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

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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