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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Branches Are All You Need: Our Opinionated ML Versioning Framework
This article presents a framework for versioning machine learning projects using Git branches. The framework aims to simplify workflows, organize data and models, and consolidate different aspects of the ML solution. It emphasizes the use of…
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USC Researchers Propose DeLLMa (Decision-making Large Language Model Assistant): A Machine Learning Framework Designed to Enhance Decision-Making Accuracy in Uncertain Environments
USC researchers have developed DeLLMa, a machine learning framework aimed at improving decision-making in uncertain environments. It leverages large language models to address the complexities of decision-making, offering structured, transparent, and auditable methods. Rigorous testing demonstrated…
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5 Levels in AI by OpenAI: A Roadmap to Human-Level Problem Solving Capabilities
The Five Levels of AI by OpenAI Practical Solutions and Value Level 1: Conversational AI AI programs like ChatGPT can converse with people, aiding in information retrieval, customer support, and casual conversation. Level 2: Reasoners AI…
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Optimize Llama Models with Meta’s New Python Toolkit: Llama Prompt Ops
The rise of open-source large language models (LLMs) like Llama has revolutionized the landscape of artificial intelligence, providing new opportunities for developers and organizations alike. However, transitioning from proprietary systems such as OpenAI’s GPT or Anthropic’s…
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Harmonizing Vision and Language: The Advent of Bi-Modal Behavioral Alignment (BBA) in Enhancing Multimodal Reasoning
The integration of domain-specific languages (DSL) into large vision-language models (LVLMs) advances multimodal reasoning capabilities. Traditional methods struggle to harmoniously blend visual and DSL reasoning. The Bi-Modal Behavioral Alignment (BBA) method bridges this gap by prompting…
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MaskLLM: A Learnable AI Method that Facilitates End-to End Training of LLM Sparsity on Large-Scale Datasets
Practical Solutions for Efficient AI Model Deployment Semi-Structured Pruning for Efficiency Implement N: M sparsity pattern to reduce memory and computational demands. Introducing MaskLLM for Enhanced Pruning MaskLLM by NVIDIA and NUS applies learnable N: M…
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Hugging Face Releases Open LLM Leaderboard 2: A Major Upgrade Featuring Tougher Benchmarks, Fairer Scoring, and Enhanced Community Collaboration for Evaluating Language Models
Hugging Face Releases Open LLM Leaderboard 2: A Major Upgrade Featuring Tougher Benchmarks, Fairer Scoring, and Enhanced Community Collaboration for Evaluating Language Models Addressing Benchmark Saturation Hugging Face has upgraded the Open LLM Leaderboard to address…
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Introduction of Microsoft Fabric
Microsoft Fabric is a new solution that aims to enhance our relationship with technology. This article discusses its features, benefits, and suitable users, providing a guide on when and how to utilize it.
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Trusting LLM Reward Models: Master-RM’s Solution to Systemic Vulnerabilities
As artificial intelligence continues to evolve, the use of large language models (LLMs) in reinforcement learning with verifiable rewards (RLVR) is becoming increasingly popular. These generative reward models evaluate responses based on comparisons to reference answers,…
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Top AgentOps Tools in 2025
Unlocking the Power of AI Agents with AgentOps Tools As AI agents become more advanced, managing and optimizing their performance is essential. The emerging field of AgentOps focuses on the tools needed to develop, deploy, and…
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Meta announces its “Emu” family of generative AI tools
Meta has unveiled two new AI tools, called “Emu Video” and “Emu Edit,” as part of its Emu AI research project. Emu Video allows users to create short video clips from text prompts, while Emu Edit…
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Enhancing Anomaly Detection with Adaptive Noise: A Pseudo Anomaly Approach
Practical AI Solution: Enhancing Anomaly Detection with Adaptive Noise Value and Practical Solutions Anomaly detection is crucial in surveillance, medical analysis, and network security. Our approach introduces a robust method to improve anomaly detection by training…
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MAmmoTH-VL-Instruct: Advancing Open-Source Multimodal Reasoning with Scalable Dataset Construction
Open-Source MLLMs: Enhancing Reasoning with Practical Solutions Open-source Multimodal Large Language Models (MLLMs) show great potential for tackling various tasks by combining visual encoders and language models. However, there is room for improvement in their reasoning…
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Kosmos: The AI Scientist Revolutionizing Data-Driven Research
Understanding Kosmos: The Autonomous AI Scientist Kosmos, created by Edison Scientific, is revolutionizing the way scientific research is conducted. This autonomous discovery system is designed to run extensive research campaigns focused on a single goal. By…
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Meta AI Unveils Brain2Qwerty: Breakthrough in Non-Invasive Sentence Decoding Using MEG and Deep Learning
Advancements in Neuroprosthetic Devices Neuroprosthetic devices have made significant progress in brain-computer interfaces (BCIs), enabling communication for individuals with speech or motor impairments caused by conditions such as anarthria, ALS, or severe paralysis. These devices decode…
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SpeechAlign: Transforming Speech Synthesis with Human Feedback for Enhanced Naturalness and Expressiveness in Technological Interactions