olmOCR is a project designed to convert PDF files and document images into structured Markdown text. It can handle equations, tables, and handwritten text, preserving the correct reading order even in the most complex multi-column layouts.
olmOCR is trained with heuristics to handle common parsing and metadata errors and supports SGLang and vLLM, where it can scale from one to hundreds of GPUs, making it a unique solution for large-scale tasks.
The key advantage of olmOCR is its cost-effectiveness. Processing 1 million PDF pages will cost only $190 (with GPU rental), which is about 1/32 of the cost of using the GPT-4o API for the same volume.
The development team created a unique method called "document anchoring" to improve the quality of the extracted text. It uses text and metadata from PDF files to improve the accuracy of processing. Image regions and text blocks are extracted, concatenated and inserted into the model prompt. When VLM requests a plain text version of the document, the "anchored" text is used along with the rasterized page image.
In tests, olmOCR showed high results compared to Marker, MinerU and GOT-OCR 2.0. During testing, olmOCR was preferred in 61.3% of cases against Marker, in 58.6% against GOT-OCR and in 71.4% against MinerU.
poppler-utilssglang with flashinfer for GPU inference# Install dependencies
sudo apt-get update
sudo apt-get install poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools
# Set up a conda env
conda create -n olmocr python=3.11
conda activate olmocr
git clone https://github.com/allenai/olmocr.git
cd olmocr
pip install -e .
# Convert a Single PDF
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/test.pdf
# Convert Multiple PDFs
python -m olmocr.pipeline ./localworkspace --pdfs tests/gnarly_pdfs/*.pdf
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Basic skills needed for ai engineer
1. Programming Skills (Essential)
Learn Python (most widely used in AI).
Basics of libraries like NumPy, Pandas (for data handling).
Understanding of loops, functions, OOPs concepts.
2. Mathematics & Statistics (Basic Level)
Linear Algebra (Vectors, Matrices, Dot Product).
Probability & Statistics (Mean, Variance, Standard Deviation).
Basic Calculus (Derivatives, Integrals – useful for ML models)
3. Machine Learning Fundamentals
Understand what Supervised & Unsupervised Learning are.
Learn about Regression, Classification, and Clustering.
Introduction to Neural Networks and Deep Learning.
4. Data Handling & Processing
How to collect, clean, and process data for AI models.
Using Pandas & NumPy to manipulate datasets.
5. AI Libraries & Frameworks
Learn Scikit-learn for ML models.
Introduction to TensorFlow or PyTorch for Deep Learning.
1. Programming Skills (Essential)
Learn Python (most widely used in AI).
Basics of libraries like NumPy, Pandas (for data handling).
Understanding of loops, functions, OOPs concepts.
2. Mathematics & Statistics (Basic Level)
Linear Algebra (Vectors, Matrices, Dot Product).
Probability & Statistics (Mean, Variance, Standard Deviation).
Basic Calculus (Derivatives, Integrals – useful for ML models)
3. Machine Learning Fundamentals
Understand what Supervised & Unsupervised Learning are.
Learn about Regression, Classification, and Clustering.
Introduction to Neural Networks and Deep Learning.
4. Data Handling & Processing
How to collect, clean, and process data for AI models.
Using Pandas & NumPy to manipulate datasets.
5. AI Libraries & Frameworks
Learn Scikit-learn for ML models.
Introduction to TensorFlow or PyTorch for Deep Learning.
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Ex_Files_Complete_Guide_NLP_with_R.zip
10.8 MB
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✅ AI Ethics Basics You Should Know 🧠⚖️
AI Ethics focuses on ensuring that artificial intelligence systems are developed and used in a responsible, fair, and transparent manner.
🔹 1. What is AI Ethics?
AI Ethics is the study of moral principles and practices that guide the development, deployment, and use of AI technologies.
