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If you’ve spotted a factual error, an outdated reference, or wish to propose an edit: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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Aaren: Rethinking Attention as Recurrent Neural Network RNN for Efficient Sequence Modeling on Low-Resource Devices
Practical AI Solutions for Sequence Modeling Introducing Aaren: Rethinking Attention as Recurrent Neural Network for Efficient Sequence Modeling on Low-Resource Devices Sequence modeling is crucial in machine learning, especially for tasks like robotics, financial forecasting, and…
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This AI Paper Unveils TrialGPT: Revolutionizing Patient-to-Trial Matching with Precision and Speed
Revolutionizing Patient-to-Trial Matching with TrialGPT Challenges in Clinical Trial Matching Matching patients with appropriate clinical trials is crucial yet difficult. It requires detailed analysis of patients’ medical histories against complex trial eligibility criteria. This process is…
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AI subjected to tests on Theory of Mind and systematic generalization
Researchers have developed FANToM, a benchmark to evaluate large language models’ (LLMs) understanding of Theory of Mind (ToM). ToM is the ability to attribute beliefs and perspectives to oneself and others. FANToM tests LLMs’ knowledge of…
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Researchers from Cambridge have Developed a Virtual Reality Application Using Machine Learning to Give Users the ‘Superhuman’ Ability to Open and Control Tools in Virtual Reality
Researchers from the University of Cambridge have developed a VR program called “HotGestures” that allows users to access and use 3D modeling tools through hand gestures. Using machine learning, the system recognizes gestures and enables quick…
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Can Chat GPT Play chess?
A Multi-Strategy AI with Deep Reinforcement Learning has achieved victory over GPT3.5 in a Chess Match. For more details, please visit Towards Data Science.
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Robot trained to read braille at twice the speed of humans
Researchers have created a robotic sensor with AI that can read braille at double the speed of human readers.
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Google AI Proposes TransformerFAM: A Novel Transformer Architecture that Leverages a Feedback Loop to Enable the Neural Network to Attend to Its Latent Representations
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FLUTE: A CUDA Kernel Designed for Fused Quantized Matrix Multiplications to Accelerate LLM Inference
Practical Solutions for Deploying Large Language Models (LLMs) Addressing Latency with Weight-Only Quantization Large Language Models (LLMs) face latency issues due to memory bandwidth constraints. Researchers use weight-only quantization to compress LLM parameters to lower precision,…
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Converting Texts to Numeric Form with TfidfVectorizer: A Step-by-Step Guide
This text provides instructions on how to calculate Tfidf values manually and using the sklearn library for Python. It can be found on the Towards Data Science website.
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This AI Paper from China Introduces StreamVoice: A Novel Language Model-Based Zero-Shot Voice Conversion System Designed for Streaming Scenarios
StreamVoice, a new streaming language model, offers real-time zero-shot voice conversion (VC) without the need for complete source speech. Developed by researchers from Northwestern Polytechnical University and ByteDance, the model employs a fully causal context-aware LM…
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Conservative Algorithms for Zero-Shot Reinforcement Learning on Limited Data
Practical Solutions and Value of Conservative Algorithms for Zero-Shot Reinforcement Learning on Limited Data Overview: Reinforcement learning (RL) trains agents to make decisions through trial and error. Limited data can hinder learning efficiency, leading to poor…
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Researchers from UCSD and Adobe Introduce Presto!: An AI Approach to Inference Acceleration for Score-based Diffusion Transformers via Reducing both Sampling Steps and Cost Per Step
Text-to-Audio and Text-to-Music Innovations Recent advancements in Text-to-Audio (TTA) and Text-to-Music (TTM) technologies have been driven by new audio models. These models outperform older methods like GANs and VAEs in creating high-quality audio. However, they struggle…
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TWLV-I: A New Video Foundation Model that Constructs Robust Visual Representations for both Motion and Appearance-based Videos
Practical Solutions for Video Analysis Challenges in Video Analysis Language Foundation Models (LFMs) and Large Language Models (LLMs) have inspired the development of Image Foundation Models (IFMs) in computer vision. However, applying these techniques to video…
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Unveiling the Shortcuts: How Retrieval Augmented Generation (RAG) Influences Language Model Behavior and Memory Utilization
Unveiling the Shortcuts: How Retrieval Augmented Generation (RAG) Influences Language Model Behavior and Memory Utilization Practical Solutions and Value Researchers from Microsoft, the University of Massachusetts, Amherst, and the University of Maryland, College Park, conducted a…
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WEBRL: A Self-Evolving Online Curriculum Reinforcement Learning Framework for Training High-Performance Web Agents with Open LLMs
Understanding WEBRL: A New Approach to Training Web Agents What are Large Language Models (LLMs)? LLMs are advanced AI systems that can understand and generate human language. They have the potential to operate as independent agents…
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The Benefits of Live Chat Support for Enhanced Customer Service
Live chat support allows businesses to engage with customers in real-time, offering immediate assistance and personalized interactions. It enhances customer service by meeting the digital age’s expectations of instant assistance, increasing engagement, and providing cost-effective solutions.…