The Revolution of Digital Marketing in the Artificial Intelligence era

The Revolution of Digital Marketing in the Artificial
Intelligence era
By: Eng. Mohamed Hanafy

How is Artificial Intelligence (AI) changing the Market world?
• Ad targeting and ads media analysis
• Identifying micro-influencers
• Data Analysis with LLMs
• Intelligent Customer Engagement
• Sentiment analysis
• Churn predictive
• Dynamic pricing
• Chatbots
• AI-driven content
• Text Generation
• Music Generation
• Generating Songs withVocals
• Text to Image
• Graphic Design
• AI video generation
• Personalized design

Introduction to AI
• Introduction to AI
• What is AI?
• History of AI
• Types ofAI
• Applications of AI
• Search and Reasoning
• Search algorithms
• Heuristics
• Logic and reasoning
• Machine Learning
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Natural Language Processing
• Text classification
• Named entity recognition
• Machine translation
• ComputerVision
• Object detection
• Image classification
• Face recognition

What is AI?
• AI is a branch of computer science that deals with the creation of intelligent
agents, which are systems that can reason, learn, and act autonomously.
• AI research has been highly successful in developing effective techniques for
solving a wide range of problems, from game playing to medical diagnosis.
• However, there is still no consensus on what constitutes true AI, and many
experts believe that we are still many years away from creating truly
intelligent machines.

History of AI
• The field of AI was founded in the 1950, when a group of researchers at
Dartmouth College proposed the creation of a "thinking machine."
• Early AI research was focused on developing symbolic AI systems, which were
designed to mimic human reasoning.
• However, these systems were often brittle and difficult to scale, and they did
not achieve the same level of success as other areas of computer science.
• In the 1980, there was a shift in AI research towards machine learning, which
is a field that focuses on developing systems that can learn from data.
• Machine learning has been much more successful than symbolic AI, and it has
led to the development of many powerfulAI systems, such as Google's search
engine and Amazon's recommendation engine.

Types of AI
• There are many different types of AI, but they can generally be divided into two
categories: narrow AI and general AI.
• Narrow AI systems are designed to solve a specific problem, such as playing chess
or Go.
• General AI systems are designed to be more versatile and can be used to solve a
wider range of problems.
• However, general AI systems are still under development, and there is no
consensus on whether they will ever be possible to create.

Search algorithms
• There are many different types of search algorithms, each with its own advantages and
disadvantages. Some of the most common search algorithms include:
• Linear search: This algorithm checks every record in a data structure until it finds the target record. It
is the simplest search algorithm, but it is also the least efficient.
• Binary search: This algorithm divides the search space in half at each step and then checks the half
that is more likely to contain the target record. It is more efficient than linear search, but it requires
the data structure to be sorted.
• Hashing: This algorithm uses a hash function to convert a key into a value that is used to index into a
data structure.This can be very efficient for finding records, but it requires the data structure to be
hashed.

Traveling Salesman Problem (TSP)
• The number ofTSP
routes grows
exponentially with the
number of cities. For 10
cities, there are over
300,000 routes and for
15 cities, over 87 billion
routes.

Heuristics
A heuristic is a mental shortcut that allows us to make quick judgments or decisions based on
limited information. Heuristics are often used in everyday life, such as when we are trying to decide
which restaurant to eat at or which movie to watch.
There are many different types of heuristics, but some of the most common ones include:
• Availability heuristic:This heuristic states that we tend to judge the probability of an event based
on how easily examples of that event come to mind. For example, if we can easily think of
several examples of people who have been in car accidents, we may be more likely to believe
that car accidents are common.
• Representativeness heuristic:This heuristic states that we tend to judge the probability of an
event based on how similar it is to our mental model of a typical example of that event. For
example, if we see a man with a beard and a long white robe, we may be more likely to believe
that he is a rabbi, even if we know that there are other people who fit that description who are
not rabbis.
• Anchoring heuristic:This heuristic states that we tend to rely too heavily on the first piece of
information we are given when making a decision. For example, if we are asked to estimate the
number of jelly beans in a jar, we may be more likely to give a number that is close to the first
number we see, even if it is not accurate.
• Heuristics can be helpful in making quick decisions, but they can also lead to errors. It is
important to be aware of the limitations of heuristics and to use them with caution.

