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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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deepsense.ai among top 50 AI providers in CEE
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Generalizable Reward Model (GRM): An Efficient AI Approach to Improve the Generalizability and Robustness of Reward Learning for LLMs
Practical Solutions and Value of Generalizable Reward Model (GRM) Improving Large Language Models (LLMs) Performance Pretrained large models can align with human values and avoid harmful behaviors using alignment methods such as supervised fine-tuning (SFT) and…
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Recognition and Generation of Object-State Compositions in Machine Learning Using “Chop and Learn”
Researchers propose a new dataset called Chop & Learn (ChopNLearn) to study compositional generalization in object recognition. They introduce two tasks, Compositional Image Generation and Compositional Action Recognition, to evaluate existing generative models and video recognition…
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This AI Paper from Harvard Introduces Q-Probing: A New Frontier in Machine Learning for Adapting Pre-Trained Language Models
Q-Probe, a new method from Harvard, efficiently adapts pre-trained language models for specific tasks. It balances between extensive finetuning and simple prompting, reducing computational overhead while maintaining model adaptability. Showing promise in various domains, it outperforms…
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LayerSkip: An End-to-End AI Solution to Speed-Up Inference of Large Language Models (LLMs)
Practical AI Solutions for Large Language Models Energy and Cost Optimization with AI Many applications utilize large language models (LLMs), but deploying them on GPU servers can result in significant energy and financial expenditures. Some acceleration…
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Stylus: An AI Tool that Automatically Finds and Adds the Best Adapters (LoRAs, Textual Inversions, Hypernetworks) to Stable Diffusion based on Your Prompt
Practical Solutions in AI for Image Generation Adopting Finetuned Adapters Using finetuned adapters in generative image models allows for customized image creation while minimizing storage requirements. This has led to expansive open-source platforms with over 100,000…
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Purdue University Researchers Introduce ETA: A Two-Phase AI Framework for Enhancing Safety in Vision-Language Models During Inference
Understanding Vision-Language Models (VLMs) Vision-language models (VLMs) are advanced AI systems that combine computer vision and natural language processing. They can analyze both images and text simultaneously, leading to practical applications in areas like medical imaging,…
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Chain-of-Associated-Thoughts (CoAT): An AI Framework to Enhance LLM Reasoning
Enhancing AI Reasoning with Chain-of-Associated-Thoughts (CoAT) Transforming AI Capabilities Large language models (LLMs) have changed the landscape of artificial intelligence by excelling in text generation and problem-solving. However, they typically respond to queries quickly without adjusting…
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Google’s Magenta RealTime: Revolutionizing AI Music Generation for Musicians and Educators
Google’s Magenta team has unveiled Magenta RealTime (Magenta RT), an innovative model designed for real-time music generation. This tool opens new avenues for musicians, composers, researchers, and educators, allowing for a more interactive and responsive music…
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Enhancing Vision-Language Models: Addressing Multi-Object Hallucination and Cultural Inclusivity for Improved Visual Assistance in Diverse Contexts
The Value of Vision-Language Models Vision-Language Models in Practical Applications The research on vision-language models (VLMs) is gaining momentum due to their potential to revolutionize various applications, such as visual assistance for visually impaired individuals. Challenges…
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MIT researchers identify new class of antibiotics using AI
MIT researchers utilized deep learning models to uncover a groundbreaking class of antibiotics, potentially combatting drug-resistant bacteria. Spearheaded by Dr. Jim Collins, the Antibiotics-AI Project targets the development of seven new antibiotic classes. By employing machine…
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MIT Researchers Developed SmartEM: An AI Technology that Takes Electron Microscopy to the Next Level by Seamlessly Integrating Real-Time Machine Learning into the Imaging Process
SmartEM, developed by researchers from MIT and Harvard, combines powerful electron microscopes with AI to quickly capture and understand details of the brain. It acts like an assistant, focusing on essential areas and helping scientists examine…
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DeepSeek AI Introduces CODEI/O: A Novel Approach that Transforms Code-based Reasoning Patterns into Natural Language Formats to Enhance LLMs’ Reasoning Capabilities
Transforming Reasoning with CODEI/O Understanding the Challenge Large Language Models (LLMs) have improved in processing language, but they still struggle with reasoning tasks. While they can excel in structured areas like math and coding, they face…
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Top Artificial Intelligence (AI) Tools That Can Generate Code To Help Programmers (2024)
AI technologies are revolutionizing programming, as AI-generated code becomes more accurate. This article discusses AI tools like OpenAI Codex, Tabnine, CodeT5, Polycoder, and others that are transforming how programmers create code. These tools support various languages…
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Researchers from the University of Washington and Google Unveil a Breakthrough in Image Scaling: A Groundbreaking Text-to-Image Model for Extreme Semantic Zooms and Consistent Multi-Scale Content Creation
New text-to-image models have advanced, enabling revolutionary applications like creating images from text. However, existing approaches struggle to consistently produce content across zoom levels. A study by the University of Washington, Google, and UC Berkeley introduces…
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OpenVoice V2: Evolving Multilingual Voice Cloning with Enhanced Style Control and Cross-Lingual Capabilities