Conscious Intelligence Requires Developmental 

Autonomous Programming For General Purposes 

Juyang Weng∗†‡

∗Department of Computer Science and Engineering, 

†Cognitive Science Program, ‡ Neuroscience Program, 

Michigan State University, East Lansing, MI, 48824 USA 

Abstract—Universal Turing Machines are well known in computer science but they are about manual programming for

general purposes. Although human children perform conscious

learning (learning while being conscious) from infancy, it is

unknown that Universal Turing Machines can facilitate not only

our understanding of Autonomous Programming For General

Purposes (APFGP) by machines, but also enable early-age

conscious learning. This work reports a new kind of AI—

conscious learning AI from a machine’s “baby” time. Instead

of arguing what static tasks a conscious machine should be able

to do during its “adulthood”, this work suggests that APFGP

is a computationally clearer and necessary criterion for us to

judge whether a machine is capable of conscious learning so

that it can autonomously acquire skills along its “career path”.

The results here report new concepts and experimental studies

for early vision, audition, natural language understanding, and

emotion, with conscious learning capabilities that are absent from

traditional AI systems.

I. INTRODUCTION 

To be conscious during adulthood, must a system be conscious from its “infancy” all the way into its “adulthood”? 

All animals [26], [15] conduct lifelong APFGP but traditional 

AI systems do not. This work argues that consciousness 

should not be an optional crown jewelry for AI [4], [11] 

but a necessity for credible AI. Unconscious AI has resulted 

in machines that are brittle because they are unaware of 

themselves and the physical world around them. Machines 

can become highly conscious by bootstrapping its degree of 

consciousness through lifelong conscious learning. 

Consciousness must apply to different contexts that a life 

experiences. Furthermore, the term involves many entities, 

such as environment, awareness, cognition and behavior, 

which is learned through lifetime based on genetically prepositioned (i.e., developed) learning capabilities [18], [17], [4]. 

For example, how does a cattle or a human in Fig. 1 learn 

consciousness from infancy so that, after it has grown up, it 

can navigate autonomously through the hustle and bustle of 

streets to reach its home? 

Can an artificial machine consciously learn to do the same 

and more? Weng et al. 2001 [25], which started this conference 

series as ICDL, proposed “autonomous mental development” 

as learning across lifetime that must be task-nonspecific. 

However, the link between “autonomous development” and 

“consciousness” still lacks a computational minimal set. 

By “minimal set”, we mean a minimal set of mechanisms 

from which a machine, natural or artificial, can bootstrap its 

Fig. 1. A conscious cattle and conscious humans navigate on a busy street 

of New Delhi, India. 

3

Δ c a t s a r e m y

Fig. 2. An example of Turing Machine. Each cell of the tape bears only a 

symbol. The controller has, at each integer time, a current state (e.g., 3). 

degree of consciousness from “early age” to “later age” so that 

a degree of consciousness is already present during learning, 

not only after a static batch of learning. This minimal set 

makes the causality of consciousness clearer. 

In order to gain understanding of what that minimal set is in 

terms of computation, let us start with a model of computation 

well known in computer science but not directly related to 

consciousness till this work. Please skip no sections. 

II. TURING MACHINES 

Turing Machines, originally proposed by Alan Turing [23] 

in 1936, although not meant to explain consciousness, can 

assist us to understand how consciousness arises from computations by a machine, both natural and artificial. 

A Turing Machine [7], [13], illustrated in Fig. 2, consists 

of an infinite tape, a read-write head, and a controller. The 

controller consists of a sequence of moves where each move 

is a 5-word sentence of the following form: 

(q, γ) → (q0

, γ0, d)

J. Weng, "Conscious Intelligence Requires Developmental Autonomous Programming For General Purposes," in Proc. IEEE 

International Conference on Development and Learning and Epigenetic Robotics, pp. 1-7, Valparaiso, Chile, Oct. 26-27, 2020.
meaning that if the current state is q and the current input 

that the head senses on the tape is γ, then the machine enters 0 to next state q , writes γ0 onto the tape, and its head moves 

in direction d (left, right, or stay) but no more than one cell 

away. 

Intuitively speaking, let us consider each symbol in the 

above 5-word expression as a “word”. Then all such 5-

word expressions are “sentences”. Thus, a human-handcrafted 

“program” is a sequence of such 5-word sentences the Turing 

Machine must follow in computation. Although such sentences 

are not a natural language, they are more precise than a natural 

language. 

III. UNIVERSAL TURING MACHINES 

How did Turing make the above machine general-purpose? 

