Talks & Topics

Zann Gill’s current talks focus on how her theoretical work, and studies in the history of ideas, reveal a clear, practical method for solving problems we now face. Reconciling once opposed views enables constructive innovation:

  • the industry view of supply chain management with the ecological view of ecosystem services, a term coined by ecologists to build this bridge;
  • the “compete to innovate” traditional Darwinian view with the power of next generation social networks;
  • individualism, which underpins collaborative autonomy and supports unique Game*Changers versus the traditional consensus model for problem-solving.

to make sense of the data cloud

Newman-network3Big Data and Predictive Analytics take for granted that, with enough data and the right tools, we can predict the future. This talk proposes instead that crowdsourcing platforms are poised for a breakthrough — capacity to evolve through use toward Ecosystem Utility that exploits synergies across computer data processing and human pattern recognition. Based on study of how evolution operates, and how thriving ecosystems provide platforms for co-evolution of species, collaborative intelligence characterizes one model for how distributed, multi-agent networks can “evolve in the cloud” as each unique agent autonomously contributes to its collaborative problem-solving ecosystem.

[talk originally given at Netflix]


ZannGill-CIQ-CDR-5-12The next generation of crowdsourcing platforms should be able to evolve, through use, toward Ecosystem Utility that exploits synergies across computer data processing and human pattern recognition. Beyond reliance on Predictive Analytics, collaborative intelligence emerges from Design Ecosystems — smart learning networks connecting humans and devices. Whereas collective intelligence identifies similarities in aggregates to improve prediction, next generation social networks are becoming crowdsourcing ecosystems where disparities among agents strengthen “genetic diversity” in the ecosystem. As in nature, each individual organism plays a unique role in a distributed, multi-agent network where each unique agent is an autonomous contributor in a collaborative intelligence ecosystem.

[talk originally given at Stanford University, LASER]


User-Driven Social Networks as Crowdsourcing Ecosystems
Gill-ACM-CHI2012Vernor Vinge proposed, “In network and interface research there is something as profound (and potentially wild) as Artificial Intelligence” an opportunity to create futures of intelligence. User experience data can co-evolve synergies across computer data processing and human capacity for pattern recognition, developing collaborative intelligence applications that engage distributed creativity, processing crowd-sourced analytics to plan and track projects, so that data gathered, bottom-up, can improve decision-making.

[talk originally given at ACM CHI]

for multi-agent innovation networks

Although we’re poised to experience the impact of what Garrett Hardin evocatively predicted in 1968 would escalate into a Tragedy of the Commons, where competition for scarce resources and “survival of the fittest” could threaten the delicate balance of Earth’s ecosystems, we’re also poised to harness the Public Internet, new media, persuasive technologies, social networks and ubiquitous computing to reverse the downward slump – to channel technological innovation in needed directions to support  social change, revitalized cities and a greener economy.

NASA-aerial2x3Zann Gill will describe how nine principles of ECO-logical design can “raise collaborative intelligence” of multi-agent networks. She’ll draw from her forthcoming book, What Daedalus told Darwin, and describe the strategic innovation network she developed for Kawasaki, Japan in the context of current related initiatives. Whereas collective intelligence identifies similarities in aggregates to improve prediction, collaborative intelligence identifies disparities among agents to spur innovation. As in nature, each individual organism plays a unique role in its ecosystem. Crowdsourcing platforms are poised for a breakthrough — capacity to evolve, through use, toward Ecosystem Utility that exploits synergies across computer data processing and human pattern recognition. Collaborative intelligence characterizes distributed, multi-agent networks where each unique agent is an autonomous contributor to its collaborative problem-solving ecosystem.

[talk originally given at Stanford University, Media X]


social nets, semantic webs & evolving systems

Although researchers have analyzed how social networks operate, from small organizations to nations and networks of people connected by similar values and objectives, insufficient attention has been paid to harnessing social networks for cross-disciplinary, collaborative problem-solving. The theory of evolution is seen by many as the greatest theoretical breakthrough of all time.
Gill-wordleZann Gill will introduce findings that call for a more complete interpretation of Darwin’s theory (Stephen Jay Gould thought “Darwinism” misrepresented Darwin) and argue that this more complete interpretation of Darwin’s theory would drive sustainable development and offer a model for seeding and evolving “innovation networks” to develop smart systems for eco-sustainability at the intersection of ICT and green tech.

