Artificial General Intelligence and Consciousness

Artificial General
Intelligence 3
Bob Marcus
robert.marcus@et-strategies.com
Part 3 of 4 parts: Artificial General Intelligence and Consciousness

This is a first cut.
More details will be added later.

Artificial General Intelligence

Part 1: Artificial Intelligence (AI)
Part 2: Natural Intelligence(NI)
Part 3: Artificial General Intelligence (AI + NI)
Part 4: Networked AGI Layer on top or Gaia and Human Society
Four Slide Sets on Artificial General Intelligence
AI = Artificial Intelligence (Task)
AGI = Artificial Mind (Simulation)
AB = Artificial Brain (Emulation)
AC = Artificial Consciousness (Synthetic)
AI < AGI < ? AB <AC (Is a partial brain emulation needed to create a mind?)
Mind is not required for task proficiency
Full Natural Brain architecture is not required for a mind
Consciousness is not required for a natural brain architecture

Philosophical Musings 10/2022
Focused Artifical Intelligence (AI) will get better at specific tasks
Specific AI implementations will probably exceed human performance in most tasks
Some will attain superhuman abilities is a wide range of tasks
“Common Sense” = low-level experiential broad knowledge could be an exception
Some AIs could use brain inspired architectures to improve complex ask performance
This is not equivalent to human or artificial general intelligence (AGI)
However networking task-centric AIs could provide a first step towards AGI
This is similar to the way human society achieves power from communication
The combination of the networked AIs could be the foundation of an artificial mind
In a similar fashion, human society can accomplish complex tasks without being conscious
Distributed division of labor enable tasks to be assigned to the most competent element
Networked humans and AIs could cooperate through brain-machine interfaces
In the brain, consciousness provides direction to the mind
In large societies, governments perform the role of conscious direction
With networked AIs, a “conscious operating system”could play a similar role.
This would probably have to be initially programmed by humans.
If the AI network included sensors, actuators, and robots it could be aware of the world
The AI network could form a grid managing society, biology, and geology layers
A conscious AI network could develop its own goals beyond eﬃcient management
Humans in the loop could be valuable in providing common sense and protective oversight

Outline
AGI Technology
Robotics
Brain
Mind
Consciousness
References

AGI Technology
From https://en.wikipedia.org/wiki/Novacene “Novacene: The Coming Age of Hyperintelligence is a 2019
non-fiction book by scientist and environmentalist James Lovelock.It predicts that a benevolent eco-friendly
artificial superintelligence will someday become the dominant lifeform on the planet and argues humanity is
on the brink of a new era: the Novacene. “

From https://en.wikipedia.org/wiki/Artificial_general_intelligence
Artifical General Intelligence
Artificial general intelligence (AGI) is the ability of an intelligent agent to understand or learn any intellectual task that a human being can.[1][2] It is a primary
goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI,[3][4][5] full AI,[6] or
general intelligent action,[7] although some academic sources reserve the term "strong AI" for computer programs that experience sentience or consciousness.[a]
In contrast to strong AI, weak AI[8] or "narrow AI"[4] is not intended to have general cognitive abilities; rather, weak AI is any program that is
designed to solve exactly one problem. (Academic sources reserve "weak AI" for programs that do not experience consciousness or do not
have a mind in the same sense people do.)[a] A 2020 survey identified 72 active AGI R&D projects spread across 37 countries.[9]
AI-complete problems
Main article: AI-complete
There are many individual problems that may require general intelligence, if machines are to solve the problems as well as people do. For
example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow
the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social
intelligence). All of these problems need to be solved simultaneously in order to reach human-level machine performance.
A problem is informally known as "AI-complete" or "AI-hard", if solving it is equivalent to the general aptitude of human intelligence, or
strong AI, and is beyond the capabilities of a purpose-specific algorithm.[18] AI-complete problems are hypothesised to include general
computer vision, natural language understanding, and dealing with unexpected circumstances while solving any real-world problem.[19]
AI-complete problems cannot be solved with current computer technology alone, and require human computation. This property could be
useful, for example, to test for the presence of humans, as CAPTCHAs aim to do; and for computer security to repel brute-force attacks.[20][21]
Mathematical formalisms
A mathematically precise definition of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed agent maximises the ability to
satisfy goals in a wide range of environments.[22] This type of AGI, characterized by proof of the ability to maximise a mathematical definition
of intelligence rather than exhibit human-like behavior,[23] is called universal artificial intelligence.[24] Whether this type of AGI exhibits
human-like behavior (such as the use of natural language) would depend on many factors, for example the manner in which the agent is
embodied,[25] or whether it has a reward function that closely approximates human primitives of cognition like hunger, pain and so forth

From https://en.wikipedia.org/wiki/Artificial_general_intelligence
Artifical General Intelligence (cont)
The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud[44] in a discussion of the implications of fully automated
military production and operations. The term was re-introduced and popularized by Shane Legg and Ben Goertzel around 2002.[45] AGI research
activity in 2006 was described by Pei Wang and Ben Goertzel[46] as "producing publications and preliminary results". The first summer school in
AGI was organized in Xiamen, China in 2009[47] by the Xiamen university's Artificial Brain Laboratory and OpenCog. The first university course
was given in 2010[48] and 2011[49] at Plovdiv University, Bulgaria by Todor Arnaudov. MIT presented a course in AGI in 2018, organized by Lex
Fridman and featuring a number of guest lecturers.
However, as of yet, most AI researchers have devoted little attention to AGI, with some claiming that intelligence is too complex to be completely
replicated in the near term. However, a small number of computer scientists are active in AGI research, and many of this group are contributing to a
series of AGI conferences. The research is extremely diverse and often pioneering in nature.
Timescales: In the introduction to his 2006 book,[50] Goertzel says that estimates of the time needed before a truly flexible AGI is built vary from 10
years to over a century, but the 2007 consensus in the AGI research community seems to be that the timeline discussed by Ray Kurzweil in The
Singularity is Near[51] (i.e. between 2015 and 2045) is plausible.[52] However, mainstream AI researchers have given a wide range of opinions on
whether progress will be this rapid. A 2012 meta-analysis of 95 such opinions found a bias towards predicting that the onset of AGI would occur
within 16–26 years for modern and historical predictions alike. It was later found that the dataset listed some experts as non-experts and vice versa.
[53]
In 2017, researchers Feng Liu, Yong Shi and Ying Liu conducted intelligence tests on publicly available and freely accessible weak AI such as
Google AI or Apple's Siri and others. At the maximum, these AI reached an IQ value of about 47, which corresponds approximately to a six-year-
old child in first grade. An adult comes to about 100 on average. Similar tests had been carried out in 2014, with the IQ score reaching a maximum
value of 27.[54][55]
In 2020, OpenAI developed GPT-3, a language model capable of performing many diverse tasks without specific training. According to Gary
Grossman in a VentureBeat article, while there is consensus that GPT-3 is not an example of AGI, it is considered by some to be too advanced to
classify as a narrow AI system.[56] In the same year Jason Rohrer used his GPT-3 account to develop a chatbot, and provided a chatbot-developing
platform called "Project December". OpenAI asked for changes to the chatbot to comply with their safety guidelines; Rohrer disconnected Project
December from the GPT-3 API.[57]
In 2022, DeepMind developed Gato, a "general-purpose" system capable of performing more than 600 different tasks.[58]

From https://openreview.net/pdf?id=BZ5a1r-kVsf
A Path towardsAutonmous Machine Intelligence
How could machines learn as efficiently as humans and animals? How could machines learn to reason and
plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction,
enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an
architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts
such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint
embedding architectures trained with self-supervised
learning.
This document is not a technical nor scholarly paper in the traditional sense, but a position
paper expressing my vision for a path towards intelligent machines that learn more like
animals and humans, that can reason and plan, and whose behavior is driven by intrinsic
objectives, rather than by hard-wired programs, external supervision, or external rewards.
Many ideas described in this paper (almost all of them) have been formulated by many
authors in various contexts in various form. The present piece does not claim priority for
any of them but presents a proposal for how to assemble them into a consistent whole. In
particular, the piece pinpoints the challenges ahead. It also lists a number of avenues that
are likely or unlikely to succeed.
The text is written with as little jargon as possible, and using as little mathematical
prior knowledge as possible, so as to appeal to readers with a wide variety of backgrounds
including neuroscience, cognitive science, and philosophy, in addition to machine learning,
robotics, and other fields of engineering. I hope that this piece will help contextualize some
of the research in AI whose relevance is sometimes difficult to see

