OpenTechAI is open source technologies for artificial intelligence

1. OpenTechAI
Jim Spohrer (IBM)
http://slideshare.net/spohrer/opentechai_20180429_v1
4/29/2018 IBM #OpenTechAI 1
FfDL – MAX – ART – INTU – COG – IBM – DBG – WATSON – CODAIT - RESEARCH – POWERAI – COGNITIVE – GITHUB – LEADERBOARDS - KAGGLE

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6. OpenTechAI on GitHub (April 29, 2018)
Code Stars (K) Forks (K)
Google TensorFlow 97 62
Keras-team Keras 28 10
Sci-kit Sci-kit 27 13
BVLC Caffe 23 14
PyTorch PyTorch 14 3
Apache MXNet 13 5
Caffe2 Caffe2 7 1
Baidu PaddlePaddle 6 1
Google Tensor2Tensor 3 0.8
Tencent ANGEL 3 0.8
4/29/2018 IBM #OpenTechAI 6

7. OpenTechAI on GitHub (April 29, 2018)
Code Stars (K) Forks (K)
Tencent NCNN 3 0.8
Google Kubeflow 3 0.3
Microsoft ONNX 3 0.3
Google TensorBoard 1 0.3
Apache SystemML 0.6 0.2
IBM (watson-intu) INTU (10 Repositories) 0.1 0.07
IBM FfDL 0.1 0.04
IBM ART 0.1 0.03
IBM MAX 0.005 0.017
LFDL AT&T Acumos (35 Repositories) 0.01 0
4/29/2018 IBM #OpenTechAI 7

8. Step Comment
GitHub Get an account and read the guide
Learn 3 R's - Read, Redo, Report Read (Medium/arXiv), Redo (GitHub), Report (Jupyter Notebook)
Kaggle Compete in a Kaggle competition
Leaderboards Compete to advance AI progress
Design New Challenges build an AI system that can take and pass any online course, then
switch to tutor-mode and help you pass
Open Source Guide Establish open source culture in your organization
4/29/2018 IBM #OpenTechAI 8

9. Leaderboards Framework
AI Progress on Open Leaderboards - Benchmark Roadmap
Perceive World Develop Cognition Build Relationships Fill Roles
Pattern
recognition
Video
understanding
Memory Reasoning Social
interactions
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 Summarizatio
n
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
4/29/2018 (c) IBM 2017, Cognitive Opentech Group 9
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->

10. Future of AI
4/29/2018
© IBM Cognitive Opentech Group (COG)
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Dota 2
“Deep Learning” for
“AI Pattern Recognition”
depends on massive
amounts of “labeled data”
and computing power
available since ~2012;
Labeled data is simply
input and output pairs,
such as a sound and word,
or image and word, or
English sentence and French
sentence, or road scene
and car control settings –
labeled data means having
both input and output data
in massive quantities.
For example, 100K images
of skin, half with skin
cancer and half without to
learn to recognize presence
of skin cancer.

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© IBM 2015, IBM Upward University Programs Worldwide accelerating regional
development
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12. Trust: Two Communities
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© IBM MAP COG2018
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Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?

13. Resilience:
Rapidly Rebuilding From Scratch
• Dartnell L (2012) The Knowledge: How to
Rebuild Civilization in the Aftermath of a
Cataclysm. Westminster London: Penguin
Books.
4/29/2018
© IBM MAP COG2018
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