Astute symposium 2013-10-10_keynote_kay_stanney

Human-‐Systems Integra0on in Adap0ve Mission Cri0cal Systems

Kay Stanney, Ph.D., C.H.F.P. Design Interac7ve, Inc., President & Founder University of Central Florida, Courtesy Appt. October 10, 2013

Sirris Symposium: Human Factors and Technologies for Pro-active, Context- aware and Data-intensive Applications

Agenda Ø  A bit about me and Design Interac7ve, Inc. Ø  Human Augmenta7on: Essen7al Emerging

Transforma7onal Technology Ø  Past Approaches to Human Augmenta7on

§  Adap7ve Automa7on §  Augmented Cogni7on

Ø  The Future of Human Systems Integra7on Ø  Conclusions

17 October 2013

A bit about me and Design Interac9ve, Inc.

Design Interac-ve, founded in 1998, is a human factors engineering firm that helps clients overcome their most pressing human performance challenges. Unlike most firms, we use deep behavioral and physiological diagnos-cs to design adap-ve, engaging solu-ons that op-mize performance and profoundly enhance the user experience.

17 October 2013

DI Divisions DIVISION' MARKET'POSITIONING'STATEMENT'

Defense&Solutions& Our&Defense&Solutions&Division&provides&operational&analysis,&performance&assessment,&and&advanced&technology&solutions&to&Department&of&Defense&clients&who&aim&to&enhance&training&effectiveness&and&efficiency.&&We&use&deep&behavioral&and&physiological&diagnostics&to&deliver&adaptive,&meaningful,&and&intuitive&learning&experiences&for&the&Warfighter.&&

Medical&Innovations& Our&Medical&Innovations&Division&provides&innovative&personal&health&solutions&for&medical&care&providers&and&consumers.&We&combine&unobtrusive&biomonitoring&technology&with&adaptive&assessment&solutions&that&continuously&analyze&collected&data&to&offer&preventative&and&corrective&measures&in&any&setting.&&

Emerging&Markets&and&Technologies&

Our&Emerging&Markets&Division&specializes&in&userDcentered&design&and&usability.&We&leverage&our&cuttingDedge&military&R&D&to&develop&innovative&design&and&evaluation&tools,&human/machine&interfaces,&and&smart&mobile&solutions&that&empower&users&and&enhance&the&user&experience.&&

& Across our Divisions, DI’s solu0ons save lives, reduce cost, enhance the user experience, and op0mize human performance -‐ while defining the future of human-‐systems integra0on.

Emerging DI Products – Low Cost EEG

Emerging DI Products -‐ New Training Solu9ons

Emerging DI Products -‐ Lessons Learned Tool

Playbook is a rapid authoring tool that can be used to capture, publish, and share operational

observations, insights, and lessons (OIL).

Playbook provides an easy-to-use platform to record and share personal experiences quickly and effectively.

DI Products – easyGaze and GazeWare

STRAP vest communicates a haptic language based on military hand signals

Emerging DI Products – STRAP

Demonstrated rapid learning and high retention rates

Emerging DI Products – New Evalua9on Tools

Emerging DI Products – SIMI Sensor Suite

Rela9ng DI’s SIMI to the ASTUTE Project Build an EEG-‐based Measure of Situa9on Awareness

Sensor: EEG

Measure: EEG Alpha & Theta

Diagnose: High Theta & Low Alpha = Low SA

MeasureIT

Human Augmenta9on: Essen9al Emerging Transforma9onal Technology

As natural human capaci-es become increasingly mismatched to data volumes, processing capabili-es, and decision speeds, augmen-ng human performance will become essen-al for gaining the benefits that other technology advances can offer.

