Editorial Policy itinai.com
At itinai.com, we take editorial integrity seriously. Our mission is to create trustworthy, useful, and verifiable content in the field of artificial intelligence, innovation, and product development.
Every article published on itinai.com undergoes human review and aligns with the principles below.
Our Editorial Principles
- Accuracy – We fact-check our content and update it when necessary.
- Transparency – We disclose the source, author, and publishing intent.
- Experience-first – Our content is written or reviewed by practitioners and domain experts.
- Human in the loop – No article is published without human editorial oversight.
- Clarity – We prioritize plain, accessible language and practical insight.
- Accountability – Errors are corrected. Feedback is encouraged and valued.
Submit a Correction or Suggest an Update
We welcome suggestions to improve our content.
If you’ve spotted a factual error, an outdated reference, or wish to propose an edit:
📬 Email: editor@itinai.com
All valid correction requests are reviewed within 72 hours.
In most cases, you will receive a reply from our editorial team.
Submit a News Item or Contribute Content
Want to submit a story, research highlight, or industry insight?
We accept contributions in the following formats:
- Short AI news (100–300 words)
- Research summary (with link to paper)
- Opinion/editorial piece
- Product case study (original only)
📥 Send your pitch to: editor@itinai.com
💡 Guest authorship is available — we credit all contributors.
Editor-in-Chief assistant
Editorial Review Process
Every piece of content published on itinai.com follows a structured editorial workflow:
- Drafting – Written by in-house authors or external contributors.
- Expert Review – Reviewed by a domain specialist (AI, product, healthcare, or law).
- Editor-in-Chief Review – Final oversight by Vladimir Dyachkov, Ph.D.
- Fact-Checking – Sources verified manually and/or via LLM-assisted tools.
- Markup – Structured data (
Article,Person,WebPage) is applied. - Publishing – With author attribution and publishing date.
- Monitoring – Regularly re-evaluated for accuracy and relevancy.
Note: If AI tools assist in drafting or summarizing, this is clearly disclosed.
User & Company Feedback, Corrections
We actively encourage users, companies, and institutions to report factual errors or request content updates.
How we handle it:
- Submissions are received
- An editor reviews the case manually within 72 hours.
- Verified changes are fact-checked again, optionally using AI models for cross-verification (e.g., citation match, entity comparison).
- If the correction significantly changes the context or outcome, we:
- Add a “Corrected on” notice to the article
- Publish a separate editorial blog post explaining the change in our Editor’s Blog
We do not silently alter content unless it’s a typo or formatting issue.
Propose a Story or Suggest an Edit
We believe in collaborative knowledge. Anyone can contribute insights or highlight gaps.
📬 To contribute:
- Factual correction – Use our correction request form
- Submit a news item – Email your pitch to editor@itinai.com
- Contribute a piece – See our Contributor Guidelines
We welcome:
- Original insights
- AI research summaries
- Localization use cases
- Startup/product case studies
Every submission is reviewed by humans. We may edit for clarity or add editorial context.
Get Involved
Follow us, contribute insights, or propose partnerships. We welcome collaboration from researchers, writers, and product leaders passionate about building ethical, usable AI.
Contact and Transparency
- Email: editor@itinai.com
- Telegram: @itinai
- LinkedIn: itinai.com company page
You can also explore:
Editorial Picks
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ChatWithYourDocs Chat App: A Python Application that Allows You to Chat with Multiple Docs Formats like PDF, WEB Pages and YouTube Videos
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Revolutionizing Deep Model Fusion: Introducing Sparse Mixture of Low-rank Experts (SMILE) for Scalable Model Upscaling
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Implementing LLM Arena-as-a-Judge for Evaluating Language Model Outputs
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Efficient Coding in Data Science: Easy Debugging of Pandas Chained Operations
This article discusses various methods for debugging chained operations in Pandas. It introduces three functions that can be used for debugging: pdbreakpoint(), pdhead(), and pddo(). The pdbreakpoint() function allows you to add a typical breakpoint to…
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Chooch AI vs Clarifai: B2B Vision Intelligence for Real-World Industries?
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The Role of Symmetry Breaking in Machine Learning: A Study on Equivariant Functions and E-MLPs
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Samsung Introduces ANSE: Enhancing Text-to-Video Diffusion Models with Active Noise Selection
Samsung Researchers Introduce ANSE: Enhancing Text-to-Video Models Samsung researchers have unveiled a groundbreaking framework named ANSE (Active Noise Selection for Generation) aimed at improving text-to-video (T2V) diffusion models. These models are vital for creating engaging video…
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This AI Paper Introduces Semantic Backpropagation and Gradient Descent: Advanced Methods for Optimizing Language-Based Agentic Systems
Revolutionizing AI with Language-Based Agentic Systems What Are Language-Based Agentic Systems? Language-based agentic systems are advanced AI tools that automate tasks like answering questions, programming, and solving complex problems. They use Large Language Models (LLMs) to…
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Llama-3.1-Storm-8B: A Groundbreaking AI Model that Outperforms Meta AI’s Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B Models on Diverse Benchmarks
Artificial Intelligence (AI) Revolution Over the past decade, AI has made significant progress in NLP, machine learning, and deep learning. The latest breakthrough, Llama-3.1-Storm-8B by Ashvini Kumar Jindal and team, sets new standards in performance, efficiency,…
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Researchers from Microsoft Research and Tsinghua University Proposed Skeleton-of-Thought (SoT): A New Artificial Intelligence Approach to Accelerate Generation of LLMs
Microsoft Research and Tsinghua University researchers have introduced a new approach called Skeleton-of-Thought (SoT) to address the sluggish processing speed of Large Language Models (LLMs) like GPT-4 and LLaMA. SoT refrains from making extensive changes to…
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Updated Versions of Command R (35B) and Command R+ (104B) Released: Two Powerful Language Models with 104B and 35B Parameters for Multilingual AI
C4AI Command R+ 08-2024: Advancements in AI Models Overview Cohere For AI introduces the C4AI Command R+ 08-2024, a groundbreaking language model with 104 billion parameters. It features Retrieval Augmented Generation (RAG) and advanced tool-use functionalities,…
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Ruliad AI Releases DeepThought-8B: A New Small Language Model Built on LLaMA-3.1 with Test-Time Compute Scaling and Deliverers Transparent Reasoning
Introducing Deepthought-8B-LLaMA-v0.01-alpha Ruliad AI has launched Deepthought-8B, a new AI model designed for clear and understandable reasoning. Built on LLaMA-3.1, this model has 8 billion parameters and offers advanced problem-solving capabilities while being efficient to operate.…
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OmniParse: An AI Platform that Ingests/Parses Any Unstructured Data into Structured, Actionable Data Optimized for GenAI (LLM) Applications
OmniParse: A Comprehensive Solution for Unstructured Data In various fields, data comes in many forms, such as documents, images, or video/audio files. Managing and making sense of this unstructured data can be overwhelming, especially for applications…