SciBite is pleased to have been recognized as a KM World trend-setting product of 2020. Recognized for helping the life sciences to unlock previously inaccessible data and knowledge from life science texts – making the process of extracting and analyzing data and information easier so organizations can continue to push understanding forward.
Recently acquired by global research publishing and information analytics provider Elsevier, SciBite’s data-first, semantic analytics software platform is designed for those who want to innovate and get more from their data. Built by scientists for scientists who believe data fuels discovery, SciBite continues to push boundaries with its cutting-edge technology applications and people-first solutions that unlock the complexities of scientific content. Leading the way by pioneering the combination of the latest in machine learning with an ontology-led approach, SciBite’s semantic infrastructure answers business-critical questions in real-time by releasing the value and full potential of unstructured data.
The KM World Trend-Setting Products list is created annually to help spread the word about new and noteworthy solutions that are helping to improve organizational systems. These offerings push the limits of what is possible with knowledge management. Some are mature and have evolved over many years by adding new features and capabilities, while others are newer entrants in the KM market. But whether time-tested or cutting-edge, the common theme is the potential value they offer organizations by transforming information into insight.
“We’re very pleased to have been recognized as a trend-setting product for 2020, our mission is to help our customers better understand the complexities of life sciences data. This recognition is yet another testament to the fantastic team we have built and continue to build on here at SciBite,” said Lee Harland, Founder & CSO at SciBite.
Large language models (LLMs) have limitations when applied to search due to their inability to distinguish between fact and fiction, potential privacy concerns, and provenance issues. LLMs can, however, support search when used in conjunction with FAIR data and could even support the democratisation of data, if used correctly…Read
Patient X, suffering from an untreatable gastrointestinal disease, chats with a large language model for advice. GPT suggests looking at clinical trials and Patient X finds 10 active recruiting trials but is unsure which to choose. Patient X consults his doctor, who recommends a trial from a pharmaceutical company. What could go wrong?Read
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