🔹 2. Why AI Ethics is Important:
• AI systems impact millions of people
• Prevents bias and discrimination
• Ensures trust and accountability
• Protects user privacy and rights
🔹 3. Key Principles of AI Ethics:
• Fairness: Avoid bias and discrimination
• Transparency: AI decisions should be explainable
• Accountability: Humans must be responsible for AI outcomes
• Privacy: Protect user data and personal information
• Safety: AI should not cause harm
🔹 4. Common Ethical Issues in AI:
• Biased algorithms
• Data privacy violations
• Surveillance misuse
• Job displacement due to automation
• Misinformation and deepfakes
🔹 5. Real World Use Cases:
• Fair hiring systems
• Ethical facial recognition
• Responsible healthcare AI
• Bias detection in financial systems
🔹 6. Examples of AI Bias:
• Gender bias in resume screening
• Racial bias in face recognition
• Language bias in NLP models
🔹 7. How to Build Ethical AI:
• Use diverse and representative datasets
• Regularly audit models for bias
• Maintain human oversight
• Clearly document AI decisions
🔹 8. AI Ethics vs AI Governance:
• AI Ethics focuses on moral values
• AI Governance focuses on rules and regulations
• Both work together for responsible AI
🔹 9. Who is Responsible for AI Ethics?
• Developers
• Companies
• Governments
• Researchers
• End users
🔹 10. Future of AI Ethics:
• Stronger regulations
• Ethical AI certifications
• More transparent AI systems
• Human centered AI development
💡 Learning AI Ethics is essential for building trustworthy and responsible AI systems.
💬 Tap ❤️ for more!
AI Ethics focuses on ensuring that artificial intelligence systems are developed and used in a responsible, fair, and transparent manner.
🔹 1. What is AI Ethics?
AI Ethics is the study of moral principles and practices that guide the development, deployment, and use of AI technologies.
🔹 2. Why AI Ethics is Important:
• AI systems impact millions of people
• Prevents bias and discrimination
• Ensures trust and accountability
• Protects user privacy and rights
🔹 3. Key Principles of AI Ethics:
• Fairness: Avoid bias and discrimination
• Transparency: AI decisions should be explainable
• Accountability: Humans must be responsible for AI outcomes
• Privacy: Protect user data and personal information
• Safety: AI should not cause harm
🔹 4. Common Ethical Issues in AI:
• Biased algorithms
• Data privacy violations
• Surveillance misuse
• Job displacement due to automation
• Misinformation and deepfakes
🔹 5. Real World Use Cases:
• Fair hiring systems
• Ethical facial recognition
• Responsible healthcare AI
• Bias detection in financial systems
🔹 6. Examples of AI Bias:
• Gender bias in resume screening
• Racial bias in face recognition
• Language bias in NLP models
🔹 7. How to Build Ethical AI:
• Use diverse and representative datasets
• Regularly audit models for bias
• Maintain human oversight
• Clearly document AI decisions
🔹 8. AI Ethics vs AI Governance:
• AI Ethics focuses on moral values
• AI Governance focuses on rules and regulations
• Both work together for responsible AI
🔹 9. Who is Responsible for AI Ethics?
• Developers
• Companies
• Governments
• Researchers
• End users
🔹 10. Future of AI Ethics:
• Stronger regulations
• Ethical AI certifications
• More transparent AI systems
• Human centered AI development
💡 Learning AI Ethics is essential for building trustworthy and responsible AI systems.
💬 Tap ❤️ for more!
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Artificial intelligence is not a single technology but a layered system where each level builds on the previous one. It starts with AI as the broad concept, moves into machine learning that learns from data, neural networks inspired by the human brain, and deep learning that powers vision, speech, and language.
On top of that comes generative AI, capable of creating text, images, and media, and finally agentic AI, which can reason, use tools, and act autonomously toward goals. Understanding these layers helps make sense of how modern AI systems work and where the future of intelligent technology is headed.
On top of that comes generative AI, capable of creating text, images, and media, and finally agentic AI, which can reason, use tools, and act autonomously toward goals. Understanding these layers helps make sense of how modern AI systems work and where the future of intelligent technology is headed.
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Not all Al is the same, and understanding the differences is becoming essential.
Traditional Al focuses on prediction, classification, and anomaly detection using historical data.
Generative Al creates content like text, code, images, and summaries from prompts. Agentic Al goes a step further by taking action, using tools, maintaining context, orchestrating workflows, and executing complex tasks with minimal human input.
As Al evolves from automation to autonomy, businesses gain speed, efficiency, and smarter decision-making. ai is no longer just about generating answers; it’s about getting real work done.
Traditional Al focuses on prediction, classification, and anomaly detection using historical data.
Generative Al creates content like text, code, images, and summaries from prompts. Agentic Al goes a step further by taking action, using tools, maintaining context, orchestrating workflows, and executing complex tasks with minimal human input.
