Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Industry 4.0.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Signoroni NatComm23 14.png|240px]]</div>
'''"[[Journal:Cybersecurity impacts for artificial intelligence use within Industry 4.0|Cybersecurity impacts for artificial intelligence use within Industry 4.0]]"'''
'''"[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]"'''


In today’s modern digital manufacturing landscape, new and emerging technologies can shape how an organization can compete, while others will view those technologies as a necessity to survive, as manufacturing has been identified as a critical infrastructure. Universities struggle to hire university professors that are adequately trained or willing to enter academia due to competitive salary offers in the industry. Meanwhile, the demand for people knowledgeable in fields such as [[artificial intelligence]], [[Informatics (academic field)|data science]], and [[cybersecurity]] continuously rises, with no foreseeable drop in demand in the next several years. This results in organizations deploying technologies with a staff that inadequately understands what new cybersecurity risks they are introducing into the company. This work examines how organizations can potentially mitigate some of the risk associated with integrating these new technologies and developing their workforce to be better prepared for looming changes in technological skill need. ('''[[Journal:Cybersecurity impacts for artificial intelligence use within Industry 4.0|Full article...]]''')<br />
Full [[laboratory automation]] is revolutionizing work habits in an increasing number of clinical [[microbiology]] facilities worldwide, generating huge streams of [[Imaging|digital images]] for interpretation. Contextually, [[deep learning]] (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic [[Bacteria|bacterial]] [[Cell culture|culture]] plates, including presumptive [[pathogen]] identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... ('''[[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Full article...]]''')<br />
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Latest revision as of 15:02, 3 June 2024

Fig1 Signoroni NatComm23 14.png

"Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology"

Full laboratory automation is revolutionizing work habits in an increasing number of clinical microbiology facilities worldwide, generating huge streams of digital images for interpretation. Contextually, deep learning (DL) architectures are leading to paradigm shifts in the way computers can assist with difficult visual interpretation tasks in several domains. At the crossroads of these epochal trends, we present a system able to tackle a core task in clinical microbiology, namely the global interpretation of diagnostic bacterial culture plates, including presumptive pathogen identification. This is achieved by decomposing the problem into a hierarchy of complex subtasks and addressing them with a multi-network architecture we call DeepColony ... (Full article...)
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