Embed Size (px)
Citation preview
1
Talk given by Etic Lab’s Stephanie Moran at Goldsmiths Visual Cultures Department on 24th October 2019.
KRAKEN? What does it mean to communicate with an Alien Intelligence
and how might we try to do it?
Astrobiology and Michael Arbib's octoplus
Astrobiology, a form of future-‐ and outer-‐space-‐oriented speculative evolutionary
biology, already considers this question.
Like Olaf Stapledon’s bacterial / microbial alien intelligence in his sci-‐fi Last and First
Men, we may not even recognise another intelligence’s arrival.
Michael Arbib’s [2011] octoplus was a thought experiment in how language could
have evolved differently, given a different set of sensory and cognitive apparatus,
based on the alien entity of the octopus. Arbib asks,
“what might be some of the properties of a language evolved from the basis of
chromatophores and body texture rather than visual control of the hand?”
Arbib develops what he calls an Octoplus – an imagined octopus-‐being that has
evolved differently in the different environment of another planet. Most octopuses
only live long enough to reproduce, about two years, and die shortly after; they do
not raise their young. So the young learn for themselves how to inhabit the world.
They mostly live on their own for most of their lives, shaped by solitary hunting and
2
danger. They camouflage, communicate, hunt and avoid predators with displays of
colour, texture and pattern that can be breathtakingly varied; so far as biologists
understand, these are produced by a system of light-‐sensing and reflecting and
colour-‐producing cells in their skin and despite apparent colour blindness, or lack of
colour vision as found in humans – their eyes, which evolved separately from the
human line of eye evolution but which is remarkably similar in many ways, appear to
see in black and white. Combined with their rapid shapeshifting capacity, from their
lack of fixed form, their distributed neural network, the highly sensitive suckers on
their arms that taste and feel, their plasticity and visual system affords them a high
degree of intelligence from and in relation to their environment.
While there are certain patterns and signals marine biologists have presumed to
interpret – the obvious cases of camouflage, and mating / warning / threatening
signals – there is a whole range of octopus display that remains uninterpretable or
incomprehensible to biologists, although it may communicate on an aesthetic level.
There are rapidly changing displays that have been called passing cloud, that could
be dynamic marine camouflage or tactics for confusing predators or prey, imitating
the effect of shadows passing overhead; more mysteriously, there are what
scientists call chromachatter and other seemingly random displays, out of the
3
context of obvious communication, hunting or hiding; they also appear to dream
while emitting rapidly changing displays.
Arbib imagines an evolution that emphasized sociality and longer lives could lead to
development of a complex language, molded by their morphology and a different set
of environmental affordances and evolutionary responses.
The mirror system hypothesis offers a gestural basis for language; it is one of several
theories suggesting that language evolved from manual dexterity, and humans’ two
opposable thumbs, and originates in pantomimed representations.
Arbib proposes that octopuses’ colour-‐, pattern-‐ and texture-‐matching “with the
statistics of what it sees around it” constitutes an equivalent “sensorimotor
transformation” to that of the human hand gesture; he posits the octoplus language
on an expanded ability to control this, their capacity for imitation forming the basis
for the visual miming of objects from memory, in conjunction with movements of
their arms. Then the development of grammar from separation out of action-‐mimes
and object-‐mimes, based in abstraction of existing split communications as when
male octopuses are courting a female with the visual signaling on one side of their
body and simultaneously emitting an aggression colour signal towards other males
on the side facing away from her. Arbib describes this as a “video screen model”
where the display becomes “an assemblage of subdisplays”. He suggests that
concepts of action and object are universal to human language, although verb and
noun categorisation varies.
The experience-‐world of an organism is subjective, “a consequence of its specialized
receptor and effector apparatus”, and its particular embodiment and social
structure; think about the ways in which humans’ sensory perceptual apparatus,
often with emphasis on sight and sound, frame how humans inhabit their worlds
(rather than dogs’ perceptual emphasis on smell for example, in reading their
environments and social relationships); and the ways in which the structure of
human societies also structure relationships to and perceptions of our
4
environment(s). Similarly, octopus concepts and syntax, formed from their
perceptions of their environments and relationships, are likely to be entirely
different from human ones.