All we need is to augment the meaning of the input on the 

tape: The tape contains two parts, program P and data x. 

In his 1936 paper [23], Turing explained in detail how 

a Turing Machine can be constructed to emulate program 

P applied to data x. This new kind of Turing Machines is 

now called Universal Turing Machines [7], [13]. We called it 

universal because the program P on the tape is open-ended, 

supplied by any users for any purposes. However, Universal 

Turing Machines are still not conscious. 

To see the link between Universal Turing Machines and 

consciousness, we must break a series of restrictions in Turing 

Machines, as explained in the next section. 

IV. EIGHT CONDITIONS FOR CONSCIOUSNESS 

The eight conditions below were not well known to be 

necessary for consciousness. However, the APFGP capability 

in the title requires all of them. That said, they are still insufficient for giving rise to APFGP without the full Developmental 

Networks (DN) to be discussed in the next section. 

To facilitate memorization, let us summarize the eight 

conditions in eight words: Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, Abstractive, giving 

acronym GENISAMA. Let us explain each of them below. 

Grounded: Grounded means sensors and effectors of a 

learner must be directly grounded in the physical world in 

which the learner lives or operates. IBM Deep Blue, IBM 

Watson, and AlphaGo are not grounded. Instead, it is humans 

who synthesize symbols from the physical world, and thus 

shield machines off from the rich physical game environments, 

including their human opponents. 

Emergent: The signals in the sensors, effectors and all 

representations inside the “skull” of the learner must emerge 

automatically through interactions between the learner and the 

physical world by way of sensors, effectors, and a genome (aka 

developmental program). A genome is meant to fit the physical 

world through the entire life, not only for a specific task during 

the life. For example, fruit flies must do foraging, fighting and 

mating. Thus, task-specific handcrafting of representation in 

sensors, effectors, and inside the “skull” is inconsistent to consciousness. This emergence requirement rules out task-specific 

and handcrafted representations, such as weights duplication 

in convolution used by deep learning. Likewise, an artificial 

genetic algorithm without lifetime learning/development does 

not have anything to emerge since each individual does not 

learn/develop in lifetime. 

Natural: The learner must use natural sensory and natural 

motor signals, instead of human hand-synthesized features 

from sensors or hand-synthesized class labels for effectors, 

because such symbols and labels are not natural without a 

human in the loop. For robots, natural signals are those directly 

available from a sensor (e.g., RGB pixel values from a camera) 

and raw signals for an effector/actuator. IBM Deep Blue, IBM 

Watson and AlphaGo all used handcrafted symbols for the 

board configurations and symbolic labels for game actions. 

Such symbols are not natural, not directly from cameras and 

not directly for robot arms. 

Incremental: Because the current action from the learner 

will affect the next input to the learner (e.g., the current action 

“turn left” allows it to see left view), learning must take 

place incrementally in time. IBM Deep Blue, IBM Watson 

and AlphaGo have used a batch learning method: all game 

configurations are available as a batch for the learner to learn. 

The learner is not conscious of how it has improved from early 

mistakes—not self-conscious. 

Skulled: The skull closes the brain of the learner so that 

any teacher’s direct manipulations with the internal brain 

representations (e.g., twisting internal parameters) are not 

permitted. For example, how can the brain be aware of what 

a neurosurgeon did inside the skull? 

Attentive: The learner must learn how to attend to various 

entities in its environment — the body and extra-body environment. The entities include location (where to attend), type 

(what to attend), scale to attend (e.g., body, face, or nose), as 

well as abstract concepts that the learner learned in life (e.g., 

am I doing the right thing?). IBM Deep Blue, IBM Watson 

and AlphaGo did not seem to think “what am I doing?”. 

Motivated: The beautiful logic that a Universal Turing 

Machine possesses to emulate any valid program does not give 

rise to consciousness as we know it. By motivation, we mean 

that the learner must learn motivation based on its intrinsic 

motives, such as pain avoidance, pleasure seeking, uncertainty 

awareness, and sensitivity to novelty. A system that is designed 

to do facial recognition does not have a motive to do things 

other than facial recognition. IBM Deep Blue, IBM Watson 

and AlphaGo did not feel happy when they won a game. 

Abstractive: Although a shallow definition of consciousness means awareness, full awareness requires a general capability to abstract higher concepts from concrete examples. By 

higher concepts here we mean those concepts that a normal 

individual of a species is expected to be able to abstract. 

Consider movie “Rain Man”: If a kiss by a lady on the lip 

is sensed only as “wet”, there is a lack of abstraction. A baby 

cannot abstract love from the first kiss, but a normal human 

adult is able to. Thus, abstraction requires learning. 