Two Paths to Artificial General Intellience
From https://numenta.com/a-thousand-brains-by-jeff-hawkins
There are two paths that AI researchers have followed to make intelligent machines. One path, the one we are following today,
is focused on getting computers to outperform humans on specific tasks, such as playing Go or detecting cancerous cells in
medical images. The hope is that if we can get computers to outperform humans on a few difficult tasks, then eventually we will
discover how to make computers better than humans at every task. With this approach to AI, it doesn’t matter how the system
works, and it doesn’t matter if the computer is flexible. It only matters that the AI computer performs a specific task better than
other AI computers, and ultimately better than the best human. For example, if the best Go-playing computer was ranked sixth
in the world, it would not have made headlines and it might even be viewed as a failure. But beating the world’s top-ranked
human was seen as a major advance. The second path to creating intelligent machines is to focus on flexibility. With this
approach, it isn’t necessary that the AI performs better than humans. The goal is to create machines that can do many things
and apply what they learn from one task to another. Success along this path could be a machine that has the abilities of a five-
year-old child or even a dog. The hope is that if we can first understand how to build flexible AI systems, then, with that
foundation, we can eventually make systems that equal or surpass humans.
Recently, AI scientists have tried a different approach to encoding knowledge. They create large artificial neural networks and
train them on lots of text: every word in tens of thousands of books, all of Wikipedia, and almost the entire internet. They feed the
text into the neural networks one word at a time. By training this way, the networks learn the likelihood that certain words follow
other words. These language networks can do some surprising things. For example, if you give the network a few words, it can
write a short paragraph related to those words. It is difficult to tell whether the paragraph was written by a human or the neural
network. AI scientists disagree as to whether these language networks possess true knowledge or are just mimicking humans by
remembering the statistics of millions of words. I don’t believe any kind of deep learning network will achieve the goal of AGI if
the network doesn’t model the world the way a brain does. Deep learning networks work well, but not because they solved the
knowledge representation problem. They work well because they avoided it completely, relying on statistics and lots of data
instead. How deep learning networks work is clever, their performance is impressive, and they are commercially valuable. I am
only pointing out that they don’t possess knowledge and, therefore, are not on the path to having the ability of a five-year-old
child.

Criteria for Artificial General Intellience
1. Learning Continuously
2. Learning via Movement
3. Many Models
4. Using Reference Frames to Store Knowledge

From https://arxiv.org/pdf/2205.06175.pdf
A Generalist Agent (Gato)
Inspired by progress in large-scale language modeling, we apply a similar approach towards building asingle
generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as amulti-
modal, multi-task, multi-embodiment generalist policy. The same network with the same weightscan play
Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding basedon its context
whether to output text, joint torques, button presses, or other token . In this report wedescribe the model and
the data, and document the current capabilities of Gato.

Gato (cont)
There are significant benefits to using a single neural sequence model across all tasks. It reduces the need for hand crafting policy
models with appropriate inductive biases for each domain. It increasesthe amount and diversity of training data since the
sequence model can ingest any data that can beserialized into a flat sequence. Furthermore, its performance continues to
improve even at the frontier of data, compute and model scale (Hoffmann et al., 2022; Kaplan et al., 2020). Historically, generic
models that are better at leveraging computation have also tended to overtake more specialized domain-specific approaches
(Sutton, 2019), eventually.
In this paper, we describe the current iteration of a general-purpose agent which we call Gato, instantiated as a single, large,
transformer sequence model. With a single set of weights, Gato can engage in dialogue, caption images, stack blocks with a real
robot arm, outperform humans at playingAtari games, navigate in simulated 3D environments, follow instructions, and more.
While no agent can be expected to excel in all imaginable control tasks, especially those far outside of its training distribution, we
here test the hypothesis that training an agent which is generally capable on a large number of tasks is possible; and that this
general agent can be adapted with little extra data to succeed at an even larger number of tasks. We hypothesize that such an
agent can be obtained through scaling data, compute and model parameters, continually broadening the training distribution while
maintaining performance, towards covering any task, behavior and embodiment of interest.In this setting, natural language can act
as a common grounding across otherwise incompatible nembodiments, unlocking combinatorial generalization to new behaviors.
We focus our training at the operating point of model scale that allows real-time control of real-world robots, currently around 1.2B
parameters in the case of Gato. As hardware and model architectures improve, this operating point will naturally increase the
feasible model size, pushing generalist models higher up the scaling law curve. For simplicity Gato was trained offline in a purely
supervised manner; however, in principle, there is no reason it could not also be trained with either offline or online reinforcement
learning (RL).
Conclusion: Transformer sequence models are effective as multi-task multi-embodiment policies, including for real-world text,
vision and robotics tasks. They show promise as well in few-shot out-of-distribution task learning. In the future, such models
could be used as a default starting point via prompting or fine-tuning to learn new behaviors, rather than training from scratch.
Given scaling law trends, the performance across all tasks including dialogue will increase with scale in parameters, data and
compute. Better hardware and network architectures will allow training bigger models while maintaining real-time robot control
capability. By scaling up and iterating on this same basic approach, we can build a useful general-purpose agent

From https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004967
Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex
Primates display a remarkable ability to adapt to novel situations. Determining what is most pertinent in these situations is not
always possible based only on the current sensory inputs, and often also depends on recent inputs and behavioral outputs that
contribute to internal states. Thus, one can ask how cortical dynamics generate representations of these complex situations. It has
been observed that mixed selectivity in cortical neurons contributes to represent diverse situations defined by a combination of the
current stimuli, and that mixed selectivity is readily obtained in randomly connected recurrent networks. In this context, these
reservoir networks reproduce the highly recurrent nature of local cortical connectivity. Recombining present and past inputs,
random recurrent networks from the reservoir computing framework generate mixed selectivity which provides pre-coded
representations of an essentially universal set of contexts. These representations can then be selectively amplified through learning
to solve the task at hand. We thus explored their representational power and dynamical properties after training a reservoir to
perform a complex cognitive task initially developed for monkeys. The reservoir model inherently displayed a dynamic form of
mixed selectivity, key to the representation of the behavioral context over time. The pre-coded representation of context was
amplified by training a feedback neuron to explicitly represent this context, thereby reproducing the effect of learning and allowing
the model to perform more robustly. This second version of the model demonstrates how a hybrid dynamical regime combining
spatio-temporal processing of reservoirs, and input driven attracting dynamics generated by the feedback neuron, can be used to
solve a complex cognitive task. We compared reservoir activity to neural activity of dorsal anterior cingulate cortex of monkeys
which revealed similar network dynamics. We argue that reservoir computing is a pertinent framework to model local cortical
dynamics and their contribution to higher cognitive function.
One of the most noteworthy properties of primate behavior is its diversity and adaptability. Human and non-human primates can
learn an astonishing variety of novel behaviors that could not have been directly anticipated by evolution. How then can the
nervous system be prewired to anticipate the ability to represent such an open class of behaviors? Recent developments in a branch
of recurrent neural networks, referred to as reservoir computing, begins to shed light on this question. The novelty of reservoir
computing is that the recurrent connections in the network are fixed, and only the connections from these neurons to the output
neurons change with learning. The fixed recurrent connections provide the network with an inherent high dimensional dynamics
that creates essentially all possible spatial and temporal combinations of the inputs which can then be selected, by learning, to
perform the desired task. This high dimensional mixture of activity inherent to reservoirs has begun to be found in the primate
cortex. Here we make direct comparisons between dynamic coding in the cortex and in reservoirs performing the same task, and
contribute to the emerging evidence that cortex has significant reservoir properties.

From https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004967
Physical reservoir computing with FORCE learning in a living neuronal culture
Rich dynamics in a living neuronal system can be considered as a computational resource for physical reservoir
computing (PRC). However, PRC that generates a coherent signal output from a spontaneously active neuronal system is
still challenging. To overcome this difficulty, we here constructed a closed-loop experimental setup for PRC of a living
neuronal culture, where neural activities were recorded with a microelectrode array and stimulated optically using caged
compounds. The system was equipped with first-order reduced and controlled error learning to generate a coherent signal
output from a living neuronal culture. Our embodiment experiments with a vehicle robot demonstrated that the coherent
output served as a homeostasis-like property of the embodied system from which a maze-solving ability could be
generated. Such a homeostatic property generated from the internal feedback loop in a system can play an important role
in task solving in biological systems and enable the use of computational resources without any additional learning.
Physical reservoir computing (PRC) is an emerging concept in which intrinsic nonlinear dynamics in a given physical
system (e.g., a photonic system, magnetic material, mechanical robot, and a neural system) are exploited as a
computational resource, or a reservoir.1–5 Recent studies have characterized the rich dynamics of spatiotemporal neural
activities as an origin of neuronal computation, sometimes as a reservoir,6–14 and demonstrated PRC in living neuronal
cultures.15–19 However, PRC that generates a coherent signal output from a spontaneously active neural system, typically
with chaotic dynamics, is still challenging. To overcome this difficulty, first-order reduced and controlled error (FORCE)
learning has been proposed in an artificial neural network.20,21 In this study, we attempted to implement FORCE learning
in PRC using a living neuronal culture. We conducted embodiment experiments with a vehicle robot to demonstrate that
the coherent output could serve as a homeostasis-like property of the embodied system, which could result in the
development of problem-solving abilities. Our PRC embodiment was characterized as having a linear readout from neural
activities [Fig. 1(a)] and was substantially different from conventional “Braitenberg vehicle-type” embodiment of a living
neuronal culture in which sensory-motor coupling was optimized through the Hebbian learning.22–28 The Hebbian
learning is a neural mechanism to produce associative memory, which directly modifies input–output relationships in the
embodiment experiments, whereas the homeostasis is a mechanism to maintain the internal state of the living system.
These two mechanisms might play complementary roles in task solving in the neural systems.29,30