Technology Horizons: A Vision for Air Force Science & Technology During 2010-‐2030

Dr. Werner J.A. Dahm United States Air Force Chief Scien7st

May 15, 2010 (p. 58)

Human Augmenta9on: Essen9al Emerging Transforma9onal Technology

Human Augmenta7on

Predic7ve & Content Analy7cs

VR & Virtual

Assistants

Biochips; Health Monitoring

Augmented Reality;

Wearable UIs

Robo7cs & UAVs

Human Augmenta9on Essen9al for Gaining Benefits of Emerging Technology Advances

Emerging Technologies

Human Augmentation Emerging Technology

Emerging Technologies Priority Mix 2013

Human Augmenta7on

Transforma7onal

ASTUTE Focused on Human Augmenta9on Ø  ASTUTE is focused on the transforma7onal

emerging technology of human augmenta7on Ø  Pro-‐ac7ve systems are one approach to human

augmenta7on Ø  ASTUTE’s goals for proac7ve systems are to:

§  Measure user state and relate it to context §  Provide pro-‐ac7ve sugges7ons based on these user

state and context data §  Thereby realizing adap7ve HMIs

that increase situa7onal awareness, improve decision making, and augment other aspects of human performance

Past Approaches to Human Augmenta9on Adap9ve Automa9on & Augmented Cogni9on

Ø  Mission cri7cal systems put immense pressure on human cogni7on

Ø  These context demand swièr, highly accurate, and ever more resilient capabili7es

§  NASA proposed adap7ve automa7on as a means to address such demands

§  DARPA proposed augmented cogni7on as new HSI paradigm through which to achieve gains in mission cri7cal performance

§  These efforts can inform ASTUTE’s proac7ve systems efforts

Past Approaches to Human Augmenta9on

Ø  Rouse’s (1998) adap7ve automa7on theory suggested that both user and system should be able to ini7ate changes in the level of system automa7on in response to situa7onal demands

Ø  Systems implemented based on these early theories generally followed a binary (on/off) approach to adap7ve automa7on

§  Some relied on physiological measures of operator state to trigger automa7on

§  Others relied on task or context based measures (e.g., cri7cal events; operator performance; task models) to trigger automa7on

Past Approaches to Human Augmenta9on: Adap9ve Automa9on

ü ASTUTE’s proac7ve systems plan to leverage both operator state & context

Workload Matched Adap9ve Automa9on

Source: hep://www.docstoc.com/docs/99925451/Adap7ve-‐Automa7on-‐Matched-‐to-‐Human-‐Mental-‐Workload

Ø  Several closed-‐loop solu7ons evolved, many of which controlled the levels of task automa7on based on physiological indices:

§  EEG measures (e.g., theta, alpha, beta, and gamma band ac7vity to develop an engagement index)

§  Cardio-‐circulatory measures (e.g., HR, HRV) §  Combina7on of physiological indicators (e.g., EEG, ERPs,

and HRV; EEG, HR, respira7on interval, and eye blinks/interblink intervals)


Ø  Adap7ve automa7on oèn 7mes substan7ally improves human performance:

§  44% reduc7on in tracking task errors and a 33% reduc7on in error rates on resource management tasks (Wilson & Russell, 2003)

§  50% improvement on UAV opera7ons (Wilson & Russell, 2007)

§  300% improvement in throughput and detec7on in image analysis tasks (Bloom, et al., 2009)


Ø  Some issues: §  Unbalanced mental workload §  Mistrust §  Overreliance §  Complacency §  Insensi7ve physiological measures §  Reduced situa9on awareness §  Decision biases


u ASTUTE’s proac7ve systems seek increased SA and enhanced decision making

Ø  Notwithstanding challenges and barriers presented by real-‐7me psychophysiological monitoring, early work in adap7ve automa7on demonstrated immense poten7al derived through systema7c integra7on of operator state and system state

Ø  Further, possibili7es of leveraging these synergies for more than adap7ve automa7on soon became evident, as they provided a means by which to augment cogni7on and thus extend the human poten7al

Past Approaches to Human Augmenta9on: Adap9ve Automa9on è Augmented Cogni9on

Ø  Augmented cogni7on has provided many of the theories, principles, and prac7ces needed to realize proac7ve systems

Ø  Augmented cogni7on R&D has primarily focused on mission cri7cal systems that put immense pressure on human cogni7on

