Editing Collaborative intelligence (section)

==AI and Collaborative Intelligence==
Although many sources warn that AI may cause the extinction of the human species,<ref>{{Cite web |last=Mitchell |first=Melanie |date=2023-04-23 |title=Do half of AI researchers believe that there's a 10% chance AI will kill us all? |url=https://aiguide.substack.com/p/do-half-of-ai-researchers-believe |website=AI: A Guide for Thinking Humans}}</ref> humans may cause our own extinction via [[climate change]], [[ecosystem|ecosystem disruption]], decline of our ocean lifeline, increasing [[Mass murder|mass murders]] and [[police brutality]], and an [[arms race]] that could trigger [[World War III]], driving humanity extinct before AI gets a chance. The surge of [[open source]] applications in generative AI demonstrates the power of collaborative intelligence (AI-human C-IQ) among distributed, autonomous agents, sharing achievements in collaborative partnerships and networks. The successes of small open source experiments in generative AI provide a model for a paradigm shift from centralized, hierarchical control to decentralized bottom-up, evolutionary development.<ref>{{Cite web |last1=Patel |first1=Dylan |last2=Ahmad |first2=Afzal |date=2023-05-04 |title=Google "We Have No Moat, And Neither Does OpenAI" |url=https://www.semianalysis.com/p/google-we-have-no-moat-and-neither |access-date=2023-08-07 |website=www.semianalysis.com |language=en}}</ref> 
The key role of AI in collaborative intelligence was predicted in 2012 when Zann Gill wrote that collaborative intelligence (C-IQ) requires “multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem-solving network.”<ref>{{cite book | chapter-url=https://dl.acm.org/citation.cfm?id=2212794 | doi=10.1145/2212776.2212794 | chapter=User-driven collaborative intelligence | title=CHI '12 Extended Abstracts on Human Factors in Computing Systems | date=2012 | last1=Gill | first1=Zann | pages=161–170 | isbn=9781450310161 | s2cid=15027953 }}</ref> Gill’s ACM paper has been cited in applications ranging from an NIH (U. S. National Institute of Health) Center for Biotechnology study of human robot collaboration,<ref>{{cite journal|pmid=33659886 |date=2021 |last1=Vinanzi |first1=S. |last2=Cangelosi |first2=A. |last3=Goerick |first3=C. |title=The collaborative mind: Intention reading and trust in human-robot interaction |journal=iScience |volume=24 |issue=2 |page=102130 |doi=10.1016/j.isci.2021.102130 |pmc=7890414 |bibcode=2021iSci...24j2130V }}</ref> to an assessment of cloud computing tradeoffs.<ref>{{Cite journal |last1=Magalhães |first1=W. F. |last2=Farias |first2=M. C. De |last3=Gomes |first3=H. M. |last4=Marinho |first4=L. B. |last5=Aguiar |first5=G. S. |last6=Silveira |first6=P. |date=June 2020 |title=Evaluating Edge-Cloud Computing Trade-Offs for Mobile Object Detection and Classification with Deep Learning |url=https://www.academia.edu/90310305 |journal=Journal of Information and Data Management |volume=11 |issue=1 |pages=3–19 |doi=10.5753/jidm.2020.2026 |issn=2178-7107|doi-access=free }}</ref> A key application domain for collaborative intelligence is risk management, where preemption is an anticipatory action taken to secure first-options in maximising future gain and/or minimising loss.<ref>{{Cite journal |last=Ng |first=Provides |title=Preemptive Futures: Entropic and Negentropic Information in Speculative Design |url=https://www.academia.edu/100218458 |journal=Proceedings of the 10th Conference on Computation, Communication, Aesthetics & X |date=2022 |page=85 |doi=10.24840/xCoAx_2022_16|doi-access=free }}</ref> Prediction of gain/ loss scenarios can increasingly harness AI analytics and predictive systems designed to maximize collaborative intelligence. Other collaborative intelligence applications include the study of social media and policing, harnessing computational approaches to enhance collaborative action between residents and law enforcement.<ref>{{Cite thesis |degree=PhD |url=https://repository.iiitd.edu.in/xmlui/handle/123456789/509 |title=Social media and policing : Computational approaches to enhancing collaborative action between residents and law enforcement |date=April 2017 |publisher=IIIT-Delhi |last1=Sachdeva |first1=Niharika |last2=Kumaraguru |first2=Ponnurangam (Advisor) }}</ref> In their Harvard Business Review essay, Collaborative Intelligence: Humans and AI Are Joining Forces –
Humans and machines can enhance each other’s strengths, authors H. James Wilson and Paul R. Daugherty report on research involving 1,500 firms in a range of industries, showing that the biggest performance improvements occur when humans and smart machines work together, enhancing each other’s strengths.<ref>{{Cite journal |last1=Wilson |first1=James H. |last2=Daugherty |first2=Paul R. |title=Collaborative Intelligence: Humans and AI Are Joining Forces |url=https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces |journal=Harvard Business Review |date=July 2018 |publication-date=July–August 2018 |pages=114–123}}</ref>