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Description

Gemini for Science enhances the process of scientific discovery by offering AI-driven tools and resources specifically designed to bolster scientific initiatives. By integrating experimental tools found in Google Labs with the science workflows offered through Google Antigravity, it aims to expedite research, improve analytical reasoning, and enable researchers to delve into the future of AI-enhanced scientific exploration. The Literature Insights feature compiles scholarly literature to uncover new research possibilities, produce well-founded research artifacts, and convert paper information into structured tables linked directly to original evidence. Meanwhile, Hypothesis Generation employs a multi-agent approach that emulates the scientific method, allowing it to pinpoint knowledge gaps, suggest viable research avenues, and outline testable research plans that could lead to significant breakthroughs. Additionally, Computational Discovery assists researchers in identifying models and algorithms through an intelligent research engine that creates and evaluates code variations according to user-specified optimization criteria, thereby streamlining the research process even further. Ultimately, these innovative tools collectively aim to revolutionize how scientific research is conducted and understood.

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.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

CC Yes 
Conspecta Yes 
Gemini Yes 
Google Antigravity Yes 

Integrations

CC No 
Conspecta No 
Gemini No 
Google Antigravity No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

ai.google/gemini-for-science/

Vendor Details

Company Name

Iris.ai

Founded

2015

Country

Norway

Website

iris.ai/

Product Features

Product Features

Data Extraction

Disparate Data Collection No 
Document Extraction No 
Email Address Extraction No 
IP Address Extraction No 
Image Extraction No 
Phone Number Extraction No 
Pricing Extraction No 
Web Data Extraction No 

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

Qualitative Data Analysis

Annotations No 
Collaboration No 
Data Visualization No 
Media Analytics No 
Mixed Methods Research No 
Multi-Language No 
Qualitative Comparative Analysis No 
Quantitative Content Analysis No 
Sentiment Analysis No 
Statistical Analysis No 
Text Analytics No 
User Research Analysis No 

Alternatives

Alternatives

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