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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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This AI Paper Introduces ReasonEval: A New Machine Learning Method to Evaluate Mathematical Reasoning Beyond Accuracy
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Google Updates its Vertex AI Search with Healthcare and Life Sciences Capabilities
Google Cloud’s Vertex AI Search is set to revolutionize the healthcare and life sciences industries by leveraging artificial intelligence (AI) to extract accurate clinical information from various sources, addressing the challenge of data overload. This advancement…
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Understanding Key Terminologies in Large Language Model (LLM) Universe
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Differentiable Adaptive Merging (DAM): A Novel AI Approach to Model Integration
Understanding Model Merging in AI Model merging is a key challenge in creating versatile AI systems, especially with large language models (LLMs). These models often excel in specific areas, like multilingual communication or specialized knowledge. Merging…
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WebChoreArena: Revolutionizing Benchmarking for Memory-Heavy Web Automation Agents
Understanding WebChoreArena WebChoreArena is a groundbreaking framework developed by researchers at the University of Tokyo to evaluate web automation agents more effectively. Unlike previous benchmarks, it focuses on tasks that require significant cognitive effort, reflecting real-world…
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MLC LLM: Universal LLM Deployment Engine with Machine Learning ML Compilation
MLC LLM: Universal LLM Deployment Engine with Machine Learning ML Compilation Deploying large language models (LLMs) can be challenging, especially as they become more complex and need to run efficiently on various platforms. MLC LLM offers…
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L3GO: Unveiling Language Agents with Chain-of-3D-Thoughts for Precision in Object Generation
AI applications translate textual instructions to 2D/3D images, facing challenges in accuracy. L3GO proposes leveraging language model agents to enhance 3D comprehension, using Blender to evaluate performance. It decomposes the creation process into parts, focusing on…
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MemoryFormer: A Novel Transformer Architecture for Efficient and Scalable Large Language Models
Transforming AI with Efficient Models What are Transformer Models? Transformer models have revolutionized artificial intelligence, enhancing applications in areas like natural language processing, computer vision, and speech recognition. They are particularly good at understanding and generating…
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Words Unveiled: The Evolution of AI-Generated Poetry and Literature
AI-generated poetry and literature are pushing the boundaries of creativity in the age of artificial intelligence. Algorithms are composing verses and stories that evoke emotions and captivate readers, merging artistry and technology. This article explores the…
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G-Retriever: Advancing Real-World Graph Question Answering with RAG and LLMs
Advancing Real-World Graph Question Answering with G-Retriever Practical Solutions and Value Large Language Models (LLMs) have made significant strides in artificial intelligence, but their ability to process complex structured data, particularly graphs, remains challenging. In our…
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TIME Framework: A Novel Machine Learning Unifying Framework Breaking Down Temporal Model Merging
Understanding Model Merging with TIME Framework What is Model Merging? Model Merging combines the strengths of specialized models into one powerful system. It involves training different versions of a base model on separate tasks until they…
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Revolutionizing Language Model Safety: How Reverse Language Models Combat Toxic Outputs
This text discusses the problematic behaviors exhibited by language models (LMs) and proposes strategies to enhance their robustness. It emphasizes automated adversarial testing techniques to identify vulnerabilities and elicit undesirable behaviors. Researchers at Eleuther AI focus…
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Researchers from Zhipu AI and Tsinghua University Introduced the ‘Self-Critique’ pipeline: Revolutionizing Mathematical Problem Solving in Large Language Models
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Zamba2-2.7B Released: A State-of-the-Art Small Language Model Achieving Twice the Speed and 27% Reduced Memory Overhead
Zamba2-2.7B: Revolutionizing Small Language Models Enhanced Performance and Efficiency Zyphra’s Zamba2-2.7B sets a new standard in small language models, achieving remarkable efficiency and performance. Trained on a substantial dataset, it matches larger models while reducing resource…
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COMCAT: Enhancing Software Maintenance through Automated Code Documentation and Improved Developer Comprehension Using Advanced Language Models
The Value of Automated Code Documentation The field of software engineering is continuously evolving, focusing on improving software maintenance and code comprehension. Automated code documentation is crucial for enhancing software readability and maintainability through advanced tools…
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Google Upgrades Gemini-exp-1121: Advancing AI Performance in Coding, Math, and Visual Understanding
The Evolution of Artificial Intelligence The world of artificial intelligence (AI) is rapidly advancing, especially with large language models (LLMs). While recent strides have been made, challenges remain. A key issue for models like GPT-4 is…