§  Such context have common ground with Emergency Dispatching domain

§  Thus, lessons-‐learned via Augmented Cogni7on R&D can inform the ASTUTE Project

Past Approaches to Human Augmenta9on: Augmented Cogni9on

ü ASTUTE’s proac7ve systems can benefit from AugCog lessons-‐learned

Problem: The one-‐two punch of informa7on overload and mul7tasking

•  Increased volume of informa7on available in command centers, while staffing levels remain constant -‐ must to try to do more, and do it faster

Augmented Cogni9on Objec9ve: Develop technologies capable of extending, by an order of magnitude or more, the informa7on management capacity of individuals working with 21st century

compu7ng technologies.

Past Approaches to Human Augmenta9on: Augmented Cogni9on – The Objec9ve

Ø  Augmented cogni7on presents a pro-‐ac7ve paradigm through which to achieve human performance gains in mission cri7cal context such as Emergency Dispatching domain –  Challenge is to real-‐7me sensor, measure and diagnose the collec7ve human state (cogni7ve, physical, affec7ve) and then use theory-‐based algorithms to proac7vely adapt to and augment innate human abili7es

– Making the unobservable – observable

Past Approaches to Human Augmenta9on: Augmented Cogni9on – The Challenge

Augmented Cogni0on: Some History

Where has it been established that such WIMPs (windows, icons, menus, pointers) are the ul-mate HSI design? What is appealing about the WIMP and internet browser as the interface for the human to the computer? Are we falling into a least common denominator trap? Can’t we do beYer?

ADM (ret.) Lee Kollmorgen

When we started the Augmented Cogni9on Program: We asked -‐ do we have it wrong?

All these designs rely on WIMP interfaces – Windows, Icons, Menus, Pointing Devices

While current HSI paradigms have empowered computer users of varying ability, they alone cannot handle many of the challenges of today’s opera9onal environments:

• Mul9-‐tasking • Mul9ple informa9on streams • Varying contexts

We need systems that ac9vely integrate the human and provide proac9ve support based on individual capabili9es and limita9ons:

• Alleviate cogni9ve bo^lenecks • Support situa9on awareness • Enhance decision making • Account for individual differences

Is it 9me for new HSI paradigms?

Command-‐Line

•  Pure text interface •  Individual or batched

commands •  Predetermined sequence à User feeds system

GUI

•  Graphical interface • Metaphorical representa7ons

• Sta7c implementa7on

•  Object-‐oriented • “Direct manipula7on”

•  Reac7ve (event-‐based) • To user input (behavioral) • To system event • Standardized reac7ons

•  Baby-‐steps towards dynamic interac7on • Personaliza7on • Smart menus

à System acts on user behavior

AugCog

•  System awareness • User state • User behavior • System state • Task context

•  Proac7ve -‐ adapts to changes in: • User performance • Task-‐context requirements

•  Provides: • Dynamic and adaptable representa7ons

• Individualized response •  Act on operator intent

à System proac7vely adapts to user, task, and context

Evolution of HSI Paradigms

Augmented Cognition Ø  A new HSI paradigm

§  Intui7ve coupling between human and machine §  Providing the right informa7on -‐ at precisely the

right 7me -‐ in the right format to amplify human capabili7es

Ø  An augmented cogni7on system has three main components: §  Sensors: Neurophysiological, physiological,

and behavioral sensors §  Measures: Cogni7ve, physical, and

affec7ve user state measures §  Adapta7on Strategies: Proac7ve

techniques to alleviate situa7ons of overload, inaeen7on, stress... and improve human performance

AugCog Sensors & Measures

Source: hep://www.spawar.navy.mil/s7/publica7ons/pubs/tr/tr1940vicond.pdf




ü ASTUTE’s proac7ve systems could learn from AugCog sensors, measures, and classifica7on methods


ü ASTUTE’s proac7ve systems could design future sensors and measures focused on increased SA and enhance decision making

AugCog Adaptive Strategies

Adapt presentation of information Modality augmentation (redundancy, switching)

