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Iris AI VS ML5.js

Compare Iris AI VS ML5.js and see what are their differences

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Iris AI logo Iris AI

Your Research Workspace - a comprehensive AI platform for all your research processing.

ML5.js logo ML5.js

Friendly machine learning for the web
  • Iris AI Landing page
    Landing page //
    2023-11-20

The Iris.ai Researcher Workspace is a flexible tool suite that allows all researchers - without a necessary AI background knowledge - to approach a project in a variety of ways. Modules include content based explorative search, machine analysis of document sets, extracting and systematizing data points, automatically writing summaries of multiple documents - and very powerful filters based on context descriptions, the machine’s analysis, or specific data points or entities. The Iris.ai engine for scientific text understanding is a powerful interdisciplinary system that can be automatically reinforced on a specific research field for much more nuanced machine understanding - without human training or annotation.

The Iris.ai Researcher Workspace can service numerous research use cases, from knowledge processing in R&D, systematic literature reviews and IP analysis to automated post-market surveillance or pharmacovigilance. Let AI take over all those tedious tasks so our best and brightest can focus on the tasks that really matter and improve our lives.

  • ML5.js Landing page
    Landing page //
    2021-10-12

Iris AI

Website
iris.ai
$ Details
-
Release Date
2015 November
Startup details
Country
Norway
State
Oslo
City
Oslo
Founder(s)
Anita Schjoll Brede
Employees
10 - 19
Edit details

ML5.js

Website
ml5js.org
$ Details
Release Date
-
Edit details

Iris AI features and specs

  • Enhanced Research Efficiency
    Iris AI uses advanced artificial intelligence algorithms to streamline the research process by fetching and summarizing relevant scientific papers, thus saving significant time and effort for researchers.
  • Semantic Search Capabilities
    The platform employs semantic search to understand the context and content of scientific papers, allowing researchers to find more relevant papers based on concepts rather than just keywords.
  • Cross-disciplinary Research Facilitation
    Iris AI is designed to assist in cross-disciplinary research by understanding diverse fields and linking relevant literature across various disciplines, thereby providing a more comprehensive view of a research area.
  • User-friendly Interface
    The platform provides an intuitive and easy-to-navigate interface that makes it accessible, even for users who are not tech-savvy or experienced in using advanced search tools.

Possible disadvantages of Iris AI

  • Dependence on Data Availability
    The effectiveness of Iris AI is significantly dependent on the availability and quality of data it can access; if certain papers or databases are not included, the tool might miss important research.
  • Learning Curve
    While the interface is user-friendly, there is still a learning curve associated with using AI-driven research tools, which might require some initial training or familiarization for optimal use.
  • Potentially Limited Access
    Access to certain features of Iris AI might be limited by institutional subscriptions or pricing models, which could prevent some researchers, particularly those from underfunded institutions, from utilizing its full capabilities.
  • Accuracy of AI Interpretations
    While Iris AI can provide streamlined search capabilities, its interpretations and summaries may not always align perfectly with human interpretations, leading to potential misunderstandings or missed nuances in literature.

ML5.js features and specs

  • Ease of Use
    ml5.js is designed with simplicity in mind, making machine learning accessible to artists, creative coders, and students, even those without a robust background in AI or machine learning.
  • Browser-based
    Operates directly in the browser, eliminating the need for any additional setup or dependencies, which makes it highly compatible with web projects.
  • Pre-trained Models
    Includes a variety of pre-trained models for quick implementation of complex machine learning tasks like image classification, pose detection, and text generation.
  • Community and Documentation
    Strong community support and well-documented guides and examples help new users get started quickly and find solutions to common issues.
  • Integration with p5.js
    Integrates seamlessly with p5.js, a popular JavaScript library for creative coding, facilitating the development of interactive and visually engaging applications.

