Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3145
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Recent developments in artificial intelligence (AI) and machine learning (ML), driven by unprecedented data and computing capabilities, have transformed fields from computer vision to medicine, beginning to influence culture at large. These advances face key challenges: accuracy and trustworthiness issues, security vulnerabilities, algorithmic bias, lack of interpretability, and performance degradation when deployment conditions differ from training data. Fields lacking large datasets have yet to see similar impacts. This paper examines AI and ML's growing influence on engineering systems-from self-driving vehicles to materials discovery-while addressing safety and performance assurance. We analyze current progress and challenges to strengthen the engineering-AI synergy.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887848 | PMC |
http://dx.doi.org/10.1093/pnasnexus/pgaf030 | DOI Listing |
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