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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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
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Editorial Picks
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Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services
Large Language Models (LLMs) are influential tools in various applications such as conversational agents and content generation. Responsible and robust evaluation of these models is essential to prevent misinformation and bias. Amazon SageMaker Clarify simplifies LLM…
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An Overview of Microsoft Fabric Going Into 2024
Microsoft Fabric is a comprehensive data and analytics platform introduced by Microsoft, aiming to cover the entire data lifecycle from collection to analytics. It integrates various existing services like Azure Synapse Analytics, Azure Data Factory, Azure…
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Microsoft AI Team Introduces Phi-2: A 2.7B Parameter Small Language Model that Demonstrates Outstanding Reasoning and Language Understanding Capabilities
Microsoft Research’s Machine Learning Foundations team researchers introduced Phi-2, a groundbreaking 2.7 billion parameter language model. Contradicting traditional scaling laws, Phi-2 challenges the belief that model size determines language processing capabilities. It emphasizes the pivotal role…
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Demystifying Generative Artificial Intelligence: An In-Depth Dive into Diffusion Models and Visual Computing Evolution
Computer graphics and 3D computer vision groups have been working on creating realistic models for various industries, including visual effects, gaming, and virtual reality. Generative AI systems have revolutionized visual computing by enabling the creation and…
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Google AI Introduces SOAR: An Algorithmic Improvement to Vector Search that Introduces Effective and Low-Overhead Redundancy to ScaNN
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GPT-4o Mini: OpenAI’s Latest and Most Cost-Efficient Mini AI Model
GPT-4o Mini: OpenAI’s Latest and Most Cost-Efficient Mini AI Model OpenAI has launched GPT-4o Mini, an affordable and powerful AI model that expands the scope of AI applications. GPT-4o Mini is significantly more cost-efficient than previous…
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This Paper from Cornell Introduces Multivariate Learned Adaptive Noise (MuLAN): Advancing Machine Learning in Image Synthesis with Enhanced Diffusion Models
Cornell University researchers introduced “Multivariate Learned Adaptive Noise” (MuLAN), a machine learning method that revolutionizes diffusion models. By employing a learned, data-driven approach to diffusion, MuLAN enhances classical models with a more tailored application of noise,…
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CMU Researchers Unveil RoboTool: An AI System that Accepts Natural Language Instructions and Outputs Executable Code for Controlling Robots in both Simulated and Real-World Environments
Carnegie Mellon University and Google DeepMind collaborated to develop RoboTool, a system using Large Language Models to enable robots to creatively use tools in tasks with physical constraints and planning. It comprises four components and leverages…
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Automate prior authorization using CRD with CDS Hooks and AWS HealthLake
Prior authorization is a crucial process in healthcare that involves the approval of medical treatments before they are carried out. The Da Vinci Burden Reduction project has rearranged the prior authorization process into three implementation guides…
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AI Content Model for Book Authors and Experts
AI-Powered Author Services: A Lean Business Plan Executive Summary: This plan outlines a rapid-launch business leveraging AI to provide value-added services to book authors and experts, utilizing the AI Business Accelerator platform (itinai.com). We’ll focus on…
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Marktechpost’s 2025 Report on Agentic AI and AI Agents: A Comprehensive Technical Overview
Marktechpost Releases 2025 Agentic AI and AI Agents Report: A Technical Overview Marktechpost AI Media has launched the 2025 Agentic AI and AI Agents Report, providing an in-depth look into the frameworks, architectures, and strategies driving…
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Enhancing AI Model Evaluation: The Critical Role of Contextualized Queries
Understanding the context in which users interact with AI models is crucial for improving their performance and evaluation. Many users pose questions that lack sufficient detail, making it difficult for AI to provide accurate and relevant…
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The Pursuit of the Platonic Representation: AI’s Quest for a Unified Model of Reality
The Pursuit of the Platonic Representation: AI’s Quest for a Unified Model of Reality As AI systems advance, a trend has emerged: their representations of data across different architectures, training objectives, and modalities seem to be…
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Adept AI Introduces Fuyu-Heavy: A New Multimodal Model Designed Specifically for Digital Agents
Adept AI researchers have introduced Fuyu-Heavy, a new multimodal model designed for digital agents. It is the world’s third-most-capable multimodal model, demonstrating commendable performance. The development faced challenges due to its scale but showed effectiveness in…
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How to Create a Simple GIS Map with Plotly and Streamlit
Plotly map functions and Streamlit UI components enable the creation of GIS-style dashboards. This integration allows for interactive and user-friendly visualization of geographical data. For further details, refer to the full article on Towards Data Science.
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Shattering AI Illusions: Google DeepMind’s Research Exposes Critical Reasoning Shortfalls in LLMs!
Google DeepMind and Stanford University’s research reveals a startling vulnerability in Large Language Models (LLMs). Despite their exceptional performance in reasoning tasks, a deviation from optimal premise sequencing can lead to a significant drop in accuracy,…