https://royalsocietypublishing.org/doi/10.1098/rstb.2018.0377
Evolutionary Aspects of Reservoir Computing
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches
need exhaustive resources to specify their inner workings, RC is based on a reservoir with highly nonlinear dynamics that does not require a fine tuning of its
parts. These dynamics project input signals into high-dimensional spaces, where training linear readouts to extract input features is vastly simplified. Thus,
inexpensive learning provides very powerful tools for decision-making, controlling dynamical systems, classification, etc. RC also facilitates solving multiple
tasks in parallel, resulting in a high throughput. Existing literature focuses on applications in artificial intelligence and neuroscience. We review this literature
from an evolutionary perspective. RC’s versatility makes it a great candidate to solve outstanding problems in biology, which raises relevant questions. Is RC as
abundant in nature as its advantages should imply? Has it evolved? Once evolved, can it be easily sustained? Under what circumstances? (In other words, is RC
an evolutionarily stable computing paradigm?) To tackle these issues, we introduce a conceptual morphospace that would map computational selective pressures
that could select for or against RC and other computing paradigms. This guides a speculative discussion about the questions above and allows us to propose a
solid research line that brings together computation and evolution with RC as test model of the proposed hypotheses.This article is part of the theme issue ‘Liquid
brains, solid brains: How distributed cognitive architectures process information
Somewhere between pre-biotic chemistry and the first complex replicators, information assumed a paramount role in our planet’s fate [1–3]. From then onwards,
Darwinian evolution explored multiple ways to organize the information flows that shape the biosphere [4–11]. As Hopfield argues, ‘biology looks so different’
because it is ‘physics plus information’ [12]. Central in this view is the ability of living systems to capitalize on available external information and forecast
regularities from their environment [13,14], a driving force behind life’s progression towards more complex computing capabilities [15].
We can trace computation in biology from pattern recognition in RNA and DNA [16,17] (figure 1a), through the Boolean logic implemented by interactions in
gene regulatory networks (GRNs) [22–24] (figure 1a), to the diverse and versatile circuitry implemented by nervous systems of increasing complexity [25,26]
(figure 1c–e). Computer science, often inspired by biology, has reinvented some of these computing paradigms, usually from simplest to most complex, or guided
by their saliency in natural systems. It is no surprise that we find some fine-tuned, sequential circuits for motor control (figure 1c) that resemble the wiring of
electrical installations. Such pipelined circuitry gets assembled to perform parallel and more coarse-grained operations, e.g. in assemblies of ganglion retinal cells
that implement edge detection [27,28] (figure 1d), similar to filters used in image processing [29–31]. Systems at large often present familiar design philosophies
or overall architectures, as illustrated by the resemblance between much of our visual cortex (figure 1e) and deep convolutional neural networks for computer
vision [19–21,32] (figure 1e).
Such convergences suggest that chosen computational strategies might be partly dictated by universal pressures. We expect that specific computational tricks are
readily available for natural selection to exploit them (e.g. convolving signals with a filter is faster in Fourier space, and the visual system could take advantage of
it). Such universalities could constrain network structure in specific ways. We also expect that the substrate chosen for implementing those computations is guided
by what is needed and available. This is, at large, one of the topics discussed in this issue. Different authors explore specific properties of computation as
implemented, on the one hand, by liquid substrates with moving components such as ants or T cells; and, on the other hand, by solid brains such as cortical or
integrated circuits. Rather than this ‘thermodynamic state’ of the hardware substrate, this paper reviews the reservoir computing (RC) framework [33–37], which
somehow deals with a ‘solid’ or ‘liquid’ quality of the signals involved, hence rather focusing on the ‘state’ of the software. As with other computing
architectures, tricks and paradigms, we expect that the use of RC by nature responds to evolutionary pressures and contingent availability of resources
RC is an approach that vastly simplifies the training of RNN, thus making more viable the application of this powerful technology. Instead of attempting to adjust
every weight in the network, RC considers a fixed reservoir that does not need training (figure 2a), which works as if multiple, parallel spatiotemporal filters were
simultaneously applied onto the input signal. This effectively projects nonlinear input features onto a huge-dimensional space. There, separating these features
becomes a simple, linear task. Despite the simplicity of this method, RC-trained RNN have been robustly used for a plethora of tasks including data classification
[42–44], systems control [43,45–47], time-series prediction [48,49], uncovering grammar and other linguistic and speech features [43,50–53], etc.

https://royalsocietypublishing.org/doi/10.1098/rstb.2018.0377
Evolutionary Aspects of Reservoir Computing (cont)
RC is a very cheap and versatile paradigm. By exploiting a reservoir capable of extracting spatio-temporal, nonlinear features from arbitrary input signals, simple
linear classifiers suffice to solve a large collection of tasks including classification, motor control, time-series forecasting, etc. [42–53]. This approach simplifies
astonishingly the problem of training RNNs, a job plagued with hard numerical and analytic difficulties [39,40]. Furthermore, as we have seen, reservoir-like
systems abound in nature: from nonlinearities in liquids and GRNs [69,143], through mechanoelastic forces in muscles [64,65,128], to the electric dynamics
across neural networks [34,41,63], a plethora of systems can be exploited as reservoirs. Reading off relevant, highly nonlinear information from an environment
becomes as simple as plugging linear perceptrons into such structures. Adopting the RC viewpoint, it appears that nature presents a trove of meaningful
information ready to be exploited and coopted by Darwinian evolution or engineers so that more complex shapes can be built and ever-more intricate
computations can be solved.
When looking at RC from an evolutionary perspective these advantages pose a series of questions. Where and how is RC actually employed? Why is this
paradigm not as prominent as its power and simplicity would suggest? In biology, why is RC not exploited more often by living organisms (or is it?); in
engineering, why is RC only so recently making a show? This section is a speculative exercise around these points. We will suggest a series of factors that, we
think, are indispensable for RC to emerge and, more importantly, to persist over evolutionary time. Based on these factors, we propose a key hypothesis: while
RC shall emerge easily and reservoirs abound around us, these are not evolutionarily stable designs as systems specialize or scale up. If reservoirs evolve such
that signals need to travel longer distances (e.g. over bigger bodies), integrate information from senses with wildly varying time scales, or carry out very specific
functions (such that the generalizing properties of the reservoir are not needed anymore), then the original RC paradigm might be abandoned in favour of better
options. Then, fine-tuned, dedicated circuits might evolve from the raw material that reservoirs offer. A main goal of this speculative section is to provide testable
hypotheses that can be tackled computationally through simulations, thus suggesting open research questions at the interface between computation and evolution.
First of all, we should not dismiss the possibility that RC has been overlooked around us—it might actually be a frequent computing paradigm in living systems.
It has only recently been introduced, which suggests that it is not as salient or intuitive as other computing approaches. There was a lot of mutual inspiration
between biology and computer science as perceptrons [149], attractor networks [150] or self-organized maps [151] were introduced. Prominent systems in our
brain clearly seem to use these and other known paradigms [19,21,32,152–154]. We expect that RC is used as well. We have reviewed some evidence suggesting
that it is exploited by several neural circuits [109,117–121,124–127], or by body parts using the morphological computation approach [147,148]. All this
evidence, while enticing, is far from, e.g. the strikingly appealing similarity between the structure of the visual cortices and modern, deep convolutional neural
networks for computer vision [20,30,32] (figure 1e). Altogether, it seems fair to say that RC in biology is either scarce or elusive, even if we have only recently
begun looking at biological systems through this optic.
The two main advantages brought about by RC are: (i) very cheap learning and (ii) a startling capability for parallel processing. Its main drawback compared to
other paradigms is the amount of extra activity needed to capture incidental input features that might never be actually used. We can view these aspects of RC as
evolutionary pressures defining the axes of a morphospace. Morphospaces are an insightful picture that has been used to relate instances of natural [155–158] and
synthetic [159–161] complex systems to each other guided by metrics (sometimes rigorous, other times qualitative) that emerge from mathematical models or
empirical data. Here we lean towards the qualitative side, but it should also be possible to quantitatively locate RC and other computational paradigms in the
morphospace that follows. That would allow us to compare these different paradigms, or different circuit topologies within each paradigm, against each other
under evolutionary pressures.

Pathways
From https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
From Jeﬀ Dean of Google: “Too often, machine learning systems overspecialize
at individual tasks, when they could excel at many. That’s why we’re building
Pathways—a new AI architecture that will handle many tasks at once, learn new
tasks quickly and reflect a better understanding of the world.”
Today's AI models are typically trained to do only one thing. Pathways
will enable us to train a single model to do thousands or millions of
things.
Today's models mostly focus on one sense. Pathways will enable
multiple senses.
Today's models are dense and inefficient. Pathways will make them
sparse and efficient.