Collaborative Intelligence describes a process that, like a spiral, converges toward a focus, while retaining the diversity of multiple agent priorities (utilities) and without requiring consensus.

Collaborative autonomy, a prerequisite for “raising collaborative IQ,” uses frameworks that facilitate autonomy of individual agents. By avoiding consensus, while enabling a Joint process to converge toward a Focused, shared outcome, rich raw material is retained and optimal results are achieved.

[talk originally given at DERI, National University of Ireland]



engaged citizens & smart systems for sustainable cities

losangeles-button-1Nature manifests directed innovation, with a series of “process design” principles that human-computer systems could emulate. Zann Gill will describe how the evolution of life harnesses innovation networks, directed processes that converge toward improved adaptation that cannot be predicted in advance as a goal. Collective intelligence taps the consensus “wisdom of crowds,” harnessing algorithms to transform diverse input into a better-than-average consensual output. In contrast, collaborative intelligence taps and retains the diversity of individuals, manifesting principles of evolutionary design, such as collaborative autonomy, to co-evolve by improving through time, innovating and adapting to continually changing ecosystems.

This talk describes how Nature’s dynamic design process principles apply to practical problems faced by enterprises, ranging from companies to social networks to cities and nations to evolve more intelligent capacity for emergent pattern recognition, decision-making, converging gradually toward sustainable operations.

[talk originally given at SAP Labs – Future Salon]



origins of life & emergence of intelligence

[talk given at NASA Ames Research Center]

The origin and synthesis of life are entwined with controversies over evolution and intelligence. Rather than argue for “one right answer” to the question of how life originated, she’ll describe how the origin of life demands thinking by analogy, logical extrapolation and design method, since material evidence is scarce. Research on the origin and evolution of life is a case study of the creative process in science, which can inform the synthetic disciplines: AI, ALife, robotics, and designing collaborative multi-agent systems.

Objective. To stir the “primordial big ideas alphabet soup” and watch how letters cluster to spell out one or more concepts worth pursuing. Zann Gill will present the hypothesis of her book: traits ascribed to mind, such as pattern recognition, may have counterparts in early life.

QUESTIONS from provocateurs & the audience, e.g.
1. Is the origin of life a proof in principle that Darwinian evolution does not fully explain how life advanced toward complexity?
2. self-aware systems. What mechanisms enable the acceleration of evolution, such as the Baldwin effect, a new deliberative Baldwin effect, group evolution, and self-improving systems? What are the implications of these mechanisms for evolving, self-aware technologies and for technology innovation?
3. Alife can be invisible to extant immune systems. Slime-9 fears: it could reproduce unchecked and run rampant. Personalized medicine dreams: it could enter living systems unmolested to perform useful tasks. Will smart biotechnologists become Intelligent Designers in our interest? Or is there another approach to “planetary protection” that we could harness if we were clever enough?
4. Sensor networks as intelligent, multi-agent systems. What is their potential to support decision-making toward environmental sustainability?
5. networked computer grids. Could they be used as a lens to understand how life began on Earth, or elsewhere in the universe?
6. Long “Turing” Bet 2029. What types of bio-inspired simulations will pass “the Turing test” to be deemed by biologists worthy of study as models revealing insights about life? What role will they perform?
7. Innovation networks for sustainability. What has NASA learned from space travel that can contribute to sustainable communities on Earth?
8. from concept to implementation.  What hurdles must we overcome to achieve more effective collaboratory initiatives?

Joe Betts-Lacroix
, Evocateur/ entrepreneur: Founder/ CTO of OQO; Harvard: earth, MIT: oceans, Caltech: proteins, IBM Research: usability
Rich Boyle, NASA Ames, Director BioVis (Bio-Visualization, Imaging and Simulation Technology Center)
Geoff Briggs, NASA Ames (ret.), Founder/ Director of the Center for Mars Exploration
Bruce Damer, CEO, DigitalSpace Corporation, Founder, Contact Consortium & (project EVOgrid)
Boris Debic, Google Software Engineer, Mars Society Project Director
Patty Jones
, NASA Ames, Division Chief (acting), Human Systems Integration Division
Chris McKay NASA Ames astrophysicist
Steve Omohundro, Self-Aware Systems
Mark Shirley 
NASA Ames Software Lead, LCROSS Lunar Mission
Jonathan Trent 
NASA Ames biologist ( nanotechnology & astrobiology)
Osher Yadgar
, Computer Scientist, SRI Artificial Intelligence Center

Filmed by Furious Ink Studios
Filmmakers: Kurt Stumbaugh & Igor Stalew


Autonomy | Pattern Recognition
APR-linkThe A-PR Hypothesis and Complex Systems

The A-PR Hypothesis characterizes how effective complexity emerges — the bootstrapping engine through which life evolves toward effective complexity.