With the above eight requirements, we are ready to discuss 

GENISAMA Universal Turing Machines as super machines 

capable of conscious learning. 
A

B

Sensory X Closed control Y Motoric Z

Location concepts

Type concepts

Higher rules

Motion concepts

Output

Input

Output

Input

Input

Output

Input

Output

A

B

p

q

Excitatory

Inhibitory

(a)

(b)

Top-down Top-down

LRF LEF

Bottom-up

Sensory X Internal Y Motoric Z

X Y Z

Lateral

Bottom-up

SRF MEF

SEF MRF

Parietal Frontal

Temporal Frontal

Occipital

Eyes,

ears,

etc.

Fig. 3. A DN in (b) grows a brain Y as a two-way bridge between the sensory 

bank X and the motor bank Z. All the connections are learned, updated and 

trimmed automatically by DN. (a) A neuron’s connections are highly recurrent 

which requires our time-unfolded explanation. 

V. GENISAMA SUPER UNIVERSAL TURING MACHINES 

Handcrafting a University Turing Machine is not hard. What 

is really hard is how to enable such a machine to grow 

automatically from the natural physical word so as to learn any 

programs and any data directly from its physical environment! 

We will see below how. 

A DN in Fig. 3 is capable of learning any GENISAMA 

Universal Turing Machine. It grows one neuron at a time, to 

learn moves incrementally, one at a time. Such a machine 

is capable of APFGP, which motivates us as an alternative 

characterization of consciousness. 

A brain is highly recurrent, meaning a neuron sends signals 

to many other neurons but other neurons also send signals 

back, directly and indirectly. This recurrence has caused great 

difficulties in our understanding how the brain works. We must 

unfold time so that the time-unfolded brain is not recurrent 

along the time axis. We consider five entities W,Z, Y, X, X0

at times t,t = 0, 1, 2, ..., as illustrated in Table I. 

The first row in Table I gives the sample times t. 

The second row denote the actable world W, such as a 

hammer acting on a nail. 

The third row is the motor Z, which has muscles to drive 

effectors, such as arms, legs, and mouth. 

The fourth row is the skull-closed brain Y . The computation 

inside the brain must be fully autonomous [25] without pregiven any tasks. 

The fifth row is the sensor X, such as cameras, microphones, and touch sensors (e.g., skin). 

The last row is the sensible world W0

, such as surfaces of 

objects that reflect light received by cameras. 

The actable world W is typically not exactly the same as 

the sensible world W0

, because where sensors sense from and 

where effectors act on can be different. 

Next, we discuss the rules about how a DN, denoted as 

N = (X, Y,Z), learns from world W and W0

. 

Extend the tape of the Turing Machine to record images 

X from sensors, instead of symbols σ. Let X be the original 

emergent version of input, e.g., a vector that contains values 

of all pixels. 

Extend the output from the Turing Machine (q0

, γ0, d) to 

be a muscle image from motor Z, instead of symbols. Thus, 

the GENISAMA Turing Machine directly acts on the physical 

world. 

Parallel computing model: We treat X and Z as external 

because they can be “supervised” by the physical environment 

as well as “self-supervised” by the network itself. The internal 

area Y is closed (hidden)—cannot be directly supervised by 

external teachers. As in the above Table, we unfold the time 

t and allow the network to have three areas X(t), Y (t), and 

Z(t) that learn incrementally through time t = 0, 1, 2, ...: 

⎡ ⎤ ⎡ ⎤ ⎡ ⎤ Z(0) Z(1) Z(2)

⎣Y (0)⎦ → ⎣Y (1)⎦ → ⎣Y (2)⎦ → ... (1) 

X(0) X(1) X(2)

where → means neurons on the left adaptively links to the 

neurons on the right. 

Note, all neurons in every column t use only the values of 

the column t − 1 to its immediate left, but use nothing from 

other columns. This is true for all columns t, with integers 

t ≥ 1. Otherwise, iterations are required. Namely, by unfolding 

time in the above expression, the highly recurrent operations 

in recurrent DN become nonrecurrent in time-unfolded DN. 

Thus, the DN runs in real time and do not have to slow down 

waiting for any iterations. 

Using Eq. (1), we outline each of the motor area Z, brain 

area Y , and sensory area X, for t = 1, 2, ..., from an embryo, 

all the way to an adult, till possible death. 