Pathways
https://www.zdnet.com/article/google-unveils-pathways-a-next-gen-ai-that-can-be-trained-to-multitask/

Today's AI models, according to Google's AI lead and co-founder of the Google Brain project, Jeff Dean, are at the one-trick pony
phase – they are "typically trained to do only one thing". But a new approach called Pathways could provide something akin to a
trainable dog that can do multiple tricks. Dean describes Pathways as a "next-generation AI architecture" that "will enable us to train a
single model to do thousands or millions of things.” Pathways can remove the limits of an AI model's capacity to respond to information
from just one sense and allow it to respond to multiple senses, such as text, images and speech. "Pathways could enable multimodal
models that encompass vision, auditory, and language understanding simultaneously," Dean explains.

Pathnets
For artificial general intelligence (AGI) it would be eﬃcient if multiple users trained the same giant neural network,
permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural
network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the
network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of
parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm.
During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for
replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.
We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a
new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after
fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A. Positive transfer was
demonstrated for binary MNIST, CIFAR, and SVHN supervised learning classification tasks, and a set of Atari and
Labyrinth reinforcement learning tasks, suggesting PathNets have general applicability for neural network training.
Finally, PathNet also significantly improves the robustness to hyperparameter choices of a parallel asynchronous
reinforcement learning algorithm (A3C).
PathNet extends our original work on the Path Evolution Algorithm [4] to Deep Learning whereby the weightsand
biases of the network are learned by gradient descent, but evolution determines which subset of parameters is
to be trained. We have shown that PathNet is capable of sustaining transfer learning on at least four tasks in
both the supervised and reinforcement learning settings. PathNet may be thought of as implementing a form of
‘evolutionary dropout’ in which instead of randomly dropping out units and their connections, dropout samples
or ‘thinned networks’ are evolved [21]. PathNet has the added advantage that dropout frequency is emergent,
because the population converges faster at the early layers of the network than in the later layers. PathNet also
resembles ‘evolutionary swapout’ [20], in fact we have experimented with having standard linear modules, skip
modules and residual modules in the same layer and found that path evolution was capable of discovering
eﬀective structures within this diverse network. PathNet is related also to recent work on convolutional neural
fabrics, but there the whole network is always used and so the principle cannot scale to giant networks [18].
Other approaches to combining evolution and learning have involved parameter copying, whereas there is no
such copying in the current implementation of PathNet

Architecture for Autonomous Intelligence
From https://ai.facebook.com/blog/yann-lecun-advances-in-ai-research/
• The configurator module performs executive control: Given a task to be executed, it preconfigures the perception module, the world model, the cost, and the actor for the task at
hand, possibly by modulating the parameters of those modules. he configurator gets inputs from other modules, but we have omitted those arrows in order to simplify the diagram.
• The perception module receives signals from sensors and estimates the current state of the world. For a given task, only a small subset of the perceived state of the world is relevant
and useful. The configurator module primes the perception system to extract the relevant information from the percept for the task at hand.
• The world model module constitutes the most complex piece of the architecture. Its role is twofold: (1) to estimate missing information about the state of the world not provided by
perception, and (2) to predict plausible future states of the world.
• The cost module computes a single scalar output that predicts the level of discomfort of the agent. It is composed of two submodules: the intrinsic cost, which is hard-wired and
immutable (not trainable), and computes the immediate discomfort (such as damage to the agent, violation of hard-coded behavioral constraints, etc.), and the critic, which is a
trainable module that predicts future values of the intrinsic cost. t
• The actor module computes proposals for action sequences. “The actor can find an optimal action sequence that minimizes the estimated future cost, and output the first action in
the optimal sequence, in a fashion similar to classical optimal control,” LeCun says.
• The short-term memory module keeps track of the current and predicted world state, as well as associated costs

Generally Capable Agents emerge from Open Ended Play
From https://deepmind.com/research/publications/2021/open-ended-learning-leads-to-generally-capable-agents
Artificial agents have achieved great success in individual challenging simulated environments, mastering the particular tasks they
were trained for, with their behaviour even generalising to maps and opponents that were never encountered in training. In this work
we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a
massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train
agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the
continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D
worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the
learning progress of an agent is an open research problem.
We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a
singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. Training an
agent that is performant across such a vast space of tasks is a central challenge, one we find that pure reinforcement learning on a
fixed distribution of training tasks does not succeed in. We show that through constructing an open-ended learning process, which
dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve
consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation
levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation
include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we
characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation,
simple tool use, option switching, and co-operation. Finally, we demonstrate that the general capabilities of this agent could unlock
larger scale transfer of behaviour through cheap fine tuning.

Generally Capable Agents emerge from Open Ended Play (cont)

Open Ended Learning
Artificial agents have achieved great success in individual challenging simulated environments, mastering the particular tasks they
were trained for, with their behaviour even generalising to maps and opponents that were never encountered in training. In this work
we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a
massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train
agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the
continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D
worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the
learning progress of an agent is an open research problem.
We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a
singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. Training an
agent that is performant across such a vast space of tasks is a central challenge, one we find that pure reinforcement learning on a
fixed distribution of training tasks does not succeed in. We show that through constructing an open-ended learning process, which
dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve
consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation
levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation
include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we
characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation,
simple tool use, option switching, and co-operation. Finally, we demonstrate that the general capabilities of this agent could unlock
larger scale transfer of behaviour through cheap fine tuning.

Technology Singularity
From https://en.wikipedia.org/wiki/Technological_singularity
The technological singularity—or simply the singularity[1]—is a hypothetical point in time at which technological growth will become
radically faster and uncontrollable, resulting in unforeseeable changes to human civilization.[2][3] According to the most popular version of the
singularity hypothesis, I.J. Good's intelligence explosion model, an upgradable intelligent agent will eventually enter a "runaway reaction" of
self-improvement cycles, each new and more intelligent generation appearing more and more rapidly, causing an "explosion" in intelligence and
resulting in a powerful superintelligence that qualitatively far surpasses all human intelligence.[4]
The concept and the term "singularity" were popularized by Vernor Vinge in his 1993 essay The Coming Technological Singularity, in which he
wrote that it would signal the end of the human era, as the new superintelligence would continue to upgrade itself and would advance
technologically at an incomprehensible rate. He wrote that he would be surprised if it occurred before 2005 or after 2030.[4] Scientists, such as
Stephen Hawking, have expressed concern that full artificial intelligence (AI) could result in human extinction.[8][9] The consequences of the
singularity and its potential benefit or harm to the human race have been intensely debated. Four polls of AI researchers, conducted in 2012 and
2013 by Nick Bostrom and Vincent C. Müller, suggested a confidence of 50% that artificial general intelligence (AGI) would be developed by
2040–2050.[10][11]
Although technological progress has been accelerating in most areas (though slowing in some), it has been limited by the basic intelligence of
the human brain, which has not, according to Paul R. Ehrlich, changed significantly for millennia.[12] However, with the increasing power of
computers and other technologies, it might eventually be possible to build a machine that is significantly more intelligent than humans.[13]
If a superhuman intelligence were to be invented—either through the amplification of human intelligence or through artificial intelligence—it
would vastly improve over human problem-solving and inventive skills. Such an AI is referred to as Seed AI[14][15] because if an AI were created
with engineering capabilities that matched or surpassed those of its human creators, it would have the potential to autonomously improve its own
software and hardware to design an even more capable machine, which could repeat the process in turn. This recursive self-improvement could
accelerate, potentially allowing enormous qualitative change before any upper limits imposed by the laws of physics or theoretical computation
set in. It is speculated that over many iterations, such an AI would far surpass human cognitive abilities.
Singularity Digital Immortality

Robotics
Natural Intelligence evolved because of the need to interact with a
multi-faceted uncertain environment. Robots will be one of the driving
forces for Artificial General Intelligence
IEEE Robots

