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AI drug development’s data problem
ScienceThe future of drug discovery may be artificial intelligence (AI), but its present is not. AI is in its infancy in the field. To help AI mature, developers need nonproprietary, open, large, high-quality datasets to train and validate models, managed by independent organizations.
E Richard, Gold, Robert, Cook-Deegan
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AI to Solve the Data Deluge: AI-Based Data Compression
2020The massive amounts of data, growing as we speak, are one of the, if not the, most accountable reasons of today’s AI systems which on many tasks exhibit human grade performance. Thanks to the enormous amounts of image data that machines can be trained to recognize scenes and steer cars. Quantities of medical imagery lead to machine provided diagnostics,
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1981
Abstract : This report documents results of a 12-month R/D effort consisting in development of MATRES III, a language understanding system for automated generation of AIS data base elements. Section 1.0 discusses the need for a high-volume message processing technology in the I/W environment, and summarizes its current state of the art exemplified in ...
Georgette M. Silva, Donald L. Dwiggins
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Abstract : This report documents results of a 12-month R/D effort consisting in development of MATRES III, a language understanding system for automated generation of AIS data base elements. Section 1.0 discusses the need for a high-volume message processing technology in the I/W environment, and summarizes its current state of the art exemplified in ...
Georgette M. Silva, Donald L. Dwiggins
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This is a talk I gave on the 24th August 2024 at Royal Holloway on a two day workshop for AI training for UK government officials.
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The World Data System hosted a workshop on AI Data readiness. The workshop was delivered by WDS-ITO Staff, in coordination with the ESIP Data Readiness Cluster. Participants were given an introduction to the ESIP AI Data Readiness Checklist which encapsulates considerations for data quality, documentation, access, and preparation.
Jenkyns, Reyna +3 more
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Jenkyns, Reyna +3 more
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AI Readiness, AI Reproducibility, and Data Stewardship
This session will discuss lessons learned and emerging community practices as they relate to AI Readiness and AI Reproducibility. Insights into ways to leverage Gen-AI and LLMs for data stewardship will be shared, as well as open research questions, and gaps in practices at the intersection of AI and data.openaire +1 more source