1. The motor area Z starting from Z(0), represents many 

muscles signals in a developing body. Muscle cells in Z(t) at 

time t take inputs from the Y (t − 1) area and the Z(t − 1), 

acting on the environment at time t using Z(t) through selfsupervision—trials, errors, and practices. 

2. Concurrently, the brain Y , starting from Y (0), also 

dynamically develops and grows. Each neuron in Y (t) gets 

multiple inputs from all three areas, X(t − 1), Y (t − 1) and 

Z(t − 1). Competition among neurons allows only few Y

neurons to win. These winner Y neurons (like an expert team) 

at the time t column represent the firing of the brain at time 

t. 

3. Likewise, the sensory area X, starting from X(0), also 

develops within a developing body. What is different between 

the motor area Z and the sensory area X is that the former 

develops neurons that drive muscles (and also “feel” the world) 

but the latter develops receptors that sense the world. 
TABLE I 

UNFOLDING TIME FOR APFGP IN A DEVELOPMENTAL NETWORK 

Time 

Actable world W

Motor Z

Skulled brain Y

Sensor X

Sensible world W0

0 

W(0)

Z(0)

Y (0)

X(0)

W0

(0)

1 

W(1)

Z(1)

Y (1)

X(1)

W0(1)

2 

W(2)

Z(2)

Y (2)

X(2)

W0(2)

3 

W(3)

Z(3)

Y (3)

X(3)

W0(3)

Now, we have the minimal set of mechanisms—Eq. (1) 

along with above paragraphs 1, 2 and 3—as the Computational 

Model of Emergence of Consciousness for natural and artificial machines to learn consciousness (and consciously learn) 

throughout lifetime: 

As time goes by, the learner looks more and 

more aware by learning a lifetime program P and 

lifetime data x from its world, while an optimal 

GENISAMA Universal Turing Machine emerges as 

proven mathematically in [24]. Inside the brain this 

machine autonomously predicts to make a larger, 

more sophisticated and increasingly integrated program P for all learned purposes that apply to world 

data x. 

In the eyes of humans, this learner becomes increasingly 

conscious. Although the lifelong-learned consciousness can 

be extremely complex, the minimal set of computational 

mechanisms is relatively simple for us to understand, thanks 

to the Universal Turing Machines. 

VI. TECHNICAL DETAIL 

A. Developmental Networks for Learning Consciousness 

A Developmental Network is meant for consciousness because it is a holistic model for a biological brain, also fully 

implementable on an artificial machine. 

The following section presents Developmental Network 1 

(DN-1). Developmental Network 2 (DN-2) is different from 

DN-1 primarily in the following sense. In DN-1, each of 

multiple Y areas has a static set of neurons so that the 

competition within each area is based on a top-k principle. 

Namely, inhibition among neurons within each area is implicitly modeled by top-k competition. 

In the DN-2, however, there is no static assignment of 

neurons to any regions, so that regions in the DN-2 automatically emerge, along their scales, cascade, and nesting. 

A major advantage of DN-2 is that a human programmer 

is not in the loop of deciding the distribution of X-Y-Z 

mechanisms, relieving human from this intractable task of 

handcrafting consciousness. A major disadvantage of DN-2 

is that its computational explanation is too sophisticated to be 

included in this paper. Let us leave DN-2 out from this paper 

and concentrate on DN-1 below. 

The hidden Y area corresponds to the entire “brain”. In the 

following, we assume the brain has a single area Y but it will 

enable many subareas to emerge. 

4 

W(4)

Z(4)

Y (4)

X(4)

W0

(4)

5 

W(5)

Z(5)

Y (5)

X(5)

W0(5)

6 

W(6)

Z(6)

Y (6)

X(6)

W0(6)

7 ... t 

W(7) ... W(t)

Z(7) ... Z(t)

Y (7) ... Y (t)

X(7) ... X(t)

W0(7) ... W0(t)

The brain takes input from vector (z, x), not just sensory 

x but also motor z, to produce an internal response vector y

which represents the best match of (z, x) with one of many 

internally stored patterns of (z, x): 

The winner-take-all learning rule, which is highly nonlinear 

and simulates parallel lateral inhibition in the internal (hidden) 

area Y is sufficient to prove in [24] that a DN that has 

sufficient hidden neurons learns any Turing Machine perfectly, 

immediately, and error-free. 

