Generative AI has revolutionized the way we approach content creation and other content-related tasks such as language translation and question-answering.

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Getting started with generative AI: A beginner’s guide
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Generative AI has revolutionized the way we approach content creation and other
content-related tasks such as language translation and question-answering. From
OpenAI’s groundbreaking language models-based applications like ChatGPT and DALL-E
to Google’s Bard, generative AI is challenging the limits of content creation, transforming
the content industry in ways that were once thought impossible.
Trained on vast amounts of data with the use of unsupervised and semi-supervised
learning algorithms, generative AI allows for creating new content from scratch. One of
the most exciting developments in this field is large language models that enable
computer programs to comprehend text and generate new content.
At the heart of generative AI is a neural network that is trained to recognize patterns in
data and then use these patterns to generate new content. By using customized
parameters during training, generative AI models can make their own conclusions and
identify features in the data. This allows them to predict the most relevant patterns to
input and generate corresponding content.
Although generative AI is capable of producing incredible results, it still requires human
intervention at the beginning and end of the training process to achieve optimal
outcomes. Human input is necessary to provide the initial data set, identify the relevant
features to be learned by the model and evaluate the output for quality and relevance. The
synergy of human and machine intelligence in generative AI enables it to unlock a whole
new world of possibilities in the realm of content generation.
In this guide, we will walk you through the basics of generative AI, how it works, and what
exciting applications it has in store for the future.
What is generative AI?
Generative AI, also known as creative AI, is a subset of machine learning that generates
new and unique content using artificial intelligence. It differs from traditional AI models
designed to recognize patterns and make predictions based on existing data.
Generative AI represents a significant departure from traditional AI, which relies on
analyzing and processing existing data to make predictions and recommendations.
Traditional AI models are trained on large datasets to recognize patterns and
relationships between variables, which are then used to make predictions and decisions
on new data inputs. On the other hand, generative AI focuses on creating new data that
does not already exist in the training dataset, unlike traditional AI systems that produce
predictable and consistent outputs based on the input data and predefined rules.
Generative AI uses algorithms and models specifically designed for generating new
content, such as images, music, and text.

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One of the most popular techniques used in generative AI is Generative Adversarial
Networks (GANs). GANs consist of two neural networks: a generator and a discriminator.
The generator creates new data based on the training data, while the discriminator
evaluates the data and determines whether it is real or fake. Through this process, the
generator improves its output until it can create content that is indistinguishable from
real data.
Another technique used in generative AI is Variational Autoencoders (VAEs). VAEs use a
probabilistic approach to learn a compressed representation of the input data, which is
then used to generate new data. Unlike GANs, VAEs do not generate exact replicas of the
training data but produce new variations of the input data.
The applications of generative AI are vast and varied. In art and design, generative AI can
be used to create unique and original designs for fashion, furniture, and architecture. In
music, it can be used to generate new and interesting compositions. In writing, it can
produce text-based content, such as news articles and stories. These applications
represent a significant departure from the traditional use cases of AI, which have typically
been focused on classification, regression, and clustering tasks.
Despite the exciting possibilities of generative AI, there are also potential risks and ethical
concerns that need to be addressed. For example, there is a risk that generative AI could
be used to create fake news or generate fake images for propaganda purposes.
Additionally, bias in the data used to train the models is also possible, resulting in biased
output. These issues need to be carefully considered by developers and stakeholders to
ensure that generative AI is used responsibly and ethically.
A brief history of generative AI
The roots of generative AI date back to the early days of artificial intelligence research in
the 1950s and 1960s, when computer programs were developed to generate simple pieces
of text or music. However, the real progress began in the 2010s with the advent of deep
learning, which brought significant strides in accuracy and realism.
A key milestone in the history of generative AI was the introduction of Experiments in
Musical Intelligence (EMI) by David Cope in 1997. EMI combined rule-based and
statistical techniques to generate new music items in the style of famous composers.
2010 represents another significant milestone when Google introduced its “autocomplete”
feature, which predicts what a user is typing and offers suggestions to complete the
sentence. This was made possible by a language model trained on vast amounts of text
data.
In 2013, the Deep Boltzmann Machine (DBM) was introduced by a team of researchers at
the University of Toronto. This generative neural network learned to represent complex
data distributions and paved the way for developing other generative models.