[As Arbib says,] “any intelligence with which we are likely to establish communication
will have vision, language and a sophisticated knowledge of applied mathematics, …
[but] can we posit convergent cultural evolution in expressiveness?”
What would octopus geometry consist of, for a being with no bodily angles that
inhabits a world with no fixed horizon line and three dimensions of travel, eight
semi-‐autonomous arms, a body with no fixed form; geometry is etymologically
derived from and originated in land measurements, rather than measurements in a
fluid all-‐round 360 degrees. Would their version of geometry be grounded in non-‐
linear, non-‐Euclidean structures? Is it possible to reverse-‐engineer a speculative
nonhuman language based on this hypothesis? Is this all based on very
anthropocentric ideas about what constitutes language or communication?
Octopuses’ short life spans, and for the most part lack of communal living, are due to
environmental pressures, which in turn have formed their quick intelligence /
cognition as predatorial and predated beings in a fast-‐moving dangerous
environment; although there is evidence of octopuses in the deeper water we know
little about, that live longer; and recent discoveries of octopus colonies cohabiting
covetable areas of ocean real estate, vast seafloor accumulations of discarded shells
that provide enough material cover for concealing camouflaged octopuses, to offer a
degree of security in a particularly hazardous space shared with sharks and other
large predators.
As in Adrian Tchaikovsky’s sci-‐fi novel Children of Time, where an anthropogenic
virus to speed up evolutionary processes and enhance other species’ intelligence is
released, there’s an assumption that other species do not have sophisticated
communication systems already; that a human-‐style language is the desirable
pinnacle of evolution; that most meaningful communication occurs via this kind of
5
formal language; and that the capacity for language in this way is an indicator of
intelligence. What do we mean by intelligence anyway? Kevin may address this in
more detail later, but for now I’m going to propose Cognitive Psychologist Ulric
Neisser’s definition of cognition as “all the processes by which sensory input is
transformed, reduced, elaborated, stored, recovered and used. (1976)” For Etic Lab
a working definition might also include the degree of connectivity with others, the
capacity for an entity to change itself as a result of an encounter, and to have impact
on the world.
Although Arbib recognizes that there is no guarantee any exolanguage will contain
any surface properties associated with human language, he does not view any
nonhuman terrestrial communication as constituting any kind of language as such.
These, like the IQ test, ground prerequisites for judging intelligence in an inherent
and unquestioned assumption of superiority of those doing the testing; which is as
ridiculous as the assumption that there can be an equivalent of Star Trek’s universal
translator, that converts an alien language into English. In experimental testing from
an imperialist perspective, we’re in danger of just testing or reflecting on ourselves,
not other cultures or species. Arbib assumes that rich language development and
advanced technologies are prerequisites of communication with extraterrestrials,
but who knows how a terrestrial microorganism might communicate with an
extraterrestrial one, in what exchange of electrical or chemical signals.
Biosemiotics
Marine Biologist Jennifer Mather’s definition of intelligence, or cognitive ability, is
about obtaining and using information, in ways afforded by environments and
evolved sensory apparatus. Biosemiotics is a biology-‐based discipline that borrows
from literary theory and Peircean semiotic analysis, which models a triadic relation
rather than the binary of Saussurean signifier-‐signified, and a more fluidly dynamic
relationship between an object, representamen (means of representation) and
6
interpretant (receiver of the message), that can, for example, begin or end with the
object.
Biosemiotics refers to this act of perceiving and communicating biological
information, including signs between organisms (literally, communication), such as
human gesture, speech, sign-‐making; or octopus displays, gesture and sign-‐
production; but also signs encoded in DNA and activated through interactions in
environments, eg. the fact we develop legs before we need them indicates we,
mostly, intend to / will walk; more abstractly, things like the rate at which different
species perceive time is a form of biological information. The rate at which time is
perceived varies across animals: evidence suggests that distinct species experience
passing time on different scales. (A 2013 study in Animal Behavior reveals that body
mass and metabolic rate determine how animals of different species perceive time.)
“[for] fish, which live on fast-‐moving prey, processes of motion may appear more
slowly in their environment, as in slow motion.”