The n neurons in Y give a response vector y =

(y1, y2, ...yn) of n neurons in which only the best-matched 

neuron fires at value 1 and all other neurons do not fire giving 

value 0:

⎧

⎨1 if j = argmax{f(ti, z, bi, x)}

yj = 1≤i≤n j = 1, 2, ...n,

⎩0 otherwise 

(2) 

where f is a function that measures the similarity between 

the top-down weight vector ti and the top-down input vector 

z [19] as well as the similarity between the bottom-up weight 

vector bi and the bottom-up input vector x. The value of 

similarity is the inner product of their length-normalized 

versions [24]. Corresponding to FA, both the top-down weight 

and the bottom-up weight must match well for f to give a high 

value as inner product. 

The response vector y the hidden Y area of DN is then used 

by Z and X areas to predict the next z and x respectively in 

discrete time t = 1, 2, , 3, ...: 

    z(t − 1) z(t + 1) → y(t) → (3) x(t − 1) x(t + 1)

where → denotes the update on the left side using the left side 

as input. The first → above is highly nonlinear because of the 

top-1 competition so that only one Y neuron fires (i.e., exactly 

one component in binary y is 1). The second → consists of 

simply links from the single firing Y neurons to all firing 

neurons on the right side. 

Like the transition function of a Turing Machine, each 

prediction of z(t+ 1) in Eq. (3) is called a transition, but now 

in real-valued vectors without any symbols. The same y(t) can 

also be used to predict the binary (or real-valued) x(t+1) ∈ X

in Eq. (3). The quality of prediction of (z(t + 1), x(t + 1))

depends on how state Z abstracts the external world sensed 

by X. The more mature the DN is in its “lifetime”’ learning, 

the better its predictions. 

The expression in Eq. (3), is extremely rich as illustrated in 

Fig. 3: Self-wiring within a Developmental Network (DN) as 
the control of GENISAMA TM, based on statistics of activities 

through “lifetime”, without any central controller, Master Map, 

handcrafted features, and convolution. 

The above vector formalization is simple but very powerful 

in practice. The pattern in Z can represent the binary pattern of 

any abstract concept — context, state, muscles, action, intent, 

object type, object group, object relation. However, as far as 

DN is concerned, they mean the same— a firing pattern of the 

Z area! 

Namely, unified numerical processing-and-prediction in DN 

amounts to any abstract concepts above. In symbolic representations, it is a human to handcraft every abstract concept as 

a symbol; but DN does not have a human in the “skull”. it 

simply learns, processes, and generates vectors. In the eyes of 

a human outside the “skull”, the DN gets smarter and smarter. 

Eq. (3)(a) shows each feature neuron has six fields in 

general: Sensory Receptive Field (SRF), Sensory Effective 

Field (SEF), Motor Receptive Field (MRF), Motoric Effective 

Field (MEF), and Lateral Receptive Field (LRF) and Lateral 

Effective Field (LEF). Eq. (3)(b) shows the resulting self-wired 

architecture of DN with Occipital, Temporal, Parietal, and 

Frontal lobes. Regulated by a general-purpose Developmental 

Program (DP), the DN self-wires by “living” in the physical 

world. The X and Z areas are supervised by body and the 

physical world which includes teachers. 

Through the synaptic maintenance, some Y neurons gradually lose their early connections (dashed lines) with X (Z) 

areas and become “later” (early) Y areas. In the (later) Parietal 

and Temporal lobes, some neurons further gradually lost their 

connections with the (early) Occipital area and become rulelike neurons. These self-wired connections give rise to a 

complex dynamic network, with shallow and deep connections 

instead of a deep cascade of areas. Object location and motion 

are non-declarative concepts and object type and language 

sequence are declarative concepts. Concepts and rules are 

abstract with the desired specificities and invariances. DN does 

not have any static Brodmann areas. 

B. Optimal Properties Proved for DN 

If a DN can learn quickly like other normal animals, we 

may have to call it retarded with only a limited consciousness 

compared to other animals of the same age. We do not want 

a DN to get stuck into a local minimum, as many nonlinear 

artificial systems have suffered. 

Fortunately, every DN is optimal in the sense of maximum 

likelihood, proven mathematically in [24]. Put intuitively, all 

DNs are optimal, given the same learning environment, the 

same learning experience, and the same number of neurons in 

the “brain”. There might be many possible network solutions 

some of which got stuck into local minima in their search 

for a good network. However, each DN is the most likely 

one, without getting stuck into local minima. This is because 

although a DN starts with random weights, all random weights 

result in the same network. 

However, this does not mean that the learning environment 

is the best possible one or the number of neurons is best 

training data collection route

blind folded testing route

regular testing route

sunny weather

sunny weather

cloudy weather

cloudy weather

sunny weather

cloudy weather

sunny weather

sunny weather

sunny weather cloudy weather

sunny weather

cloudy weather

Fig. 4. Training, regular testing, and blind-folded testing sessions conducted 

on campus of a university under different times of day and different natural 

lighting conditions (e.g., there are extensive shadows in images). Disjoint 

testing sessions were conducted along paths that the machine has not learned. 