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However, the real breakthrough came in 2014 with the introduction of Generative
Adversarial Networks (GANs) by Ian Goodfellow and his colleagues. GANs generated new
data by pitting two networks against each other in a game-like setting, creating realistic
images and videos that could fool human observers.
In 2015, Amazon introduced Alexa, a voice assistant that responded to natural language
queries and helped popularize the use of voice assistants in daily life.
In 2019, OpenAI released its GPT-2 language model, capable of producing human-like
text in various styles and genres, generating long and coherent pieces of text that could
easily pass as being written by a human.
In 2021, OpenAI announced the release of GPT-3.5, an improved version of GPT-3 that is
more powerful and efficient. With its advanced language capabilities and the ability to
generate coherent and meaningful text, GPT-3.5 has gained popularity among developers
and businesses worldwide.
Moreover, OpenAI has also announced its recent release of GPT-4, a multimodal model
expected to be even more advanced and capable than its predecessor. With the promise of
improved natural language processing and more advanced capabilities, GPT-4 is
anticipated to revolutionize the field of generative AI.
These milestones demonstrate the continuous innovation and breakthroughs in machine
learning research, leading to increasingly sophisticated and realistic generative AI models.
With the development of new technologies, we can expect to see even more impressive
and creative applications of generative AI in the future.
Different types of generative AI models
There are several types of generative AI, including:
Generative Adversarial Networks (GANs)

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Generative Adversarial Networks (GANs) are a type of deep learning model used for
generative tasks, such as creating new images, music, or other types of content.
The architectural components of GAN consist of two neural networks: a generator and a
discriminator. The generator is responsible for generating new data samples intended to
be similar to the original training data, whereas the discriminator tries to distinguish
between the generated samples and real ones.
During training, the generator starts by creating random noise as input and tries to
generate data samples similar to the training data. The discriminator receives both real
and generated samples and tries to distinguish between them. The generator uses the
feedback from the discriminator to improve its samples and the discriminator adjusts its
weights to better distinguish between real and generated data. This process continues
iteratively until the generator can produce samples that are indistinguishable from real
data.
One of the key advantages of GANs is their ability to generate highly realistic samples that
are difficult to distinguish from real data. GANs have been used to create realistic images
of faces, landscapes, and even art. They can also be used in other applications, such as
generating music or speech.
However, training GANs can be challenging, as it requires balancing the generator and
discriminator networks and preventing the model from getting stuck in a local minimum.
There are also issues with mode collapse, where the generator produces only a limited
subset of the possible outputs.

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Despite these challenges, GANs remain a powerful tool for generative tasks and have the
potential to be used in a wide range of applications.
Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) are a type of generative model that can learn a latent
representation of input data by training an encoder and decoder network in an
unsupervised manner. They are often used for image generation, data compression and
feature extraction tasks.
The basic idea behind VAEs is to learn a probabilistic distribution of the data being fed to
the model. The encoder network maps the input data to a probability distribution in the
latent space, which can be thought of as a compressed representation of the input data.
The decoder network then maps the latent representation back to the original input data.
The goal is to minimize the reconstruction error between the input and output data while
also regularizing the latent distribution to ensure that it follows a pre-defined probability
distribution, such as a Gaussian.
The key difference between VAEs and traditional autoencoders is that VAEs learn a
probabilistic distribution in the latent space, whereas traditional autoencoders learn a
deterministic mapping. This means that VAEs can generate new data samples by
sampling from the learned latent distribution, which can be useful for tasks such as image
generation.
To train a VAE, we first define a loss function that consists of two terms: a reconstruction
loss that measures the difference between the input and output data and a regularization
loss that measures the difference between the learned latent distribution and the pre-
defined probability distribution. The overall objective is to minimize this loss function
using stochastic gradient descent.
Autoregressive models
Autoregressive models are a class of statistical models that use a regression approach to
model the dependencies between consecutive observations in a time series. These models
are widely used in various applications, such as natural language processing, speech
recognition, and time-series analysis.
In autoregressive models, the next value in a time series is predicted based on the
previous values. The model assumes that the current observation is dependent on the
previous observations, which are captured by a set of parameters learned during training.
Specifically, the model takes the previous values of the time series as inputs and predicts
the next value. This process is repeated for each time step in the series.
Autoregressive models come in different forms, depending on the type of input and
output variables. For instance, if the input variable is the same as the output variable, the
model is considered a univariate autoregressive model. In contrast, if the input variable