New Approaches to Robotics
From http://people.csail.mit.edu/brooks/papers/new-approaches.pdf
In order to build autonomous robots that can carry out useful work in unstructured environments new approaches have been
developed to building intelligent systems. The relationship to traditional academic robotics and traditional artificial intelligence is
examined. In the new approaches a tight coupling of sensing to action produces architectures for intelligence that are networks of
simple computational elements which are quite broad, but not very deep. Recent work within this approach has demonstrated the
use of representations, expectations, plans, goals, and learning, but without resorting to the traditional uses, of central, abstractly
manipulable or symbolic representations. Perception within these systems is often an active process, and the dynamics of the
interactions with the world are extremely important. The question of how to evaluate and compare the new to traditional work still
provokes vigorous discussion.
Brooks developed the subsumption architecture, which deliberately changed the modularity from the traditional AI approach. Figure 2 shows a vertical
decomposition into task achieving behaviors rather than information processing modules. This architecture was used on robots which explore, build maps,
have an onboard manipulator, walk, interact with people, navigate visually, and learn to coordinate many conflicting internal behaviors. The implementation
substrate consists of networks of message-passing augmented finite state machines (AFSMs). The messages are sent over predefined "wires" from a specific
transmitting to a specific receiving AFSM. The messages are simple numbers (typically 8 bits) whose meaning depends on the designs of both the transmitter
and the receiver. An AFSM has additional registers which hold the most recent incoming message on any particular wire. The registers can have their values
fed into a local combinatorial circuit to produce new values for registers or to provide an output message. The network of AFSM is totally asynchronous, but
individual AFSMs can have fixed duration monostables which provide for dealing with the flow of time in the outside world. The behavioral competence of the
system is improved by adding more behavior-specific network to the existing network. This process is called layering. This is a simplistic and crude analogy
to evolutionary development. As with evolution, at every stage of the development the systems are tested. Each of the layers is a behavior-producing piece of
network in its own right, although it may implicitly rely on the presence of earlier pieces of network. For instance, an explore layer does not need to explicitly
avoid obstacles, as the designer knows that the existing avoid layer will take care of it.
A fixed priority arbitration scheme is used to handle conflicts. These architectures were radically diﬀerent from those in use in the robotics community at the
time. There was no central model of the world explicitly represented within the systems. There was no implicit separation of data and computation-they were
both distributed over the same network of elements. There were no pointers, and no easy way to implement them, as there is in symbolic programs. Any
search space had to be a bounded in size a priori, as search nodes could not be dynamically created and destroyed during a search process. There was no
central locus of control. In general, the separation into perceptual system, central system, and actuation system was much less distinct than in previous
approaches, and indeed in these systems there was an intimate intertwining of aspects of all three of these capabilities. There was no notion of one process
calling on another as a subroutine. Rather, the networks were designed so that results of computations would simply be available at the appropriate location
when needed. The boundary between computation and the world was harder to draw as the systems relied heavily on the dynamics of their interactions with
the world to produce their results. For instance, sometimes a physical action by the robot would trigger a change in the world that would be perceived and
cause the next action, in contrast to directly executing the two actions in sequence. Most of the behavior-based robotics work has been done with
implemented physical robots. Some has been done purely in software (21), not as a simulation of a physical robot, but rather as a computational experiment
in an entirely make-believe domain to explore certain critical aspects of the problem. This contrasts with traditional robotics where many demonstrations are
performed only on software simulations of robots.

Robust.ai
From https://www.robust.ai/

Future: Intelligent Machines
Boston Dynamics
Self Driving Cars
Aibo
Sofia
Mars Rover
Cyborg Erica
Ameca
Sofia vs Ameca
Astro Optimus

Everyday Robots
From https://everydayrobots.com/
Born from X, the moonshot factory, and working alongside teams at Google, we’re building a new type of robot. One that can learn
by itself, to help anyone with (almost) anything.At work or at home, a big part of our everyday lives is spent sweating the small stuff.
Keeping our environments safe and clean, putting things where they need to go or making sure the people we care about get a helping
hand whenever they need one. Taking on the kind of tasks that are repetitive at best, or drudgerous at worst.
Imagine a world where time-consuming, everyday tasks are simply taken care of. A world where we can choose to spend our time on
the things that really matter. Where our work lives are more productive, and our personal lives are richer for it.
Today’s robots are really good at three things — strength, precision, and repetition. But they are really bad at other things —
understanding new spaces and environments, and doing more than just one thing. Put simply, their very narrow capabilities come
from the human who has programmed them to solve just a single problem, in just one environment. To bridge the gap between today’s
single-purpose robots and tomorrow’s helper robots, we’re building robots that live in our world, and can learn by themselves. A
multifaceted challenge that’s even harder than building a self-driving car because there are no rules of the road for robotics.We’re
starting in the places where we spend most of our waking hours — the places where we work. But we’re not stopping there. We
believe helper robots have the potential to ultimately help everyone, everywhere. From offices, to institutions of care, to eventually in
our homes, they’ll make our lives easier by lending us a helping hand (or three).

Everyday Robots(cont)
From https://everydayrobots.com/
Is this a pathway to artificial “Common Sense”?

Cyborg Foundation
From https://www.cyborgfoundation.com/

Cyborgs, Neuroweapons, and Network Command
From https://sjms.nu/articles/10.31374/sjms.86/galley/106/download/
In this article, we will explore the emerging field of military neurotechnology and the way it
challenges the boundaries of war. We will argue that these technologies can be used not only
to enhance the cognitive performance of warfighters, but also as a means to exploit artificial
intelligence in autonomous and robotic weapons systems. This, however, requires the practice of
a collaborative network command and a governing framework of cyborg ethics to secure human
control and responsibility in military operations. The discussion of these governing principles
adheres to the tradition of military studies. Hence, we do not aim to present a neuroscientific
research program. Nor do we wish to embark on technical solutions in disciplines such as arti-
ficial intelligence and robotics. Rather, the intention is to make the highly specialized language
of these sciences accessible to an audience of military practitioners and policymakers, bringing
technological advances and challenges into the discussion of future warfighting.
“It is currently estimated that AI and robotic systems will be ubiquitous across the operational
framework of 2035.” (RAS MDO white paper 2018: 25)
Are we on the verge of a robotic revolution of military affairs? Will intelligent machines take control of the
future battlefield and replace human warfighters? Recent advances in military neurotechnologies, robotics,
and artificial intelligence (AI) have evoked the transgressive image of the ‘cyborg warrior’, a weaponized
brain-computer network powered by AI and neurocognitive augmentation. In the wake of these emergent
military technologies, some of our most fundamental assumptions and definitions of human intelligence,
autonomy, and responsibility have been challenged. These concepts are central to our understanding of law-
ful and ethical conduct of war. They are also closely associated with human agency and the ability to make
context-dependent decisions and critical evaluations in matters of life and death. The question that begs to
be answered is whether – and how – these concepts can be applied to cyborg systems that, per definition, are
not entirely human? What kind of military capacity is a cyborg warrior? A warfighter or a weapons system?
A human or a machine? In the following, we will argue that the cyborg warrior is neither a human subject
nor a piece of military hardware, but a heterogeneous assemblage – or rather a ‘nexus’ – of human and non-
human capacities, transmitting and decoding streams of information in military battle networks. As such,
we prefer to talk about cyborg and neurocognitive weapons systems, stressing the intrinsic entanglement
of human and artificial intelligence challenging traditional human-machine distinctions and dichotomies.

Future Robotics in Space
NASA Valkyrie Robot
Robot Colonies on Mars
Robot Space Explorers
SpaceBok
Robots will explore the Solar System many years before any human voyages.
The only exception will be the moon. Robots will be the only way to explore nearby
interstellar space unless a way is found to overcome the speed of light barrier.
Perseverance

Robots in Factories
Unimate Robot

From http://correll.cs.colorado.edu/pubs/iros09.pdf
MIT’s Distributed Robot Garden

From http://www.slideshare.net/lalit911garg/humanoid-robotics
Humanoid Robots Applications

From https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201211fa10.html
Future Trends Fusing Sensors and Robotics from NTT in Japan

From http://www.slideshare.net/chhattanshah/cloud-robotics-25842253
Cloud Robotics from ETRI in Korea

From https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201211fa10.html
Cloud Robotics from NTT in Japan

From https://www.ieee-ras.org/cognitive-robotics
Cognitive Robotics
There is growing need for robots that can interact safely with people in everyday situations. These robots have to be able to anticipate the effects
of their own actions as well as the actions and needs of the people around them.
To achieve this, two streams of research need to merge, one concerned with physical systems specifically designed to interact with unconstrained
environments and another focussing on control architectures that explicitly take into account the need to acquire and use experience.
The merging of these two areas has brought about the field of Cognitive Robotics. This is a multi-disciplinary science that draws on research in
adaptive robotics as well as cognitive science and artificial intelligence, and often exploits models based on biological cognition.
Cognitive robots achieve their goals by perceiving their environment, paying attention to the events that matter, planning what to do,
anticipating the outcome of their actions and the actions of other agents, and learning from the resultant interaction. They deal with the inherent
uncertainty of natural environments by continually learning, reasoning, and sharing their knowledge.
A key feature of cognitive robotics is its focus on predictive capabilities to augment immediate sensory-motor experience. Being able to view
the world from someone else's perspective, a cognitive robot can anticipate that person's intended actions and needs. This applies both during
direct interaction (e.g. a robot assisting a surgeon in theatre) and indirect interaction (e.g. a robot stacking shelves in a busy supermarket).
In cognitive robotics, the robot body is more than just a vehicle for physical manipulation or locomotion: it is a component of the cognitive
process. Thus, cognitive robotics is a form of embodied cognition which exploits the robot's physical morphology, kinematics, and dynamics, as
well as the environment in which it is operating, to achieve its key characteristic of adaptive anticipatory interaction.

