Responsible TinyML
1
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How can TinyML support Responsible AI?
2
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How can TinyML support Responsible AI?
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Accessibility / Education
Sustainability / Conservation
Privacy / Security
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Accessibility / Education
4
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Promoting Accessibility / Education
5
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Promoting Accessibility / Education
6
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Global Embedded ML Education Opportunities:
7
Low Resource Requirements
1
Low Power
Low Cost
Low Connectivity
Interdisciplinary Focus
and Applied Learning
2
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Global Embedded ML Education Opportunities:
8
Low Resource Requirements
1
Interdisciplinary Focus
and Applied Learning
2
Low Power
Low Cost
Low Connectivity
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Global Embedded ML Education Opportunities:
9
250 Billion
MCUs today
Software and Hardware Fragmentation
1
Challenges
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Global Embedded ML Education Opportunities:
10
Language
and Local
Relevance
Software and Hardware Fragmentation
1
Affordability Barriers and Localization Roadblocks
2
Challenges
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Global Embedded ML Education Opportunities:
11
Software and Hardware Fragmentation
1
Affordability Barriers and Localization Roadblocks
2
Educator Readiness and Research Incentives
3
Challenges
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12
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Towards a Modular Curriculum
13
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Towards a Modular Curriculum
14
Please feel free to remix our materials and please consider sharing back your materials for the community!
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Calls to Action
15
Assessing Our
Educational Programs
1
Maintaining Open-Source Software and Courseware
2
Embedded ML Model
and Data Zoo
3
Improving Accessibility
of Hardware
4
Growing a Research Community
5
Increased Outreach
and Diversity Efforts
6
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Calls to Action
16
Assessing Our
Educational Programs
1
Maintaining Open-Source Software and Courseware
2
Embedded ML Model
and Data Zoo
3
Improving Accessibility
of Hardware
4
Growing a Research Community
5
Increased Outreach
and Diversity Efforts
6
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CC BY-NC-SA 4.0
Calls to Action
17
As has been noted in related works, this kind of methodology has many flaws and does not take into account much of the nuance in gender, including issues of bias, misperception, and nonbinary identities [7], [8]. However, we hope that this initial study will help add to the robotics community’s understanding of the current state of gender diversity and, at a minimum, provide directionally correct data to help with future diversity, equity, and inclusion efforts.
Underrepresentation of Women in Robotics Research
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Sustainability / Conservation
18
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Promoting Sustainability / Conservation
19
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20
TinyML can support the SDGs but comes with costs. What is the net impact?
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Building Representative Systems
21
High Cost
Medium Cost
Low Cost
Image Classification
Keyword Spotting
Cost Level
Application
Size
Large
Compact
Compact
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22
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TinyML Systems in Context
23
5x to 38x Savings over a 3-year lifespan!
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What if we scale to 250bn devices?
24
There are around 250bn MCUs deployed today and around 15bn IoT devices
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What if we scale to 250bn devices?
25
If our devices have longer lifespans this data starts to look even better!
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Privacy / Security
26
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IoT 2.0: Intelligence on Things
27
Google Assistant
nest
IoT 1.0:
Internet
of Things
TinyML will soon be everywhere!
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What is a Machine Learning Sensor?
28
Sensor 2.0
Privacy by Design
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We suggest transparency as a core value
29
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Materiality and Risk in the Age of Pervasive AI Sensors
30
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Evolution of Sensors…
31
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… and their impact on Responsible AI
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Valid and Reliable
Safe
Secure and Resilient
Accountable and Transparent
Explainable and Interpretable
Privacy Enhanced
Fair with Harmful Bias Managed
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How can TinyML support Responsible AI?
33
Accessibility / Education
Sustainability / Conservation
Privacy / Security
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