Possible disadvantages of ML5.js

  • Performance Limitations
    Since it runs in the browser, it may not be suitable for performance-intensive applications or those requiring real-time processing of large datasets.
  • Limited Customization
    While it offers pre-trained models, there is limited functionality for training new models from scratch compared to more comprehensive libraries like TensorFlow.js.
  • Dependency on Web Standards
    Depends on the performance and capabilities of the client's browser, which can vary significantly between different users and devices.
  • Size of Models
    Some pre-trained models can be quite large, which may affect loading times and performance on slower network connections or less powerful devices.
  • Scope
    Focused on high-level tasks and applications, which might not be sufficient for advanced machine learning requirements or niche functionalities outside its provided models.

Analysis of ML5.js

Overall verdict

  • ML5.js is considered a good tool for web developers who want to incorporate machine learning into their projects without needing extensive knowledge of the underlying algorithms. It's particularly noted for its simplicity and ease of use.

Why this product is good

  • ML5.js is a high-level JavaScript library built on top of TensorFlow.js. It is designed to make machine learning accessible to web developers with minimal expertise in machine learning or deep learning. The library provides easy-to-use functions for tasks such as image and video classification, pose detection, text generation, and more. Its simplicity and ease of integration make it ideal for rapid prototyping and educational purposes.

Recommended for

    ML5.js is recommended for educators, beginners, artists, and developers who want to quickly implement machine learning models in web applications. It is also suitable for creative coding projects and interactive applications where simplicity and ease of use are important.

Iris AI videos

+ Add

Iris.ai Researcher Workspace

ML5.js videos

+ Add

ml5.js: Train Your Own Neural Network

More videos:

  • Review - ml5.js: Image Classification with MobileNet
  • Review - Image classification to gif with ML5.js | Vue.js Virtual Meetup

Category Popularity

0-100% (relative to Iris AI and ML5.js)

Iris AI

ML5.js

Productivity
100 100%
Productivity
0% 0
AI
32 32%
AI
68% 68
Developer Tools
0 0%
Developer Tools
100% 100
Tech
75 75%
Tech
25% 25

User comments

Share your experience with using Iris AI and ML5.js. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, ML5.js seems to be more popular. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Iris AI mentions (0)

We have not tracked any mentions of Iris AI yet. Tracking of Iris AI recommendations started around Mar 2021.

ML5.js mentions (10)

  • How AI is Transforming Front-End Development in 2025!
    Ml5.js: Built on top of TensorFlow.js, it provides a user-friendly interface for implementing machine learning in web applications.​. - Source: dev.to / about 2 months ago
  • Riffr - Create Photo Montages in the Browser with some ML Magic✨
    Important APIs - ml5 for in-browser detection, face-api that uses tensorflow-node to accelerate on-server detection. VueUse for a bunch of useful component tools like the QR Code generator. Yahoo's Gifshot for creating gif files in-browser etc. - Source: dev.to / over 2 years ago
  • Brain.js: GPU Accelerated Neural Networks in JavaScript
    See also: https://ml5js.org/ "The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.". - Source: Hacker News / almost 3 years ago
  • [Showoff Saturday] I made a captcha prototype that requires a banana
    I used ml5js.org , p5js.org and https://teachablemachine.withgoogle.com to train the Banana images. When you create a new image project on Teachable Machine, you can output the p5js and basically use it right out of the box - I customized js, css, and html from there. Source: over 3 years ago
  • My First 30 Days of 100 Days of Code.
    Going forward: I'll be 100% into JavaScript. You can use JavaScript in so many fields nowadays. Websites React, Mobile Apps React Native, Machine Learning TensorFlow & ML5, Desktop Applications Electron, and of course the backend Node as well. It's kind of a no-brainer. Of course, they all have specific languages that are better, but for now, JavaScript is a bit of a catch-all. - Source: dev.to / over 3 years ago
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What are some alternatives?

When comparing Iris AI and ML5.js, you can also consider the following products

ScienceBox - Simple data science collaboration & productivity on the web

Amazon Machine Learning - Machine learning made easy for developers of any skill level

Enago Read - All In One AI-Powered Reading Assistant. A Reading Space to Ideate, Create Knowledge and Collaborate on Research

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

FirstIgnite - Matching scientific research to business needs

Apple Machine Learning Journal - A blog written by Apple engineers

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