From a Biosemiotic perspective, human cultural production is also a form of
biosemiotics; it encodes signs, communication and information, and is an expression
of relationships. This works the other way too, cultural production as biosemiotics
affect our perceptions of the world, and what we give attention to.
For Biosemioticians, “interpretation is the defining form of semiosis and life”;
“the ability to reach a conclusion from sensory inputs whose result can vary
according to circumstances, memory, experience, and learning. In a way, it is the
ability to ‘‘jump-‐to-‐conclusions”… from a limited number of data, with results that
may not be perfect but are good enough for the purpose of survival… [it is] a form of
semiosis because it involves signs and meanings.” (Barbieri)
“The transformation of the signals received by the sense organs into mental images,
or high level neural states, is based on sets of rules that are often referred to as
neural codes, because neurobiology has made it abundantly clear that there are no
necessary connections between sensory inputs and mental, or neural, images… What
7
organisms interpret… is not the world but representations of the world, and neural
representations are formed by neural networks made of many different types of
cells”.
AI combines code and networks, largely based on the computational aspect of how
biological brains may work in machine learning.
Machine Learning and Interpretation
As artists and designers we are interested in the phenomenology of things, the way
their operations structure experience. Although some of you may be much more
familiar with the principles of machine learning than I am, I am assuming many of
you are not, so I’m going to give a very basic description of how it works.
There are a number of machine learning techniques. I’ll go through a few of them
briefly here.
8
Predicting [regression]
Supervised learning algorithms make predictions based on a set of examples, known
as regression. The programme is trained on the data to find the equation from the
desired end state. It starts by filling in the missing value, the multiplier, with a
random value.
Image from Tariq Rashid’s Make Your Own Neural Network
9
It calculates the result and the error margin, adjusts the multiplier correspondingly,
and tries again, and again, iteratively, until it has the correct, or close enough to
correct, output.
This is the basis of how a machine learns, using a model to adjust parameters based
on known values. In this case, it’s a predictor model because it takes an input and
predicts what the output should be.
Neural Networks, Deep Learning and human brains
But there are usually a number of layers of code, called neural networks as they are
modeled on neuronal computation in the biological brains, and their network of
neurons. The ML neural net passes data in parallel through a sequence of layers,
based on an idea of how computation works in a biological brain. Layers of input-‐
output nodes are processed through a threshold activation function that mimics the
neuronal output thresholds.
10
Images from Neurocomic: Neurocomic, a graphic novel about how the brain works by
neuroscientists Matteo Farinella and Hana Ros.
11
In biological brains, neurons process information from the environment (light,
sound, touch, etc.); each neuron is an individual unit that receives inputs, processes
the information, and if the input is large enough, pass it on to more neurons. These
are most concentrated in the brain for humans. The human brain has about 100
billion neurons. African Elephants have 257 billion neurons in their brain, although
most of those are in the cerebellum rather than the cerebral cortex where you find
the highest concentration of human brains. Octopuses have around 500 million
neurons but more than half are in their arms. But even animals with much lower
neuron counts can outperform computers on many complex tasks. It’s not all about
the neurons or the processing power, although that is what neural networks are
modeled on and what I’ll be talking about today; biological brains and nervous
systems have a lot more going on.
In biological brains, networks of neurons pass information from one to another;
whether each receiver processes and passes on the information depends on whether
the information passes a certain threshold: that is, they suppress the input until it
becomes large enough to trigger an output. And they process signals in parallel. Each
neuron receives inputs from many others, and passes its signal on to many others
when it outputs.
Sigmoid function, from Tariq Rashid’s Make Your Own Neural Network
12
When a neural net node receives a signal, it too suppresses that information until it
reaches a threshold, which is calculated by an activation function. In order to imitate
the biological threshold, the activation function chosen is generally one that
produces a smooth gradient, such as the s-‐shaped sigmoid function [y=1/1+1e-‐x].
Neural net, from Tariq Rashid’s Make Your Own Neural Network
In each layer, inputs are weighted in various combinations, summed, and passed on
to the next layer (fed forwards) if they pass the threshold.
