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Spontaneity, Imagination and Wonder…
The set of expressions resulting from these is where I truly feel at ease. Yet the impression of these, I imagine, seems where ego (with its status or its bragging with myths of what ought to be) seems to get in the way.
Human paradox, how of beauty you are!
While feigning ignorance toward an other’s dismissal, these are my engines of multi-directionality, diversity and the holding of paradoxes —metaphorically, as I imagine quantum entanglement to be experienced; If only I consciously could.
I love life free of the fixed idea; spontaneous. It’s a purely imagined place for my consciousness. A place for those strange loops of mine to roam free, feeding-back within my brain. It is where my childhood’s unicorns, knights, dragons or blue extraterrestrials reside. That life is with spontaneity, imagination and wonder.
A noticeable number of humans seem to slap these down with various shouts that non of them are found in-between the stats, the data, the models, the forecasts, the plans, the urgencies, the importance of seeming busy and better, the P&Ls, the geopolitics, the ideologies, the macroeconomics, the engineered manipulations, calculations, processes, surfaces or mechanisms. There hardly seems a place for wonder, spontaneity, and imagination in the decorum of the imposed real.
Depending on who the judge is: realism, pragmatism, utilitarianism, practicality, rationality or rather cynicism, are unnecessarily referenced as the daggers to their doom; begone spontaneity, imagination and wonder? Is then the real that what is ignited by a degree of fear?
I also wonder… are they paradoxically even more so there, in those who deny the existence, importance and influences of that same yet less matured spontaneity, imagination or wonder?
Are they who dismiss these then also they who resort to external escapes, constructed by the wonder, imagination and spontaneity of others?
Could it be that if one may not wonder, one may not ask; If one may not ask one may not imagine; If one may not imagine one may not express spontaneity; If one may not use these one might not mature these?
You do not have to say good-bye to those cheap tadalafil pills awkward canicule of abortive acclamation and lower self-esteem. Poor or weak erections are also the symptoms of your ED, talk to your doctor who may recommend you kamagra or click here cheap levitra. Heartburn and bloating are two common symptoms that one can expertise for diverticulitis is tenderness within the decrease left facet of the stomach which could be cheap levitra 20mg mild or abruptly flares up to severe pain. This is buying online viagra the same chemical released by the brain that is formed when one consumes drugs.
A beautiful and clearly-explained introduction to Neural Networks is offered in a 20 minute video by Grant Sanderson in his “3Blue1Brown” series.[1] One is invited to view this and his other enlightening pieces.
The traditional Artificial Neural Network (ANN)[2] is, at a most basic level, a kind of computational model for parallel computing between interconnected units. One unit could be given more or less numerical ‘openness’ (read: weight & bias)[3] then another unit, via the connections created between the units. This changing of the weight and the bias of a connection (which means, the allocation of a set of numbers, turning them up or down as if changing a set of dials), is the ‘learning’ of the network by means of a process, through a given algorithm. These changes (in weight and bias) will influence which signal will be propagated forwardly to which units in the network. This could be to all units (in a traditional sense) or to some units (in a more advanced development of a basic neural network, e.g. such as with Convoluted Neural Networks).[4] An algorithm processes signals through this network. At the input or inputs (e.g. the first layer) the data is split across these units. Each unit within the network can hold a signal (e.g. a number) and contains a computational rule, allowing activation (via a set threshold controlled by, for instance, a sigmoid function, or the recently more often applied “Rectified Linear Unit,” or ReLu for short), to send through a signal (e.g. a number) over that connection to a next unit or to a number of following units in a next layer (or output). The combination of all the units, connections and layers might allow the network to label, preferably correctly, the entirety of a focused-on object, at the location of the output or outputs layer. The result is that the object has been identified (again, hopefully, correctly or, at least, according to the needs).
