This file assists in planning a presentation at the Jefferson Lab's AI Lunch Series that I am to give on 2 September 2020, as per #688 .
Situating AI on the road from data sharing to societal impact
By its very nature, Artificial Intelligence depends on the availability of data at scale. In this presentation - which shall be available at https://doi.org/10.5281/zenodo.3996019 when it starts - we will look at a range of factors that influence the nature and scale of data sharing, from open science to disasters, from research infrastructures to ethics, from cooperation to competition. We will then delve into how these factors affect the data life cycle and the research cycle and explore how data sharing (or the lack of it) translates into societal impact. On that journey, we will watch out for ways in which AI can and does contribute to or benefit from the sharing of data and associated resources (or not), which will then form the basis of our discussion.
- For a list of past presentations of mine that have been recorded, see https://github.com/Daniel-Mietchen/events/blob/master/recordings.md.
- The societal impact of data sharing seen through the lens of AI
- The societal impact of data sharing in light of AI
- Societal impact of data sharing in light of AI
- Societal impact of data sharing, and what AI has got to do with it
- From data sharing to societal impact via AI
- AI on the road from data sharing to societal impact
- Situating AI on the road from data sharing to societal impact
- The societal impact of data sharing, considered through the lens of AI
- The societal impact of data sharing and what it means for AI
- Artificial intelligence in the context of data sharing, open science and disasters
- Playing the Wikipedia game from AI to Open science and back
- Putting AI on the red team
- Research with AI on the red team
- Research as an adversarial network playing against nature
- What if AI were (also) on science's red team?
- Wikipedia as training data
- Omdena is a collaborative platform to build innovative, ethical, and efficient AI solutions to real-world problems
- Climate TRACE Coalition
- Data4COVID19
- Wikidata concept tree generator
- Too Much Information: Can AI Cope With Modern Knowledge Graphs?
- Semantic AI
- Knowledge Graphs And Machine Learning -- The Future Of AI Analytics?
- Artificial Intelligence - The Next Digital Frontier?
- Knowledge Graph part 1 – Using AI insights to connect the dots and make better business decisions
- On the role of knowledge graphs in explainable AI
- Why Knowledge Graphs Are Foundational to Artificial Intelligence
- Knowledge Graphs For eXplainable AI
- Here are 10 ways AI could help fight climate change
- Climate change and machine learning
- How AI Is Helping Solve Climate Change
- Fighting climate change with AI
- Machine learning and artificial intelligence to aid climate change research and preparedness
- The Amazing Ways We Can Use AI To Tackle Climate Change
- Artificial Intelligence—A Game Changer for Climate Change and the Environment
- AI can fight climate change but there's a catch: Optimization doesn't automatically equal emissions reduction
- AI and Climate Change: How they’re connected, and what we can do about it
- Tackling Climate Change with Machine Learning
- How artificial intelligence can tackle climate change
- Climate Change AI
- AI for social good: unlocking the opportunity for positive impact
- AI for Good
- Why facilitating data sharing is of great importance to Artificial Intelligence
- New framework promotes ethical, secure data sharing for developing AI
- AI Strategy in The Age of Vertical Federated Learning and Data Sharing
- Machine Learning & EU Data Sharing Practices
- The impact of data access regimes on artificial intelligence and machine learning
- Data Sharing Challenges & AI
- Davos 2019: Why data sharing is key to AI in Industry 4.0
- The global AI agenda: Promise, reality, and a future of data sharing
- Data, metadata and the AI horizon
- Surfing the Mavericks of Data with AI
- Metadata: Fuel that Powers System Integrations and AI Context Recognition
- How AI and metadata are taking the hard work out of content discovery
- Metadata and Artificial Intelligence
- Got Metadata? Your AI Models Depend On It, But Your Storage Systems Probably Aren’t Collecting Enough
- AI and Metadata: Networking Lunch
- Using AI for Metadata Creation
- How AI Enhances Metadata Creation
- Global Corruption Report: Climate Change
- Depixellation? Or hallucination?
- Robot scientist
- Unconventional computing
- Collective Intelligence Design Playbook (beta)
- How can AI-enhanced collective intelligence enable new forms of community responses to the climate crisis?
- NESTA: Artificial Intelligence
- Teamwork in Health Care: Maximizing Collective Intelligence via Inclusive Collaboration and Open Communication
- UpRiver — a game that teaches the principles of flood prediction, specifically along the Zambezi River
- Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network
- Harnessing Collective Intelligence to Address Global Climate Change
- The changing face of expertise and the need for knowledge transfer
- Solving public problems with collective intelligence
- Is AI causing collective intelligence research to become less diverse?
- Alternative visions for the future of AI
- Mobilising collective intelligence to tackle the COVID-19 threat
- Reinventing Discovery: The New Era of Networked Science
- The research frontier: where next for AI and collective intelligence?
- Crisis mapping
- The Role of Cooperation in Responsible AI Development
- The Future of Minds and Machines: How artificial intelligence can enhance collective intelligence
- Experiments in distributed AI — Improving collective decisions of human groups
- Digitally Nudging Team Processes to EnhanceCollective Intelligence
- Tourist Aviation Emissions: A Problem of Collective Action
- Strategy in the Age of Artificial Intelligence
- Data governance: Organizing data for trustworthy Artificial Intelligence
- Data Governance as a Collective Action Problem
- Law and Norms in Collective Action: Maximizing Social Influence to Minimize Carbon Emissions
- The outbreak of cooperation
- Formal Models of Collective Action
- The collective action problem in primate territory economics
- Sending Out an S.O.S.: Public Safety Communications Interoperability as a Collective Action Problem
- FAIR data
- All Together Now: Collective Intelligence for Computer-Supported Collective Action
- Physarum machines imitating a Roman road network: the 3D approach
- Category:Physarum polycephalum
- Reaction-Diffusion Computing
- Al Gore's Nobel Acceptance Speech
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We must abandon the conceit that individual, isolated, private actions are the answer. They can and do help. But they will not take us far enough without collective action.
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We must understand the connections between the climate crisis and the afflictions of poverty, hunger, HIV-Aids and other pandemics. As these problems are linked, so too must be their solutions. We must begin by making the common rescue of the global environment the central organizing principle of the world community.
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- Collective action problem
- Dagen H
- Collective intelligence
- Advances in Physarum Machines
- Slime mould attacks simulates Tokyo rail network
- Encoding movies and data in DNA storage
- Cities in motion: how slime mould can redraw our rail and road maps
- Storing data in DNA is a lot easier than getting it back out
- Survey of Information Encoding Techniques for DNA
- The Rise of DNA Data Storage
- DNA could store all of the world's data in one room
- High information capacity DNA-based data storage with augmented encoding characters using degenerate bases
- DNA digital data storage
- DNA storage: research landscape and future prospects
- The Capacity of DNA for Information Encoding
- DNA Data Storage Is Closer Than You Think
- MAPLE (modular automated platform for large-scale experiments), a robot for integrated organism-handling and phenotyping
- AI Should not Leave Structured Data Behind!