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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Optimizing Test-Time Compute for LLMs with Meta-Reinforcement Learning
Enhancing Reasoning Abilities of LLMs Improving the reasoning capabilities of Large Language Models (LLMs) by optimizing their computational resources during testing is a significant research challenge. Current methods often involve fine-tuning models using search traces or…
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Top AI Tools for Fashion Designers in 2024
Top AI Tools for Fashion Designers in 2024 The New Black The New Black is a fashion idea generator that creates original designs from user-supplied sketches or text, promoting creativity and personalization. Botika Botika automates clothing…
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Mechanisms of Localized Receptive Field Emergence in Neural Networks
Understanding Localization in Neural Networks Key Insights Localization in the nervous system refers to how specific neurons respond to small, defined areas rather than the entire input they receive. This is crucial for understanding how sensory…
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Automating Behavioral Testing in Machine Translation
Behavioral testing in NLP evaluates system capabilities by analyzing input-output behavior. However, current tests for Machine Translation are limited and manually created. To overcome this, our proposal suggests using Large Language Models (LLMs) to generate diverse…
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This AI Research from Stanford and UC Berkeley Discusses How ChatGPT’s Behavior is Changing Over Time.
Practical AI Solutions for Business Overview Large Language Models (LLMs) like GPT 3.5 and GPT 4 have gained attention in the AI community for their ability to process data and produce human-like language. These models can…
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LMSYS ORG Introduces Arena-Hard: A Data Pipeline to Build High-Quality Benchmarks from Live Data in Chatbot Arena, which is a Crowd-Sourced Platform for LLM Evals
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MISATO: A Machine Learning Dataset of Protein-Ligand Complexes for Structure-based Drug Discovery
AI Solutions for Drug Discovery and Structural Biology Addressing Challenges with MISATO In the field of AI technology, the drug discovery community faces challenges in creating precise models for drug design. MISATO, developed by leading research…
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PersonaGym: A Dynamic AI Framework for Comprehensive Evaluation of LLM Persona Agents
Practical Solutions for Persona Agents Challenges in Persona Agent Development Large Language Model (LLM) agents are diversifying rapidly, from chatbots to robotics, creating a need for personalized experiences. Developing persona agents that embody specific personas is…
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Is Your LLM Agent Enterprise-Ready? Salesforce AI Research Introduces CRMArena: A Novel AI Benchmark Designed to Evaluate AI Agents on Realistic Tasks Grounded on Professional Work Environments
Transforming Customer Relationship Management with AI Understanding CRM and AI Integration Customer Relationship Management (CRM) systems are essential for managing customer interactions and data. By integrating advanced AI, businesses can automate routine tasks, provide personalized experiences,…
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Meet MMToM-QA: A Multimodal Theory of Mind Question Answering Benchmark
Recent advancements in machine learning show potential in understanding Theory of Mind (ToM), crucial for human-like social intelligence in machines. MIT and Harvard introduced a Multimodal Theory of Mind Question Answering (MMToMQA) benchmark, assessing machine ToM…
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Meta AI Researchers Introduce Mixture-of-Transformers (MoT): A Sparse Multi-Modal Transformer Architecture that Significantly Reduces Pretraining Computational Costs
Advancements in AI: Multi-Modal Foundation Models Recent developments in AI have led to models that can handle text, images, and speech all at once. These multi-modal models can change how we create content and translate information…
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This AI Paper by UC Berkeley Explores the Potential of Self-play Training for Language Models in Cooperative Tasks
The Potential of Self-play Training for Language Models in Cooperative Tasks Advancements in AI AI has made significant strides in game-playing, such as AlphaGo’s superhuman performance using self-play techniques. These techniques have pushed AI capabilities beyond…
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Cohere AI Introduces Rerank 3.5: A New Era in Search Technology
Transforming Search and Information Retrieval with AI Searching for information has gone beyond just finding data; it now plays a vital role in improving business efficiency and productivity. Companies depend on effective search systems for customer…
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AMD Instella: Fully Open-Source 3B Parameter Language Model Released
Introduction In today’s fast-changing digital world, the demand for accessible and efficient language models is clear. While traditional large-scale models have significantly improved natural language understanding and generation, they are often too expensive and complex for…
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How Self-RAG Could Revolutionize Industrial LLMs
The article discusses Self-RAG, a method that improves upon the standard Retrieval Augmented Generation (RAG) architecture. Self-RAG uses fine-tuned language models to determine the relevance of a context and generates special tokens accordingly. It outperforms other…
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Uptake vs IBM Maximo APM: Which AI Solution Detects Equipment Issues Faster?
Comparing AI-Powered Asset Performance Management: Uptake vs. IBM Maximo APM Purpose of Comparison: This comparison aims to determine which AI-powered solution, Uptake or IBM Maximo APM, is more effective at detecting equipment issues faster. This is…