Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

2010 ◽  
Vol 5 (4) ◽  
pp. 13-18 ◽  
Author(s):  
I Arel ◽  
D C Rose ◽  
T P Karnowski
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Intelligence Research ◽  
New Frontier

Where the Sun never Shines

2018 ◽  
Vol 4 (1) ◽  
pp. 133-154
Author(s):  
Johannes Bruder
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Neuroscience ◽  
Machine Learning Algorithms ◽  
The Sun ◽  
Artificial Intelligence Research ◽  
Creative Cognition ◽  
Long Time ◽  
Creative Problem ◽  
Spontaneous Thought

Abstract In this paper, I elaborate on deliberations of “post-enlightened cognition” between cognitive neuroscience, psychology and artificial intelligence research. I show how the design of machine learning algorithms is entangled with research on creativity and pathology in cognitive neuroscience and psychology through an interest in “episodic memory” and various forms of “spontaneous thought”. The most prominent forms of spontaneous thought - mind wandering and day dreaming - appear when the demands of the environment abate and have for a long time been stigmatized as signs of distraction or regarded as potentially pathological. Recent research in cognitive neuroscience, however, conceptualizes spontaneous thought as serving the purpose of, e. g., creative problem solving and hence invokes older discussions around the links between creativity and pathology. I discuss how attendant attempts at differentiating creative cognition from its pathological forms in contemporary psychology, cognitive neuroscience, and AI puts traditional understandings of rationality into question.

scholarly journals The A.I. storyteller: Perpetuating orality using developments in machine learning

2021 ◽  
Author(s):  
Soo Anne Mahabir Mahabir
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Physical Computing ◽  
Generative Art ◽  
Artificial Intelligence Research ◽  
Theatrical Design ◽  
Video Recordings ◽  
Art Philosophy ◽  
Key Aspects

Oral histories are a part of all cultures and societies, however, our knowledge and interest in these practices has waned, and arguably with it, a sense of social identity and belonging in many contemporary communities and cultures. This paper pulls from aspects of experiential and theatrical design, generative art philosophy, physical computing, and machine learning in artificial intelligence research combined with a theoretical foundation of Adorno, Benjamin, and Ong to discuss and propose the creation of an embodied and immersive story experience. This project will overturn key aspects of traditional orality to encourage interactivity with, and ownership of, the stories and will prompt discussion about its use as an archival process that will promote perpetuation rather than preservation, moving beyond the current processes of audio and video recordings.

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

2020 ◽  
pp. 084653712095429
Author(s):  
Hussam Kaka ◽  
Euan Zhang ◽  
Nazir Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Multiple Sclerosis ◽  
Deep Learning ◽  
Intracranial Aneurysm ◽  
Intracranial Hemorrhage ◽  
Tumor Imaging ◽  
Stroke Imaging ◽  
Spine Imaging ◽  
New Frontier

There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.

scholarly journals Mapping the landscape of Artificial Intelligence applications against COVID-19

2020 ◽  
Vol 69 ◽  
pp. 807-845 ◽  
Author(s):  
Joseph Bullock ◽  
Alexandra Luccioni ◽  
Katherine Hoffman Pham ◽  
Cynthia Sin Nga Lam ◽  
Miguel Luengo-Oroz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Open Science ◽  
Data Sources ◽  
World Health ◽  
Artificial Intelligence Research ◽  
Non Invasive ◽  
Multiple Data ◽  
The World ◽  
Health Organization

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

scholarly journals The A.I. storyteller: Perpetuating orality using developments in machine learning

2021 ◽  
Author(s):  
Soo Anne Mahabir Mahabir
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Theoretical Foundation ◽  
Physical Computing ◽  
Generative Art ◽  
Artificial Intelligence Research ◽  
Theatrical Design ◽  
Video Recordings ◽  
Art Philosophy ◽  
Key Aspects

Oral histories are a part of all cultures and societies, however, our knowledge and interest in these practices has waned, and arguably with it, a sense of social identity and belonging in many contemporary communities and cultures. This paper pulls from aspects of experiential and theatrical design, generative art philosophy, physical computing, and machine learning in artificial intelligence research combined with a theoretical foundation of Adorno, Benjamin, and Ong to discuss and propose the creation of an embodied and immersive story experience. This project will overturn key aspects of traditional orality to encourage interactivity with, and ownership of, the stories and will prompt discussion about its use as an archival process that will promote perpetuation rather than preservation, moving beyond the current processes of audio and video recordings.

Analogizing the Internet of Things

2020 ◽  
Author(s):  
Scott J. Shackelford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Space Exploration ◽  
The Internet ◽  
New Frontier ◽  
The Internet Of Things

As any new frontier opens or industry matures, it’s natural to search for analogies and historical precedents to guide both our actions and perceptions. President Kennedy famously compared space exploration to seafaring.1 The rise of artificial intelligence and machine learning discussed further...

scholarly journals Automated Creation of Expert Systems with the InteKRator Toolbox

2021 ◽  
Author(s):  
Daan Apeldoorn ◽  
Torsten Panholzer
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Medical Informatics ◽  
Learning Approaches ◽  
Artificial Intelligence Research ◽  
New Evidence

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning.

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