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Reading list

What is NLP? Applications of NLP
Understanding Text Pre-processing Tokenization in NLP Byte Pair Encoding Tokenizer Free Language Modeling with Pixels Stopword Removal Stemming vs Lemmatization Text Mining
Spacy Tutorials Gensim Tutorials
What are Regular Expressions? Regular Expressions
String Similarity
Spelling Correction
Introduction to Topic Modeling Latent Dirichlet Allocation (LDA) Implement Topic Modeling
Introduction to Feature Engineering for Text Data Implement Text Feature Engineering Techniques Introduction to One Hot Encoding Implement One Hot Encoding Limitations of One Hot Encoding Count Vectorizer and TF-IDF Solving Text classification using TF-IDF
Information Retrieval System Explained in Simple terms! How does Google Rank Search Results? Knowledge Graph
Understanding Word2Vec Understanding Skip Gram and Continous Bag Of Words Word2Vec Implementation in Gensim Visualizing Word2Vec
Word Senses and Word Sense Ambiguity
Why Are We Interested in Syntatic Strucure? What is a Dependency Grammar? Neural Dependency Parsing
Introduction to Language Models N-Gram Language Models Neural Language models
Why Sequence models? Usecases of Sequence models Introduction to RNN Implement RNN
Shortcomings of RNN What is Long Short Term Memory (LSTM) Implementing LSTM Build Your Own Fake News Classification Model What is Gated Recurrent Unit (GRU)? Implementing GRU
Introduction to Machine Translation Multilingualism in NLP Drawbacks of Seq2Seq model Mathematical Calculation of Attention
Understand Positional Encoding Introducing Transformers Model Key Query Value Attention in Tranformer Encoder
Pretrained Language Models in NLP Generative Pre-training (GPT) for Natural Language Understanding(NLU) Finetuning GPT-2 Understanding BERT Finetune Masked language Modeling in BERT Implement Text Classification using BERT Finetuning BERT for NER Extensions of BERT: Roberta, Spanbert, ALBER MobileBERT GPT-3 Prompt Engineering in GPT-3 Bigbird T5 and large language models
Implement Question Answering on SQUAD
Text Summarization
Named Entity Recognition (NER) in Python with Spacy
Coreference Resolution
Visualizing Sounds Using Librosa Machine Learning Library! Audio Processing Audio Analysis Audio Classification using Deep Learning
Automatic Speech Recognition Implement Automatic Speech Recognition Can Voice Conversion Improve ASR in Low-Resource Settings?
Audio Separation
Building Chatbots Building Chatbots using Rasa
Automate NLP Tasks using EvalML Library

Google Tests ‘Tracking Protection’ to Eliminate Third-Party Cookies: A Privacy Revolution Unfolding

Nitika Sharma
Nitika Sharma Last Updated : 08 Jan, 2024
2 min read

In a world where privacy is increasingly becoming a concern, Google Chrome has taken a significant step forward. The tech giant has started blocking data tracking cookies, a move that has sent ripples across the digital landscape. Let’s look into the implications of this decision and what it means for users and advertisers.

Google Tests Tracking Protection

The Dawn of a New Era

Google Chrome, the world’s most popular web browser, has begun blocking third-party cookies, a common tool used by advertisers to track users’ online activities. This move is part of Google’s broader initiative to enhance user privacy and security. The decision has sparked mixed reactions, with some applauding it as a step towards a more private internet, while others express concerns about its impact on digital advertising.

Understanding Cookies and their Role

Websites store small pieces of data called cookies on your computer, remembering information like login details. However, Google Chrome is now blocking third-party cookies, often used for tracking and targeted ads.

Also Read: Google DeepMind to Build Intelligent Helper Robots

The Impact on Users and Advertisers

For users, this move means enhanced privacy and less intrusive advertising. However, it also means that some websites may not function as they used to, as they rely on third-party cookies for various features. For advertisers, it’s a significant blow, as they lose a valuable tool for tracking user behavior and serving targeted ads. However, Google is working on alternatives that balance user privacy with the needs of advertisers.

Google’s Privacy Sandbox Initiative

Privacy

Google’s decision to block third-party cookies is part of its Privacy Sandbox initiative. This project aims to create a set of open standards to enhance privacy on the web while giving advertisers the tools they need. One of the proposed technologies is Federated Learning of Cohorts (FLoC), which groups users with similar interests together, allowing advertisers to serve relevant ads without tracking individual users.

Also Read: Google and Stanford Develop AI Housemaid

Our Say

Google Chrome’s decision to block third-party cookies is a bold move that signals a shift towards a more privacy-focused internet. While it presents challenges for advertisers, it also opens up opportunities for innovation in digital advertising. As users, it’s a welcome change that gives us more control over our online privacy. However, it’s crucial to stay informed and understand the implications of these changes. As the digital landscape continues to evolve, we can expect more such initiatives aimed at balancing user privacy with the needs of the digital economy.

Follow us on Google News to stay updated with latest innovations in the world of AI, Data Science and GenAI.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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