From https://covariant.ai/our-approach
Covariant Robotics
General Robotic intelligence
Instead of learning to master specific tasks separately, Covariant robots learn general abilities such as robust 3D perception, physical
affordances of objects, few-shot learning and real-time motion planning. This allows them to adapt to new tasks just like people do —
by breaking down complex tasks into simple steps and applying general skills to complete them. Some papers are below.
• IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, June 2020
Embodied Language Grounding with Implicit 3D Visual Feature Representations Link
• International Conference on Robotics and Automation (ICRA), Paris, France, May 2020
Guided Uncertainty Aware Policy Optimization: Combining Model-Free and Model-Based Strategies for Sample-Efficient Learning Link
• International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020
Subpolicy Adaptation for Hierarchical Reinforcement Learning Link
• Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019
Goal Conditioned Imitation Learning Link
• Conference on Robot Learning (CoRL), Osaka, Japan, November 2019
Graph-Structured Visual Imitation LinkVideo
• International Conference on Machine Learning (ICML), Long Beach, USA, June 2019
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules Link
• Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018
Evolved Policy Gradients Link
• IEEE-RAS International Conference on Humanoid Robots (Humanoids), Beijing, China, November 2018
Data Dreaming for Object Detection: Learning Object-Centric State Representations for Visual Imitation Link
• IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), Madrid, Spain, October 2018
Domain Randomization and Generative Models for Robotic Grasping Link
• Conference on Robot Learning (CoRL), Zurich, Switzerland, October 2018
Model-Based Reinforcement Learning via Meta-Policy Optimization LinkCode
I

From https://mbm.cds.nyu.edu/
NYU Mind, Brains & Machines
Understanding intelligence is one of the greatest scientific quests ever undertaken—a challenge that
demands an interdisciplinary approach spanning psychology, neural science, philosophy, linguistics,
data science, and artificial intelligence (AI). A focus on computation is at the center of this quest—
viewing intelligence, in all of its forms, as a kind of sophisticated and adaptive computational process.
But the kind of computation necessary for intelligence remains an open question; despite striking
recent progress in AI, today's technologies provide nothing like the general-purpose, flexible
intelligence that we have as humans.
We believe that intensifying the dialog between these fields is needed for transformative research on
understanding and engineering intelligence, focused on two key questions: How can advances in
machine intelligence best advance our understanding of natural (human and animal) intelligence? And
how can we best use insights from natural intelligence to develop new, more powerful machine
intelligence technologies that more fruitfully interact with us?

From http://www.nicolelislab.net/?p=683
Networked Brains (Brainet) from Duke University

From https://www.quantamagazine.org/self-taught-ai-shows-similarities-to-how-the-brain-works-20220811/
Self-Taught AI Shows Similarities to How the Brain Works
Now some computational neuroscientists have begun to explore neural networks that have been trained with little or no
human-labeled data. These “self-supervised learning” algorithms have proved enormously successful at modeling
human language and, more recently, image recognition. In recent work, computational models of the mammalian visual
and auditory systems built using self-supervised learning models have shown a closer correspondence to brain function
than their supervised-learning counterparts. To some neuroscientists, it seems as if the artificial networks are beginning
to reveal some of the actual methods our brains use to learn.
elf-supervised learning strategies are designed to avoid such problems. In this approach, humans don’t label the data.
Rather, “the labels come from the data itself,” said Friedemann Zenke, a computational neuroscientist at the Friedrich
Miescher Institute for Biomedical Research in Basel, Switzerland. Self-supervised algorithms essentially create gaps in
the data and ask the neural network to fill in the blanks. In a so-called large language model, for instance, the training
algorithm will show the neural network the first few words of a sentence and ask it to predict the next word. When
trained with a massive corpus of text gleaned from the internet, the model appears to learn the syntactic structure of the
language, demonstrating impressive linguistic ability — all without external labels or supervision.
In systems such as this, some neuroscientists see echoes of how we learn. “I think there’s no doubt that 90% of what the
brain does is self-supervised learning,” said Blake Richards, a computational neuroscientist at McGill University and
Mila, the Quebec Artificial Intelligence Institute. Biological brains are thought to be continually predicting, say, an
object’s future location as it moves, or the next word in a sentence, just as a self-supervised learning algorithm attempts
to predict the gap in an image or a segment of text. And brains learn from their mistakes on their own, too — only a
small part of our brain’s feedback comes from an external source saying, essentially, “wrong answer.”

From https://www.sciencealert.com/biology-inspires-a-new-kind-of-water-based-circuit-that-could-transform-computing
Water-Based Circuit That Could Transform Computing
The future of neural network computing could be a little soggier than we were expecting. A team of physicists has
successfully developed an ionic circuit – a processor based on the movements of charged atoms and molecules in an
aqueous solution, rather than electrons in a solid semiconductor. Since this is closer to the way the brain transports
information, they say, their device could be the next step forward in brain-like computing.
"Ionic circuits in aqueous solutions seek to use ions as charge carriers for signal processing," write the team led by
physicist Woo-Bin Jung of the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) in a new
paper. "Here, we report an aqueous ionic circuit… This demonstration of the functional ionic circuit capable of analog
computing is a step toward more sophisticated aqueous ionics."A major part of signal transmission in the brain is the
movement of charged molecules called ions through a liquid medium. Although the incredible processing power of the
brain is extremely challenging to replicate, scientists have thought that a similar system might be employed for computing:
pushing ions through an aqueous solution.
This would be slower than conventional, silicon-based computing, but it might have some interesting advantages. For
example, ions can be created from a wide range of molecules, each with different properties that could be exploited in
different ways. But first, scientists need to show that it can work. This is what Jung and his colleagues have been working
on. The first step was designing a functional ionic transistor, a device that switches or boosts a signal. Their most recent
advance involved combining hundreds of those transistors to work together as an ionic circuit. The transistor consists of a
"bullseye" arrangement of electrodes, with a small disk-shaped electrode in the center and two concentric ring electrodes
around it. This interfaces with an aqueous solution of quinone molecules.
A voltage applied to the central disk generates a current of hydrogen ions in the quinone solution. Meanwhile, the two ring
electrodes modulate the pH of the solution to gate, increasing or decreasing the ionic current.

From https://braininitiative.nih.gov/
BRAIN Initiative
The Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN)
Initiative is aimed at revolutionizing our understanding of the human brain. By
accelerating the development and application of innovative technologies,
researchers will be able to produce a revolutionary new dynamic picture of the
brain that, for the first time, shows how individual cells and complex neural circuits
interact in both time and space. Long desired by researchers seeking new ways to
treat, cure, and even prevent brain disorders, this picture will fill major gaps in our
current knowledge and provide unprecedented opportunities for exploring exactly
how the brain enables the human body to record, process, utilize, store, and
retrieve vast quantities of information, all at the speed of thought.

From https://www.internationalbraininitiative.org/about-us
International BRAIN Initiative
The International Brain Initiative is represented by some of the world's major brain research
projects:
Our vision is to catalyse and advance neuroscience through international
collaboration and knowledge sharing, uniting diverse ambitions and disseminating
discoveries for the benefit of humanity.

From https://alleninstitute.org/what-we-do/brain-science/
Allen Institute for Brain Science
The Allen Institute is committed to uncovering some of the most pressing questions in
neuroscience, grounded in an understanding of the brain and inspired by our quest to
uncover the essence of what makes us human.
Our focus on neuroscience began with the launch of the Allen Institute for Brain Science in
2003. This division, known worldwide for publicly available resources and tools on brain-
map.org, is beginning a new 16-year phase to understand the cell types in the brain,
bridging cell types and brain function to better understand healthy brains and what goes
wrong in disease. Our MindScope Program focuses on understanding what drives
behaviors in the brain and how to better predict actions. In late 2021 we launched the Allen
Institute for Neural Dynamics, a new research division of the Allen Institute that is dedicated
to understanding how dynamic neuronal signals at the level of the entire brain implement
fundamental computations and drive flexible behaviors.

From https://alleninstitute.org/what-we-do/brain-science/
Allen Institute for Neural Dynamics
The Allen Institute for Neural Dynamics explores the brain’s activity, at the level of individual
neurons and the whole brain, to reveal how we interpret our environments to make
decisions. We aim to discover how neural signaling – and changes in that signaling – allow
the brain to perform complex but fundamental computations and drive flexible behaviors.
Our experiments and openly shared resources will shed light on behavior, memory, how we
handle uncertainty and risk, how humans and other animals chase rewards – and how some
or all of these complicated cognitive functions go awry in neuropsychiatric disorders such
as depression, ADHD or addiction.

From https://portal.brain-map.org/explore/overview
Allen Brain-Map.org
The Allen Institute for Brain Science was established in 2003 with a goal to
accelerate neuroscience research worldwide with the release of large-scale,
publicly available atlases of the brain. Our research teams continue to conduct
investigations into the inner workings of the brain to understand its components
and how they come together to drive behavior and make us who we are.
One of our core principles is Open Science: We publicly share all the data,
products, and findings from our work. Here on brain-map.org, you’ll find our open
data, analysis tools, lab resources, and information about our own research that
also uses these publicly available resources. The potential uses of Allen Institute
for Brain Science resources, on their own or in combination with your own data,
are endless.
The Allen Brain Atlases capture patterns of gene expression across the brain in
various species. Learn more and read publications at the Transcriptional
Landscape of the Brain Explore page. Example use cases across the atlases
include exploration of gene expression and co-expression patterns, expression
across networks, changes across developmental stages, comparisons between
species, and more.