As Al evolves from automation to autonomy, businesses gain speed, efficiency, and smarter decision-making. ai is no longer just about generating answers; it’s about getting real work done.
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🧠 AI isn’t a single switch you flip.
It is a sophisticated stack of overlapping technologies that has evolved over seven decades.
Understanding this hierarchy is the difference between chasing hype and building a scalable enterprise strategy.
The AI Stack:
1950s: Artificial Intelligence (The Foundation)
1980s: Machine Learning (The Engine)
2010s: Deep Learning (The Scale)
2020s: Generative AI (The Innovation)
2025+: Agentic AI (The Frontier)
We are currently witnessing the most significant shift yet: the transition from AI as an assistant to AI as an orchestrator.
Capgemini’s 2025 Agentic AI report finds 37% of organizations now piloting (23%) or scaling (14%) AI agents, marking the shift from assistants to orchestration.
These systems don’t just “chat.” They plan and execute multi-step workflows independently.
Enterprises will deploy autonomous agents from 2025 as tools transition from assistants to orchestration systems.
The goal is no longer just processing information. It is autonomous action.
It is a sophisticated stack of overlapping technologies that has evolved over seven decades.
Understanding this hierarchy is the difference between chasing hype and building a scalable enterprise strategy.
The AI Stack:
1950s: Artificial Intelligence (The Foundation)
1980s: Machine Learning (The Engine)
2010s: Deep Learning (The Scale)
2020s: Generative AI (The Innovation)
2025+: Agentic AI (The Frontier)
We are currently witnessing the most significant shift yet: the transition from AI as an assistant to AI as an orchestrator.
Capgemini’s 2025 Agentic AI report finds 37% of organizations now piloting (23%) or scaling (14%) AI agents, marking the shift from assistants to orchestration.
These systems don’t just “chat.” They plan and execute multi-step workflows independently.
Enterprises will deploy autonomous agents from 2025 as tools transition from assistants to orchestration systems.
The goal is no longer just processing information. It is autonomous action.
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Introducing Vanna: An Open-Source Text-to-SQL Tool | Daily Dose of Data Science posted on the topic | LinkedIn
Finally! A Text-to-SQL tool that actually works!
(100% open-source, 20k+ stars)
Vanna is an open-source RAG framework for complex Text-to-SQL generation, designed for handling dynamic datasets.
Works in 2 easy steps:
1️⃣ Train a RAG “model” on your data.
2️⃣ Ask questions in natural language which will return SQL queries that can be set up to automatically run on your database.
Key features:
🎯 High accuracy on complex datasets
🤖 Self-learning: improves with each query
🔒 Secure: data never leaves your environment
🌐 Connect to any SQL DB (Snowflake, Redshift, etc.)
🧩 Multiple front-end integrations (Jupyter, Slack, etc.)
🌐 Vanna GitHub: https://github.com/vanna-ai/vanna
Finally! A Text-to-SQL tool that actually works!
(100% open-source, 20k+ stars)
Vanna is an open-source RAG framework for complex Text-to-SQL generation, designed for handling dynamic datasets.
Works in 2 easy steps:
1️⃣ Train a RAG “model” on your data.
2️⃣ Ask questions in natural language which will return SQL queries that can be set up to automatically run on your database.
Key features:
🎯 High accuracy on complex datasets
🤖 Self-learning: improves with each query
🔒 Secure: data never leaves your environment
🌐 Connect to any SQL DB (Snowflake, Redshift, etc.)
🧩 Multiple front-end integrations (Jupyter, Slack, etc.)
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AI Outperforms Average Human in Some Creativity Tests
Recent research shows that advanced AI models are now scoring above the average human on certain standard tests of creative thinking — including idea generation and problem-solving tasks.
While these benchmarks don’t capture the full range of human creativity, the results suggest AI is making measurable progress in areas once seen as uniquely human.
The findings are part of ongoing work to understand how AI can assist in creative workflows, not just automate routine tasks.
Recent research shows that advanced AI models are now scoring above the average human on certain standard tests of creative thinking — including idea generation and problem-solving tasks.
While these benchmarks don’t capture the full range of human creativity, the results suggest AI is making measurable progress in areas once seen as uniquely human.
The findings are part of ongoing work to understand how AI can assist in creative workflows, not just automate routine tasks.
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