This is the first time for visual awareness to be learned by GENISAMA Turing 

Machines. 

possible one for many lifetime tasks. Search for a better 

educational environment will be human challenge for their 

future children, both natural and artificial kinds. 

C. Experiments 

This seems the first time where general-purpose vision, 

general-purpose audition, and general-purpose natural language, as the three well-known bottleneck areas in AI has 

been learned by a single type of network integrated with 

motivational learning. Other systems include [5], [8], [22], [3], 

[9], [12], [16], [6]. 

We conducted experiments in which a learning system acts 

as an emergent Turing Machine that learns one of three wellrecognized bottleneck problems in AI, vision, audition and 

natural language acquisition. Hopefully, when such systems 

are mature enough after “living” and “learning” in the real 

physical world, they look as though they have a certain degree 

of animal-like consciousness in the eyes of humans. 

Vision from a “lifelong” retinal sequence: How does 

a DN become visually conscious demonstrated by its motor 

behaviors? Let it learn by artificially “living” in the real world! 

Fig. 4 provides an overview of the extensiveness of the 

training, regular training, and blindfolded testing sessions. 

The inputs to the DN were from the same mobile phone 

that performs computation. They include the current image 

from the monocular camera, the current desirable direction 

from the Google Map API and the Google Directions API. 

If the teacher imposes the state in Z, this is treated as the 

supervised state. Otherwise, the DN outputs its predicted state 

from Z. The DN learned to attend critical visual information in 

the current image (e.g., scene type, road features, landmarks, 

and obstacles) depending on the context of desired direction 

and the context state. Each state from DN includes heading 

direction or stop, the location of the attention, and the type 

of object to be detected (which detects a landmark), and the 
Fig. 5. The sequences of concept 1 (dense, bottom) and concept 2 (sparse, 

top) for phoneme /u:/. The latest DNs do not need human to provide any 

labels. Instead, they self-supervise themselves. 

scale of attention (global or local), all represented as binary 

patterns. None is a symbol. 

Below, we will see that an auditory consciousness also uses 

the same DN, but using different “innate” parameters. 

Audition from a “lifelong” cochlear sequence: How does 

a DN become auditory conscious demonstrated by its motor 

behaviors? Let it learn by artificially “living” in the real world! 

For the audition modality, each input image to X is the 

pattern that simulates the output from an array of hair cells 

in the cochlea. We model the cochlea in the following way. 

The cells in the base of the cochlea correspond to filters with 

a high pass band. The cells in the top correspond to filters 

with a low pass band. At the same height, cells have different 

phase shifts. Potentially, such a cochlear model could deal 

with music and other natural sound, more general than the 

popular Mel Frequency Cepstral Coefficients (MFCCs) that 

are mainly for human speech processing. The performance 

will be reported elsewhere due to the limited space. 

Take the phoneme /u:/ as an example shown in Fig. 5. The 

state of concept 2 keeps as silence when inputs are silence 

frames. It becomes a “free” state when phoneme frames are 

coming in, and changes to /u:/ state when first silence frame 

shows up at the end. At the same time, the states of concept 

1 count temporally dense stages. 

One may ask, what about higher consciousness such as 

natural language understanding? 

Natural languages from a “lifelong” word sequence:

How does a DN become language conscious demonstrated by 

its motor behaviors? Let it learn by artificially “living” in the 

real world! Here, we assume grounded words are emergent 

patterns, not symbols. 

As far as we know, this seems to be the first work that deals 

with language acquisition in a bilingual environment, largely 

because the DN learns directly from emergent patterns, both in 

word input and in action input (supervision), instead of static 

symbols. 

The input to X is a 12-bit binary pattern, each represents 

Fig. 6. The finite automaton for the English and French versions of some 

sentences. The DN learned a much larger finite automaton. Cross-language 

meanings of partial- and full-sentences are represented by the same state of 

meaning context qi, i = 0, 1, 2, ..., 24. See, e.g., q1, q3, q4, and q5. But the 

language specific context is represented by another concept: language type. 

The last letter is the return character that indicates the end of a sentence. 

a word, which potentially can represent 212 words using 

binary patterns. The system was taught 1,862 English and 

French sentences from [20], using 2, 338 unique words (case 

sensitive). As an example of the sentences: English: “Christine 

used to wait for me every evening at the exit.” French: 

“Christine m’attendait tours les soirs a` la sortie.” 

