Discovery Systems Accelerating Scientific Discovery at NASA Barney

Discovery Systems: Accelerating Scientific Discovery at NASA Barney Pell, Ph. D. NASA Ames Research Center Barney. D. Pell @@ nasa. gov Presentation at IAAI-04 panel on The Broader Role of Artificial Intelligence in Large-Scale Scientific Research

Outline of Talk • Trends and Challenges affecting Scientific Discovery at NASA • Distributed Data Search, Access, and Analysis • Machine-Assisted Model Discovery and Refinement • Exploratory Environments and Collaboration • Vision for the future and summary of AI technologies • Closing remarks

Science Discovery Acceleration • NASA conducts missions to take measurements that produce large amounts of data to support ambitious science goals – In-situ observation of deep space for origin and evolution of life – Earth-orbiting satellites for global cause and effect relationships – Biological experiments to support life in space • Too much work and expertise required to perform each of many steps in a discovery cycle to understand this data – Detailed knowledge of the heritage of data and models – Hard to invert through a complex processing pipeline – Constant reprocessing and reanalyzing as new info available • The specialized expertise slows the process and also restricts the set of users and scientists using NASA products

Discovery Steps and Architectures • Examples of discovery steps - finding and organizing distributed data assessing, filtering, cleaning and post-processing the data reconciling the differences across diverse data exploring the data sets to discover regularities using the regularities to formulate and evaluate hypotheses testing the hypotheses and comparing alternate hypotheses against each other integrating the data into models linking separate models together running simulations to generate predictive data to compare against observations • Current technology programs addressing difficulties of individual steps, typically in isolation – Eg. machine-learning algorithms detect regularities in underlying phenomena but also artifacts of the data collection/processing system. • ML algorithms developed without consideration of the deeper processes by which the data is generated, distributed, and used • Data system put together without characterizing the data stream to enable new users to analyze the data in unanticipated ways.

Trends affecting NASA • Improvements in sensors, communications, and computing – orders of magnitude more data, in more varieties, and at higher rates than ever before. • NASA’s science questions are becoming increasingly large-scale and interdisciplinary. – forming and evaluating theories across a wide variety of data – integrating a complex set of models produced by diverse communities of scientists – virtual projects comprising distributed teams • Socioeconomic demands are requiring increased quality – Eg. many customers for weather and climate model predictions – Need characterization of confidence in data, models, results • Faster feedback loops in observing/simulation systems – make it possible to gather more precise data, often in real-time, if only we could understand the existing data quickly enough. • NASA required to enable public access and benefit from the data to the same extent as the mission science team

Distributed Search, Access and Analysis • Objective – Develop and demonstrate technologies to enable investigating interdisciplinary science questions by finding, integrating, and composing models and data from distributed archives, pipelines; running simulations, and running instruments. – Support interactive and complex query-formulation with constraints and goals in the queries; and resource-efficient intelligent execution of these tasks in a resource-constrained environment. – Milestone: Enable novel what-if and predictive question answering • • • Across NASA’s complex and heterogeneous data and simulations By non data-specialists Use world-knowledge and meta-data Support query formulation and resource discovery Example query: “Within 20%, what will be the water runoff in the creeks of the Comanche National Grassland if we seed the clouds over southern Colorado in July and August next year? ”

Years-To-Centuries Chemistry CO 2, CH 4, N 2 O ozone, aerosols Climate Temperature, Precipitation, Radiation, Humidity, Wind Heat Moisture Momentum Aerodynamics Energy Water Biogeophysics Microclimate Canopy Physiology Evaporation Transpiration Snow Melt Infiltration Runoff Intercepted Water Snow CO 2 CH 4 N 2 O VOCs Dust Biogeochemistry Carbon Assimilation Decomposition Mineralization Phenology Hydrology Soil Water Days-To-Weeks Minutes-To-Hours Terrestrial Biogeoscience Involves Many Complex Processes and Data Bud Break Leaf Senescence Gross Primary Production Plant Respiration Microbial Respiration Nutrient Availability Species Composition Ecosystem Structure Nutrient Availability Watersheds Surface Water Subsurface Water Geomorphology Hydrologic Cycle Ecosystems Species Composition Ecosystem Structure Vegetation Dynamics (Courtesy Tim Killeen and Gordon Bonan, NCAR) Disturbance Fires Hurricanes Ice Storms Windthrows

Solution Construction via Composing Models modeled phenomenon evaporation model runoff model snow melt metadata preparation surface water community snow coverage snow and ice DAAC (NASA) service interface: required inputs, provided outputs, data descriptions, events binary data streams climate model Each model typically has a community of experts that deal with the complexity of the model and its environment parameterized phenomenon rainfall Nat. Weather Service evaporati runoff mo topography USGS snow melt metadata preper modeled surface water phenomenon community modeled phenomenon snow melt metadata surface water community

