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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Hugging Face Releases Text Generation Inference (TGI) v3.0: 13x Faster than vLLM on Long Prompts
Text Generation: A Key to Modern AI Text generation is essential for applications like chatbots and content creation. However, managing long prompts and changing contexts can be challenging. Many systems struggle with speed, memory use, and…
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Frame-Dependent Agency: Implications for Reinforcement Learning and Intelligence
Understanding Agency in AI What is Agency? Agency is the ability of a system to achieve specific goals. This study highlights that how we assess agency depends on the perspective we use, known as the reference…
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Marqo Releases Advanced E-commerce Embedding Models and Comprehensive Evaluation Datasets to Revolutionize Product Search, Recommendation, and Benchmarking for Retail AI Applications
Marqo’s New E-commerce Solutions Introduction of Advanced Models Marqo has launched four innovative datasets and advanced e-commerce embedding models that enhance product search, retrieval, and recommendations. The models, named Marqo-Ecommerce-B and Marqo-Ecommerce-L, significantly improve accuracy and…
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Meet the OCR Toolkit: A Versatile Python Package for Seamlessly Integrating and Experimenting with Various OCR and Object Detection Frameworks
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Clarifai 9.9: AI Assist
The text is about the new updates in Python SDK, AI-assisted labeling, and a growing library of generative models.
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DALL·E 3 system card
This text requests a summary of an article about AI, specifically focusing on solutions.
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Meet BiTA: An Innovative AI Method Expediting LLMs via Streamlined Semi-Autoregressive Generation and Draft Verification
Recent advancements in large language models (LLMs) like Chat-GPT and LLaMA-2 have led to an exponential increase in parameters, posing challenges in inference delay. To address this, Intellifusion Inc. and Harbin Institute of Technology propose Bi-directional…
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Top 25 AI Tools for Content Creators in 2025
Unlock the Power of AI for Content Creation Creating engaging and high-quality content is now easier than ever with AI-powered tools. These innovative platforms are changing how creators and marketers produce videos, write blogs, edit images,…
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Researchers at Peking University Introduce A New AI Benchmark for Evaluating Numerical Understanding and Processing in Large Language Models
Understanding the Challenges of Large Language Models (LLMs) Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex reasoning and mathematical tasks. However, they struggle with basic numerical concepts, which are crucial for advanced…
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TransMLA: Transforming GQA-based Models Into MLA-based Models
Understanding the Importance of Large Language Models (LLMs) Large Language Models (LLMs) are becoming essential tools for boosting productivity. Open-source models are now performing similarly to closed-source ones. These models work by predicting the next token…
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HuggingFace Releases Parler-TTS: An Inference and Training Library for High-Quality, Controllable Text-to-Speech (TTS) Models
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Anthropic Releases Claude 3 Haiku: The Fastest and Most Cost-Effective Artificial Intelligence (AI) Model in Its Intelligence Class
Anthropic released Claude 3 Haiku, the fastest and most cost-effective AI model in its class. It outperforms competitors in speed and affordability, processing 21,000 tokens per second. Haiku also prioritizes enterprise-class security with strict testing and…
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This Paper from MBZUAI Introduces 26 Guiding Principles Designed to Streamline the Process of Querying and Prompting Large Language Models
Large Language Models (LLMs) have revolutionized processing multimodal information, leading to breakthroughs in multiple fields. Prompt engineering, introduced by researchers at MBZUAI, focuses on optimizing prompts for LLMs. Their study outlines 26 principles for crafting effective…
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This AI Paper Unveils HiFi4G: A Breakthrough in Photo-Real Human Modeling and Efficient Rendering
New AI paper introduces HiFi4G, a compact 4D Gaussian representation combining nonrigid tracking with Gaussian Splatting for realistic human performance rendering. The study’s dual-graph approach efficiently recovers spatially-temporally consistent 4D Gaussians with a complementary compression method,…
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Interactive Dashboards in Excel
This article provides a step-by-step tutorial on how to create an interactive dashboard in Excel using the Superstore dataset from Tableau. It covers topics such as creating pivot tables, pivot charts, maps, slicers, and formatting techniques…
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This AI Paper by Toyota Research Institute Introduces SUPRA: Enhancing Transformer Efficiency with Recurrent Neural Networks
NLP Advancements and Challenges Natural language processing (NLP) has seen significant advancements, especially with transformer models, but they come with high memory and computational requirements. This poses practical challenges for long-context work applications. Research and Solutions…