Add or change mode of information presentation

Transposition Change information type from verbal to spatial or vice versa

Cueing Augment display to capture attention of user

Decluttering Reduce amount/complexity of information displayed

Context-sensitive help Provide information specific to system state at time help is needed


AugCog Adaptive Strategies Adapt scheduling of information

Pacing Hold low priority information until current high priority tasks completed

Sequencing Simultaneous events converted into sequential form

Decompose tasks into smaller portions and re-arrange subtasks

Adapt system autonomy

Delegate Transfer tasks to fully-automated system

Mixed Initiative Provide operator with most appropriate level of control for situation; both operator and system can adjust system autonomy


ü ASTUTE’s proac7ve systems could use AugCog Adap7ve Strategies

Innova9ve Adap9ve Techniques

-‐ Empathe7c proac7ve system -‐ Use predic7ve cogni7ve, physical, and affec7ve state

Potential Future Adaptive Strategies

New Adapta9on Objec9ves

-‐ today: Cogni7ve boelenecks

-‐ tomorrow: Increase SA, reduce confusion…

-‐ tomorrow: Reduce inaeen7veness, fear…

Individual Cogni9ve Profiles

-‐ Aiding novice dispatchers -‐ Suppor7ng seasoned dispatchers

-‐ Use ambient environment

-‐  Music tempo/genre change

-‐ today: Reduce distrac7ons

ü ASTUTE’s proac7ve systems could design future adap7ve strategies focused on enhanced SA and DM

Results of Augmented Cogni9on Program Summarized in AugCog Prac99oner's Guide

ü ASTUTE’s proac7ve systems can benefit from AugCog lessons-‐learned

Where we were at end of Augmented Cogni9on Program: Need for broadening scope of human state assessment

Adap7ve Smart Home

Adap7ve Smart Phone

Adap7ve Smart Glasses

Adap7ve Smart Tablets

Adap7ve Smart Cars

Sensors to Measure

Cogni7ve State

Workload, Uncertainty,

Confusion, etc.

Sensors to Measure

Physical State

Body Temp., O2 Level,

Physical Fa7gue

Sensors to Measure

Affec7ve State

Anxiety, Fear, Stress,

Confidence

Next Steps: Develop plethora of sensors & measures that drive adap9ve systems

Extensions aber Augmented Cogni9on Where DI has taken AugCog R&D

Appling SIMI to Image Analysis

SIMI for Image Analysis DI used EEG and eye-‐tracking to develop real-‐7me neurophysiological indicators of ‘interest’ during image analysis Sensor: Eye tracking Measure:

Ø  Capture parameters to assist in determining ‘interest’ (e.g., fixa7on dura7on, pupilometry)

Sensor: Electroencephalography (EEG) Measures:

Ø  Previously used to indicate individual’s workload, arousal, aeen7on, drowsiness, percep7on of events

Ø  Image level analysis: iden7fy images that contain one or more points of interest

Ø  FLERPs: Fixa7on level analysis: iden7fy ‘interest’ at specific fixa7on points within an image

ü ASTUTE’s proac7ve systems should consider use of FLERP’s

SIMI for Image Analysis Depic7on of all fixa7on points on the image drawn on an Area Of Interest (AOI) layer

SIMI for Image Analysis •  Fixa7on dura7on

(ms) in addi7on to the loca7on

Fixa7ons greater than 700ms Behavior-‐based classifica7ons

•  Define Hits, Misses, Correct Rejec7ons, False Alarms

Appling SIMI to Instrument Flight Panel Training

HIGH WORKLOAD

ERROR

Altitude out of range

Sensors: Eye Tracking, EEG, & Performance Measures: Visual Aeen7on Alloca7on, Controls Engaged, Errors, Cogni7ve Measures Diagnosis: Skill Deficiencies – Not looking at Al7meter; Workload High

Performance Feedback

Look at Error Details!

ü ASTUTE’s proac7ve systems should consider linking errors to physiological data for more in depth diagnos7cs

Error Detail Screen Cont.

