The End Of Knowledge - Vault 4/56 - AI For Good - 2020 - Food Revolution + Evolution of AI for Good

graph LR classDef main fill:#f9f9f9, font-weight:bold, font-size:14px classDef food fill:#ffcc99, font-weight:bold, font-size:14px classDef ethics fill:#ccff99, font-weight:bold, font-size:14px classDef aiGood fill:#99ccff, font-weight:bold, font-size:14px classDef scalability fill:#ff99cc, font-weight:bold, font-size:14px classDef impact fill:#ccccff, font-weight:bold, font-size:14px A[Food Revolution +
Evolution of AI
for Good] A --> B[Food Revolution] B --> B1[Food revolution
related to pandemic. 1] B --> B2[Monocropping reduces
habitats, spreads viruses. 2] B --> B3[Food system relies on
6 plant species. 3] B --> B4[Rural women produce
50% of food. 4] B --> B5[AI as common good
for agriculture. 5] B --> B6[Lack of connectivity
in rural Africa. 6] B --> B7[Danones carbon neutrality,
regenerative agriculture. 7] B --> B8[Food sovereignty,
relocalized food systems. 8] B --> B9[AI should accept
diversity, avoid standardization. 9] A --> C[Evolution of
AI for Good] C --> C1[Overview of AI ethics
evolution. 10] C --> C2[2015 AI conference
on beneficial AI. 11] C --> C3[2017 Asilomar AI
ethics principles. 12] C --> C4[Partnership on AI
formed in 2016. 13] C --> C5[Companies AI ethics principles,
IEEE initiative. 14] C --> C6[Consistent global AI
ethics principles. 15] A --> D[Early AI
for Good] D --> D1[Early AI for
good initiatives. 16] D --> D2[Early AI for social
good programs. 17] D --> D3[UN SDGs vision
for beneficial AI. 18] D --> D4[Scaling AI for good
needs collaboration. 19] D --> D5[AI for Good Global Summits
success. 20] D --> D6[AI Commons initiative
for collaboration. 21] A --> E[AI Challenges] E --> E1[AI excels in correlations,
struggles with causality. 22] E --> E2[Expanding AI capabilities
for causal reasoning. 23] E --> E3[Progress in AI ethics,
need for practice. 24] E --> E4[Connecting near-term AI risks,
long-term vision. 25] E --> E5[Developing platforms,
tools for AI scalability. 26] E --> E6[Economic sustainability
for AI for good. 27] A --> F[Impact and Implementation] F --> F1[Sector-specific AI ethics
frameworks needed. 28] F --> F2[AIs impact on planetary
health, gender equity. 29] F --> F3[Implementing AI ethics
principles in practice. 30] class A main class B,B1,B2,B3,B4,B5,B6,B7,B8,B9 food class C,C1,C2,C3,C4,C5,C6 ethics class D,D1,D2,D3,D4,D5,D6 aiGood class E,E1,E2,E3,E4,E5,E6 scalability class F,F1,F2,F3 impact

Resume:

1.- Emmanuel Faber, Chairman and CEO of Danone, discussed the food revolution and how the pandemic is related to the way we live and produce food.

2.- Monocropping agriculture has reduced natural habitats, bringing humans into contact with new viruses. Global travel enabled the virus to spread worldwide.

3.- The food system relies on just 6 plant species for 75% of calorie intake, resulting in a collapse of biodiversity.

4.- 1.7 billion women in rural areas produce 50% of worldwide food but own only 2% of land and receive little aid.

5.- AI must be governed as a common good to empower people. It can reduce food waste, support regenerative agriculture, and measure CO2 footprint.

6.- Lack of connectivity infrastructure in rural Africa is a barrier to farmers accessing data and benefiting from AI.

7.- Danone aims to be carbon neutral by working with farmers on regenerative agriculture practices and supporting a shift to plant-based diets.

8.- The food revolution means people want food sovereignty and a relocalized food system based on local food traditions and agricultural conditions.

9.- AI should be designed to accept diversity and exceptions to avoid standardizing thought. Danone invests in agricultural biodiversity startups.

10.- Francesca Rossi gave an overview of the evolution of AI ethics and AI for good over the past 5 years.

11.- In 2015, the Future of Life Institute conference on AI brought together a multidisciplinary group to discuss beneficial AI.

12.- In 2017, the Asilomar conference led to 23 AI ethics principles being published, focusing on values, research practices, and long-term issues.

13.- In 2016, major tech companies formed the Partnership on AI to address AI ethics issues through multi-stakeholder collaboration.

14.- Companies like IBM published their own AI ethics principles. IEEE launched an initiative on ethical considerations in AI.

15.- Many sets of AI ethics principles have been published globally, with consistent themes. The principles now need to be put into practice.

16.- AI for good initiatives started earlier, such as using AI for computational sustainability in 2008.

17.- Other early AI for good programs focused on social good, public health, conservation, and educating the next generation of AI researchers.

18.- The 17 UN Sustainable Development Goals provide a vision for beneficial AI, but society is not on track to achieve the goals.

19.- Scaling AI for good requires collaboration platforms, reusable patterns, sustainable business models, and convening problem owners and problem solvers.

20.- The AI for Good Global Summit has successfully brought together UN agencies and AI experts to connect problems with AI solutions.

21.- The AI Commons initiative aims to provide a collaborative environment to develop, experiment with, and share AI solutions for social good.

22.- Current AI excels at finding correlations in big data but struggles with causality, learning from small data, and adapting to disruptions.

23.- Expanding AI capabilities in causal reasoning, adaptability, and learning efficiency by studying the human mind could benefit AI for good applications.

24.- Significant progress has been made in AI ethics and AI for good, but more work is needed to put principles into practice.

25.- Initiatives must connect efforts to proactively address near-term AI risks with a long-term vision for beneficial AI.

26.- Platforms, tools, and business models to scale AI for good need further development, as do data and model sharing approaches.

27.- Making AI for good economically sustainable requires input from funders, stakeholders, domain experts, and technologists.

28.- Sector-specific AI ethics frameworks for areas like climate change, healthcare, and gender equity are needed.

29.- AI will impact planetary health and gender equity through both technical solutions and non-technical approaches like education and judicial guidelines.

30.- Ensuring AI ethics principles drive desired behaviors requires detailed interpretation and implementation beyond high-level tenets. Developing nations must be included.

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