Undermind

Undermind is a scientifically-grounded application that employs advanced AI technology to conduct academic research on behalf of its users. It meticulously examines a myriad of academic papers, extracting exact information as dictated by its users, imitating the careful, systematic human discovery process. Users can articulate their needs to Undermind as though conversing with a colleague.

Unlike traditional search engines, Undermind has revolutionized search processes to imitate a human's systematic approach, and not only regurgitates search results. It adapts its search strategy based on what it learns from reading hundreds of papers. Furthermore, it doesn't stop searching until it has found all relevant information.

Undermind utilizes language models in its search strategy. By reading hundreds of papers on a subject, it learns and adapts its search engine progressively. This adaptive approach helps it to identify and focus on papers, or sections of papers, that are most likely to contain the information it is looking for.

Undermind can help with academic research by meticulously examining a multitude of academic papers and extracting the exact information a user needs. It can effectively find specific solutions for complex problems in a variety of different fields, with users precisely outlining what they require.

Undermind is versatile and can be used in various fields. Some examples include Quantum Computing, Artificial Intelligence, Information Science and more. The user can articulate their specific need to Undermind, which then proceeds to scan academic papers related to the topic and deliver the information required.

Undermind uses advanced AI algorithms to decide when it has found all relevant information. It employs intelligent adaptation, which allows it to learn from each document it reads and to adjust its strategies accordingly. Once it no longer finds new or relevant information in the documents it scans, it understands that it has found all the relevant information.

At present, Undermind primarily searches the abstracts and metadata of scientific articles in the Semantic Scholar database. Although, plans to incorporate full text discovery are in the works to cater to more complex discovery objectives.

Undermind sources its data from the Semantic Scholar database, which consists of over 200 million articles from a variety of databases.

Undermind makes use of language models to recognize essential information. These models enable it to decipher relevant details from an array of academic papers and adjust its search strategy accordingly. This allows Undermind to adapt and provide accurate, focused search results.

Undermind is designed to work with complexity. Users can describe exactly what they want in detail, and Undermind adapts its search to meet these specific requirements. It examines and learns from hundreds of papers, identifying relevant pieces of information, no matter how intricate the topic is.