Logic and reasoning
• Reasoning is the process of using logic to reach a conclusion.When we reason, we start
with a set of premises, which are statements that we believe to be true. We then use these
premises to reach a conclusion.The conclusion is a statement that follows logically from
the premises.
• There are many different types of reasoning, but some of the most common ones include:
• Deductive reasoning: Deductive reasoning is a type of reasoning in which the conclusion is
guaranteed to be true if the premises are true. For example, if we know that all dogs are
mammals and that Spot is a dog, then we can deduce that Spot is a mammal.
• Inductive reasoning: Inductive reasoning is a type of reasoning in which the conclusion is
likely to be true, but it is not guaranteed to be true. For example, if we observe that all of
the swans we have seen are white, then we can induce that all swans are white. However, it
is possible that there is a swan that is not white.
• Abductive reasoning: Abductive reasoning is a type of reasoning in which we make the
best explanation for a set of facts. For example, if we find a dead body in the library, we
might abduce that the person was murdered. However, there are other possible
explanations for the person's death, such as a heart attack or an accident.

AI
Chatbots
Robots
Knowledge
representation
Personal
Assistants
Recommenders
Emotions
Detection
Autonomous
Vehicles
Object
Detection
Computer
vision
Speech
recognition
Machine
reasoning
Natural
Language
processing
Autonomics
Virtual
agent
Image
recognition
Applications of AI

Mathematics
Cloud Computing
AWS
Microsoft Azure
Watson
Software
Algorithms
Data Structures
Programming
DataVisualization
Multivariate Statistics
Statistics
Signals & Systems
Optimization
Probability
Multivariate Calculus
LinearAlgebra
SingleVariableCalculus
Discrete Mathematics
Machine Learning
Deep Learning
Artificial Intelligence
The Roots

Machine Learning (ML)
• Machine Learning (ML) is a subfield of artificial intelligence (AI).
• It focuses on the use of data and algorithms to enable computers to learn and
improve their performance on a specific task without being explicitly programmed.
• Machine learning algorithms can be trained on a dataset to make classifications or
predictions, and to uncover key insights in data mining projects.
• There are several types of machine learning, including supervised learning,
unsupervised learning, and reinforcement learning.
• Each type involves different approaches to training algorithms and making
predictions or decisions based on data.

Types of Machine Learning (ML)
• Supervised learning is a methodology in data science that creates a model to
predict an outcome based on labeled data. Labeled data contains a collection
of variables (features) and a specific output that we are trying to predict.
• Unsupervised learning is a type of machine learning that involves finding
patterns or relationships in data without using labeled data. It is often used
for exploratory analysis and anomaly detection because it helps to see how
the data segments relate and what trends might be present.
• Reinforcement learning is a type of machine learning that involves an agent
learning to make decisions based on rewards and punishments.The agent is
not given explicit feedback about what is right or wrong but must learn from
its own actions and experiences.

The Predictive Data Analytics Project Lifecycle: CRISP-DM
Cross Industry Standard Process for Data Mining (CRISP-DM)
analytics base table (ABT)

Predictive Data AnalyticsTools
• Application-based, or point-and-click tools
• IBM SPSS
• SAS Enterprise Mine
• Knime Analytics Platform
• RapidMiner Studio
• Weka
• Programming language
• R
• Python

Aim
• to predict four different customer life events: moving,
birth of a child, new relationship, and end of a
relationship.
• Reveal a pseudo-social network that supports the derivation of
behavioral similarity measures.To advance decision support
systems literature
• this study validates the proposed customer life event prediction
model in a real-world setting in the financial services industry.
Objectives
Leveraging fine-grained
transaction data for customer life
event predictions