Error Detail Screen Cont.

Error Detail Screen

Error Detail Screen

LOW WORKLOAD/ LOW AROUSAL

ERROR DETECTED!

Altitude out of range

Diagnosis: Looking at Al7meter; Skill Aeained… Now: Arousal Issue – Time to move on to next training objec7ve

Appling SIMI to Baggage Screening

Individualized training for visual search: –  Sensors: U7lizes eye and EEG-‐based sensor technology –  Measures: Real-‐7me cogni7ve state and performance evalua7on –  Diagnosis: Non-‐op7mal cogni7ve state, exper7se level, and

deficiencies/inefficiencies in screening performance –  Adapts in real-‐7me to op7mize training:

– Tailored feedback and training » Exposure training » Discrimina7on training

–  Image generator: » Allows instructor upload of new threats, distractors, bags

» Produces endless combina7ons of image components to avoid image repe77on

Provides individualized training for visual search skills

SIMI for Baggage Screening: ScreenADAPT

Sensors & Measures Performance: Error Classifica7on

–  Hit, Miss, Correct Rejec7on, False Alarm –  Recogni7on error -‐ looked at threat, didn’t flag threat –  Scanning error -‐ didn’t even look at threat

Cogni7ve State via EEG –  Readiness to Learn

•  Workload and drowsiness

Eye Tracking –  Gaze paeerns –  Recogni7on Error

ü ASTUTE’s proac7ve

systems should consider use of recogni7on and scanning error diagnos7cs

Two Adap9ve Training Techniques Exposure training used to strengthen object detec7on ability when trend of False Alarms is iden7fied

–  Includes both immediate and delayed feedback to support prac7ce and training

Discrimina7on training used to strengthen object recogni7on when trend of Misses is iden7fied

ü ASTUTE’s proac7ve systems should consider use of adap7ve feedback based on performance trends

SIMI Applied to Emergency Dispatch Which sensors, measures, diagnoses, and adap9ve strategies?

•  ASTUTE’s objec7ves are to increase situa7onal awareness and improve decision making •  Situa7onal Awareness:

•  How are you sensing SA? •  How are you measuring SA? •  How are you diagnosing SA? •  How are you adap7ng to SA?

•  Decision Making: •  How are you sensing decision making? •  How are you measuring decision making? •  How are you diagnosing decision making? •  How are you adap7ng to decision making?

SensorIT – MeasureIT -‐ DiagnoseIT then Proac9vely ADAPT!

ü ASTUTE’s proac7ve systems should consider how to sensor, measure, diagnose, and adapt to SA level and DM performance

SIMI Applied to Emergency Dispatch

SIMI Applied to Emergency Dispatch

EEG Captures Data

Gamma waves reveal high engagement Beta waves reveal over-‐arousal

Link to Context – Find Decision Error – over

arousal and high engagement were

due to CONFUSION

Add clarifying info to address decision

error

Augmented Cogni9on: Remaining Hard Problems

Ø  Sensors §  Real-‐7me, noninvasive, highly sensi7ve and reliable

sensors to gather neuro/physiological data rela7ng to human cogni7ve, physical, and affec7ve state

Ø  Building generic human state classifiers §  Determining which technology yields the best results

(e.g. AI vs neural networks vs machine learning) §  Using the specific and extrapola7ng to the 'generic’

Ø  Measures – measures – measures §  Developing valid, reliable measures of a plethora

of cogni7ve, physical, and affec7ve state


Ø  Designing seamless adapta7on techniques Ø  When to adapt:

Ø  Valid and reliable classifiers used to gauge when to adapt Ø  Classifiers available today are isolated single measures of 1

state (e.g., arousal, workload) -‐ live tutors take in the user experience as a whole – classifiers need to be mul7dimensional

Ø  Threshold that triggers when to adapt Ø  Range of performance within which to adapt at vs. above/

below a given threshold Ø  ‘Generalized theory’ [if there is one] that can drive

adapta7on triggers -‐ want to avoid the yo-‐yo effect of 'mi7ga7on on', 'mi7ga7on off' paeern

Ø  Level of granularity where adapta7on takes place -‐ target 'paeerns of error' or individual instances of error?


