Smart Data Webinar: The Road to Autonomous Applications

The Road to Autonomous Applications

Adrian Bowles, PhDFounder, STORM Insights, Inc.

Lead Analyst, AI, Aragon Research

[email protected]

DECEMBER 14, 2017

mailto:[email protected]

• Overview and Opportunities• Enabling Technologies • Autonomous Application Categories• A Matter of Trust

Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.

AGENDA

ROBOCOP IS COOL, BUT…ROOMBA IS REAL


Autonomous: The ability to make one’s own decisions.

Automatic: A system that responds to environmental input with pre-programmed responses.

Semi-autonomous: A system capable of making [some] decisions based on context, and relying on human intervention or override for others.

A single system may have multiple modes.

AUTONOMOUS VS AUTOMATIC


OrientAct

Observe

Decide

WorldModel

Boyd’s LoopJohn Boyd (1927-1997)

FORMALIZING THE DECISION-MAKING PROCESS



Autonomous Intelligent

AUTONOMY VS INTELLIGENCE

Abstract

GeneralizeLearn

Understand

ReasonIndependent


Listen Look

Touch

Smell Taste

Your System

LearnModel

Reason

Understand

INTERACTING WITH THE OUTSIDE WORLD

AutonomousSystems

Robots

Semi-AutonomousRobot/automaton/android

Machine that performs one or morephysical tasks determined by preprogrammed instructions or determined by autonomous reasoning.

AutonomousSystems

ROBOTS VS AUTONOMOUS SYSTEMS


AutonomousSystems

Consumer

Enterprise

Robots

Home CleaningEldercare

Semi-Autonomous

Automobiles

InfoRetrieval

AutonomousSystems

Robot/automaton/android




AutonomousSystems

Consumer

Public Sector Robots


Semi-Autonomous

UAVs

Automobiles

Buses

Trucks

Multi-peds

InfoRetrieval

AutonomousSystems

Robot/automaton/android




AutonomousSystems

Consumer

Public Sector

Enterprise

Robots


ManufacturingSemi-Autonomous

UAVs

Automobiles

Buses

TrucksInventory Mgmt

Multi-peds

InfoRetrieval

Cog

niti

ve C

omm

erce

App

s

NetworkMonitoring

AutonomousSystems

Robot/automaton/androidhine that performs one or more physical tasks determined

by preprogrammed instructionsor autonomous reasoning.



Low High

AutonomyAutomatic/Reflexive Autonomous

Independent InterdependentIntegration

World ModelComprehensiveNone

CONTROL & COMPLEXITY


BigDog is a dynamically stable quadruped robot created in 2005 by Boston Dynamics with Foster-Miller, the NASA Jet Propulsion Laboratory, and the Harvard University Concord Field Station.[1] It was funded by DARPA, but the project was shelved after the BigDog was deemed too loud for combat.[2]

BigDog. (2016, August 19). In Wikipedia, The Free Encyclopedia. Retrieved 19:15, September 7, 2016, from https://en.wikipedia.org/w/index.php?title=BigDog&oldid=735286400

MQ-9 ReaperUnmanned Areal Vehicle (UAV/Drone)

SOMETIMES, A LITTLE AUTONOMY GOES A LONG WAY.


https://en.wikipedia.org/wiki/Quadruped

https://en.wikipedia.org/wiki/Robot

https://en.wikipedia.org/wiki/Boston_Dynamics

https://en.wikipedia.org/wiki/Foster-Miller

https://en.wikipedia.org/wiki/Jet_Propulsion_Laboratory

https://en.wikipedia.org/wiki/Harvard_University_Concord_Field_Station

https://en.wikipedia.org/w/index.php?title=BigDog&oldid=735286400

WHEN DO WE NEED HUMANS IN THE LOOP?


SAE AUTONOMOUS VEHICLE CLASSIFICATION


9/7/2016homeai.info

LEVEL 4 IN 2021?


Google Patents Emergency VehicleDetection for Autonomous Cars

9/6/2016

INTEROPERABILITY WITH THE EXISTING INFRASTRUCTURE IS CRITICAL


Autonomous Systems Artificial General Intelligence

MachineLearning

Cognitive ComputingThe Internet of Things

UnderstandReasonLearn

Domain-independentproblem solving

Connectivity

Control

AUTONOMOUS SYSTEMS IN CONTEXT


Enabling Technologies

IoT……Sensors,Intelligence at the Edge…

Vision Technologies…LIDAR

Supervised Unsupervised

Deep

GeneralReinforcement

Learning by example,using training data. Strategies based

on performancefeedback.

Discovers patterns basedon experience with data.

Biologically-inspired ML approach.Leverages simple processing units - analogous to neurosynaptic elements -organized in layers that collaborate to solve complex problems.

Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved. Copyright (c) 2017 by STORM Insights Inc. All Rights Reserved.

MACHINE LEARNING FOR AUTONOMOUS APPLICATIONS


CONTRASTING AI APPROACHES USED FOR INTELLIGENT AUTONOMOUS SYSTEMS

Knowledge-Centric Data-Centric/Deep Learning

Representation LearningUse ML to discover the representation

Lots of Up-Front EffortDeveloping the Algorithms

or Rules

Should have Complete Transparency

Identify the CategoriesLet the Data Drive the Process

Can Become a Black Box

ATTRIBUTES

APPROACH Use ML to discover the mappingUse experts to create therepresentation and mapping


HOW TO DELEGATE AUTONOMY

ALGORITHMS&

RULES

DATA

AutoSys

Decision-making…Does it plan?

Generative planning?Use feedback?Coordinate?

Can it:move?

in the air?on land?on/under water?

see?hear?smell?taste?feel?learn?

CLASSIFYING AUTONOMOUS SYSTEMS


Segmentaton

Train


A MATTER OF TRUST

Verify

Validate

Delegate

Evaluate

TRUST

DecisionsPreparation

Copyright (c) 2017 by STORM Insights Inc. All Rights reserved.

PROGRESS?

Alex Coppel / Herald Sun/Getty Images

Fernando Alonso Walks Away from a 46G ImpactWill F1 become fully autonomous?


FROM INFORM/ADVISE TO AUTOMATE

DESIGN CONSTRAINTS - DECISIONS UNDER DURESS



DESIGN DECISIONS/CONSTRAINTS

Independent vs Collaborative

Execution Constraints

Augment vs Automate

OrientAct

Observe

Decide

SmallModel

OrientAct

Observe

Decide

SmallModel

Feedback

Sensor

Sensor

Effector

Effector

COLLABORATIVE AUTONOMY - MULTIPLE SENSORS & EFFECTORS


• A robot may not injure a human being or, through inaction, allow a human being to come to harm.

• A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

• A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

“Handbook of Robotics, 56th Edition, 2058AD”in

Runaround, a 1942 short story

ISAAC ASIMOV - THREE LAWS OF ROBOTICS


[email protected]

Twitter @ajbowlesSkype ajbowles

If you would like to connect on LinkedIn, pleaselet me know that you that you registered for theSmart Data webinar series.

KEEP IN TOUCH

2018 SmartData Webinar Dates & Topics

January 11 AI At The Edge: Pushing Intelligence to Fog Computing Nodes

February 8 A Pragmatic AI Maturity Model: Choosing the Right AI Technologies Based on Application Requirements and User Attributes

March 8 Machine Learning Update: An Overview of Technology Maturity and Product Vendors

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