The Z area was taught two concepts: language type (English, French, and language neutral, e.g., a number or name) 

represented by 3 neurons (top-1 firing), and the languageindependent meanings as meaning states, as shown in Fig. 6. 

The latter is represented by 18 neurons (18-bit binary pattern), always top 5 neurons firing, capable of representing 

C(18, 5) = 8, 568 possible combinations as states, but only 

6, 638 actual meanings were recorded. Therefore, the Z area 

has 3+ 18 = 21 neurons, potentially capable of representing a 

huge number 221 binary patterns if all possible binary patterns 

are allowed. 

However, the DN actually observed only 8, 333 Z patterns 

(both concepts combined) from the training experience, and 

10, 202 distinct (Z, X) patterns—FA transitions. Consider a 

traditional symbolic FA using a symbolic transition table, 

which has 6, 638 × 3 = 19, 914 rows and 2, 338 columns. 

This amounts to 19, 914 × 2, 338 = 46, 558, 932 table entries. 

But only 10, 202/46, 558, 932 ≈ 0.022% of the entries were 

detected by the hidden neurons, representing that only 0.02%

of the FA transition table was observed and accommodated 

by the DN. Namely, the DN has a potential to deal with ntuples of words with a very large n but bounded by DN size, 

because most un-observed n-tuples are never represented. The 

FA transition table is extremely large, but never generated. 

Without adding noise to the input X, the recognition error is 

zero, provided that there is a sufficient number of Y neurons. 

We added Gaussian noise into the bits of X. Let α represent 
the relative power of the signal in the noisy signal. When 

α is 60%, the state recognition rate of DN is around 98%. 

When α is 90%, the DN has reached 0% error rate, again 

thanks to the power of DN internal interpolation that converts 

a huge discrete (symbolic) problem into a considerably smaller 

continuous (numeric) problem. 

Emotional learning using the same network: One may 

wonder, does this type of consciousness enable emotion? The 

DN model considers emotion to belong to a wider category 

known in neuroscience as motivation. 

Motivation is very rich [2]. It has two major aspects (a) 

and (b) in the current DN model. All reinforcement-learning 

methods other than the DN, as far as we know, are for 

symbolic methods (e.g., Q-learning [21], [14]) and are in 

aspect (a) below exclusively. The DN uses concepts (e.g., 

important events) instead of the rigid time-discount in Qlearning to avoid the failure of far goals. 

(a) Pain avoidance and pleasure seeking to speed up learning 

important events. Signals from pain (aversive) sensors release 

a special kind of neural transmitters (e.g., serotonin [1]) that 

diffuse into all neurons that suppress Z firing neurons but 

speed up the learning rates of the firing Y neurons. Signals 

from sweet (appetitive) sensors release a special kind of neural 

transmitters (e.g., dopamine [10]) that diffuse into all neurons 

that excite Z firing neurons but also speed up the learning 

rates of the firing Y neurons. Higher pains (e.g., loss of 

loved ones and jealousy) and higher pleasure (e.g., praises and 

respects) develop at later ages from lower pains and pleasures, 

respectively. 

(b) Synaptic maintenance—the growing and trimming of 

the spines of synapses— segments object/event and motivates 

curiosity. Each synapse incrementally estimates the average 

error β between the pre-synaptic signal and the synaptic conductance (weight), represented by a kind of neural transmitter 

(e.g., acetylcholine [27]). Each neuron estimates the average  ̄ deviation β as the average across all its synapses. The ratio 

β/β ̄ is the novelty represented by a kind of neural transmitters 

(e.g., norepinephrine, [27]) at each synapse. The synaptogenic 

factor f(β, β ̄) at each synaptic spine and full synapse enables 

the spine to grow if the ratio is low (1.0 as default) and to 

shrink if the ratio is high (1.5 as default). See Fig. 3(b) for how 

a neuron can cut off their direct connections with Z to become 

early areas in the occipital lobe or their direct connections 

with the X areas to become latter areas inside the parietal 

and temporal lobes. However, we cannot guarantee that such 

“cut off” are 100% based on the statistics-based wiring theory 

here. 

D. Conclusions 

APFGP inside a network has a minimal set of computational mechanisms for conscious systems, natural and 

artificial. The new APFGP characterization is clearer than 

existing other characterizations for the notoriously vague term 

“consciousness” as we discussed in the first section. Hopefully, APFGP will give rise to richer animal-like artificial 

consciousness so that conscious AI receives a long-overdue 

credibility for developmental AI. APFGP might also be useful 

as a computational model for unifying natural consciousness 

and artificial consciousness, due to its holistic nature backed 

by the new capability—APFGP of GENISAMA Universal 

Turing Machines. Much exciting practical work on learning 

consciousness remains to be done in the future. 

