Knowledge Vault 4 /7 - AI For Good 2017
Investments, Economic Aspects and Designing the Future
Eric Horvitz
< Resume Image >
Link to IA4Good VideoView Youtube Video

Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef collaboration fill:#d3f4ff, font-weight:bold, font-size:12px classDef models fill:#ffd3e0, font-weight:bold, font-size:12px classDef data fill:#d3ffd4, font-weight:bold, font-size:12px classDef methods fill:#fff3d3, font-weight:bold, font-size:12px classDef applications fill:#f3d3ff, font-weight:bold, font-size:12px classDef challenges fill:#f9f9d4, font-weight:bold, font-size:12px A[Investments, Economic Aspects
and Designing the
Future] A --> B[AI for good needs
deep collaboration. 1] B --> C[Engagement models must
be custom-tailored. 2] C --> D[Coalitions around expertise,
data essential. 3] D --> E[Leverage existing infrastructure,
hidden data. 4] E --> F[AI includes various
sub-disciplines, methods. 5] A --> G[Useful perspective:
data to actions. 6] G --> H[Decision support,
human-computer collaboration common. 7] H --> I[Cholera: AI models
predict outbreaks. 8] I --> J[Predictive models provide
critical insights. 9] A --> K[Incredible data assets
often already exist. 10] K --> L[Cloud service uses
flight tracking data. 11] L --> M[Call patterns predicted
Rwanda earthquake epicenter. 12] M --> N[Call density disruptions
indicate needed assistance. 13] A --> O[Planning models triage
surveillance resources. 14] O --> P[Build shared data resources
through coalitions. 15] P --> Q[Share data, infrastructure
experiences widely. 16] A --> R[Existing AI methods
need domain engagement. 17] R --> S[No cookie-cutter approach,
tailor models. 18] S --> T[Lab success to services:
stakeholder collaboration. 19] A --> U[Healthcare AI progress
slower than expected. 20] U --> V[Understand human factors,
ergonomics, workflows. 21] V --> W[Lab ideas often premature,
need adaptation. 22] A --> X[Convincing industries
to adopt AI challenging. 23] X --> Y[Curation, iteration needed
for AI success. 24] A --> Z[Speaker missed summit,
attended remotely. 25] Z --> AA[Talk delivered remotely
at early hour. 26] A --> AB[Examples illustrate AI
challenges, opportunities. 27] AB --> AC[Engaging governments,
industries remains challenging. 28] AC --> AD[Panel resonated with
discussed challenges. 29] AD --> AE[Speaker appreciated
sharing thoughts remotely. 30] class B,C,D,E,F collaboration class G,H,I,J applications class K,L,M,N data class O,P,Q collaboration class R,S,T data class U,V,W methods class X,Y challenges class Z,AA challenges class AB,AC,AD,AE challenges

Resume:

1.- Harnessing AI for good requires deep engagement and collaboration between AI experts, domain experts, government, industry, academia and civil society.

2.- Models of engagement vary and require custom tailoring for each application.

3.- Success requires coalitions around expertise, infrastructure, and data. Expertise helps select problems, identify pain points and understand possibilities.

4.- Existing infrastructure and human resources in domains can often be leveraged, along with access to hidden data streams and sets.

5.- "AI" refers to many different sub-disciplines and methods like vision, speech, planning, robotics etc. which must all be considered for solutions.

6.- A useful perspective is the pipeline from sense data to probability distributions to actions, then considering the value of additional data.

7.- Applications often involve decision support and human-computer collaboration rather than full automation.

8.- Cholera is an example high-payoff area, where AI models predicting outbreaks could optimize hydration therapy and vaccine distribution, saving lives.

9.- Predictive models using heterogeneous data provide critical insights for optimal infrastructure and resource allocation that reactive approaches lack.

10.- Incredible data assets often already exist if we look for them, e.g. using airplanes as weather sensors via flight tracking data.

11.- A cloud service was built showing live updated wind maps based on leveraging existing flight tracking data from the FAA.

12.- Cell phone call patterns after the 2008 Rwanda earthquake were used to predict the epicenter location within 17km.

13.- Disruptions in call density patterns over time and space can indicate areas needing assistance after disasters.

14.- Decision-theoretic planning models can triage where to send limited surveillance/assistance resources given uncertain inferences of need.

15.- The AI community should build shared data resources through coalitions of governments, private sector and civil society.

16.- Experiences and best practices with data and infrastructure should be widely shared.

17.- Much can be done with existing AI methods, but it requires deep engagement with domain experts to understand opportunities for impact.

18.- There is no cookie cutter approach - models must be iterated and tailored for each application and domain.

19.- Turning lab successes into real-world services is challenging and requires working with many stakeholders to understand how inferences are used.

20.- Healthcare is an example domain where AI progress has been shockingly slow in translating from lab to real-world use.

21.- Real-world challenges involve human factors, ergonomics, and the realities of existing workflows that must be deeply understood and accommodated.

22.- Ideas that seem mature in the lab often turn out to be premature or ill-suited for messy real-world contexts without adaptation.

23.- Convincing entrenched industries to adopt new AI technologies can be very challenging even when there are clear benefits.

24.- A long process of curation, iteration and working with stakeholders is needed to really make AI work in each application area.

25.- Speaker wishes he could have attended the full AI for Good week but had to be in the US.

26.- Talk was delivered remotely at 4:55am from Seattle, just after speaker arrived there from Washington DC.

27.- Examples given aimed to illustrate some key challenges and opportunities in harnessing AI for societal benefit.

28.- Engaging governments and industries to adopt beneficial AI solutions remains a major challenge for the field.

29.- The current panel resonated with the key challenges discussed around convincing stakeholders to harness AI capabilities.

30.- Speaker appreciated the opportunity to share thoughts and examples with the AI for Good community despite the early hour.

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