The A-PR Hypothesis and the Origins of Life 
If the threshold of the origin of life was the moment when the first A-PR cycle occurred, then the A-PR Hypothesis resolves debates about how to define life.

The A-PR Hypothesis and Evolutionary Computing
Next generation social networks can become problem-solving ecosystems. Beyond targeting us as buyers, recommender systems can use our profiles to identify how we can best contribute to a problem-solving ecosystem. Future crowdsourcing platforms can improve capacity to aggregate data so that microtasks engaging A-PR cycles of distributed agents can be integrated, enabling our collaborative intelligence to address complex problems.

[talks originally given at ICCS (International Conference on Complex Systems, Origins (International Conference on the Origin of Life and Bioastronomy, and GECCO (International Conference on Genetic Algorithms & Evolutionary Computing)



from predicting to pioneering the future

ZannGill-DraperUTalk-6-13Making sense of today’s deluge of “Big Data” is a great IT challenge, requiring Design Synthesis. Predicting that crowdsourcing platforms will evolve into next generation social networks to support the emerging field of collaborative intelligence, Zann Gill’s talk describes a thought leadership space where supply chain management and sustainability are co-dependent. This space offers emerging potential for a range of new ventures dedicated to turning social networks into problem-solving ecosystems. Student discussion (last 20 minutes of the film) identified a range of new ventures conceived by this Draper U cohort, pioneering next generation social network business concepts, engaging several modules in the Draper U curriculum.

[talk originally given at Draper University]


Decision Support & Sustainable Remediation

san_francisco_baySustainable remediation taps all four quadrants of the prediction compass: First, accurate measurement and statistical prediction characterizes the hard sciences. Second, calculation of probabilities and risks within tolerance ranges characterizes the life sciences. Third, cost-benefit analysis of tradeoffs, subjective assessments and forecasts recognize that how experts and the community perceive the problem, and respond to proposed remediation plans, plays an active role in determining outcomes. And finally, innovation, harnessing new discoveries and technologies, may require new procedures and methods to analyze problems and implement solutions.
ZannGill-ParadoxofPredictionThe top two quadrants on the prediction compass are passive, assuming that prediction does not influence what it predicts. In contrast, the lower two quadrants are active, since how we perceive and represent the problem affects what action plan we design and how a solution is implemented. When many stakeholders with different expertise, priorities, and agendas must be engaged in formulating a sustainable remediation strategy, the challenge lies in creatively harnessing the inevitable uncertainty of this process.

[talk originally given at the Sustainable Remediation Forum SURF]


Natural Sustainability via Worldgaming
Bucky-linkThe World Game concept needed collaborative intelligence methods, the internet, ubiquitous mobile computing, and social networks that only now, half a century later, can implement this concept, harnessing game methods to environmental sustainability challenges.

There is only one revolution tolerable to all men, all societies, all political systems: revolution by design and invention.
Buckminster Fuller

[talk originally given at Rhode Island School of Design]


artificial life, decision support, & the environment
ventrellapostFrom ALife to “terraforming Mars,” our speculative thought experiments enhance awareness of Earth’s intricate web of life. Trying to synthesize life or to simulate life’s behaviors are both ways we speculate about evolution and the future. Updating Buckminster Fuller’s vision of World Game for eco-sustainability and world peace will promote constructive speculation and harness models developed by the artificial life community, moving beyond deterministic to emergent systems able to cope with uncertainty.

[originally a Biota Live podcast]


Learning = Innovation for Sustainability

Launch the Foothill College initiative to be a model sustainable community college campus, building a coalition between the campus as a meeting ground for Silicon Valley municipalities and innovators and its communities around a clean energy future.
[originally given as a keynote for the Foothill College Sustainability Launch]


Related topics:
       Zann Gill, short bio
       Zann Gill, a Journey
       Seven Mavericks
       Quotes & Questions
       Talks & Topics