• Combines aggregated customer data including
customer demographics, behavior and contact with
the firm, with fine-grained transaction data
• large European financial services provider
• —approximately 60 million debit transactions
involving around 132,000 customers to 1.5 million
different counterparties over a one-year period—
Dataset

Fine-grained transaction data representation

• First, the components pertaining to the seed customers, which we called
behavioral similarity terms, are calculated differently.
• Second, two variants down-weight popular merchants based on inverse
consumer frequency (ICF) or a cross-validated beta distribution.

?
• How can machine learning be used to improve the targeting of marketing
campaigns?
• How can machine learning be used to personalize the customer experience?
• How can machine learning be used to predict customer behavior?
• How can machine learning be used to optimize marketing budgets?
• How can machine learning be used to measure the effectiveness of
marketing campaigns?

Machine learning can be used to improve digital
marketing in a variety of ways.
• Targeting: Machine learning can be used to identify the most likely
customers to respond to a particular message.
• Personalization: Machine learning can be used to deliver content and offers
that are tailored to each individual's interests.
• Prediction: Machine learning can be used to predict customer behavior,
such as which products they are likely to buy or when they are likely to make
a purchase.
• Budget optimization: Machine learning can be used to allocate res

What is Deep Learning?
Machine Learning focuses only on solving real-world problems. It also takes a few ideas
from Artificial Intelligence. Machine Learning goes through the Neural Networks that are
designed to mimic human decision-making capabilities. ML tools and techniques are the
two key narrow subsets that only focus more on Deep Learning. We need to apply it to
solve any problem that requires thought — human or artificial. Any Deep Neural Network
will consist of three types of layers:
• The Input Layer
• The Hidden Layer
• The Output Layer
We can say Deep Learning is the newest term in the field of Machine Learning. It’s a way
to implement Machine Learning.

Deep LearningTheory
 System is “dumb” (i.e. mechanical)
 “Learns” with big data (lots of input examples) and trial-and-error
guesses to adjust weights and bias and establish key features
 Creates a predictive system to identity new examples
 SameAI argument: big enough data makes a difference (“simple” algorithms run over large data
sets)
Input: Big Data (e.g.;
many examples)
Method: Trial-and-error
guesses to adjust node weights
Output: system identifies
new examples
5

The Linear Unit
The Linear Unit: y=wx+b

Deep Neural Networks
• Layers
• Neural networks typically organize their neurons into layers.When we
collect together linear units having a common set of inputs we get
a dense layer.
• The Activation Function
• An activation function is simply some function we apply to each of a
layer's outputs (its activations).
• The most common is the rectifier function max(0,x)

Deep Neural Networks
Python Code (keras)
• from tensorflow import keras
• from tensorflow.keras import layers
• model = keras.Sequential([
• # the hidden ReLU layers
• layers.Dense(units=4, activation='relu', input_shape=[2]),
• layers.Dense(units=3, activation='relu'),
• # the linear output layer
• layers.Dense(units=1),
• ])

Deep Learning Architectures
• RNN: Recurrent Neural Networks
• LSTM: Long Short-Term Memory
• CNN: Convolutional Neural Networks
• DBN: Deep Belief Network
• DSN: Deep Stacking Network