The
signal (e.g. a number) could be a representation of, for instance, 1 pixel in
an image.[5]
Note, an image is digitally composed of many pixels. One can imagine many of
these so-called ‘neurons’ are needed to process only 1 object (consisting of
many pixels) in a snapshot of only the visual
data (with possibly other objects and backgrounds and other sensory information
sources) from an ever changing environment, surrounding an autonomous
technology, operated with an Artificial Neural Network. Think of a near-future
driverless car driving by in your street. Simultaneously, also imagine how many
neurons and even more connections between neurons, a biological brain, as part
of a human, might have. Bring to mind a human (brain) operating another car
driving by in your street. The complexity of the neural interconnected working,
the amount of data to be processed (and to be ignored) might strike one with
awe.
The
oldest form of such artificial network is a Single-layer Perceptron Network,
historically followed by the Multilayer Perceptron Network. One could argue
that ‘ANN’ is a collective name for any network that has been artificially made
and that has been exhibiting some forms and functions of connection between (conceptual)
units.
An
ANN was initially aimed (and still is) at mimicking (or modeling, or
abstracting) the brain’s neural network (i.e. the information processing
architecture in biological learning systems).
Though,
the term, Artificial Neural Network, contains the word ‘neural’, we should not
get too stuck on the brain-like implications of this word which is derived from
the word ‘neuron’. The word ‘neuron’ is not a precise term in the realm of AI
and its networks. At times
instead of ‘neuron’ the word ‘perceptron’ has been used, especially when
referring to as specific type of (early) artificial neural network using
thresholds (i.e. a function that allows for the decision to let a signal
through or not; for instance, the previously-mentioned sigmoid function).
Nevertheless, maybe some brainy context and association might spark an interest in one or other learner. It might spark a vision for future research and development to contextualize these artificial networks by means of analogies with the slightly more tangible biological world. After all, these biological systems we know as brains, or as nervous systems, are amazing in their signal processing potentials. A hint of this link can also be explored in Neuromorphic Engineering and Computing.
The
word “neuron” comes from Ancient Greek and means ‘nerve’. A ‘neuron,’ in
anatomy or biology at large, is a nerve cell within the nervous system of a
living organism (of the animal kingdom, but not sponges), such as mammals (e.g.
humans). By means of small electrical (electro-chemical) pulses (i.e. nerve
impulses), these cells communicate with other cells in the nervous system. Such
a connection, between these types of cells, is called a synapse. Note, neurons
cannot be found among fungi nor among plants (these do exchange signals, even
between fungi and plants yet, in different chemical ways)… just maybe they are
a steppingstone for one or other learner to imagine an innovative way to
process data and compute outputs!
The
idea here is that a neuron is “like a logic gate [i.e.
‘a processing element’] that receives
input and then, depending on a calculation, decides either to fire or not.”[6]
Here, the verb “to fire” can be understood as creating an output at the
location of the individual neuron. Also note, that a “threshold” is again
implied here.
An
Artificial Neural Network can then be defined as “…а computing ѕуѕtеm made up of a number of
ѕimрlе, highlу intеrсоnnесtеd рrосеѕѕing elements, which рrосеѕѕ infоrmаtiоn by
thеir dуnаmiс ѕtаtе response to еxtеrnаl inputs.”[7]
Remember,
‘neuron’, in an ANN, it should be underlined again, is a relatively simple
mathematical function. It is, in general, agreed that this function is analogous
to a node. Therefore, one can state that an Artificial Neural Network is built
up of layers of interconnected nodes.[8]
So, one can notice, in or surrounding the field of AI, that words such as unit,
node, neuron or perceptron used interchangeably, while these are not identical
in their deeper meaning. More recently the word “capsule” has been introduced,
presenting an upgraded version of the traditional ‘node,’ the latter equaling
one ‘neuron.’ Rather, a capsule is a node in a network equaling a collection of
neurons.[9]
A little bit of additional information on this can be found here below.
How could
an analogy with the brain be historically contextualized? In the early 1950s,
with the use of electron microscopy, it was proven that the brain exists of
cells, which preceding were labelled as “neurons”.[10]
It unequivocally showed the interconnectedness (via the neuron’s extensions,
called axons and dendrites) between these neurons, into a network of a large
number of these cells. A single of these type of locations of connection
between neurons has been labeled as a “synapse”.