13
The weightedness for each input is randomly assigned. These weights are important
to machine learning, they are what moderate the learning, and they are what is
adjusted to get the desired output. As with the simpler example earlier, the
computer uses the margin of error compared with the training data to refine its
results. In this case the error needs to be distributed across the whole network in
order for it to continue to function as a neural net. One way of doing this is by what’s
called backpropagation, where the error margins are fed back across the network,
divided between the links proportionally to their initial size.
14
Backpropagating errors
This is done iteratively, in very small steps:
The whole process mimics the feedforward and feedbackwards in biological
neuronal networks. This combination of simple calculations results in the ability to
learn sophisticated class boundaries and data trends; many-‐layered networks of this
sort perform what is known as "deep learning". Deep learning is useful for data
15
resilience – eg. moderating imperfect signals: errors or anomalies in the data are
evened out across the network’s distributed learning; it has more sophistication, and
speed gained from parallel processing.
Biological Brains
There are some particular broad differences from biological wetware as I understand
it: the computational model above is based on the morphology of biological brains,
although generally in ordered, uniform layers which are easier for computation,
rather than an organically distributed; in a biological brain, neurons wouldn’t be
structured in a grid – the signals would pass through multiple neurons in a much less
linear way. AI arguably has a form of electrophysiology (the use of electrical signals
to communicate) and the use of synchronicity in cognitively tying together particular
experiences (understandings of cause and effect), but not in the same way the
human brain sends electrical signals to communicate and is thought to synchronise
experience (which we don’t entirely understand anyway); it also doesn’t use
memory in the same way. It doesn’t have a correlate for biological pharmacology
(internal chemistry) or neuroplasticity.
Where, as Catherine Malabou suggests, the human brain could be said to represent
the intersection between the symbolic and the biological (Malabou), what
represents thought in octopuses and AIs? What might the specific morphology of AI
and its digital environment afford, and what might speculative mechanistic modeling
based on an octopus brain reveal to inquisitive humans? What might it say about
alien and artificial intelligences?
Octopuses, as has been often pointed out, come from a branch of the evolutionary
tree that diverged from the human one millions of years ago. They belong to a
branch we class as mollusks, a group of soft-‐bodied water dwellers ranging from very
simple organisms to complex and sophisticated ones including also squid and
cuttlefish.
16
Developing an AI Octopus Using Reinforcement Learning
In reinforcement learning, the algorithm learns from unknowns. It is hard-‐coded to
enjoy inhabiting a particular state, and will try to return to that state as often as it
can by manipulating its environment.
action>environment>observation, reward>agent
The algorithm modifies its strategy in order to achieve the highest reward, as in this
basic reinforcement learning algorithm Etic Lab’s Alex Hogan has constructed (see
reinforcement learning link in blog text).
If we made a digital octopus in a digital environment using reinforcement learning, it
could manipulate its environment to induce changes in itself. It learns through
reinforcement, in response to and constrained by the affordances of its digitally
modeled environment. It chooses an action in response to each data point, which is
rewarded by a signal indicating how ‘good’ the decision was. A digital octopus is a
set of variables, representing for example skin colouration and arm movements (8 of
them!). We could add more variables to make it more complex, representing eg skin
17
pattern, skin texture, jet propulsion. These together could respond to variations in
its digital environment – based on another set of variables, representing for example
depth, light, flow, etc.
The octopus and its environment would constitute a black box, which returns a
mathematical output signals. We could convert, synaesthetically translate, or
transduce the variation in output, the resulting signals, into a set of shifting aesthetic
outputs, remixing for example image, colour and sound from a database of objects
created by Maggie. As the algorithm learns, some signals will be repeated more
frequently; then as the digital environment changes, the octopus AI will have to
adapt its behavior to return to its preferred state.
Expressive Machine by Laura Dekker, installation shot at the V&A, image courtesy the artist.
