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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Google DeepMind Researchers Propose Matryoshka Quantization: A Technique to Enhance Deep Learning Efficiency by Optimizing Multi-Precision Models without Sacrificing Accuracy
Understanding Quantization in Deep Learning What is Quantization? Quantization is a key method in deep learning that helps reduce computing costs and improve the efficiency of models. Large language models require a lot of processing power,…
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Paperlib: An Open-Source AI Research Paper Management Tool
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3 Ways to Run Llama 3 on Your PC or Mac
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Mistral.rs: A Lightning-Fast LLM Inference Platform with Device Support, Quantization, and Open-AI API Compatible HTTP Server and Python Bindings
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Meet Slope TransFormer: A Large Language Model (LLM) Trained Specifically to Understand the Language of Banks
Slope TransFormer is a new solution developed to understand bank transactions. Traditional methods struggle with the variety of transaction forms, while existing solutions have limitations. TransFormer overcomes these challenges by being a Large Language Model (LLM)…
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The Text-to-Speech-Client Tool by Xenova: A Robust and Flexible AI Platform for Producing Natural-Sounding Synthetic Speech
Xenova’s text-to-speech client utilizes transformer-based neural networks to generate natural-sounding synthetic speech. It offers high-quality synthetic speech that is indistinguishable from human voice, supports various voices and languages, and allows fine-grained control over speech synthesis. The…
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How satellite images and AI could help fight spatial apartheid in South Africa
Raesetje Sefala, a South African activist, is using computer vision and satellite imagery to address the effects of spatial apartheid. She aims to map out and analyze racial segregation in housing, hoping to prompt systemic change…
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ETH Zurich Researchers Introduced EventChat: A CRS Using ChatGPT as Its Core Language Model Enhancing Small and Medium Enterprises with Advanced Conversational Recommender Systems
Conversational Recommender Systems for SMEs Revolutionizing User Decision-Making Conversational Recommender Systems (CRS) offer personalized suggestions through interactive dialogue interfaces, reducing information overload and enhancing user experience. These systems are valuable for SMEs looking to enhance customer…
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Researchers from University College London Introduce DSP-SLAM: An Object Oriented SLAM with Deep Shape Priors
Deep Learning advancements in AI, specifically in SLAM technology, have been made by University College London researchers with DSP-SLAM. This system accurately maps environments and tracks camera movement, utilizing object shape and pose estimation to improve…
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LightOn and Answer.ai Releases ModernBERT: A New Model Series that is a Pareto Improvement over BERT with both Speed and Accuracy
Introduction to ModernBERT Since 2018, BERT has been a popular choice for natural language processing (NLP) due to its efficiency. However, it has limitations, especially with long texts, as it can only handle 512 tokens. Modern…
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Project Alexandria: Democratizing Scientific Knowledge with Structured Fact Extraction
Introduction Scientific publishing has grown significantly in recent decades. However, access to vital research remains limited for many, especially in developing countries, independent researchers, and small academic institutions. Rising journal subscription costs worsen this issue, restricting…
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MicroPython Testbed for Federated Learning Algorithms (MPT-FLA) Framework Advancing Federated Learning at the Edge
The Practical Solutions and Value of MPT-FLA Framework for Federated Learning at the Edge Introduction The MPT-FLA (MicroPython Testbed for Federated Learning Algorithms) framework provides practical solutions for developing decentralized and distributed applications for edge systems.…
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EDLM: A New Energy-based Language Model Embedded with Diffusion Framework
Advancements in Language Modeling Recent developments in language modeling have improved natural language processing, allowing for the creation of coherent and contextually relevant text for various uses. Autoregressive (AR) models, which generate text sequentially from left…
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Tencent Research Introduces DRT-o1: Two Variants DRT-o1-7B and DRT-o1-14B with Breakthrough in Neural Machine Translation for Literary Texts
Understanding Neural Machine Translation (NMT) Neural Machine Translation (NMT) is an advanced technology that translates text between languages using machine learning. It plays a crucial role in global communication, particularly for tasks like technical document translation…
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Google Researchers Reveal Practical Insights into Knowledge Distillation for Model Compression
Practical Insights into Knowledge Distillation for Model Compression Introduction Many computer vision tasks are dominated by large-scale vision models, which often exceed hardware capabilities. Google Research Team focuses on reducing the computational costs of these models…
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This Machine Learning Research Attempts to Formalize Generalization in the Context of GFlowNets and to Link Generalization with Stability
Practical Solutions for Sampling from Unnormalized Probability Distributions Addressing Complex Sampling Challenges with GFlowNets Generative Flow Networks (GFlowNets) offer a robust framework for efficient sampling from unnormalized probability distributions in machine learning. By learning a policy…