Frank Stein (IBM), Chuck Howell (MiITRE), Jim Spohrer (IBM)

Friday November 10, 2017

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Cognitive Assistance in GovernmentAnd Public Sector Applications

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Day 1

• Keynote: Mark Maybury (MITRE)– AI Progress - saving lives, but bad actors growing too e.g., fake

news

• William Regli (DARPA) – AI changing the way we think - students performing like experts

• Randy Bailey (NASA)– AI Pilot - deskilling is becoming a problem ("push button one, it

solves everything"), partnership better with roles and responsibilities (R2D2)

• Kevin Burns (MITRE)– AI Ergonomics for safety - better ways of measuring cognitive

load for different types of work (science of human-machine teaming)

• Steve Meckl, Gheorghe Tecuci (GMU)– Cog for advanced cyber security threat detection

• Jim Whitmore (Dickinson)– Cog for Cyber Security risk analysis

• Joshi (UMBC), Branting (MITRE), Chan (USC)– Cog for Fed Acq. Req., Admin Adjudication, Homeless Youth

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Day 2

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• Keynote: Ken Forbus (Northwestern)– How system can learn as much with 10

examples as with 10,000

• Alun Preece (Cariff)– How people and chatbots can increase

situational awareness

• Chieko Asakawa (IBM)– Cogs for blind

• Matt Dering, Conrad Tucker (PSU)– Blame and responsibilities

• Jim Spohrer (IBM)– A view on the future

• Faruqe (GWU). Skeivik( (IBM), Watkins (GWU), Grubbs (VA), Medsker (GWU)– Cogs for health, autism, education,

government

Leaderboards Framework

AI Progress on Open Leaderboards - Benchmark RoadmapPerceive World Develop Cognition Build Relationships Fill Roles

Pattern recognition

Videounderstanding

Memory Reasoning Socialinteractions

Fluent conversation

Assistant & Collaborator

Coach & Mediator

Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions

Chime Thumos SQuAD SAT ROC Story ConvAI

Images Context Episodic Induction Plans Intentions Summarization

Values

ImageNet VQA DSTC RALI General-AI

Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation

WMT DeepVideo Alexa Prize ICCMA AT

Learning from Labeled Training Data and Searching (Optimization)

Learning by Watching and Reading (Education)

Learning by Doing and being Responsible (Exploration)

2015 2018 2021 2024 2027 2030 2033 2036

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Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?

Approx.YearHumanLevel ->

Every 20 years, compute costs are down by 1000x

• Cost of Digital Workers– Moore’s Law can be thought of as

lowering costs by a factor of a…• Thousand times lower

in 20 years• Million times lower

in 40 years• Billion times lower

in 60 years

• Smarter Tools (Terascale)– Terascale (2017) = $3K– Terascale (2020) = ~$1K

• Narrow Worker (Petascale)– Recognition (Fast)– Petascale (2040) = ~$1K

• Broad Worker (Exascale)– Reasoning (Slow)– Exascale (2060) = ~$1K

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2080204020001960

$1K

$1M

$1B

$1T

206020201980

+/- 10 years

$1

Person AverageAnnual Salary(Living Income)

Super ComputerCost

Mainframe Cost

Smartphone Cost

T

P

E

T P E

AI Progress on Open LeaderboardsBenchmark Roadmap to solve AI/IA

GPD/Employee

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(Source)

Lower compute costs translate into increasing productivity and GDP/employees for nations

Increasing productivity and GDP/employees should translate into wealthier citizens

AI Progress on Open LeaderboardsBenchmark Roadmap to solve AI/IA

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But this stuff is still really hard…

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