Since
then it has been established that, for instance, the human cerebral cortex contains
about 160 trillion synapses (that’s a ‘160’ and another 12 zeros:
160000000000000) between about a 100 billion neurons (100000000000). Synapses
are the locations between neurons where the communication between the cells is
said to occur.[11] In
comparison some flies have about 100000 neurons and some worms a few hundreds.[12]
The brain is a “complex,
nonlinear, and parallel computer (information-processing system)”.[13]
The complexity of the network comes with the degree of interconnectedness (remember,
in a brain that’s synapses).
Whereas
it is hard for (most) humans to multiply numbers at astronomically fast speeds,
it is easy for a present-day computer. While it is doable for (most) humans to
identify what a car is and what it might be doing next, this is (far) less
evident for a computer to (yet) handle. This is where, as one of many examples,
the study and developments of neural networks (and now also Deep Learning) within
the field of AI has come in handy, with increasingly impressive results. The
work is far from finished and much can still be done.
The field of study of Artificial Neural Networks is widely believed to have started a bit earlier than the proof of connectivity of the brain and its neurons. It is said to have begun with the 1943 publication by Dr. McCulloh and Dr. Pitts, and their Threshold Logic Unit (TLU). It was then followed by Rosenblatt’s iterations of their model (i.e. the classical perceptron organized in a single layered network) which in turn was iterated upon by Minsky and Papert. Generalized, these were academic proposals for what one could understand as an artificial ‘neuron’, or rather, a mathematical function that aimed to mimic a biological neuron, and the network made therewith, as somewhat analogously found within the brain.[14]
Note,
the word ‘threshold’ is of use to consider a bit further. It implies some of
the working of both the brain’s neurons and of ANNs’ units. A threshold in
these contexts, implies the activation of an output if the signal crosses the
mathematically-defined “line” (aka threshold). Mathematically, this activation
function can be plotted by, for instance, what is known as a sigmoid function
(presently less used). The sigmoid function was particularly used in the units
(read in this case: ‘nodes’ or ‘neurons’ or ‘perceptrons’) of the first Deep
Learning Artificial Neural Networks. Presently, the sigmoid function is at
times being substituted with improved methods such as what is known as “ReLu”
which is short for ‘Rectified Linear Unit’. The latter is said to allow for
better results and is said to be easier to manage in very deep networks.[15]
Turning
back to the historical narrative, it was but 15 years later than the time of
the proposal of the 1943 Threshold Logic Unit, in 1958, with Rosenblatt’s invention
and hardware design of the Mark I
Perceptron —a machine aimed at pattern recognition in images (i.e. image
recognition) — that a more or less practical application of such network had
been built.[16] As
suggested, this is considered being a single-layered neural network.
This
was followed by a conceptual design from Minsky and Papert, considering the
multilayered perceptron (MLP), using a supervised learning technique. The name
gives it away, this is the introduction of the multi-layered neural network.
While hinting at nonlinear functionality,[17]
yet this design was still without the ability to perform some basic non-linear logical
functions. Nevertheless, the MLP was forming the basis for the neural network
designs as they are developed presently. Presently, Deep Learning research and
development has advanced beyond these models.
Simon
Haykin puts it with a slight variation in defining a neural network when he
writes that it is a “massively parallel distributed processor, made up of
simple processing units, that has a natural propensity for storing experiential
knowledge and making it available for use. It resembles the brain in two
respects: 1. Knowledge is acquired by the network from its environment through
a learning process. 2. Inter-neuron connection strengths, known as synaptic
weights, are used to store the acquired knowledge.”[18]
Let
us shortly touch on the process of learning in the context of an ANN and that
with a simplified analogy. One way to begin understanding the learning process,
or training, of these neural networks, in a most basic sense, can be done by
looking at how a network would (ignorantly) guess the conversion constant
between kilometers and miles without using algebra. One author, Tariq Rashid,
offers the following beautifully simple example in far more detail. The author
details an example where one can imagine the network honing in on the
conversion constant between, for instance, kilometers and miles.