Machine
Learning
Supervised
Unsupervised
Classical
Learning
Ensemble
Methods
Neural NETs
& Deep
Learning
Reinforcement
Learning
Convolutional Neural
Networks (CNN)
Recurrent Neural
Networks (RNN)
Generative Adversarial
Networks (RNN)
Autoencoders Perceptron (MLP)
• DCNN
• VGG
• ResNet
• LSM
• LSTM
• GRU
Stacking
Bagging
Boosting
• Random Forest
• XGBoost
• LightGBM
• CatBoost
• AdaBoost
Clustering
Pattern Search
Dimension
Reduction
(Generalization)
• FP-Growth
• K-Means
• Mean-shift
• FuzzyC-Means
• DBSCAN
• FP-Growth
• Euclat
• Apriori
• T-SNE
• PCA
• LSA
• SVD
• LDA
Classification
Regression
• DecisionTrees
• Naïve Bayes
• SVM
• K-NN
• Logistic Regression
• Linear Regression
• Polynomial Regression
• Ridge / Lasso Regression
Machine Learning Algorithms

Natural language processing (NLP)

Natural language processing (NLP)
Natural language processing (NLP) is a field of computer science that studies
how computers can understand and process human language. NLP is a
subfield of artificial intelligence (AI).
• Machine translation
• Speech recognition
• Question Answering
• Sentiment analysis
• Text summarization
• Information Extraction
natural language
understanding (NLU)
natural language
generation (NLG)
key techniques NLP
• Named Entity Recognition
• Sentiment Analysis
• Text Summarization
• Aspect Mining
• Topic Modeling

Large Language Models(LLM)
• LLMs are language models consisting of a neural network with many parameters (typically
billions of weights or more).
• They are trained on large quantities of unlabeled text using self-supervised learning or
semi-supervised learning.
• LLMs emerged around 2018 and perform well at a wide variety of tasks.
• They have shifted the focus of natural language processing research away from the
previous paradigm of training specialized supervised models for specific tasks.
• LLMs are general-purpose models that excel at a wide range of tasks, as opposed to being
trained for one specific task.
• The skill with which they accomplish tasks and the range of tasks at which they are capable
seems to be a function of the amount of resources (data, parameter-size, computing
power) devoted to them.
• Though trained on simple tasks such as predicting the next word in a sentence, neural
language models with sufficient training and parameter counts are found to capture much
of the syntax and semantics of human language.
• In addition, large language models demonstrate considerable general knowledge about the
world and are able to “memorize” a great quantity of facts during training.

Text pre-processing
• Stop word removal involves removing common words that do not carry
significant meaning, such as “and”, “the”, “is”, etc.These words are called stop
words and are usually removed from the text to reduce noise and improve the
performance of natural language processing algorithms.
• Tokenization is the process of breaking down a text into individual units called
tokens.These tokens can be words, phrases, or even sentences, depending on
the level of granularity required for the task at hand.Tokenization is an
essential step in text preprocessing as it allows algorithms to work with text
data at a more granular level.
• Stemming is the process of reducing words to their base or root form. For
example, the words “running”, “runs”, and “ran” all have the same root form
“run”. Stemming algorithms work by removing the suffixes (and in some cases
prefixes) from a word to obtain its root form.This can help reduce the
dimensionality of the data and improve the performance of natural language
processing algorithms.

Tokenization
• Tokenization is the process of breaking down a text into individual units called tokens.These
tokens can be words, phrases, or even sentences, depending on the level of granularity required
for the task at hand.Tokenization is an essential step in text preprocessing as it allows algorithms
to work with text data at a more granular level.
Tools for tokenization
• NLTK (Natural LanguageToolkit)
• TextBlob
• spaCy
• Stanford CoreNLP
• Gensim

Embedding
• embedding refers to the process of representing words, phrases, or even
entire documents as fixed-length vectors of real numbers.These vectors
are designed to capture the meaning and context of the text data in a way
that can be easily understood and manipulated by machine learning
algorithms.

Embedding techniques
• Word2Vec: Developed by Google,Word2Vec is a neural network-based model that
learns word embeddings from text data. It uses a skip-gram or continuous bag-of-
words (CBOW) architecture to predict the context words given a target word or
vice versa.
• GloVe: GloVe (GlobalVectors forWord Representation) is another word embedding
technique developed by Stanford University. It combines global matrix
factorization and local context window methods to learn word embeddings from
co-occurrence statistics.
• fastText: Developed by Facebook, fastText is a word embedding technique that
extends the Word2Vec model by incorporating subword information.This allows it
to learn embeddings for rare words and even out-of-vocabulary words by
representing them as a sum of their character n-grams.

