Download - Iftf reconfiguring reality 20171010 v3
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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