Ø  Designing seamless adapta7on techniques Ø  How to adapt:

Ø  How to transi7on between mi7ga7ons -‐ when one supersedes another and/or mul7ple mi7ga7ons may be used -‐ how to effec7vely insert mi7ga7on without distrac7ng the user

Ø  Validated mi7ga7on strategies proven to improve opera7ons or training for a given diagnosis (e.g., is mi7ga7on different if error was found due to frustra7on vs. boredom?)

Ø  Determining how to leverage different interfaces to achieve the same results (text versus speech versus earcons versus hap7c language)

ü ASTUTE could tackle many of these challenges as they develop their proac7ve system concept

The Future of HSI An R&D agenda to direct the HSI field through 2050

Where to from here? Augmented Cogni7on – field started ~2000

–  Paradigm shi` from ‘dumbing down interac7ons’ via WIMP interfaces to dissolving the user interface through direct brain-‐computer interfaces

–  Brought about interdisciplinary teams focused on monitoring and mi7ga7ng human processing limita7ons within opera7onal environments

–  Focused on revolu7onizing human-‐system integra7on aèr decades of being “locked” in the WIMP paradigm

Aber a decade of AugCog, it’s 9me to ask again… Where should HSI go from here?

First: Iden9fied HSI “Enablers” Other HSI Enablers Beyond Human Augmenta9on

Eliminate Augment

Enhancing Human Performance HSI Enabler: Augmenta9on

ü ASTUTE’s proac7ve systems are incorpora7ng human augmenta7on

Increase dynamic range and number of couplings through next genera7on neuroadap7ve systems that achieve synergis7c coopera7on among human physical, cogni7ve, and affec7ve states

Eliminate Simplify

Augment

Big Data

Iden7fy how best to leverage big data to simplify user decision making without overwhelming a user’s analy7cal capabili7es

Enhancing Human Performance HSI Enabler: Big Data

ü Is ASTUTE considering use of big data?

Eliminate Simplify

Combine

Augment

Big Data Autonomy

Enhancing Human Performance HSI Enabler: Autonomy

Iden7fy how best to design autonomy into a mission such that performance is op7mized and unintended opera7onal consequences are avoided

ü Is ASTUTE considering use of autonomy?

Eliminate

Simplify

Combine

Resequence

Augment

Big Data Autonomy

Transgenics

Gene7cally alter the human via gene therapy, gene7c breeding, and gene7c engineering

Enhancing Human Performance HSI Enabler: Transgenics

ü Is ASTUTE considering use of transgenics (e.g., gene7c engineering to enhance heat acclima7on)?

Second: Iden9fied HSI “Eras” What’s beyond the digital era?

Ø  To map out an HSI R&D agenda through 2050, the current and future eras that human-‐systems interac7on will traverse through were considered

81

Enhancing Human Performance HSI Eras

Ø  Individualism Op7miza7on: –  Brain era seeks to achieve “super-‐intelligence” –  Physical-‐feat era seeks to achieve “super-‐humans”

through symbio7c coupling of human and machines to overcome universal human limita7ons

Ø  Collec7vism Op7miza7on: –  Human quantum-‐entanglement era, which will support

human-‐human communica7on, where crea7on of human “superorganisms” is the end goal

–  Ecological quantum-‐entanglement era, which will support synergy between humans and their environment, where “super-‐symbiosis” is the end goal

Enhancing Human Performance HSI Eras

Seeks to achieve super-‐intelligence –  Ar7ficial intelligence becomes an exocortex to eliminate need for brainpower

–  Big data algorithms transform data into intelligence

–  Extend innate intelligence with cogni7ve prostheses

–  Resequence our brains to fundamentally improve intellectual capacity

Enhancing Human Performance HSI Eras: Individualism Op9miza9on – Brain Era

ü ASTUTE is considering use of tablet-‐based and PDA cogni7ve prostheses

Seeks to achieve super-‐humans –  Exoskeletons and psychos7mulants used to enhance human physical ability

