scholarly journals Artificial Intelligence: Global Risk and Long Term Strategy for Ending Fast Take off

2016 ◽  
Vol 3 (2) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Global Risk

The association of artificial intelligence-enabled electrocardiogram-derived age (physiologic age) with atherosclerotic cardiovascular events in the community

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Medina-Inojosa ◽  
A Ladejobi ◽  
Z Attia ◽  
M Shelly-Cohen ◽  
B Gersh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bypass Graft ◽  
P Value ◽  
Funding Source ◽  
Atherosclerotic Cardiovascular Disease ◽  
Age Gap ◽  
Predicted Risk ◽  
Age And Sex

Abstract Background We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low<5%, intermediate 5–9.9%, high 10–19.9%, and very high≥20%. The primary endpoint was ASCVD and included fatal and non-fatal myocardial infarction and ischemic stroke; the secondary endpoint also included coronary revascularization [Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG)], TIA and Cardiovascular mortality. Events were validated in duplicate. Follow-up was truncated at 10 years for PCE analysis. The association between Age-Gap with ASCVD and expanded ASCVD was assessed with cox proportional hazard models that adjusted for chronological age, sex and risk factors. Models were stratified by PCE risk categories to evaluate the effect of PCE predicted risk. Results We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p<0.0001. The mean Age-Gap was 0.87±7.38 years. After adjusting for age and sex, those considered older by ECG, compared to their chronologic age had a higher risk for ASCVD when compared to those with <−2 SD age gap (considered younger by ECG). (Figure 1A), with similar results when using the expanded definition of ASCVD (data not shown). Furthermore, Age-Gap enhanced predicted capabilities of the PCE among those with low 10-year predicted risk (<5%): Age and sex adjusted HR 4.73, 95% CI 1.42–15.74, p-value=0.01 and among those with high predicted risk (>20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B). Conclusion The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

AS-032 Validation of the Global Risk Classification for Prediction of Long-Term Outcome after Unprotected Left Main Coronary Revascularization

2012 ◽  
Vol 109 (7) ◽  
pp. S17-S18
Author(s):  
Yong-Kyu Park ◽  
Duk-Woo Park ◽  
Yong-Giun Kim ◽  
Gyung-Min Park ◽  
Ki-Won Hwang ◽  
...  
Keyword(s):  
Coronary Revascularization ◽  
Risk Classification ◽  
Global Risk ◽  
Left Main ◽  
Term Outcome ◽  
Long Term Outcome

scholarly journals 100 Years of Progress in Applied Meteorology. Part III: Additional Applications

2019 ◽  
Vol 59 ◽  
pp. 24.1-24.35 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Branko Kosović ◽  
Scott W. McIntosh ◽  
Fei Chen ◽  
Kathleen Miller ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Food Security ◽  
Fire Behavior ◽  
Climate Information ◽  
Security Sector ◽  
Environmental System ◽  
Applied Meteorology ◽  
Agriculture And Food ◽  
Behavior Models

AbstractApplied meteorology is an important and rapidly growing field. This chapter concludes the three-chapter series of this monograph describing how meteorological information can be used to serve society’s needs while at the same time advancing our understanding of the basics of the science. This chapter continues along the lines of Part II of this series by discussing ways that meteorological and climate information can help to improve the output of the agriculture and food-security sector. It also discusses how agriculture alters climate and its long-term implications. It finally pulls together several of the applications discussed by treating the food–energy–water nexus. The remaining topics of this chapter are those that are advancing rapidly with more opportunities for observation and needs for prediction. The study of space weather is advancing our understanding of how the barrage of particles from other planetary bodies in the solar system impacts Earth’s atmosphere. Our ability to predict wildland fires by coupling atmospheric and fire-behavior models is beginning to impact decision-support systems for firefighters. Last, we examine how artificial intelligence is changing the way we predict, emulate, and optimize our meteorological variables and its potential to amplify our capabilities. Many of these advances are directly due to the rapid increase in observational data and computer power. The applications reviewed in this series of chapters are not comprehensive, but they will whet the reader’s appetite for learning more about how meteorology can make a concrete impact on the world’s population by enhancing access to resources, preserving the environment, and feeding back into a better understanding how the pieces of the environmental system interact.

Introducing an operational method to forecast long-term regional drought based on the application of artificial intelligence capabilities

2015 ◽  
Vol 127 (1-2) ◽  
pp. 361-380 ◽  
Author(s):  
Mohammad Reza Kousari ◽  
Mitra Esmaeilzadeh Hosseini ◽  
Hossein Ahani ◽  
Hemila Hakimelahi
Keyword(s):  
Artificial Intelligence ◽  
Operational Method ◽  
Regional Drought

scholarly journals Artificial Intelligence as a positive and negative factor in global risk

2008 ◽  
Author(s):  
Eliezer Yudkowsky
Keyword(s):  
Artificial Intelligence ◽  
Gene Pool ◽  
Cognitive Biases ◽  
Time Pressure ◽  
Upper Bounds ◽  
Global Risk ◽  
Theory Of Evolution ◽  
Cognitive Busyness ◽  
Stellar Astronomy ◽  
Biological Adaptation

By far the greatest danger of Artificial Intelligence (AI) is that people conclude too early that they understand it. Of course, this problem is not limited to the field of AI. Jacques Monod wrote: ‘A curious aspect of the theory of evolution is that everybody thinks he understands it’ (Monod, 1974). The problem seems to be unusually acute in Artificial Intelligence. The field of AI has a reputation for making huge promises and then failing to deliver on them. Most observers conclude that AI is hard, as indeed it is. But the embarrassment does not stem from the difficulty. It is difficult to build a star from hydrogen, but the field of stellar astronomy does not have a terrible reputation for promising to build stars and then failing. The critical inference is not that AI is hard, but that, for some reason, it is very easy for people to think they know far more about AI than they actually do. It may be tempting to ignore Artificial Intelligence because, of all the global risks discussed in this book, AI is probably hardest to discuss. We cannot consult actuarial statistics to assign small annual probabilities of catastrophe, as with asteroid strikes. We cannot use calculations from a precise, precisely confirmed model to rule out events or place infinitesimal upper bounds on their probability, as with proposed physics disasters. But this makes AI catastrophes more worrisome, not less. The effect of many cognitive biases has been found to increase with time pressure, cognitive busyness, or sparse information. Which is to say that the more difficult the analytic challenge, the more important it is to avoid or reduce bias. Therefore I strongly recommend reading my other chapter (Chapter 5) in this book before continuing with this chapter. When something is universal enough in our everyday lives, we take it for granted to the point of forgetting it exists. Imagine a complex biological adaptation with ten necessary parts. If each of the ten genes is independently at 50% frequency in the gene pool – each gene possessed by only half the organisms in that species – then, on average, only 1 in 1024 organisms will possess the full, functioning adaptation.

scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

scholarly journals Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery

2021 ◽  
Vol 96 (12) ◽  
pp. 3062-3070
Author(s):  
Abdulah A. Mahayni ◽  
Zachi I. Attia ◽  
Jose R. Medina-Inojosa ◽  
Mohamed F.A. Elsisy ◽  
Peter A. Noseworthy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Surgery ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Long Term Mortality

scholarly journals How should we regulate artificial intelligence?

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170360 ◽  
Author(s):  
Chris Reed
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Decision Making ◽  
Current System ◽  
General System ◽  
Fundamental Rights ◽  
Ex Post ◽  
Ex Ante ◽  
Human Decision

Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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