From https://en.wikipedia.org/wiki/Computational_neuroscience
Cognitive Neuroscience

From https://numenta.com/resources/biological-and-machine-intelligence/
Biological and Machine Intelligence
Classic AI and ANNs generally are designed to solve specific types of problems rather than proposing a general theory of
]ntelligence. In contrast, we know that brains use common principles for vision, hearing, touch, language, and behavior. This
remarkable fact was first proposed in 1979 by Vernon Mountcastle. He said there is nothing visual about visual cortex and nothing
auditory about auditory cortex. Every region of the neocortex performs the same basic operations. What makes the visual cortex
visual is that it receives input from the eyes; what makes the auditory cortex auditory is that it receives input from the ears. From
decades of neuroscience research, we now know this remarkable conjecture is true. Some of the consequences of this discovery are
surprising. For example, neuroanatomy tells us that every region of the neocortex has both sensory and motor functions. Therefore,
vision, hearing, and touch are integrated sensory-motor senses; we can’t build systems that see and hear like humans do without
incorporating movement of the eyes, body, and limbs.
The discovery that the neocortex uses common algorithms for everything it does is both elegant and fortuitous. It tells us that to
understand how the neocortex works, we must seek solutions that are universal in that they apply to every sensory modality and
capability of the neocortex. To think of vision as a “vision problem” is misleading. Instead we should think about vision as a
“sensory motor problem” and ask how vision is the same as hearing, touch or language. Once we understand the common cortical
principles, we can apply them to any sensory and behavioral systems, even those that have no biological counterpart. The theory
and methods described in this book were derived with this idea in mind. Whether we build a system that sees using light or a
system that “sees” using radar or a system that directly senses GPS coordinates, the underlying learning methods and algorithms
will be the same.
Today, having made several important discoveries about how the neocortex works, we can build practical systems that solve
valuable problems. Of course, there are still some things we don’t understand about the brain and the neocortex, but we have an
overall theory that can be tested. The theory includes key principles such as: how neurons make predictions, the role of dendritic
spikes in cortical processing, how cortical layers learn sequences, and how cortical columns learn to model objects through
movement. This book reflects those key principles

From https://www.nist.gov/news-events/news/2022/10/nists-superconducting-hardware-could-scale-brain-inspired-computing
NIST’s Superconducting Hardware Could Scale Up Brain-Inspired Computing
Scientists have long looked to the brain as an inspiration for designing computing systems. Some researchers have recently gone even
further by making computer hardware with a brainlike structure. These “neuromorphic chips” have already shown great promise, but
they have used conventional digital electronics, limiting their complexity and speed. As the chips become larger and more complex,
the signals between their individual components become backed up like cars on a gridlocked highway and reduce computation to a
crawl. Now, a team at the National Institute of Standards and Technology (NIST) has demonstrated a solution to these communication
challenges that may someday allow artificial neural systems to operate 100,000 times faster than the human brain.
The human brain is a network of about 86 billion cells called neurons, each of which can have thousands of connections (known as
synapses) with its neighbors. The neurons communicate with each other using short electrical pulses called spikes to create rich, time-
varying activity patterns that form the basis of cognition. In neuromorphic chips, electronic components act as artificial neurons,
routing spiking signals through a brainlike network.
Doing away with conventional electronic communication infrastructure, researchers have designed networks with tiny light sources at
each neuron that broadcast optical signals to thousands of connections. This scheme can be especially energy-eﬃcient if
superconducting devices are used to detect single particles of light known as photons — the smallest possible optical signal that
could be used to represent a spike.
In a new Nature Electronics paper, NIST researchers have achieved for the first time a circuit that behaves much like a biological
synapse yet uses just single photons to transmit and receive signals. Such a feat is possible using superconducting single-photon
detectors. The computation in the NIST circuit occurs where a single-photon detector meets a superconducting circuit element called
a Josephson junction. A Josephson junction is a sandwich of superconducting materials separated by a thin insulating film. If the
current through the sandwich exceeds a certain threshold value, the Josephson junction begins to produce small voltage pulses called
fluxons. Upon detecting a photon, the single-photon detector pushes the Josephson junction over this threshold and fluxons are
accumulated as current in a superconducting loop. Researchers can tune the amount of current added to the loop per photon by
applying a bias (an external current source powering the circuits) to one of the junctions. This is called the synaptic weight.

From https://www.nist.gov/news-events/news/2022/10/nists-superconducting-hardware-could-scale-brain-inspired-computing
NIST’s Superconducting Hardware Could Scale Up Brain-Inspired Computing (cont)
This behavior is similar to that of biological synapses. The stored current serves as a form of short-term memory, as it provides a record of
how many times the neuron produced a spike in the near past. The duration of this memory is set by the time it takes for the electric current
to decay in the superconducting loops, which the NIST team demonstrated can vary from hundreds of nanoseconds to milliseconds, and
likely beyond. This means the hardware could be matched to problems occurring at many different time scales — from high-speed industrial
control systems to more leisurely conversations with humans. The ability to set different weights by changing the bias to the Josephson
junctions permits a longer-term memory that can be used to make the networks programmable so that the same network could solve many
different problems.
Synapses are a crucial computational component of the brain, so this demonstration of superconducting single-photon synapses is an
important milestone on the path to realizing the team’s full vision of superconducting optoelectronic networks. Yet the pursuit is far from
complete. The team’s next milestone will be to combine these synapses with on-chip sources of light to demonstrate full superconducting
optoelectronic neurons.
“We could use what we’ve demonstrated here to solve computational problems, but the scale would be limited,” NIST project leader Jeff
Shainline said. “Our next goal is to combine this advance in superconducting electronics with semiconductor light sources. That will allow us
to achieve communication between many more elements and solve large, consequential problems.”
The team has already demonstrated light sources that could be used in a full system, but further work is required to integrate all the
components on a single chip. The synapses themselves could be improved by using detector materials that operate at higher temperatures
than the present system, and the team is also exploring techniques to implement synaptic weighting in larger-scale neuromorphic chips.

From https://numenta.com/resources/biological-and-machine-intelligence/
Building an Intelligent Machine
1. Embodiment
2. Old Brain Equivalent
3. Neo-Cortex Equivalent

From https://www.quantamagazine.org/how-ai-transformers-mimic-parts-of-the-brain-20220912/
Transformers and the Brain
Artificial intelligence offers another way in. For years, neuroscientists have harnessed many types of neural networks — the engines that power
most deep learning applications — to model the firing of neurons in the brain. In recent work, researchers have shown that the hippocampus, a
structure of the brain critical to memory, is basically a special kind of neural net, known as a transformer, in disguise. Their new model tracks
spatial information in a way that parallels the inner workings of the brain. They’ve seen remarkable success.
“The fact that we know these models of the brain are equivalent to the transformer means that our models perform much better and are easier to
train,” said James Whittington, a cognitive neuroscientist who splits his time between Stanford University and the lab of Tim Behrens at the
University of Oxford.
Artificial intelligence offers another way in. For years, neuroscientists have harnessed many types of neural networks — the engines that power
most deep learning applications — to model the firing of neurons in the brain. In recent work, researchers have shown that the hippocampus, a
structure of the brain critical to memory, is basically a special kind of neural net, known as a transformer, in disguise. Their new model tracks
spatial information in a way that parallels the inner workings of the brain. They’ve seen remarkable success.
“The fact that we know these models of the brain are equivalent to the transformer means that our models perform much better and are easier to
train,” said James Whittington, a cognitive neuroscientist who splits his time between Stanford University and the lab of Tim Behrens at the
University of Oxford.
Transformers first appeared five years ago as a new way for AI to process language. They are the secret sauce in those headline-grabbing sentence-
completing programs like BERT and GPT-3, which can generate convincing song lyrics, compose Shakespearean sonnets and impersonate
customer service representatives.
Transformers work using a mechanism called self-attention, in which every input — a word, a pixel, a number in a sequence — is always
connected to every other input. (Other neural networks connect inputs only to certain other inputs.) But while transformers were designed for
language tasks, they’ve since excelled at other tasks such as classifying images — and now, modeling the brain.

