Global AI Predictor and Future Forecaster: How to win the ‘war’ against pandemics, climate change, poverty and non-intelligence
Global Predictor and Future Forecaster AI Platform: Learning the Rules to Predict the Future on topic ranging from disease outbreaks to space exploration and machines taking over the world
It is the problem of uncertainty and uncertainty quantification, which pervades our life, not mentioning the future.
"You are uncertain... about everything in the future; much of the past is hidden from you; and there is a lot of the present about which you do not have full information. Uncertainty is everywhere and you cannot escape from it".
Many problems in the natural and social sciences and engineering are abundant with sources of uncertainty, subjective and objective; statistic and epistemic, structural and experimental; algorithmic or parametric.
Computer experiments on computer modelling and simulations are the most common approach to study problems in uncertainty quantification, propagation and management.
Creating a supercomputer with powerful predictive capacities as an AI Predictor and/or Future Forecaster could become a great and much needy invention.
The COVID-19 pandemic shows that the world needs a new systemic AI-based approach to predicting and forecasting, planning and performance management—one that informs rapid realignment of plans and actions and ensures human society resilience.
AI could play a critical role in waging a war against massive poverty, inequality, climate change, epidemics and pandemics, such as the current Covid-19 coronavirus one, stepping up to prepare humanity better for future global risks and pandemics.
The head of the World Health Organisation (WHO) warned in 2018: “A devastating epidemic can start in any country at any time, and kill millions of people, because we are not prepared.”
In a 2015 TED Talk The next outbreak? We’re not ready, Bill Gates used computer models to predict that a pathogen as virulent as the 1918 Spanish flu would kill 33 million people worldwide in just nine months. Gates laments that governments regularly conduct war simulations to test their preparedness, “war games”, but not pandemic simulations, “germ games”.
Global AI Predictor can help governments prepare their readiness for the next global threats with computer modelling and simulations in the same way as Narrow ML/DL/AI helps prepare nations for war through AI for military simulation and AI for military readiness.
Global AI Predictor and Future Forecaster is to run a global, AI-enabled world's data system that can predict what, why, how, when and where critical "black swan" events might occur providing advice, summon assistance and issue warnings in real time.
Global AI Predictor
AI as the last Black Swan event
https://www.linkedin.com/pulse/ai-last-black-swan-event-azamat-abdoullaev/?published=t
Why and How to Build Digital Superintelligence: Real AI, Superhuman Intelligent Machines, Superintelligent Machines, or Superintelligent AI
https://www.linkedin.com/pulse/why-how-build-digital-superintelligence-real-ai-azamat-abdoullaev/
The global Covid-19 pandemic: WWIII
https://www.linkedin.com/pulse/global-covid-19-pandemic-wwiii-azamat-abdoullaev/
A Global Strategy for the Post Pandemic World: AI for Everybody and Everything (AI4EE)
https://www.linkedin.com/pulse/ai-everybody-everything-ai4ee-universal-artificial-azamat-abdoullaev/
Global Artificial Intelligence (GAI): Narrow AI, Applied AI, ML&DL; Strong AI, Full AI, AGI; Global AI, Real AI, Superhuman Intelligence
https://www.linkedin.com/pulse/global-artificial-intelligence-gai-narrow-ai-applied-mldl-abdoullaev/
Artificial Global Intelligence (AGI)
https://www.linkedin.com/pulse/artificial-global-intelligence-converging-general-big-abdoullaev/
Additional Materials
Coronavirus: The role of AI in the ‘war’ against epidemics and pandemics
What role can artificial intelligence play in battling against epidemics and pandemics, such as the current Covid-19 coronavirus one? Data science is stepping up to back up medical science and to prepare humanity better for future pandemics.
In a perfect world of ubiquitous, connected, affordable global healthcare – as advocated by the WHO – an infected person quickly receives medical attention and details of the illness are shared into a global, AI-enabled data system that can provide advice, summon assistance and issue warnings in real time.
Predictive analytics from McKinsy Global Institute
Planning for uncertainty: Performance management under COVID-19
https://www.mckinsey.com/…/planning-for-uncertainty-perform…
Post-Pandemic World Outlook: zero virus life, zero waste to landfill, zero fossil fuel energy use, zero process water use, and zero greenhouse-gas emissions
How growth can help Europe’s companies face the coming economic crisis
https://www.mckinsey.com/…/how-growth-can-help-europes-comp…
https://www.mckinsey.com/…/covid-19-implications-for-busine…
Crushing coronavirus uncertainty: The big ‘unlock’ for our economies
https://www.mckinsey.com/…/crushing-coronavirus-uncertainty…
To emerge stronger from the COVID-19 crisis, companies should start reskilling their workforces now
https://www.mckinsey.com/…/to-emerge-stronger-from-the-covi…
Leadership in a crisis: Responding to the coronavirus outbreak and future challenges
https://www.mckinsey.com/…/leadership-in-a-crisis-respondin…
How to restart national economies during the coronavirus crisis
https://www.mckinsey.com/…/how-to-restart-national-economie…
The Bio Revolution: Innovations transforming economies, societies, and our lives
Taking ownership of a sustainable future
https://www.mckinsey.com/…/the-bio-revolution-innovations-t…
https://www.mckinsey.com/…/taking-ownership-of-a-sustainabl…
The Future of Strategic Decision-Making
https://www.globalforesightsummit.com/talks/roger-spitz-the-future-of-strategic-decision-making
FACEBOOK'S PROPHET
https://facebook.github.io/prophet/
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Pandemic Predictors of zoonotic diseases
Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.
Predict, set up by Dennis Carroll in 2009. It estimated that there are 1.6 million unknown viral species in animals, of which 700,000 could infect humans. Predict’s funding was withdrawn by the US government in October 2019.
In the past 50 years, more than 1,500 new pathogens have been discovered, 70% of which have proved to be of animal origin, according to WHO (2018) statistics.
COVID-19: Imperial researchers model likely impact of public health measures
Combining multiple measures
In the current absence of vaccines and effective drug treatments, there are several public health measures countries can take to help slow the spread of the COVID-19. The team focused on the impact of five such measures, alone and in combination:
- Home isolation of cases – whereby those with symptoms of the disease (cough and/or fever) remain at home for 7 days following the onset of symptoms
- Home quarantine – whereby all household members of those with symptoms of the disease remain at home for 14 days following the onset of symptoms
- Social distancing – a broad policy that aims to reduce overall contacts that people make outside the household, school or workplace by three-quarters.
- Social distancing of those over 70 years – as for social distancing but just for those over 70 years of age who are at highest risk of severe disease
- Closure of schools and universities
Modelling available data, the team found that depending on the intensity of the interventions, combinations would result in one of two scenarios.
In the first scenario, they show that interventions could slow down the spread of the infection but would not completely interrupt its spread. They found this would reduce the demand on the healthcare system while protecting those most at risk of severe disease. Such epidemics are predicted to peak over a three to four-month period during the spring/summer.
In the second scenario, more intensive interventions could interrupt transmission and reduce case numbers to low levels. However, once these interventions are relaxed, case numbers are predicted to rise. This gives rise to lower case numbers, but the risk of a later epidemic in the winter months unless the interventions can be sustained.
https://www.imperial.ac.uk/news/196234/covid19-imperial-researchers-model-likely-impact/
The Perils of Applying AI Prediction to Complex Decisions
Recent research sheds light on the negative outcomes of blurring correlation and cause when applying machine learning.
https://sloanreview.mit.edu/article/the-perils-of-applying-ai-prediction-to-complex-decisions/