Summarized
here: The neural network could be referencing examples. Let us, as a simple
example, assume it ‘knows’ that 0 km equals 0 miles. It also ‘knows’, from another
given example, that 100 km is 62.137 miles. It could ‘guess’ a number for the
constant, given that it is known that 100 (km) x constant = some miles. The
network randomly could, very fast, offer a pseudo-constant guessed as 0.5.
Obviously, that would create an error compared to the given example. In a
second guess it could offer 0.7. This would create a different kind of error.
The first is too small and the second is too large. The network consecutively
undershot and then overshot the needed value for the constant.
By
repeating a similar process, whereby a next set of numbers (= adjusted
parameters internal to the network) is between 0.5 and 0.7 with one closer to
the 0.5 and the others closer to 0.7, the network gets closer in estimating the
accurate value for its needed output (e.g. 0.55 and 0.65; then next 0.57 and
0.63, and so on). The adjusting of the parameters would be decided by how right
or wrong the output of the network model is compared to the known example that
is also known to be true (e.g. a given data set for training). Mr. Rashid’s
publication continues the gentle introduction into supervised training and
eventually building an artificial neural network.
In
training the neural network to become better at giving the desired output, the
network’s weights and biases (i.e. its parameters) are tweaked. If the output
has a too large an error, the tweaking processes is repeated until the error in
the output is acceptable and the network has turned out to be a workable model
to make a prediction or give another type of output.
In
the above example one moves forward and backward until the smallest reasonable
error is obtained. This is, again somewhat over-simplified how a
backpropagation algorithm functions in the training process of a network
towards making it a workable model. Note, “propagate” means to grow, extend, spread,
reproduce (which, inherently, are forward movements over time).
These
types of network, ANNs or other, are becoming both increasingly powerful and
diversified. They also are becoming increasingly accurate in identifying and
recognizing patterns in certain data sets of visual (i.e. photos, videos),
audio (i.e. spoken word, musical instruments, etc.) or other nature. These are
becoming more and more able to identify patterns, as well as humans are able to
and beyond what humans are able to handle.[19]
Dr.
HINTON, Geoffrey[20] is widely considered as one of the leading
academics in Artificial Neural Networks (ANNs) and specifically seen as a
leading pioneer in Deep Learning.[21]
Deep Learning, a type of Machine Learning, is highly dependent on various types
of Artificial Neural Network.[22]
Dr. Hinton’s student, Alex Krizhevsky, noticeably helped to boost the field of
computer vision by winning the 2012 ImageNet Competition and this by being the
first to use a neural network.
To round
the specific ‘ANN’ introduction up, let us imagine, perhaps in the processes of
AI research and specifically in its area similar to those of ANNs, solutions
can be thought up or are already being thought of that are less (or more)
brain-like or for which the researchers might feel less (or more) of a need to
make an analogy with a biological brain. Considering processes of innovation,
one might want to keep an open-mind to these seemingly different meanderings of
thought and creation.
Going
beyond the thinking of ANNs, one might want to fine-tune an understanding and
also consider diversity in forms and functions of these or other such networks.
There are, for instance, types going around with names such as ‘Deep Neural
Networks’ (DNNs) which, are usually extremely large and are usually applied to
process very large sets of data.[23]
One can also find terminologies such as the ‘Feedforward Neural Networks’
(FFNNs), which is said to be slightly more complex than the traditional and
old-school perceptron networks;[24]
‘Convolutional Neural Networks’ (CNNs), which are common in image recognition; ‘Recurrent
Neural Networks’ (RNNs) and its sub-type of ‘Long Short-term Memory’ networks
(LSTM), which apply feedback connection and which are used in Natural Language
Processing. These latter networks are claimed to still apply sigmoid functions,
contrary to the increased popularity of other functions.[25]
All of these and more are studied and developed in the fields of Machine
Learning and Deep Learning. All these networks would take us rather deep into
the technicalities of the field. You are invited to dig deeper and explore some
of the offered resources.