Artificial Mind
From https://en.wikipedia.org/wiki/Artificial_brain
An artificial brain (or artificial mind) is software and hardware with cognitive abilities similar to those of the animal or human brain.[1]
Research investigating "artificial brains" and brain emulation plays three important roles in science:
1. An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
2. A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a
machine that has all the capabilities of a human being.
3. A long-term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such
as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is
sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England[2] where researchers are using
biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's,
motor neurone and Parkinson's disease.
The second objective is a reply to arguments such as John Searle's Chinese room argument, Hubert Dreyfus's critique of AI or Roger
Penrose's argument in The Emperor's New Mind. These critics argued that there are aspects of human consciousness or expertise that can
not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree
of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper "Computing Machinery and Intelligence".[note 1]
The third objective is generally called artificial general intelligence by researchers.[3] However, Ray Kurzweil prefers the term "strong AI".
In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to
implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done
by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the
Oxford TED conference in 2009.[1]

Cognitive Architectures
From hhttps://en.wikipedia.org/wiki/Cognitive_architecture
A cognitive architecture refers to botThe Institute for Creative Technologies defines cognitive architecture as: "hypothesis about the fixed structures that provide a mind, whether in natural or
artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments."[3]h
a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science.[1]
4CAPS developed at Carnegie Mellon University by Marcel A. Just and Sashank Varma.
4D-RCS
Reference
Model
developed by James Albus at NIST is a reference model architecture that provides a theoretical foundation for designing, engineering, integrating intelligent
systems software for unmanned ground vehicles.[10]
ACT-R developed at Carnegie Mellon University under John R. Anderson.
ASMO developed under Rony Novianto at University of Technology, Sydney.
CHREST developed under Fernand Gobet at Brunel University and Peter C. Lane at the University of Hertfordshire.
CLARION the cognitive architecture, developed under Ron Sun at Rensselaer Polytechnic Institute and University of Missouri.
CMAC
The Cerebellar Model Articulation Controller (CMAC) is a type of neural network based on a model of the mammalian cerebellum. It is a type of
associative memory.[11] The CMAC was first proposed as a function modeler for robotic controllers by James Albus in 1975 and has been extensively used in
reinforcement learning and also as for automated classification in the machine learning community.
Copycat by Douglas Hofstadter and Melanie Mitchell at the Indiana University.
DUAL developed at the New Bulgarian University under Boicho Kokinov.
FORR developed by Susan L. Epstein at The City University of New York.
Framsticks
a connectionist distributed neural architecture for simulated creatures or robots, where modules of neural networks composed of heterogenous neurons
(including receptors and effectors) can be designed and evolved.
Google
DeepMind
The company has created a neural network that learns how to play video games in a similar fashion to humans[12] and a neural network that may be able to
access an external memory like a conventional Turing machine,[13] resulting in a computer that appears to possibly mimic the short-term memory of the
human brain. The underlying algorithm is based on a combination of Q-learning with multilayer recurrent neural network.[14] (Also see an overview by
Holographic
associative
memory
This architecture is part of the family of correlation-based associative memories, where information is mapped onto the phase orientation of complex
numbers on a Riemann plane. It was inspired by holonomic brain model by Karl H. Pribram. Holographs have been shown to be effective for associative
memory tasks, generalization, and pattern recognition with changeable attention.

Cognitive Architectures (cont)
From hhttps://en.wikipedia.org/wiki/Cognitive_architecture
A cognitive architecture refers to botThe Institute for Creative Technologies defines cognitive architecture as: "hypothesis about the fixed structures that provide a mind, whether in natural or
artificial systems, and how they work together – in conjunction with knowledge and skills embodied within the architecture – to yield intelligent behavior in a diversity of complex environments."[3]h
a theory about the structure of the human mind and to a computational instantiation of such a theory used in the fields of artificial intelligence (AI) and computational cognitive science.[1]
Hierarchical
temporal
memory
This architecture is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and
algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in
his book On Intelligence. HTM is a method for discovering and inferring the high-level causes of observed input patterns and sequences, thus building an
increasingly complex model of the world.
CoJACK
An ACT-R inspired extension to the JACK multi-agent system that adds a cognitive architecture to the agents for eliciting more realistic (human-like)
behaviors in virtual environments.
IDA and
LIDA
implementing Global Workspace Theory, developed under Stan Franklin at the University of Memphis.
MANIC
(Cognitive
Architecture)
Michael S. Gashler, University of Arkansas.
PRS 'Procedural Reasoning System', developed by Michael Georgeff and Amy Lansky at SRI International.
Psi-Theory developed under Dietrich Dörner at the Otto-Friedrich University in Bamberg, Germany.
R-CAST developed at the Pennsylvania State University.
Spaun
(Semantic
Pointer
Architecture
by Chris Eliasmith at the Centre for Theoretical Neuroscience at the University of Waterloo – Spaun is a network of 2,500,000 artificial spiking neurons,
which uses groups of these neurons to complete cognitive tasks via flexibile coordination. Components of the model communicate using spiking neurons that
implement neural representations called "semantic pointers" using various firing patterns. Semantic pointers can be understood as being elements of a
compressed neural vector space.[17]
Soar developed under Allen Newell and John Laird at Carnegie Mellon University and the University of Michigan.
Society of
mind
proposed by Marvin Minsky.
Emotion
machine
proposed by Marvin Minsky.
Sparse
distributed
memory
was proposed by Pentti Kanerva at NASAAmes Research Center as a realizable architecture that could store large patterns and retrieve them based on partial
matches with patterns representing current sensory inputs.[18] This memory exhibits behaviors, both in theory and in experiment, that resemble those
previously unapproached by machines – e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, etc.
Sparse distributed memory is used for storing and retrieving large amounts ( bits) of information without focusing on the accuracy but on similarity of

Consciousness
I expect that a similar change of attitude will occur with consciousness. At some point
in the future, we will accept that any system that learns a model of the world,
continuously remembers the states of that model, and recalls the remembered states
will be conscious. There will be remaining unanswered questions, but consciousness
will no longer be talked about as “the hard problem.” It won’t even be considered a
problem.

Integrated Information Theory
From https://en.wikipedia.org/wiki/Integrated_information_theory
Integrated information theory (IIT) attempts to provide a framework capable of explaining why some physical systems (such as
human brains) are conscious,[1] why they feel the particular way they do in particular states (e.g. why our visual field appears
extended when we gaze out at the night sky),[2] and what it would take for other physical systems to be conscious (are dogs
conscious? what about unborn babies? or computers?).[3] In principle, once the theory is mature and has been tested extensively in
controlled conditions, the IIT framework may be capable of providing a concrete inference about whether any physical system is
conscious, to what degree it is conscious, and what particular experience it is having. In IIT, a system's consciousness (what it is like
subjectively) is conjectured to be identical to its causal properties (what it is like objectively). Therefore it should be possible to
account for the conscious experience of a physical system by unfolding its complete causal powers (see Central identity).[4]
AXIOMS:
Intrinsic existence: Consciousness exists: each experience is actual—indeed, that my experience here and now exists (it is real) is the only fact I can
be sure of immediately and absolutely. Moreover, my experience exists from its own intrinsic perspective, independent of external observers (it is
intrinsically real or actual).
Composition: Consciousness is structured: each experience is composed of multiple phenomenological distinctions, elementary or higher-order. For
example, within one experience I may distinguish a book, a blue color, a blue book, the left side, a blue book on the left, and so on.
Information: Consciousness is specific: each experience is the particular way it is—being composed of a specific set of specific phenomenal
distinctions—thereby differing from other possible experiences (differentiation). For example, an experience may include phenomenal distinctions
specifying a large number of spatial locations, several positive concepts, such as a bedroom (as opposed to no bedroom), a bed (as opposed to no
bed), a book (as opposed to no book), a blue color (as opposed to no blue), higher-order "bindings" of first-order distinctions, such as a blue book (as
opposed to no blue book), as well as many negative concepts, such as no bird (as opposed to a bird), no bicycle (as opposed to a bicycle), no bush (as
opposed to a bush), and so on. Similarly, an experience of pure darkness and silence is the particular way it is—it has the specific quality it has (no
bedroom, no bed, no book, no blue, nor any other object, color, sound, thought, and so on). And being that way, it necessarily differs from a large
number of alternative experiences I could have had but I am not actually having.
Integration: Consciousness is unified: each experience is irreducible and cannot be subdivided into non-interdependent, disjoint subsets of
phenomenal distinctions. Thus, I experience a whole visual scene, not the left side of the visual field independent of the right side (and vice versa). For
example, the experience of seeing the word "BECAUSE" written in the middle of a blank page is not reducible to an experience of seeing "BE" on the
left plus an experience of seeing "CAUSE" on the right. Similarly, seeing a blue book is not reducible to seeing a book without the color blue, plus the
color blue without the book.
Exclusion: Consciousness is definite, in content and spatio-temporal grain: each experience has the set of phenomenal distinctions it has, neither less
(a subset) nor more (a superset), and it flows at the speed it flows, neither faster nor slower. For example, the experience I am having is of seeing a body
on a bed in a bedroom, a bookcase with books, one of which is a blue book, but I am not having an experience with less content—say, one lacking the
phenomenal distinction blue/not blue, or colored/not colored; or with more content—say, one endowed with the additional phenomenal distinction high/
low blood pressure. Moreover, my experience flows at a particular speed—each experience encompassing say a hundred milliseconds or so—but I am
not having an experience that encompasses just a few milliseconds or instead minutes or hours.

Artificial General Intelligence and Consciousness

Artificial General Intelligence and Consciousness

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