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?”

Carbon Assimilation

CO2 CH4

N2O VOCsDust

HeatMoistureMomentum

ClimateTemperature, Precipitation,Radiation, Humidity, Wind

ChemistryCO2, CH4, N2Oozone, aerosols

MicroclimateCanopy Physiology

Species CompositionEcosystem StructureNutrient Availability

Water

DisturbanceFiresHurricanesIce StormsWindthrows

EvaporationTranspirationSnow MeltInfiltrationRunoff

Gross Primary ProductionPlant RespirationMicrobial RespirationNutrient Availability

Ecosystems

Species CompositionEcosystem Structure

WatershedsSurface Water

Subsurface WaterGeomorphology

Biogeophysics

En

erg

y

Wa

ter

Ae

ro-

dyn

am

ics

Biogeochemistry

MineralizationDecomposition

Hydrology

So

il W

ate

r

Sn

ow

Inte

r-ce

pte

dW

ate

r

Phenology

Bud Break

Leaf Senescence

HydrologicCycle

VegetationDynamics

Min

ute

s-T

o-H

ou

rsD

ays-

To

-Wee

ks

Yea

rs-T

o-C

en

turi

es

Terrestrial Biogeoscience Involves Many Complex Processes and Data

(Courtesy Tim Killeen and Gordon Bonan, NCAR)

Solution Construction via Composing Models

surface watercommunity

snow coverage

snow and iceDAAC (NASA)

snow meltmetadata

runoff model

evaporationmodel

rainfall

Nat. WeatherService

topography

USGS

data preparation

service interface:required inputs,provided outputs,data descriptions,events

climate model

parameterizedphenomenon

modeledphenomenon

modeledphenomenon

modeledphenomenon

binary data streams

Each model typically has acommunity of experts thatdeal with the complexity of themodel and its environment

Materialized Data Catalogue

MetadataCatalogue

Virtual Data Grid Example

Application: Three data types of interest: is derived from , is derived from , which is primary data(interaction and and operations proceed left to right)

Need

is known. Contact

Materialized Data Catalogue.

Need

Abstract Planner(for materializing data)

Need tomaterialize

Virtual Data Catalogue(how to generate

and )

How to generate ( is at LFN)

Estimate forgenerating

Concrete Planner(generates workflow)

Grid compute resources

Data Grid replica services

Grid storage resources

Grid workflow engine

data and LFN

Have Proceed?

LFN = logical file namePFN = physical file namePERS = prescription for generating unmaterialized data

PERSrequires

Need

Need

As illustrated, easy to deadlock w/o QoS and SLAs.

Need

Materialize with PERS

ismaterialized

at LFN

Exact steps to generate Resolve

LFN

PFN

Store an archival copy, if so requested. Record existence of cached copies.

Inform that is materialized

Request

Notifythat exists

LFN for

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 1000s of dimensions– ability to estimate parameters from tera-scale data.

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.

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.

JFM1998PredictedPrecipitation

19991997

Prediction of the 97/98 El Nino

User Community

Observing System of the

Future

• Information Synthesis

• Access to Knowledge

•Advanced Sensors

•Sensor Web

InformationInformation

•Partners•NASA•DoD•Other

Govt•Commerci

al•Internatio

nal

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 scienceTechnical 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