pre-coded language knowledge; it learns only through communication with a human interlocutor, thanks to two fundamental mechanisms, which are also present in the biological brain: synaptic plasticity and neural gating. Synaptic plasticity is the ability of the connection between two neurons to increase its efficiency when the two neurons are often active simultaneously, or nearly simultaneously. This mechanism is essential for learning and for long-term memory. Neural gating mechanisms are based on the properties of certain neurons (called bistable neurons) to behave as switches that can be turned 'on' or 'off' by a control signal coming from other neurons. When turned on, the bistable neurons transmit the signal from a part of the brain to another, otherwise they block it. The model is able to learn, due to synaptic plasticity, to control the signals that open and close the neural gates, so as to control the flow of information among different areas
How many humans does it take to spark
AI?
It
could be the start of a future joke, but at present it is something which
interests me. Because if the brain sends out electric currents between
interconnected neurons, then what happens if humans – working on the same field
– connect using the electric currents of the Internet? Something to look
forward to. (Image credit Bruno Golosio)