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differs from the output variable, the model is considered a multivariate autoregressive
model.
Autoregressive models can be further categorized based on the order of autoregression.
For example, a first-order autoregressive model (AR(1)) only considers the previous
observation in the time series to predict the next value. A second-order autoregressive
model (AR(2)) considers the two previous observations and so on.
Autoregressive models are often trained using maximum likelihood estimation, which
involves finding the parameters that maximize the likelihood of the observed data which
is done by minimizing the negative log-likelihood of the model using gradient descent.
Flow-based models
Flow-based models are a type of generative AI model that learns a mapping from a simple
distribution, such as a Gaussian distribution, to the more complex distribution of the
input data. This is achieved by applying a series of invertible transformations to the base
distribution. The result of these transformations is a bijective mapping between the input
and output distributions, which allows the model to generate new samples from the input
distribution. Since the transformations are invertible, both the forward and inverse
mappings can be computed efficiently.
Flow-based models are trained using maximum likelihood estimation, which involves
minimizing the negative log-likelihood of the data under the model. In other words, the
model is trained to minimize the difference between the output distribution of the model
and the true distribution of the input data.
To do this, the log-likelihood of the data under the model is computed, which measures
how well the model is able to generate new samples that match the input data. This log-
likelihood is then used to calculate the gradients, which are used to update the parameters
of the model through backpropagation.
One of the key advantages of flow-based models is that they can generate new data
samples by sampling from the base distribution and then applying the series of
transformations in reverse order. This means that the model can generate high-quality
samples that closely resemble the training data. Additionally, flow-based models can be
used for tasks such as data compression and anomaly detection since they can compress
the data into a lower-dimensional latent space and detect outliers based on their
likelihood under the learned distribution.
Rule-based models
Rule-based models are a class of models that use logical rules to represent knowledge and
make predictions about a given domain. These models are widely used in various
applications, such as expert systems, decision support systems and natural language
processing.

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In rule-based models, a set of rules are defined based on the knowledge and expertise of
domain experts. These rules are usually expressed as if-then statements, where the
antecedent specifies the conditions under which the rule is applicable, and the consequent
specifies the action to be taken when the conditions are met. For example, a rule in a
medical expert system might state, “If a patient has a fever and a cough, then they may
have pneumonia.”
The rules in a rule-based model can be represented using different types of logic, such as
propositional logic or first-order logic. In propositional logic, the rules are expressed in
simple logical propositions such as “if A is true, then B is true.” In first-order logic, the
rules are expressed in terms of objects, variables and quantifiers.
Rule-based models can be used for various tasks such as classification, prediction, and
diagnosis. The rules assign a class label to an input based on its features in classification
tasks. In prediction tasks, the rules are used to predict the system’s future state based on
its current state. In diagnosis tasks, the rules identify the causes of a particular symptom
or condition.
One of the main advantages of rule-based models is their ability to represent complex
knowledge and expertise transparently and interpretably. This makes them particularly
useful in domains such as medicine and law, where the decisions made by the model need
to be explainable to human experts. Additionally, rule-based models can handle noisy and
incomplete data and can be easily updated as new knowledge becomes available.
How does generative AI work?
Generative AI creates new digital content, such as images, videos, audio, text, or code,
using unsupervised learning methods. The inner working of generative AI can vary from
solution to solution, but certain characteristics are common among them.
Generative AI differs from discriminatory AI, which makes classifications between inputs.
Discriminatory AI aims to judge incoming inputs based on what it learned during
training. On the other hand, generative AI models aim to create synthetic data.
During the training phase, generative AI models are provided with restricted parameters.
This strategy challenges the model to formulate its own judgments on the most significant
characteristics of the training data. In other words, the model learns to identify the
underlying patterns and features of the training data, which it can then use to generate
new content similar to the original data.
Generative AI models can use different techniques to generate new content, such as
generative adversarial networks, transformer-based models, or variational auto-encoders.
Generative AI is often semi-supervised, meaning it uses both labeled and unlabeled
training data to learn the underlying patterns and features of the dataset. This allows the
model to understand the dataset better and generate new content beyond what is
explicitly provided.