Transformer (machine
learning model)
• The transformer is a type of deep
learning architecture introduced in a
2017 paper byVaswani et al.
• It uses self-attention mechanisms to
weigh the significance of different
parts of the input data.
• The transformer architecture follows
an encoder-decoder structure and
consists of two main components: the
encoder stacks and the decoder stacks.
• It has been implemented in standard
deep learning frameworks such as
TensorFlow and PyTorch.
• The transformer has been widely
adopted for natural language
processing tasks and has also been
applied to computer vision tasks.
“Attention Is AllYou Need”

Large Language Models(LLM)
• GPT-3 (Generative Pre-trainedTransformer 3) developed byOpenAI.
• BERT (Bidirectional Encoder Representations fromTransformers) developed by Google AI.
• RoBERTa (RobustlyOptimized BERT Approach) developed by Facebook AI.
• T5 (Text-to-TextTransferTransformer) developed by Google AI.
• CTRL (ConditionalTransformer Language Model) developed by Salesforce Research.
• Megatron-Turing NLG developed by Google AI and NVIDIA.
• Ernie 3.0Titan developed byTencent AI Lab.
• Claude developed by Hugging Face.
• GLaM (Generalist Language Model) developed by Google AI.

Open Source Resources for Large Language
Models
• Hugging Face: Hugging Face provides a variety of pre-trained Large
Language Models and tools for fine-tuning them.
• TensorFlow:TensorFlow provides several Large Language Models, such as
BERT and GPT-2, along with tools for training and fine-tuning them.
• PyTorch: PyTorch provides several Large Language Models, such as
Transformer-XL and GPT-2, along with tools for training and fine-tuning
them.

It is the third-generation language prediction model in the
GPT-n series created by OpenAI, a San Francisco-based
artificial intelligence research laboratory.

GPT-3
• GPT-3 was introduced by Open AI earlier in May 2020 as a
successor to their previous language model (LM) GPT-2.
• Generative Pre-trainedTransformer 3 (GPT-3) is an
autoregressive language model that uses deep learning to
produce human-like text.
• OpenAI trained GPT-3 on a massive corpus of text with
more than 175 billion parameters, making it the largest
language model
• Trained on a massive dataset (from sources like Common
Crawl,Wikipedia, and more).
• GPT-3 has seen millions of conversations and can calculate
which word (or even character) should come next in
relation to the words around it.

GPT-3 Dataset
• The CommonCrawl data was downloaded from 41 shards of monthly
CommonCrawl covering 2016 to 2019.
• Constituting 45TB of compressed plaintext before filtering and 570GB after
filtering
• Roughly equivalent to 400 billion byte-pair-encoded tokens.

What capabilities does GPT-3 AI offer?
• LanguageTranslation
• Text Classification
• Sentiment Extraction
• Reading Comprehension
• Named Entity Recognition
• Question Answer Systems
• News Article Generation

Background to BERT
• Bidirectional Encoder Representations from Transformers (BERT) is a machine learning model, which
uses natural language processing techniques to transform text. BERT is pre-trained by Google.
• Context-free models such as word2vec or GloVe generate a single word embedding representation for
each word in the vocabulary, where BERT takes into account the context for each occurrence of a given
word.
• This enables better contextualization of the model’s output, which considers not only the importance of
the word and how frequently it’s used but also the context of the use.
• In 2019, Google Search started applying BERT models for search queries in English. In three months'
time, BERT had already expanded to over 70 languages. A year after its introduction, almost every single
English-based query was processed by BERT.