Artist Laura Dekker describes a similar but partially physically manifest process in her
Expressive Machine project, using outputs from multiple, decentralized software and
hardware interfaces that form installations with elements responsive to, for
example, visitors’ sound, touch, webfeeds, that are transduced “across multiple
modes” into stream-‐of-‐consciousness text displays, icons and sound; as she says:
“from touch to sound, to word, to vision, to taste, to uniquely machinic states with
no particular human analogue. These stimuli are processed in various
interpretations, elaborations, in a relatively unstructured ‘data soup’. Asynchronous
18
processes consume data from the soup. When trigger conditions for a particular
expressive process are satisfied, the machine produces externalized outputs in
various forms: sound, shift of attention, fragments of narrative, and so on.”
Similarly, what we ultimately want, rather than constructing an interpretation of a
digital octopus in a digital environment, is an AI based on real-‐world interactions. We
want a real octopus to programme the AI. Kevin will talk more about that next.
The way Laura Dekker sees it, “what can be considered as creativity arises as an
emergent property – a serendipitous by-‐product of the machine working through its
experiences, rather than an explicit creative process.” (EVA conference proceeding
London 2019)
Etic Lab has another perspective on computational creativity, around the effects of
algorithmic intelligence on human culture. We suggest, from our experience working
on recent commercial projects, that Algorithmic AI is a new, creative actor in the
field of online identity-‐construction and cultural production; that is, it is actively
affecting human culture.
We’ve written a paper about this, Guru Code, which you can read on the Etic Lab
website, so I won’t go into detail here, but just to summarise:
Iteratively responsive metrics produced by an algorithm can turn the perspective of
its producer’s assumptions and the datasets it is based on into the ground of the
truth, altering or reifying the subjective realities of participating users, acting as a
kind of guru. There have been studies of online identity construction since the 90s
(Friedman and Schultermandl 2016; Papacharissi 2011; Turkle 1995), but we have
entered a new phase of online identity construction mediated by machine learning
algorithms. Online identities are now constructed on an ever-‐shifting ground, the
longer-‐term effects of which are yet to be seen. ML algorithms have not been
analysed so much from this perspective; research has tended to focus either on
technologies of surveillance or on bias in relation to algorithmically-‐impacted
identities (Cheney-‐Lippold 2017; Eubanks 2018; Noble 2018; O’Neill 2016; Zuboff
19
2019). [something Ramon has written about] The algorithm creates new subjective
realities, reshaping and framing markets. Like any futures market or guru, this
operates largely on belief in a brand or cult leader’s mastery of knowledge,
expertise, charisma and capacity to influence. What we’re calling Guru Codes have
the capacity to responsively alter or reify subjective realities through their
entanglement with human users, in ways that were not possible before the
introduction of sophisticated Machine Learning algorithms. Moreover, we suggest
that these Guru Codes operate between human and machine perception-‐worlds,
environments that function on very different principles; these form a constitutive
miscommunication and misapprehension about what is happening, which counter-‐
intuitively contribute to its effectiveness.
Embodied Interpretation and cognition
To go back to my starting point of the octoplus and embodied cognition in relation to
environments. Humans are physically, bodily surface-‐bound, with free movement on
the horizontal plane but limited vertically, which are reflected in spatial semantics.
As Linguist Arthur Holmer points out:
“all human languages possess concepts… [that] distinguish upwards from
downwards and posses corresponding verbs such as rise and fall. Meanwhile, they do
not universally distinguish directions of horizontal motion: some languages do but
the distinctions are entirely language-‐specific. Horizontal motion verbs focus on the
manner of movement: eg., walking, running, crawling, or floating… our
categorization of spatial semantics and motion verbs is determined by our lifeworld.”
(Arthur Holmer, Greetings Earthling! In History and Philosophy of Astrobiology,
p178)
By this logic, if we imagine a species whose lifeworld is characterized by
weightlessness, concepts of up and down are much less relevant for them. AI does
not possess a human-‐like set of sensory apparatus; it lacks defined morphology,
20
pharmacology, embodiment, but can be hard-‐coded with teleological purpose; like
humans, this can give it the emergent property of appearing to have a conscious
purpose.
Decentring narratives
We’re interested in decentring anthropocentric narratives; the nonhuman
intelligence of AI may both help and hinder with this. Kevin is going to speak next
about how we intend to use AI to communicate with an octopus, and how an
octopus might program a very different kind of AI.
© Stephanie Moran and Etic Lab 2019
