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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Advancing Scalable Text-to-Speech Synthesis: Llasa’s Transformer-Based Framework for Improved Speech Quality and Emotional Expressiveness
Recent Advances in Text-to-Speech Technology Understanding the Benefits of Scaling Recent developments in large language models (LLMs), like the GPT series, show that increasing computing power during both training and testing phases leads to better performance.…
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Revolutionizing Video Editing: How LAVE and AI are Democratizing Creative Expression
LAVE, a groundbreaking project by University of Toronto, UC San Diego, and Meta’s Reality Labs, revolutionizes video editing by integrating Large Language Models (LLMs). It simplifies the process using natural language commands, automating tasks and offering…
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Bill Gates Doubts Major Advancements in ChatGPT 5
According to Bill Gates, Generative AI like ChatGPT has reached its peak and may not see significant improvements, even with the release of GPT-5. However, Gates acknowledges that he could be wrong. He believes AI will…
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Open Deep Search: Democratizing AI Search with Open-Source Reasoning Agents
Introducing Open Deep Search (ODS): A Revolutionary Open-Source Framework for Enhanced Search The landscape of search engine technology has evolved rapidly, primarily favoring proprietary solutions like Google and GPT-4. While these systems demonstrate strong performance, their…
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Unlocking the Power of Tables with Large Language Models: A Comprehensive Survey on Automating Data-Intensive Tasks
Researchers at Renmin University of China propose approaches to enhance Large Language Models’ (LLMs) ability to process table data. They focus on instruction tuning, prompting, and agent-based methods to improve LLMs’ performance on table-related tasks. These…
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AI startups feel the heat as OpenAI adds ChatGPT features
OpenAI has introduced new features to ChatGPT Plus, affecting AI startups. Users can now access all ChatGPT tools without switching, including Browsing, Advanced Data Analysis, and DALL-E. PDF analysis, previously available through plugins, is now integrated.…
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Amazon AI Researchers Introduce Chronos: A New Machine Learning Framework for Pretrained Probabilistic Time Series Models
The introduction of Chronos, a revolutionary forecasting framework by Amazon AI researchers in collaboration with UC San Diego and the University of Freiburg, redefines time series forecasting. It merges numerical data analysis with language processing, leveraging…
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Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing
Practical Solutions and Value of Arena Learning Large language models (LLMs) like chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services. Challenges Faced The challenge is the efficient post-training of…
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Large language models can do jaw-dropping things. But nobody knows exactly why.
Yuri Burda and Harri Edwards of OpenAI experimented with training a large language model to do basic arithmetic, discovering unexpected behaviors like grokking and double descent. These odd phenomena challenge classical statistics and highlight the mysterious…
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![Exploring Well-Designed Machine Learning (ML) Codebases [Discussion]](https://itinai.com/wp-content/uploads/2025/03/itinai.com_russian_handsome_charismatic_models_scrum_site_dev_96579955-dded-4288-b857-3ee0b72c8d7a_2.png)
Exploring Well-Designed Machine Learning (ML) Codebases [Discussion]
The Reddit post initiated a discussion on well-designed ML projects. Beyond Jupyter was recommended for enhancing ML software architecture, emphasizing OOP and design concepts. Scikit-learn stood out for intuitive design and user-friendliness. Other projects like Easy…
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Five things you need to know about the EU’s new AI Act
After months of negotiations, EU lawmakers have reached a deal on the groundbreaking AI Act, introducing strict rules on transparency and ethics for tech companies, creating enforcement mechanisms, and setting up fines for noncompliance. The Act…
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Writer Researchers Introduce Writing in the Margins (WiM): A New Inference Pattern for Large Language Models Designed to Optimize the Handling of Long Input Sequences in Retrieval-Oriented Tasks
Practical Solutions and Value of Writing in the Margins (WiM) for Large Language Models Introduction Artificial intelligence (AI) and natural language processing (NLP) have made significant progress, particularly in the development of large language models (LLMs)…
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AI Knowledge Base Management: The Brain of Customer Support
AI knowledge base management is a tool that utilizes advanced algorithms and technologies to store, organize, and retrieve vast amounts of information. It enables support agents to quickly analyze and respond to customer queries by accessing…
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REBEL: A Reinforcement Learning RL Algorithm that Reduces the Problem of RL to Solving a Sequence of Relative Reward Regression Problems on Iteratively Collected Datasets
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Mistral AI Team Releases The Mistral-7B-Instruct-v0.3: An Instruct Fine-Tuned Version of the Mistral-7B-v0.3
The practical value of AI language models The field of AI involves creating systems that can perform tasks requiring human-like intelligence, such as language translation, speech recognition, and decision-making. Researchers are dedicated to developing advanced models…
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Welcome to a New Era of Building in the Cloud with Generative AI on AWS
Generative AI is rapidly transforming customer experiences, with many companies launching applications on AWS, including major brands and startups. AWS is democratizing advanced generative AI technology, making it more accessible and secure across three layers of…