The Revolution of Digital Marketing in the Artificial Intelligence era

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

2. 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

3. 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

4. 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.

5. 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.

8. 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.

9. 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.

10. 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.

11. 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.

12. 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.

13. 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

14. 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

15. 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.

16. 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.

17. Machine Learning

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

19. 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

20. 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

21. • 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

23. Fine-grained transaction data representation

24. • 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.

25. ? • 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?

26. 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

27. Deep Learning

28. 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.

29. 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

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

31. 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)

32. 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), • ])

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

34. Amazon Machine Learning ONNX

35. 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

36. Natural language processing (NLP)

37. 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

38. 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.

39. 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.

40. 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

41. 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.

42. 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.

43. 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”

44. 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.

45. 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.

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

47. 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.

48. 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.

50. GPT-3 Dataset

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

54. 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.

55. 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

57. How to Auto-Generate Meta Descriptions Using Python and BERT

58. 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

60. How to OptimizeYour Meta Descriptions for Better CTR

62. 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.

63. Revolutionizing Data Analysis with Large Language Models

64. 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.

65. Hands-on Session

66. 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.

68. Identifying micro-influencers

69. • 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

70. Intelligent Customer Engagement

71. 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

73. 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

75. AI-driven content

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

77. Music Generation Using Deep Learning

78. • 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

80. Text Generation Using AI

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

82. 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/

83. https://adcopy.ai

85. AITools for Generating Songs withVocals

86. 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

88. AI tools for generating text to speech

89. 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.

90. Text2Speech (AlexaVoice)

91. Descript.com

93. 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.

95. Text to Image

96. 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

97. https://firefly.adobe.com

98. https://designer.microsoft.com/

99. https://lexica.art

100. https://www.imiprompt.com/

101. https://prompt.noonshot.com/

102. https://promptomania.com/

103. https://lumalabs.ai/

106. Graphic AI

107. 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.

108. AI video generation

109. 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

111. Deep Fakes

112. DeepFakes 2017

113. • 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

117. AITools

120. Find AITools https://aitools.fyi

121. • GPT for your files humata.ai

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

123. Tome.app

124. How to Create a ChatGPT & BARD Account

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

127. https://chat.openai.com/

129. https://www.bing.com/

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

131. https://bard.google.com/

132. https://poe.com/

135. Prompt

136. https://www.prompt-genie.com/

137. AIPRM

139. https://keywordseverywhere.com/

140. https://www.promptvibes.com/

141. https://quickref.me/chatgpt.html

143. Building a ChatGPT using Personal or Organizational Data

146. ThankYou Eng. Mohamed Hanafy mohanafy@gmail.com

The Revolution of Digital Marketing in the Artificial Intelligence era

The Revolution of Digital Marketing in the Artificial Intelligence era

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The Revolution of Digital Marketing in the Artificial Intelligence era

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