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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Kaspersky Fraud Prevention vs FICO Falcon: Who’s Better at Stopping Digital Channel Fraud?
Comparing AI Fraud Prevention: Kaspersky Fraud Prevention vs. FICO Falcon Purpose of Comparison: Digital channel fraud is exploding, costing businesses billions. Choosing the right fraud prevention solution is critical. This comparison aims to provide a clear,…
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CopilotKit’s CoAgents: The Missing Link that Makes It Easy to Connect LangGraph Agents to Humans in the Loop
CopilotKit: Streamlining AI Integration for Modern Applications Practical Solutions and Value: Discover CopilotKit, a leading open-source framework simplifying AI integration into applications. It offers tools like CopilotChat and CopilotTextarea for building AI features seamlessly. With components…
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Meet OpenCodeInterpreter: A Family of Open-Source Code Systems Designed for Generating, Executing, and Iteratively Refining Code
The development of OpenCodeInterpreter represents a significant advancement in automated code generation systems. It seamlessly bridges the gap between code generation and execution by incorporating execution feedback and human insights into the iterative refinement process. This…
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Utilizing active microparticles for artificial intelligence
Physicists have developed a new type of neural network using active colloidal particles instead of electricity. This physical system shows promise for artificial intelligence and time series prediction, offering an alternative to traditional microelectronic chip-based digital…
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Revolutionizing Data Reconstruction: AI’s Compact Solution for Broad Information Retrieval
Researchers at Los Alamos National Laboratory have developed a new artificial intelligence (AI) approach called Senseiver that allows for efficient data processing. Senseiver uses a neural network to represent extensive data with minimal computational resources, reducing…
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Refining Classifier-Free Guidance (CFG): Adaptive Projected Guidance for High-Quality Image Generation Without Oversaturation
Understanding Classifier-Free Guiding (CFG) Classifier-Free Guiding (CFG) plays a crucial role in improving image generation quality in diffusion models. It helps ensure that the images produced closely match the input conditions. However, using a high guidance…
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Pinokio 2.0: A New Pinokio Browser Version that Lets You Locally Install, Run, and Automate Any AI on Your Computer
Pinokio 2.0: Redefining Offline Web and AI Apps Offline web and AI apps often pose challenges, requiring users to navigate multiple steps for app setup and customization. These processes can be confusing and time-consuming, especially for…
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Meet Mem0: The Memory Layer for Personalized AI that Provides an Intelligent, Adaptive Memory Layer for Large Language Models (LLMs)
Mem0: The Memory Layer for Personalized AI Intelligent, Adaptive Memory Layer for Large Language Models (LLMs) In today’s digital age, personalized experiences are crucial across various domains such as customer support, healthcare diagnostics, and content recommendations.…
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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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USC Researchers Present Safer-Instruct: A Novel Pipeline for Automatically Constructing Large-Scale Preference Data
Practical Solutions for AI Language Model Alignment Enhancing Safety and Competence of AI Systems Language model alignment is crucial for strengthening the safety and competence of AI systems. Deployed in various applications, language models’ outputs can…
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Positioning Your Analytics Team on the Right Projects
The article discusses the importance of project prioritization in the analytics world. It emphasizes considering impact, risks, and time constraints to make better decisions. The analogy of being a venture capitalist in choosing where to invest…
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Meta AI Introduces AnyMAL: The Future of Multimodal Language Models Bridging Text, Images, Videos, Audio, and Motion Sensor Data
Researchers have developed AnyMAL, a groundbreaking multimodal language model that enables machines to understand and generate human language in conjunction with various sensory inputs. AnyMAL integrates visual, auditory, and motion cues, allowing for a shared understanding…
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Blue Prism vs WorkFusion: Scale Product Automation with Minimal Cost
Technical Relevance In today’s fast-paced business environment, organizations are increasingly turning to automation to enhance operational efficiency and service delivery. Blue Prism stands out as a leading robotic process automation (RPA) tool that enables businesses in…
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ConceptAgent: A Natural Language-Driven Robotic Platform Designed for Task Execution in Unstructured Settings
Challenges in Robotic Task Execution Robots face big challenges in real-world environments because these places are unpredictable and varied. Traditional systems often struggle with unexpected objects and unclear tasks. They are usually designed for controlled settings,…
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Microsoft’s GeckOpt Optimizes Large Language Models: Enhancing Computational Efficiency with Intent-Based Tool Selection in Machine Learning Systems
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Zyphra Releases Zamba2-7B: A State-of-the-Art Small Language Model
Zyphra Launches Zamba2-7B: A Powerful Language Model What is Zamba2-7B? Zamba2-7B is a cutting-edge language model that excels in performance while being compact. It surpasses competitors like Mistral-7B and Google’s Gemma-7B in both speed and quality.…