–  Cloud used to monitor human ac7vi7es to fuel big data algorithms that can realize vast expansion of human physical poten7al and op7mize health

–  Cyborgs become reality – implants, prosthe7cs, psycho-‐pharmacological agents

–  Gene7c muta7ons op7mize human physical capacity to protect against disease

Enhancing Human Performance HSI Eras: Individualism Op9miza9on – Physical Feat

ü Is ASTUTE considering means to enhance physical ability and resilience?

Seeks to achieve superorganisms –  Robots synergis7cally augment human collec7ve –  Big data algorithms combine carbon and silicon-‐based intelligence into a single collec7ve consciousness

–  Human to human quantum entanglement for collec7ve intelligence

–  Gene7cally alter humans to allow for chemical communica7on, telepathy, and other means to support communica7on

Enhancing Human Performance HSI Eras: Collec9vism Op9miza9on – Human Quantum-‐Entanglement Era

ü Is ASTUTE considering means of achieving collec7ve intelligence?

Seeks to achieve super-‐symbiosis –  Biosphere morphs to op7mize human environment collec7ve

–  Directly sense, measure, and understand molecular processes in collec7ve environment

–  Sensor-‐enabled ecological scavengers to predict and adapt the environment to op7mize symbiosis

–  Gene7cally alter human such that they are beeer suited for their environment, thereby elimina7ng heat sensi7vity and other maladapta7ons

Enhancing Human Performance HSI Eras: Collec9vism Op9miza9on – Ecological Quantum-‐Entanglement Era

ü ASTUTE is collec7ng and leveraging environmental data; are they considering use of adaptable biospheres?

Emerging HSI Eras: Big Data HSI Enabler Extends Info Highway

Brain Era populates the Neurosphere

Physical Feat Era populates the Physiosphere

Human Quantum-‐Entanglement Era populates the Noosphere

Ecological Quantum-‐Entanglement Era populates the Biosphere

ü Is ASTUTE collec7ng and coordina7ng data from neurosphere, physiosphere, noosphere, and biosphere?

HSI Emerging Concepts: HSI Eras & Enablers

HSI R&D Agenda through 2050

ü ASTUTE can enhance human performance by considering 4 HSI Eras and 4 HSI Enablers

The future of HSI…

Conclusions Ø  ASTUTE aims to implement proac7ve systems that

increase SA and enhance decision making Ø  Lessons-‐learned from adap7ve automa7on and

augmented cogni7on R&D can inform the design of ASTUTE’s proac7ve systems

Ø  ASTUTE should also look to future HSI emerging concepts: Ø  Consider HSI enablers beyond human

augmenta7on, to include autonomy, big data, and transgenics

Ø  Consider advances in other HSI eras beyond the brain era, to include physical-‐feat, human quantum-‐entanglement, and ecological quantum-‐entanglement eras

Acknowledgments Special thanks goes to Innoviris for their support of this presenta-on. Any opinions, findings and conclusions or recommenda-ons expressed in this material are those of the author and do not necessarily reflect the views or the endorsement of Innoviris.

Acknowledgments This material is based upon work supported in part by the Office of Naval Research (ONR) under contracts N0001413M0047 and N00014-‐09-‐M-‐0385, Department of Homeland Security (DHS) under contracts N10POC20028 and D11PC20053, Defense Advanced Research Projects Agency (DARPA) under contracts W31P4Q-‐06-‐C-‐0041 and W31P4Q-‐07-‐C-‐0214, and the Air Force Research Laboratory (AFRL) under contracts FA8550-‐06-‐C-‐0151 and FA8550-‐06-‐C-‐0151. Any opinions, findings and conclusions or recommenda-ons expressed in this material are those of the author and do not necessarily reflect the views or the endorsement of ONR, DHS, DARPA, and AFRL.