Virtual Data Grid Example Application: Three data types of interest: is derived from , which is primary data (interaction and operations proceed left to right) Need is known. Contact Materialized Data Catalogue. Metadata Catalogue Need Have Proceed? Need How to generate ( is at LFN) Estimate for generating Abstract Planner (for materializing data) Need to materialize Request LFN for Concrete Planner (generates workflow) Notify that exists PERS requires Materialize with PERS Materialized Data Catalogue LFN = logical file name PFN = physical file name PERS = prescription for generating unmaterialized data As illustrated, easy to deadlock w/o Qo. S and SLAs. Exact steps to Resolve generate LFN Grid workflow PFN is engine materialized at LFN data and LFN Virtual Data Catalogue (how to generate and ) Inform that is materialized Grid storage resources Grid compute resources Data Grid replica services Store an archival copy, if so requested. Record existence of cached copies.

Machine assisted model discovery and refinement • Develop and demonstrate methods to – assist discovery of and fit physically descriptive models with quantifiable uncertainty for estimation and prediction – improve the use of observational or experimental data for simulation and assimilation applied to distributed instrument systems (e. g. sensor web) – integrate instrument models with physical domain modeling and with other instruments (fusion) to quantify error, correct for noise, improve estimates and instrument performance. • Eg. Metrics – 50% reduction in scientist time forming models – 10% reduction in uncertainty in parameter estimates or a 10% reduction in effort to achieve current accuracies – 10% reduction in computational costs associated with a forward model – ability to process data on the order of 1000 s of dimensions – ability to estimate parameters from tera-scale data.

Prediction of the 97/98 El Nino JFM 1998 Predicted Precipitation 1997 1999 A reasonable 15 month prediction of the 97/98 El Nino is achieved when ocean height, temperature and surface wind data are combined to initialize the model.

Observing System of the Future • Partners • • • NASA Do. D Other Govt Commercial International • Advanced Sensors • Information Synthesis • Access to Knowledge • Sensor Web Information User Community

Exploratory Environments and Collaboration • Objective – Develop exploratory environments in which interdisciplinary and/or distributed teams visualize and interact with intelligently combined and presented data from such sources as distributed archives, pipelines, simulations, and instruments in networked environments. – Demonstrate that these environments measurably improve scientists’ capability to answer questions, evaluate models, and formulate follow-on questions and predictions.

Multi-parameter Explorations

Vision for future science Technical Area Today Tomorrow Distributed Data Search Access and Analysis Answering queries requires specialized knowledge of content, location, and configuration of all relevant data and model resources. Solution construction is manual. Search queries based on high-level requirements. Solution construction is mostly automated and accessible to users who aren’t specialists in all elements. Machine integration of data / QA Publish a new resource takes 1 -3 years. Assembling a consistent heterogeneous dataset takes 1 -3 years. Automated data quality assessment by limits and rules. Publish a new resource takes 1 week. Assembling a consistent heterogeneous dataset in real-time. Automated data quality assessment by world models and cross-validation. Machine Assisted Model Discovery and Refinement Physical models have hidden assumptions and legacy restrictions. Machine learning algorithms are separate from simulations, instrument models, and data manipulation codes. Prediction and estimation systems integrate models of the data collection instruments, simulation models, observational data formatting and conditioning capabilities. Predictions and estimates with known certainties. Exploratory environments and collaboration Co-located interdisciplinary teams jointly visualize multi-dimensional preprocessed data or ensembles of running simulations on wall-sized matrixed displays. Distributed teams visualize and interact with intelligently combined and presented data from such sources as distributed archives, pipelines, simulations, and instruments in networked environments.

Discovery Systems: AI Technology Elements – Distributed data search, access and analysis • • • Grid based computing and services Information retrieval Databases Planning, execution, agent architecture, multi-agent systems Knowledge representation and ontologies – Machine-assisted model discovery and refinement • Information and data fusion • Data mining and Machine learning • Modeling and simulation languages – Exploratory environments and Collaboration • • Visualization Human-computer interaction Computer-supported collaborative work Cognitive models of science

Closing remarks • NASA science is challenging • Need to improve in existing capabilities and address emerging trends • AI technologies have a crucial role for future science – Distributed Data Search, Access, and Analysis – Machine-Assisted Model Discovery and Refinement – Exploratory Environments and Collaboration • Many of these themes are shared with science (or research) at large