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Generative AI technology comes in various forms, but three primary types are commonly
used:
Generative Adversarial Networks or GANs: This technology can generate
visual or multimedia outputs from image and language inputs. GANs consist of two
neural networks that work together: a generator and a discriminator. The generator
generates synthetic data, while the discriminator evaluates the authenticity of the
data. The two networks are trained together, with the generator attempting to create
realistic outputs that fool the discriminator into thinking they are real.
Transformer-based models: This type of technology, like Generative Pre-
Trained (GPT) language models, leverages Internet-led data to generate textual
material, such as website articles, press releases, and whitepapers. Transformer
models use a self-attention mechanism that allows them to capture the relationships
between different words in a sentence. This approach has led to impressive results
in natural language processing tasks such as language translation, question-
answering, and text generation.
Variational auto-encoders: This type of technology consists of an encoder and a
decoder. The encoder codes the input data into a compressed code, while the
decoder decompresses this code and reproduces the original information.
Variational auto-encoders can be trained on a wide range of data types, including
images, audio, and text. They have shown promising results in generating high-
quality synthetic data, as well as in data compression and representation learning.
Role of natural language processing in generative AI
Generative AI is a rapidly evolving field that is revolutionizing how we interact with
technology. At the heart of this revolution is natural language processing (NLP), a subfield
of artificial intelligence that enables computers to understand and interpret human
language. NLP is a critical component of generative AI, allowing for the creation of new
text and speech data such as product descriptions, news articles, and synthetic voices for
text-to-speech applications.
NLP technologies, such as tokenization, part-of-speech tagging, named entity recognition,
sentiment analysis, machine translation, and text summarization, form the foundation of
many generative AI applications. Recent advancements in deep learning have led to the
development of powerful neural NLP models like RNNs, BERT, GPT-3, XLNet, RoBERTa,
T5, and ELECTRA, which have significantly improved the accuracy and efficiency of
generative AI applications that rely on NLP technologies.
RNNs are particularly powerful neural networks that can process sequential data like text
or speech, making them well-suited for language modeling, speech recognition, machine
translation, and chatbots. Transformer models like BERT and GPT-3 have taken NLP to
the next level, leveraging large-scale pre-training and fine-tuning to achieve state-of-the-
art performance on a wide range of NLP tasks.

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With the help of these neural NLP models, generative AI can generate new text data with
higher accuracy and relevance, create more advanced chatbots and virtual assistants that
can understand and respond to natural language queries with greater accuracy and
efficiency and even generate synthetic voices that sound human-like. These capabilities
have profound implications for a wide range of industries, from e-commerce and media to
healthcare and education.
Applications of generative AI
Generative AI has revolutionized the way we create and interact with digital content. Its
numerous applications span various fields, from creative industries to medical
applications, financial services, and chatbots.
Creative industries
Generative AI is revolutionizing creative industries such as art, music, and fashion. With
the help of machine learning algorithms, artists and designers can create fresh and
innovative content that would be challenging or impossible to create manually. For
instance, generative AI has opened up new avenues for digital art, such as deep dream
images, which rely on convolutional neural networks to generate abstract and surreal
images. In music, generative AI can be used to generate entirely new musical styles or
create original compositions. In the realm of fashion, generative AI can design new
clothing styles or generate digital models that can be used for virtual try-on applications.
By harnessing the power of generative AI, creative professionals can explore uncharted
territory, take their work to new heights, and unleash their imagination to create truly
one-of-a-kind content.
Industrial applications of generative AI
The impact of generative AI extends beyond the confines of science fiction and the tech
industry. This innovative technology has proven to be a game-changer in industrial
settings, revolutionizing manufacturing and product design. By harnessing the power of
generative AI, engineers can take product design to the next level by creating highly
efficient and cost-effective designs. These cutting-edge designs reduce development time
and resources and enhance the product’s overall quality.
One of the most notable applications of generative AI is in aircraft component design.
Engineers can use this technology to optimize the design of aircraft components, creating
lighter and more fuel-efficient designs that meet the industry’s stringent safety standards.
In the automotive manufacturing sector, generative AI has also proven to be a valuable
asset. By leveraging generative AI, engineers can design components such as engine parts
or suspension systems, resulting in more efficient and reliable vehicles built to last.
Medical applications of generative AI

