OpenWhisk, HyperLedger, AI Leaderboards

October 10, 2017

https://www.slideshare.net/spohrer/iftf-reconfiguring-reality-20171001-v3

10/10/2017 (c) IBM 2017, Cognitive Opentech Group 1

Apache OpenWhisk

• David Krook (IBM)

10/10/2017 (c) IBM 2017, Cognitive Opentech Group 2

Linux Foundation HyperLedger• Chris Ferris (IBM)

10/10/2017 (c) IBM 2017, Cognitive Opentech Group 3

Leaderboards FrameworkAI Progress on Open Leaderboards - Benchmark Roadmap

Perceive 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 ->

AI Trends

10/10/2017© IBM Cognitive Opentech Group (COG)

5

Dota 2

“Deep Learning” for“AI Pattern Recognition”depends on massiveamounts of “labeled data”and computing poweravailable since ~2012;

Labeled data is simplyinput and output pairs,such as a sound and word,or image and word, orEnglish sentence and Frenchsentence, or road sceneand car control settings –labeled data means havingboth input and output datain massive quantities.

For example, 100K imagesof skin, half with skincancer and half without tolearn to recognize presenceof skin cancer.

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

Other Technologies: Bigger impact? Yes.

• Augmented Reality (AR)/Virtual Reality (VR) • Game worlds

grow-up

• Blockchain/Security Systems• Trust and security

immutable

• Advanced Materials/Energy Systems• Manufacturing as cheap,

local recycling service (utility fog, artificial leaf, etc.)

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