REFERENCES 

[1] N. D. Daw, S. Kakade, and P. Dayan. Opponent interactions between 

serotonin and dopamine. Neural Networks, 15(4-6):603–616, 2002. 

[2] R. J. Dolan. Emotion, cognition, and behavior. Science, 

298(5596):1191–1194, 2002. 

[3] C. Eliasmith, T. C. Stewart, X. Choo, T. Bekolay, T. DeWolf, Y. Tang, 

and D. Rasmussen. A large-scale model of the functioning brain. 

Science, 338:1202–1205, 2012. 

[4] J. L. Elman, E. A. Bates, M. H. Johnson, A. Karmiloff-Smith, D. Parisi, 

and K. Plunkett. Rethinking Innateness: A connectionist perspective on 

development. MIT Press, Cambridge, Massachusetts, 1997. 

[5] C. R. Gallistel. Themes of thought and thinking. Science, 285:842–843, 

1999. 

[6] E. A. Holm. In defense of the black box. Science, 364(6435):26–27, 

April 5 2019. 

[7] J. E. Hopcroft, R. Motwani, and J. D. Ullman. Introduction to Automata 

Theory, Languages, and Computation. Addison-Wesley, Boston, MA, 

2006. 

[8] R. Jenkins and A. M. Burton. 100% accuracy in automatic face 

recognition. Science, 319(5862):435, 2008. 

[9] M. I. Jordan and T. M. Mitchell. Machine learning: Trends, perspectives, 

and prospects. Science, 349:255–260, July 17 2015. 

[10] S. Kakade and P. Dayan. Dopamine: generalization and bonuses. Neural 

Network, 15:549–559, 2002. 

[11] C. Koch. What is consciousness? Scientific American, 318(6):60–64, 

June 2018. 

[12] B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum. Human-level 

concept learning through probabilistic program induction. Science, 

350:1332–1338, 2016. 

[13] J. C. Martin. Introduction to Languages and the Theory of Computation. 

McGraw Hill, Boston, MA, 3rd edition, 2003. 

[14] V. Mnih and et al. Human-level control through deep reinforcement 

learning. Nature, 518:529–533, 2015. 

[15] P. R. Montague, P. Dayan, C. Person, and T. J. Sejnowski. Bee foraging 

in uncertain environments using predictive Hebbian learning. Nature, 

377:725–728, 1995. 

[16] M. Moravcik and et al. Deepstack: Expert-level artificial intelligence in 

heads-up no-limit poker. Science, 356:508–513, 2017. 

[17] J. Piaget. The Construction of Reality in the Child. Basic Books, New 

York, 1954. 

[18] J. Piaget and M. Cook. The Origins of Intelligence in Children. 

International Universities Press, Madison, New York, 1952. 

[19] Y. B. Saalmann, I. N. Pigarev, and T. R. Vidyasagar. Neural mechanisms 

of visual attention: How top-down feedback highlights relevant locations. 

Science, 316:1612 – 1615, 2007. 

[20] R. Scriven, G. Amiot-Cadey, and Collins. Collins French grammar. 

HarperCollins, Glasgow, 2011. 

[21] R. S. Sutton and A. Barto. Reinforcement Learning. MIT Press, 

Cambridge, Massachusetts, 1998. 

[22] J. B. Tenenbaum, C. Kemp, T. L. Griffiths, and N. D. Goodman. How to 

grow a mind: Statistics, structure, and abstraction. Science, 331:1279– 

1285, 2011. 

[23] A. M. Turing. On computable numbers with an application to the 

Entscheidungsproblem. Proc. London Math. Soc., 2nd series, 42:230– 

265, 1936. A correction, ibid., 43, pp. 544-546. 

[24] J. Weng. Brain as an emergent finite automaton: A theory and three 

theorems. International Journal of Intelligent Science, 5(2):112–131, 

2015. 

[25] J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, 

and E. Thelen. Autonomous mental development by robots and animals. 

Science, 291(5504):599–600, 2001. 

[26] K. Wynn. Addition and subtraction by human infants. Nature, 358:749– 

750, 1992. 

[27] A. J. Yu and P. Dayan. Uncertainty, neuromodulation, and attention. 

Neuron, 46:681–692, 2005.