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The incredible potential of generative AI extends beyond technological innovation and
industrial progress. In medicine, this cutting-edge technology is transforming how we
approach drug discovery and medical imaging, leading to better patient outcomes.
By leveraging generative AI, medical professionals can gain new insights into complex
diseases and develop innovative treatments that were previously out of reach. For
instance, generative AI can analyze the chemical properties of existing drugs and generate
new chemical structures that have the potential to be more effective. This capability
allows researchers to design more precise drugs with improved efficacy and fewer side
effects.
Generative AI can also be a game-changer in medical imaging. By analyzing X-rays or
MRI scans, generative AI can assist doctors in identifying potential health issues more
quickly and accurately, enabling timely interventions that can ultimately save lives. This
technology can also help medical professionals predict disease progression, monitor
treatment effectiveness and develop personalized treatment plans.
Financial services
The impact of generative AI in the financial services industry is remarkable. This
innovative technology has opened up new avenues for banks, hedge funds, and other
financial institutions to manage risk and detect fraud more effectively.
By harnessing the power of generative AI, financial institutions can analyze large financial
data sets, gain valuable insights into customer behavior, and identify potential risks and
fraudulent activities. This capability helps financial institutions protect themselves from
potential threats and enhances customer trust by ensuring the safety and security of their
financial transactions.
Hedge funds, in particular, can benefit greatly from generative AI technology. By
leveraging the processing power of this technology, hedge funds can quickly analyze vast
amounts of financial data, extract key insights and make data-driven decisions in real-
time, which enables hedge funds to benchmark against comparables and generate
insightful reports in a matter of minutes, reducing the need for traditional financial
analysts who rely on manual data processing.
Moreover, the use of generative AI in financial services could mark the end of the “models
and bottles” era in financial analysis. With the help of intelligent applications, generative
AI solutions enable faster and more accurate decision-making, reducing the need for
human intervention and enabling financial institutions to stay ahead of the curve.
Chatbots and voice bots
The widespread use of chatbots and voice bots is changing how businesses interact with
customers, streamlining the customer experience and making interactions more efficient
and personalized. Generative AI has revolutionized the development of chatbots and voice

