Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:
Resume:
1.- Workshop on building a decision augmentation platform to address global issues and enable teams worldwide to collaborate on solving local/global problems.
2.- Risto Miikkulainen presented history, goals, and MVP of the project. Keynotes provided overview of this and similar projects, data, and architecture.
3.- Breakout sessions held on use cases, requirements, and new ideas. Groups reported back findings after lunch.
4.- Project started during pandemic to enable global collaboration on prediction and prescription for COVID-19 mitigation using AI.
5.- Built demo in a few months showing what AI can do. Expanded into global XPRIZE competition.
6.- Project Resilience aims to generalize the platform beyond pandemic to other societal challenges using same technology.
7.- Platform allows teams to work on common core, share data. Provides cloud-based decision support to those without access to data scientists.
8.- Data-based modeling approach used, in contrast to traditional epidemiological modeling. Allows prediction and prescription without assumptions.
9.- System discovered interesting solutions early on, like importance of schools/workplaces. Predicted Delta surge in India.
10.- Advised Iceland government on school reopenings in 2021. Showed potential to communicate with decision makers and have positive impact.
11.- Limitations: Missed Omicron surge as it happened everywhere at once with no early data to learn from.
12.- Rules were also evolved to provide explainable prescriptions, not just opaque neural networks, to help decision makers understand recommendations.
13.- XPRIZE pandemic response challenge engaged global teams to develop predictors and prescriptors. Informed some government policies.
14.- Found AI can leverage collective human insights by using human-designed prescriptions as starting point for evolutionary optimization.
15.- Continuing work on multiple fronts - with GPAI on multiple models, NSF on communication. Project Resilience broadening to new domains.
16.- Toby Phillips presented Oxford COVID-19 Government Response Tracker collecting data on policies across 180+ countries to enable modeling work.
17.- Key lessons: Political will a barrier more than tech for global data sharing. Systems established early as hard to add data later.
18.- Subnational data exponentially increases collection effort but national-level loses important granular policy variations. Careful design decisions needed.
19.- Relied on huge volunteer effort to manually collect policy data daily. Motivating long-term participation is hard. Frequency impacts viability.
20.- Used GitHub to share data but has limitations. Lacked standard APIs. Custom platform had to be built to use the data.
21.- Mongchul Lee emphasized considering entire data lifecycle, ensuring common understanding through ontologies/models, building trust, governance, decentralized user-centric approaches.
22.- Important to harmonize technologies like cloud, networks, IoT, big data, AI, and cybersecurity in an overall framework.
23.- Open architectures and data spaces needed to share data and create value. Marketplace and transactions mechanisms for incentives.
24.- Common global standards (e.g. from ITU and others) are key to enable interoperability and reuse of data at scale.
25.- Future architectures must consider end-to-end data, networking and AI - "DNA". Privacy, ethics, policy, regulation are critical elements.
26.- Olivier Francon demoed applying same framework used for COVID-19 to land use optimization for carbon capture.
27.- Models trained on historical land use and emissions data. Prescriptions suggest land use changes to maximize capture and minimize cost.
28.- Pareto front shows tradeoffs between competing objectives. Visualizations help decision makers understand impacts of policy choices.
29.- Can optimize at different geographical granularities. Local optimizations may have negative global impacts that need to be considered.
30.- Prem Natarajan presented an architecture enabling secure collaborative development and deployment of models by distributed teams.
31.- Key requirements: security, tools, CI/CD, data management, cost efficiency, access control, insights sharing, and "bring your own model" support.
32.- Uses AWS services like Studio and Model Registry, but aims to avoid lock-in. Enables end-to-end ML pipelines.
33.- Designed for re-use across multiple problem domains and use cases. Focuses on iterative MVPs to manage costs.
34.- Breakout 1 on use cases: Education to optimize resource allocation. Poverty reduction via policy simulation. Agriculture to optimize land use.
35.- Other use cases: Weapons systems and autonomous conflict. Assisting space debris avoidance maneuvers. Fishing stock management.
36.- Need to consider multi-objective optimization and unintended global consequences of local decisions. Work closely with domain experts.
37.- Breakout 2 on collaboration: Define clear objectives and value proposition. Identify stakeholders, sponsors, domain experts, volunteers.
38.- Plan tailored communication and engagement for each group. Build strategic partnerships for reputation and trust.
39.- Define resources, milestones, data sources, implementation roles. Consider data accuracy. Have regular demos and progress updates.
40.- Build partnerships before crises emerge to enable rapid response when needed. Data aggregation across organizations a key missing role.
41.- Breakout 3 on technical challenges: Visualizing and explaining data and model uncertainty is critical but difficult.
42.- Need reusable project templates to accelerate new applications. Must consider data as a commodity and enable exchange.
43.- Explore conversational interfaces for reports, explanations and Q&A. But beware over-reliance on generative AI.
44.- Continuous model updating, handling decision/effect time lags, and complex objective interdependencies are challenges.
45.- Must address compute access and cost barriers for developing country participation. Transfer learning may help with limited data.
46.- Post-its yielded use case ideas: sustainable peace, lifestyle optimization, surgery, resource management, education, migration, water, fishing, budgets.
47.- On collaboration: Easier data collection, engaging public and domain experts, common vocabularies, tailored communication, know when to end failing efforts.
48.- Technical ideas: Reusable templates, data-for-compute exchange, conversational AI, model uncertainty, generative AI for transparency, engaging startups.
49.- Visualizing uncertainty was top technical challenge. Also latency, complex objective trade-offs, explainability, infrastructure access.
50.- Generative AI holds promise for easier explanations but has risks. No-code tools can accelerate adoption. Model cards aid transparency.
51.- Bias cannot be eliminated as it is fundamental to human and AI reasoning. But diversity of perspectives helps.
52.- Current decision makers often don't directly use analytical/AI tools. Need to tailor UX to their workflows to get adoption.
53.- Best to engage decision makers from the start. Provide relevant outputs in their preferred formats. Show tangible benefits.
54.- Enable them to interactively explore tradeoffs and uncertainties. Support the entire decision lifecycle, not just the decision point.
55.- Adoption may be easier if it provides "cover" for unpopular but necessary decisions. Build trust gradually.
56.- Consider the baseline - AI may not be perfect but can still improve on human-only decision making in complex environments.
57.- Communicate probabilistic uncertainty intuitively. Set appropriate expectations that there will still be some errors.
58.- Project Resilience relies heavily on volunteers and needs help with development, testing, deployment, outreach and more.
59.- Bi-weekly open community meetings held to coordinate work. Recordings available. New contributors actively welcomed.
60.- Workshop materials to be shared via website and email. Ongoing collaboration enabled via mailing list. More workshops planned.
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