It
might be worthwhile to share that CNN solutions are particularly well-established
in computer vision. The neurons specialized in the visual cortex of the brain
and how these do or do not react to the stimuli coming into their brain region
from the eyes, were used as an inspiration in the development of the CNN. This
design helped to reduce some of the problems that were experienced with the
traditional artificial neural networks. CNNs do
have some shortcomings, as many of these cutting-edge inventions stil need to
be further researched and fine-tuned.[26]
In the process of improvement, innovation and fine-tuning, there are new networks continuously being invented. For instance, in answering some of the weaknesses of ‘Convolutional Neural Networks’ (CNNs), the “Capsule Networks (CapsNets)” are a relative recent invented answer, from a few years ago, by Hinton and his team.[27] It is also believed that these CapsNets mimic better how a human brain processes vision then what the CNNs have been enabled to offer up till now.
To put it too simple, it’s an improvement onto the previous versions of nodes in a network (a.k.a. ‘neurons’) and a neural network. It tries to “perform inverse graphics”, where inverse graphics is a process of extracting parameters from a visual that can identify location of an object within that visual. A capsule is a function that aids in the prediction of the “presence and …parameters of a particular object at a given location.”[28] The network hints at outperforming the traditional CNN in a number of ways such as the increased ability to identify additional yet functional parameters associated with an object. One can think of orientation of an object but also of its thickness, size, rotation and skew, spatial relationship, to name but a few.[29] Although a CNN can be of use to identify an object, it cannot offer an identification of that object’s location. Say a mother with a baby can be identified. The CNN cannot support the identification whether they are on the left of one’s visual field versus the same humans but on the right side of the image.[30] One might imagine the eventual use of this type of architectures in, for instance, autonomous vehicles.
This
type of machine learning method, a Generative Adversarial Network (GAN), was
invented in 2014 by Dr. Goodfellow and Dr. Bengio, among others.[31]
It’s
an unsupervised learning technique that allows to go beyond historical data
(note, it is debatable that, most if not all data is inherently historical from
the moment following its creation). In a most basic sense, it is a type of
interaction, by means of algorithms (i.e. Generative Algorithms), between two
Artificial Neural
Networks.
The GANs allow to create new data (or what some refer to as “lookalike data”)[32] by applying features, by means of certain identified features, from the historical referenced data. For instance, a data set, existing of what we humans perceive as images, and then of a specific style, can allow this GANs’ process to generate a new (set of) image(s) in the style of the studied set. Images are but one media. It can handle digital music, digitized artworks, voices, faces, video… you name it. It can also cross-pollinate between media types, resulting in a hybrid between a set of digitized artworks and a landscape, resulting in a landscape “photo” in a style of the artwork data set. The re-combinations and reshufflings are quasi unlimited. Some more examples are of GANs types are those that can
…allow for black and white imagery to be turned
into colorful ones in various visual methods and styles.[33]
…turn descriptive text of, say different birds
into photo-realistic bird images.[34]
…create new images of food based on their
recipes and reference images.[35]
…turn a digitized oil painting into a photo-realistic
version of itself; turning a winter landscape into a summer landscape, and so
on.[36]
If
executed properly, for instance, the resulting image could make an observer
(i.e. a discriminator) decide that the new image (or data set) is as (authentic
as) the referenced image(s) or data set(s) (note: arguably, in the digital or
analog world, an image or any other media of content is a data set).
It
is also a technique whereby two neural networks contest with each other. They
do so in a game-like setting as it is known in the mathematical study of models
of strategic decision-making, entitled “Game Theory.” Game Theory is not to be
confused with the academic field of Ludology, the latter which is the social,
anthropological and cultural study of play and game design. While often one
network’s gain is the other network’s loss (i.e. a zero-sum game), this is not
always necessarily the case with GANs.
It
is said that GANs can also function and offer workable output with relatively
small data sets (which ic an advantage compared to some other techniques).[37]
It
has huge applications in the arts, advertising, film, animation, fashion
design, video gaming, etc. These professional fields are each individually
known as multi-billion dollar industries. Besides entertainment it is also of
use in the sciences such as physics, astronomy and so
on.