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bots, allowing businesses to create computer programs that simulate natural
conversations with human users.
Chatbots have a wide range of applications, from e-commerce to customer service, and
are particularly useful in providing customers with relevant information about products,
helping them make purchasing decisions, and providing post-purchase support. With the
help of generative AI, chatbots can be programmed to respond to customer queries
naturally and personally, enhancing customer satisfaction and building brand loyalty.
Similarly, voice bots offer a hands-free and intuitive way for customers to interact with
businesses. Voice bots can be used in various environments, such as cars, homes, and
workplaces, providing users instant access to information, entertainment, and assistance
without manual interaction. With the help of generative AI, voice bots can be
programmed to understand natural language, respond to user queries, and even
anticipate user needs, making interactions seamless and effortless.
Generative AI use cases
Generative AI has a wide range of applications in various industries, including marketing,
healthcare, music, and engineering. Let’s dive into the details of each use case:
Image generation for illustrations: With generative AI, anyone can create
graphics based on a specified context, topic, or place by converting words into
visuals. This application can be beneficial for marketing campaigns, helping
designers produce realistic graphics that are visually appealing.
Image-to-photo conversion: This use case finds applicability in the healthcare
industry, as it enables the creation of realistic depictions based on rudimentary
pictures or sketches. It is also used in map design and visualizing X-ray results.
Image-to-image generation: This application involves altering an image’s
external characteristics, such as color, material, or shape while preserving its
fundamental properties. This use case can benefit retail and video/image
surveillance industries by transforming daylight photographs into nocturnal ones.
Music experience optimization: Generative AI can produce fresh audio
material for ads and creative purposes, improving the listening experience on social
media or music platforms like Spotify. This application can also generate short clips
or audio snippets for various purposes.
Text generation: This use case involves generating dialogues, headings, and ads,
and it has enormous applications in the marketing, gaming, and communications
sectors. Generative AI can produce real-time chats with customers or create product
details, blogs, and social media materials.
Equipment design: Generative AI can generate machine components and
subassemblies, optimizing designs with material efficiency, clarity, and
manufacturing efficiency in mind. In some cases, the design can be fed into a 3D
printing machine, allowing for the automatic production of parts.

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Coding: Generative AI can generate code without human coding, making it an
essential tool for professionals and non-technical individuals. This application
represents the next step in the evolution of no-code application development,
making software development more accessible and efficient.
The future of generative AI
Generative AI is a game-changer in the field of AI because it enables computers to be
creative and generate something new, which sets it apart from most AI systems that are
primarily used as classifiers. Its impact is being felt not only in the art and marketing
industries but also in fields such as pharmaceuticals, medtech and biotech, where it has
the potential to drive new discoveries and solutions that can benefit humankind.
In the pharmaceutical industry, for example, generative AI can be used to discover new
drugs and treatments by analyzing vast amounts of data and generating new hypotheses
based on that data. It can help speed up the process of drug discovery by reducing the
time and resources needed to identify promising compounds, which could ultimately lead
to faster development and approval of new medicines.
Similarly, in the field of medtech, generative AI can be used to create personalized
treatment plans for patients based on their medical history, symptoms and other factors,
which can also be used to design medical devices and implants tailored to patients’ needs
for more effective treatments and improved outcomes.
Another area where generative AI is making an impact is in the field of architecture and
design. Architects and designers can use generative AI to create new and innovative
designs that are optimized for specific functions, such as energy efficiency or structural
stability, leading to the development of buildings and structures that are more sustainable
and environmentally friendly.
Moreover, generative AI is also used in the gaming industry to create more realistic and
immersive gaming environments. It is used to generate new game content, characters,
and storylines that are unique and engaging, providing players with a highly personalized
gaming experience.
As generative AI continues to evolve and improve, its applications are expected to expand
further, with new use cases emerging across industries. With the advent of advanced
machine learning models like OpenAI’s GPT-4, we can expect to see more generative AI
advancements that could transform how we use the internet and interact with digital
content.
Endnote
The power of generative AI is truly extraordinary, transforming how businesses operate.
By automating simple tasks, creating high-quality content and even addressing complex
medical issues, generative AI has already begun to revolutionize industries across the

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board. Its potential applications are virtually limitless, offering endless possibilities for
innovation and advancement for businesses.
To make the most of generative AI, it’s crucial to consider the different types of models
available, such as GANs, VAEs, and autoregressive models, and understand how each is
suited to different applications. By doing so, businesses can unlock new possibilities and
drive innovation in their respective fields.
AI has numerous benefits beyond these obvious ones, and failing to adopt this technology
could pose significant risks for businesses. Missed opportunities and inefficiencies are two
such risks that could set companies behind their competitors. By being open-minded
about the potential of generative AI and recognizing humans’ vital role in the process,
businesses can stay ahead of the curve and unlock new possibilities for growth and
innovation.
Leverage the power of generative AI and position yourself at the forefront of innovation.
Consult with LeewayHertz’s AI experts and explore the possibilities for your business
today!
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