GPT-3 vs BERT
• GPT-3 has been trained on 175 billion parameters, while BERT has been trained on
340 million parameters
• BERT requires elaborate fine-tuning, while GPT-3 uses few-shot learning to quickly
predict output results with minimal input
• GPT-3 isn't publicly available (you need to be accepted to OpenAI's waitlist),
whereas BERT is a publicly accessible open-sourced model

How to Auto-Generate Meta Descriptions
Using Python and BERT

Website Meta Descriptions Using Python And BERT
• AI can be used to automatically generate
meta descriptions for web pages
• Several tools available that use AI to
generate meta descriptions
• Tools typically require you to input the
title and target keyword for your post
• AI then generates a meta description for
you
• Examples of tools include: SEO Toolbelt,
Dashword, Frase.io, and Copy.ai

How to OptimizeYour Meta Descriptions for
Better CTR

Important note about the model’s capabilities to
process text
• One important thing to note is that BERT will only be able to generate a meta
description for texts, longer than 400 words.This also corresponds with Google’s
understanding of thin pages.
• Pages with less than 400 words will typically be flagged in the audits of tools such
as Screaming Frog or Sitebulb. Ideally, you should strive on providing sufficient
content on all of your indexable pages for both users and search engines to
navigate your site successfully.
Then check if the keyword is contained within the meta
description and if not (yet it is relevant to the content on
the page), try to add it in a sentence.

Revolutionizing Data Analysis with
Large Language Models

Revolutionizing Data Analysis with Large Language Models
• LLMs are transforming data engineering by
providing a powerful tool for processing and
understanding large volumes of text-based
data.
• With advanced NLP capabilities, LLMs can
help extract valuable insights from
unstructured data.
• LLMs can be integrated into data
engineering workflows to enhance data
processing, improve data quality, and
increase the speed and accuracy of data
analysis.
• LLMs have the ability to integrate with a
large number of data sources.

Introducing Microsoft 365 Copilot
• Microsoft has introduced a new capability for LLMs
called Microsoft 365 Copilot.
• Copilot combines the power of LLMs with your data
in the Microsoft Graph and the Microsoft 365 apps to
turn your words into a powerful productivity tool.
• Copilot works alongside you in the Microsoft 365
apps to unleash creativity, unlock productivity, and
uplevel skills.
• For example, Copilot in Word can write, edit,
summarize, and create right alongside you.

• Micro-influencers are individuals with a
smaller but more engaged following on
social media, who are considered experts in
their respective niche.
• Micro-influencer marketing has become a
popular and effective strategy for
marketers.
• Micro-influencers can have a bigger impact
on purchasing decisions than top influencers
due to their proximity, accessibility, and
shared experiences with their followers.
Identifying micro-influencers

Intelligent Customer Engagement

Predictive Analytics for Customer Behaviour
• Uses historical data and machine learning to
predict future customer behavior
• Lead scoring: predicts which leads are most
likely to convert
• Predicts consumer behaviors and assesses
Customer Lifetime Value (CLV)
• Predicts optimal pricing and frequency for
posting
• Predicts customer defection

Suggested predictive audiences
• Purchasing users who are likely to not visit your
property in the next 7 days.
• Users who are likely to not visit your property in the
next 7 days.
• Users who are likely to make a purchase in the next
7 days.
• Users who are likely to make their first purchase in
the next 7 days.
• Users who are predicted to generate the most
revenue in the next 28 days.
Predictive audiences

Generative Adversarial Networks
Designed by Ian Goodfellow and his colleagues in June 2014
Ian Goodfellow

Music Generation Using Deep Learning

• Deep learning techniques have been used for music
generation for the past two decades
• Recurrent Neural Networks (RNNs) and Convolutional Neural
Networks (CNNs) have been used to generate music
• Deep generative models such asVariational Autoencoders
(VAEs) and Generative Adversarial Networks (GANs) can be
used to create realistic synthetic data
• These models have been used to create novel neural network
architectures that can generate new music
Tools
•AIVA
• Amper Music
•Jukebox by OpenAI
•DeepComposer by AWS
Music Generation Using Deep Learning

