forked from harvard-edge/cs249r_book
-
Notifications
You must be signed in to change notification settings - Fork 0
/
ai_for_good.qmd
112 lines (65 loc) · 7.39 KB
/
ai_for_good.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
# AI for Good
All of this technological discussion around embedded AI and TinyML we have been talking about so far has the potential to transform humanity's greatest challenges. Initiatives under "AI for Good" promote the development of AI to further the UN Sustainable Development Goals using embedded AI technologies, expanding access to AI education, amongst other things. By aligning AI progress with human values, goals, and ethics, the ultimate goal of ML systems (at any scale) is to be a technology that reflects human principles and aspirations.
> The "AI for Good" movement plays a critical role in cultivating a future where an AI-empowered society is more just, sustainable, and prosperous for all of humanity.
::: {.callout-note collapse="true"}
## Learning Objectives
* coming soon.
:::
## Introduction
To give ourselves a framework around which to think about AI for social good, we will be following the UN Sustainable Development Goals (SDGs). The UN SDGs are a collection of 17 global goals adopted by the United Nations in 2015 as part of the 2030 Agenda for Sustainable Development. The SDGs address global challenges related to poverty, inequality, climate change, environmental degradation, prosperity, and peace and justice.
[](https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.un.org%2Fsustainabledevelopment%2Fblog%2F2015%2F12%2Fsustainable-development-goals-kick-off-with-start-of-new-year%2F&psig=AOvVaw1vppNt_HtUx3YM8Tzd7s_-&ust=1695950945167000&source=images&cd=vfe&opi=89978449&ved=0CBAQjRxqFwoTCOCG1t-TzIEDFQAAAAAdAAAAABAD)
In the context of our book, here is how TinyML could potentially help advance at least _some_ of these SDG goals:
- **Goal 1 - No Poverty**: TinyML could help provide low-cost solutions for tasks like crop monitoring to improve agricultural yields in developing countries.
- **Goal 2 - Zero Hunger**: TinyML could enable localized and precise crop health monitoring and disease detection to reduce crop losses.
- **Goal 3 - Good Health and Wellbeing**: TinyML could help enable low-cost medical diagnosis tools for early detection and prevention of diseases in remote areas.
- **Goal 6 - Clean Water and Sanitation**: TinyML could monitor water quality and detect contaminants to ensure access to clean drinking water.
- **Goal 7 - Affordable and Clean Energy**: TinyML could optimize energy consumption and enable predictive maintenance for renewable energy infrastructure.
- **Goal 11 - Sustainable Cities and Communities**: TinyML could enable intelligent traffic management, air quality monitoring, and optimized resource management in smart cities.
- **Goal 13 - Climate Action**: TinyML could monitor deforestation and track reforestation efforts. It could also help predict extreme weather events.
The portability, lower power requirements, and real-time analytics enabled by TinyML make it well-suited for addressing several sustainability challenges faced by developing regions. Widespread deployment of power solutions has the potential to provide localized and cost-effective monitoring to help achieve some of the UN SDGs. In the rest of the sections, we will dive into the details of how TinyML is useful across many of the sectors that have the potential to address the UN SDGs.
## Healthcare
Explanation: Healthcare is a critical sector where timely interventions can save lives. TinyML can enable real-time monitoring and predictions, making healthcare more proactive and personalized.
- Remote health monitoring: Using TinyML for wearable health devices.
- Disease prediction and prevention: Early detection systems.
- Assistive technologies: Devices for the differently-abled.
## Agriculture
Explanation: With the global population rising, sustainable and efficient farming is crucial. TinyML can optimize farming practices, ensuring food security and sustainability.
- Precision agriculture: Monitoring soil, weather, and crop conditions.
- Pest and disease detection: Early warning systems.
- Sustainable farming practices: Optimizing water and fertilizer use.
## Science
Explanation: Scientific research often requires data collection in challenging environments or over extended periods. TinyML can empower researchers with real-time data analysis, enabling more efficient and in-depth exploration in various scientific domains.
- Space Exploration: Using TinyML for onboard data processing in satellites and rovers, enabling quicker decision-making in environments with communication lags.
- Oceanography: Deploying embedded systems for monitoring marine life, water quality, and underwater geological activities.
- Ecology: Utilizing TinyML for tracking animal migration, studying habitats, and understanding ecological changes.
- Physics and Chemistry: Enhancing experimental setups with real-time data analysis, aiding in quicker hypothesis testing and validation.
## Conservation and Environment
Explanation: The planet's ecosystems are under threat, and timely data can aid conservation efforts. TinyML can provide real-time insights into environmental conditions and wildlife behavior.
- Wildlife monitoring: Using embedded devices for tracking and behavior analysis.
- Pollution detection: Monitoring air and water quality in real-time.
- Climate change: Data collection and analysis for climate models.
## Disaster Response
Explanation: Natural disasters can have devastating effects, but early warnings and efficient post-disaster management can mitigate their impact. TinyML can enhance these systems, making them more responsive.
- Early warning systems: Predicting and detecting natural disasters.
- Post-disaster management: Using TinyML for damage assessment and resource allocation.
## Education and Outreach
Explanation: TinyML can help personalize learning experiences and provide assistive tools, democratizing education.
- STEM education access via tinyML
- Assistive tools: Devices to help with learning disabilities.
## Accessibility
Explanation: For a more inclusive world, tools and devices need to cater to people of all abilities. TinyML can power devices that bridge the gap, ensuring everyone has access to essential services and experiences.
- Devices for the differently-abled: Hearing aids, mobility devices, etc.
- Translation and communication tools: Breaking down language barriers.
## Infrastructure and Urban Planning
Explanation: As urban areas grow, managing infrastructure efficiently becomes paramount. TinyML can optimize various urban systems, making cities more livable and sustainable.
- Traffic management: Optimizing traffic flow and reducing congestion.
- Energy management: Smart grids and energy consumption optimization.
## Challenges and Considerations
Explanation: While AI for Good has immense potential, it's essential to address the challenges and ethical considerations to ensure the technology benefits all without causing harm.
- Data privacy and security in AI for Good applications.
- Ensuring equitable access to AI-powered solutions.
- Avoiding unintended negative consequences.
## Conclusion
Explanation: A reflective section that underscores the transformative potential of TinyML in various sectors, emphasizing the importance of responsible and ethical development.
- Reflecting on the transformative potential of TinyML.
- Encouraging responsible and ethical development and deployment.