NEO BRD Concept Note

Solar System

For this challenge, we invite you to become "virtual contributors" to the Asteroid Grand Challenge and develop a hypothetical method, concept note or simple prototype that demonstrates how Machine Learning could be used to help us avoid the same fate as the dinosaurs.

Explanation

Model using NASA NEO data to produce a 3D model modified as the Atari 2600 Game “Asteroids” which Google Deepmind uses to target/ confirm NEOs

Business Requirements Document (BRD) NASA NEO BRD Concept Note

NEO-AI

April 2016

Version 1.1

NASA

Acknowledgements:

1.Ben Noble (PA Consulting- SpaceApps London DataCamp 2016)

2.Shaun Moss (Author of The International Mars Research Station)

3.Dr Demis Hassabis (Google Deepmind AI)

4.Ed Rex (Jukedeck AI)


5.

1Document Revisions

Date

Version Number

Document Changes

23/04/2016

0.1

Initial Draft

2Approvals

Role

Name

Title

Signature

Date

Project Sponsor

NASA

Business Owner

NASA

Project Manager

Brett Gallie

System Architect

Brett Gallie

Development Lead

Brett Gallie

User Experience Lead

Brett Gallie

Quality Lead

Brett Gallie

Content Lead

Brett Gallie


3Introduction

3.1 Project Summary

3.1.1Objectives

To develop a hypothetical method, concept note or simple prototype that demonstrates how Machine Learning could be used to help us avoid the same fate as the dinosaurs.

3.1.2Background

This Challenge is part of the 2016 NASA SpaceApps Challenge.

3.1.2.1Business Drivers

Near Earth Objects can have potentially catastrophic(E.L.E) impacts or huge economic (ie.insurance) impact

Near Earth Objects can also be harvested for resources in the future by asteroid mining Companies this could be harnessed as a funding resource for cash strapped research projects and related NEO finding affiliates.


3.2Project Scope

3.2.1In Scope Functionality

  • Explore the existing framework for NEOs
  • Create algorithms and solutions using AI/ Machine learning resources
  • Raise issues flagged during the research phase

3.3System Perspective

3.3.1Assumptions

Existing list of NEO’s have been verified

Jupiter / our moon will take care of 90% of the NEOs

3.3.2Constraints

Our data may only go as far back as the 1900’s. The Ancient Chinese kept records dating back for 3,000 years and should be accessed for historical impacts or observations that may be useful.

http://idp.bl.uk/4DCGI/education/astronomy/history...

3.3.3Risks

Unidentified NEOs reaching earth – ie. We missed the observation

An existing NEO’s projected trajectory is altered by Solar Flare or other space phenomina bringing it into collision with earth

3.3.4Issues

The Key Issue is to ensure our 3D model of NEOs is as accurate as possible for the Deepmind AI to target the NEOs.


4Business Process Overview

4.1Current Business Process (As-Is)

Data from verified NEO objects are handled by

Repository of all located NEOs and NEAs: http://www.minorplanetcenter.net

4.2Proposed Business Process (To-Be)

1.Data from the Minor Planet Center to be fed into a 3D simulation model


2.The simulation model will resemble a modified Atari 2600 game called “Asteroids” and the 3D model would resemble existing apps. Eg: GoSkyWatch App

3.Additional data will be fed in from an App similar to Seti- at- Home which amateur astronomers NEO observations are electronically sent through by the app for confirmation

4.A modified version of Google Deepmind’s AI machine learning will be used to target NEO’s.

A list of Google Deepmind’s approaches can be found at the following site:

https://deepmind.com/publications.html


5Business Requirements

The requirements in this document are prioritized as follows:

Value

Rating

Description

1

Critical

This requirement is critical to the success of the project. The project will not be possible without this requirement.

2

High

This requirement is high priority, but the project can be implemented at a bare minimum without this requirement.

3

Medium

This requirement is somewhat important, as it provides some value but the project can proceed without it.

4

Low

This is a low priority requirement, or a “nice to have” feature, if time and cost allow it.

5

Future

This requirement is out of scope for this project, and has been included here for a possible future release.

5.1Functional Requirements

Req#

Priority

Description

Rationale

Use Case Reference

Impacted Stakeholders

General / Base Functionality

FR-G-001

1

3D Simulation model created of the NEO data using Big Data crunching applications (eg.Hadoop)

3D Simulation model will provide background for Atari 2600 “Asteroid” game for Google’s Deepmind to process

Development teams

Infrastructure engineers

FR-G-002

1

Google Deepmind’s AI will “target” NEO’s generating a database of “hits” indicating the NEO has a high probability of earth impact

Deepmind’s AI uses games to refine its search and targeting method with a high level of accuracy

FR-G-003

FR-G-004

FR-G-005

Security Requirements

FR-S-001

1

NEO report findings should be encrypted and sent to NASA

Reporting Requirements

FR-R-001

2

“Hits” detected by the AI should generate a detailed report which astronomers should be able to verify

Usability Requirements

FR-U-001

1

Data generating the 3D model should be transferred into a format that can be used by the AI

Audit Requirements

FR-A-001

1

A full audit report of AI activity needs to be generated after each session

5.2Non-Functional Requirements

ID

Requirement

NFR-001

Multiple nodes of the AI program can run the simulation at the same time and overnight as the data is updated with new observations

NFR-002

NFR-003

NFR-004

NFR-005


6Appendices

6.1List of Acronyms

NEO – Near Earth Object

ELE- Extinction level event

6.2Glossary of Terms

Google Deepmind - Google DeepMind is a British artificial intelligence company founded in September 2010 as DeepMind Technologies. It was renamed when it was acquired by Google in 2014.

6.3Related Documents

Near Earth Object Machine Learning

Asteroid Mining

Resources Used

The abovementioned is available as a word document with images

Opensource Pygame example of Atari 2600 "Asteroids"

JukeDeck's AI to provide a soundtrack for the video

Stupeflix to customise the video

Resources on GitHub provided by NASA

Made inEverywhere Planet Earth
from the minds of
How they did it