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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

At Iris.ai we have spent the last 6 years building an award-winning AI engine for scientific text understanding. Our algorithms for text similarity, tabular data extraction, domain-specific entity representation learning and entity disambiguation and linking measure up to the best in the world. On top of that, our machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it and also give feedback to the system. The Iris.ai Researcher Workspace is a flexible tool suite that allows 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.

Description

Autotuning in programming has shown significant improvements in performance and portability across various fields. Nevertheless, the portability of autotuners is often limited when transitioning between different projects, primarily due to the necessity of a domain-informed search space representation for optimal outcomes and the fact that no single search method is universally effective for all challenges. OpenTuner has emerged as a novel framework designed to create multi-objective program autotuners that are domain-specific. This framework offers fully customizable configuration representations, an extensible technique representation for incorporating domain-specific methodologies, and a user-friendly interface to interact with the programs being tuned. One of OpenTuner's standout features is its ability to utilize a combination of diverse search techniques simultaneously; those that demonstrate strong performance are allocated larger testing budgets, while those that underperform are phased out. Consequently, this adaptability enhances the overall efficiency and effectiveness of the autotuning process.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

VMware Cloud

Integrations

VMware Cloud

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Iris.ai

Founded

2015

Country

Norway

Website

iris.ai/

Vendor Details

Company Name

OpenTuner

Website

opentuner.org

Product Features

Data Extraction

Disparate Data Collection
Document Extraction
Email Address Extraction
IP Address Extraction
Image Extraction
Phone Number Extraction
Pricing Extraction
Web Data Extraction

Natural Language Processing

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

Product Features

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