AI Content Generator
• Rytr
• Shortlyai
• betterwriter.ai
• Jasper
• Copysmith
• Writesonic
• Kafkai
• Article Forge
• Articoolo
• CopyAI
• Peppertype
• copyshark.ai
• qalam.ai
• katteb

Al write
• https://ai-writer.com
Writesonic
• https://writesonic.com
QuillBot
• https://quillbot.com
Article Forge
• https://www.articleforge.com
Katteb
• https://katteb.com/ar/

AITools for Generating Songs withVocals

AITools for Generating Songs withVocals
• Several tools and applications available that use
AI to generate songs with vocals
• Popular tools includeVoicemod and Uberduck
• These tools use deep learning algorithms to
generate realistic-sounding vocals
Heart On My Sleeve

AI tools for generating text to speech

Whisper
OpenAI'sWhisper is a speech to text, or
automatic speech recognition model. It
is a "weakly supervised" encoder-
decoder transformer trained on 680,000
hours of audio. Not only can it transcribe
English, it can transcribe 96 other
languages along with also being able to
translate from those languages to
English.

AI tools for generating text to speech
• There are several AI tools available that can generate text to speech.
• These tools use advanced machine learning algorithms to convert written text into natural-sounding
speech.
• Some popularAI tools for generating text to speech include :
• Google CloudText-to-Speech,
• Microsoft AzureText to Speech
• Murf AI
• These tools offer features such as customizable voices, fine-grained audio controls, and support for
multiple languages.
• They can be used to create voiceovers for videos, podcasts, and professional presentations.

Text to Image
• DALL-E - https://openai.com/dall-e-2
• Midjourney
• Stable Diffusion
• Fotor
• NightCafe
• Jasper Art
• Starry AI
• Pixray
• Generated.photos
• Thispersondoesnotexist.com

https://designer.microsoft.com/

AI in Graphic Design: Supporting Creativity and SavingTime
• AI can support designers and non-designers in
creating beautiful designs.
• AI can expedite the design process, saving time
for other projects or tasks.
• AI can automate repetitive tasks, suggest design
options, and provide data-driven insights.
• AI can be used for image editing, classification,
color manipulation, and font design.

AI video generation
• AI video generation technology allows you to produce
videos by typing in text and choosing characters and
voice.
• AI video generation platforms can create professional
videos quickly without equipment or editing skills.
• AI can automate repetitive tasks, suggest design
options, and provide data-driven insights.
• There are different types of AI video generators,
including video editors with AI tools, generative text-to-
video apps, and video productivity apps.
• Tools:
• synthesia.io
• movio.la

• There has to be a source and a destination.
• You can think of it as the source from which we cut out the face and paste it onto
the destination.
• Collect a database of both the person you are trying to put a face on, and of the
person from which you will borrow the face.
• Extracting Frames
• You can use ffmpeg
• DeepFaceLab
• https://github.com/iperov/DeepFaceLab
• Isolating Faces
• Sorting Faces
• Training the Model
• Merging the Faces
• Compositing
Step By Step Deepfake Guide

Find AITools
https://aitools.fyi

• GPT for your files
humata.ai

• Turn Any Question to {Code}
www.useblackbox.io

How to Create a ChatGPT & BARD Account

How to Create a ChatGPT Account
• Browsec
• https://www.textverified.com ($2.5)
• Number2go (App)
• PingMe (App)

https://web.telegram.org/k/#@OpenAiChat_bot

https://keywordseverywhere.com/

https://quickref.me/chatgpt.html

Building a ChatGPT using Personal or
Organizational Data

ThankYou
Eng. Mohamed Hanafy
mohanafy@gmail.com

The Revolution of Digital Marketing in the Artificial Intelligence era

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