One can learn how to understand and build ANNs online via a
number of resources. Here below are a
few hand-picked projects that might offer a beginner’s taste to the technology.
Project #___: Making Machine Learning Neural Networks (for K12 students by
Oxford University)
Project#___: Rashid, T. (2016). Make Your Own Neural Network.
A project-driven book examining the very basics of neural networks and aiding a learning step by step into creating a network. Published as eBook or paper via CreateSpace Independent Publishing Platform.
This might be easily digested by Middle Schools students or learners who cannot spend too much effort yet do want to learn about neural networks in an AI context.
[1] Schrittwieser, J. et al. (2020). Mastering Atari, Go, Chess and Shogi by
Planning with a Learned Model. Online: arXiv.org, Cornell University;
Retrieved on April 1, 2020 from https://arxiv.org/abs/1911.08265
[8] Rashid, T.
(2016). Make Your Own Neural Network.
CreateSpace Independent Publishing Platform
[9] Sabour, S. et al.
(2017). Dynamic Routing Between Capsules.
Online: arXiv.org, Cornell University; Retrieved on April 22, 2020 from
https://arxiv.org/pdf/1710.09829.pdf
[10] Sabbatini, R. (Feb
2003). Neurons and Synapses. The History
of Its Discovery. IV. The Discovery of the Synapse. Online:
cerebromente.org. Retrieved on April 23, 2020 from http://cerebromente.org.br/n17/history/neurons4_i.htm
[11] Tang
Y. et al (2001). Total regional and
global number of synapses in the human brain neocortex. In Synapse 2001;41:258–273.
[12] Zheng, Z., et al. (2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila
melanogaster. In Cell, 174(3),
730–743.e22
[13] Haykin, S. (2008).
Neural Networks and Learning Machines. New York: Pearson Prentice Hall. p.1
[17] Samek, W. et al (2019). Explainable AI: Interpreting, Explaining
and Visualizing Deep Learning. Lecture Notes in Artificial Intelligence.
Switzerland: Springer. p.9
[21] Rumelhart,
David E.; Hinton, Geoffrey E.; Williams, Ronald J. (9 October 1986). “Learning
representations by back-propagating errors”. Nature. 323 (6088):
533–536
[23] de Marchi, L. et al.
(2019). Hands-on Neural Networks. Learn
How to Build and Train Your First Neural Network Model Using Python. Birmingham
& Mumbai: Packt Publishing. p. 9.
[24] Charniak, E. (2018). Introduction to Deep Learning.
Cambridge, MA: The MIT Press. p. 10
[26] Géron, A. (February,
2018). Introducing capsule networks. How
CapsNets can overcome some shortcomings of CNNs, including requiring less
training data, preserving image details, and handling ambiguity. Online:
O’Reilly Media. Retrieved on April 22, 2020 from https://www.oreilly.com/content/introducing-capsule-networks/
[32] Skanski, S. (2020). Guide to Deep Learning. Basic Logical, Historical and Philosophical Perspectives. Switzerland: Springer Nature. p. 127
[33] Isola, P. et al.
(2016, 2018). Image-to-Image Translation
with Conditional Adversarial Networks. Online: arXiv.org, Cornell University; Retrieved on April 16,
2020 from https://arxiv.org/abs/1611.07004
[34] Zhang, H. et al.
(2017). StackGAN: Text to Photo-realistic
Image Synthesis with Stacked Generative Adversarial Networks. Online: arXiv.org, Cornell University;
Retrieved
on April 16, 2020 from https://arxiv.org/pdf/1612.03242.pdf
[35] Bar El, O. et al.
(2019). GILT: Generating Images from Long
Text. Online: arXiv.org,
Cornell University; Retrieved
on April 16, 2020 from https://arxiv.org/abs/1901.02404
[36] Zhu, J. (2017).
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks. Online: arXiv.org,
Cornell University; Retrieved
on April 16, 2020 from https://arxiv.org/pdf/1703.10593.pdf