Undermind constitutes a scientifically-grounded application that uses state-of-the-art AI technology to perform academic research on behalf of its user.
It is devised to meticulously examine a myriad of academic papers, distilling from them the exact information requested, regardless of the inherent complexity.
Undermind surpasses traditional search engines by reconceptualizing search processes to imitate the careful, systematic human discovery process. This process prompts Undermind to scrutinize results in stages, utilizing language models for critical tasks such as the acknowledgment of essential information and adjusting the search strategy.
Undermind has been employed effectively to find pinpoint solutions for various complex problems in different fields such as Quantum Computing, Artificial Intelligence, Information Science and more.
Regardless of the topic, users can precisely describe what they need to Undermind as they would discuss with a colleague. The service prides itself on intelligent adaption.
It does not merely regurgitate search results as most engines do, but it adapts, based on what it learns from reading hundreds of papers. It is additionally programmed to recognize when it has found all relevant information.
Future plans involve incorporating full text discovery for even more complex discovery goals. Notably, Undermind sources its data from the Semantic Scholar database which includes over 200 million articles from diverse databases.

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Pros & Cons
Scientifically-grounded application
Performs academic research
Examines myriad of academic papers
Reconceptualizes search processes
Utilizes language models
Adjusts search strategy
Effective in various fields
Intelligent adaption
Recognizes relevant information
Future full text discovery
Sources from Semantic Scholar
Trusted by renowned institutions
Accurate results
Comprehensive discovery
Brainstorm with a copilot
Solves real, complex problems
Adaptive search methods
Searches abstracts and metadata
Redesigned search experience
Sourced by two PhDs
Limited to Semantic Scholar database
No full text search
Searches take 2-3 minutes
Might miss specific papers
Search process in stages
Potential information overload
No multi-language support
No collaborative research feature
Unclear update schedule
Complex search can confuse
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