Artificial intelligence, robotics and eye surgery: are we overfitted?

AbstractEye surgery, specifically retinal micro-surgery involves sensory and motor skill that approaches human boundaries and physiological limits for steadiness, accuracy, and the ability to detect the small forces involved. Despite assumptions as to the benefit of robots in surgery and also despite great development effort, numerous challenges to the full development and adoption of robotic assistance in surgical ophthalmology, remain. Historically, the first in-human–robot-assisted retinal surgery occurred nearly 30 years after the first experimental papers on the subject. Similarly, artificial intelligence emerged decades ago and it is only now being more fully realized in ophthalmology. The delay between conception and application has in part been due to the necessary technological advances required to implement new processing strategies. Chief among these has been the better matched processing power of specialty graphics processing units for machine learning. Transcending the classic concept of robots performing repetitive tasks, artificial intelligence and machine learning are related concepts that has proven their abilities to design concepts and solve problems. The implication of such abilities being that future machines may further intrude on the domain of heretofore “human-reserved” tasks. Although the potential of artificial intelligence/machine learning is profound, present marketing promises and hype exceeds its stage of development, analogous to the seventieth century mathematical “boom” with algebra. Nevertheless robotic systems augmented by machine learning may eventually improve robot-assisted retinal surgery and could potentially transform the discipline. This commentary analyzes advances in retinal robotic surgery, its current drawbacks and limitations, and the potential role of artificial intelligence in robotic retinal surgery.

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Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽

10.32473/edis-ae529-2018 ◽

2018 ◽

Vol 2018 (6) ◽

Author(s):

Yiannis Ampatzidis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Remote Sensing ◽

Computer Vision ◽

Precision Agriculture ◽

Agricultural Systems ◽

Disease Identification ◽

Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

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Artificial Intelligence & Machine Learning: a model for a new judicial system?

Revista Internacional Jurídica y Empresarial ◽

10.32466/eufv-r.i.j.e.2020.3.593.81-91 ◽

2020 ◽

pp. 81-91

Author(s):

Paula C. Arias

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

System Integration ◽

Judicial System ◽

Modern Life ◽

Technological Advances ◽

The Creation ◽

Almost All

Artificial Intelligence and Machine Learning are a result not only of technological advances but also of the exploitation of information or data, which has led to its expansion into almost all aspects of modern life, including law and its practice. Due to the benefits of these technologies, such as efficiency, objectivity, and transparency, the trend is towards the integration of Artificial Intelligence and Machine Learning in the judicial system. Integration that is advocated at all levels and, today, has been achieved mostly under the implementation of tools to assist the exercise of the judiciary. The "success" of this integration has led to the creation of an automated court or an artificially intelligent judge as a futuristic proposal.

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Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge

SN Applied Sciences ◽

10.1007/s42452-020-03559-4 ◽

2020 ◽

Vol 2 (11) ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Rob Walton ◽

Max Van Kleek ◽

Rafael Mantilla Montalvo ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Academic Literature ◽

The Past ◽

Technological Advances ◽

New Process ◽

Technical Systems ◽

Iot Devices ◽

Cyber Risk

AbstractWe explore the potential and practical challenges in the use of artificial intelligence (AI) in cyber risk analytics, for improving organisational resilience and understanding cyber risk. The research is focused on identifying the role of AI in connected devices such as Internet of Things (IoT) devices. Through literature review, we identify wide ranging and creative methodologies for cyber analytics and explore the risks of deliberately influencing or disrupting behaviours to socio-technical systems. This resulted in the modelling of the connections and interdependencies between a system's edge components to both external and internal services and systems. We focus on proposals for models, infrastructures and frameworks of IoT systems found in both business reports and technical papers. We analyse this juxtaposition of related systems and technologies, in academic and industry papers published in the past 10 years. Then, we report the results of a qualitative empirical study that correlates the academic literature with key technological advances in connected devices. The work is based on grouping future and present techniques and presenting the results through a new conceptual framework. With the application of social science's grounded theory, the framework details a new process for a prototype of AI-enabled dynamic cyber risk analytics at the edge.

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Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology

Toxicologic Pathology ◽

10.1177/0192623319881401 ◽

2019 ◽

Vol 48 (2) ◽

pp. 277-294 ◽

Cited By ~ 10

Author(s):

Oliver C. Turner ◽

Famke Aeffner ◽

Dinesh S. Bangari ◽

Wanda High ◽

Brian Knight ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Digital Pathology ◽

Data Generation ◽

Health Issues ◽

Analog To Digital ◽

Technological Advances ◽

Business Cases ◽

To Come ◽

Scientific Questions

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]

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Lions and Librarians: a Plea for the Intelligent Conservation of an Unnecessarily Endangered Species

Legal Information Management ◽

10.1017/s1472669619000197 ◽

2019 ◽

Vol 19 (02) ◽

pp. 88-91 ◽

Cited By ~ 1

Author(s):

Daniel Greenberg

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Endangered Species ◽

Legal Services ◽

Legal Information ◽

Vital Importance ◽

Technological Advances ◽

Wide Range ◽

Information Professional

AbstractThe role of the law librarian or legal information professional is thought by some to have been diminished significantly by technological advances which provide instant access to an enormous range of materials direct to individual users at their desks. The reality is that the wide range of instantly accessible materials makes the experience and knowledge of the information professional more important, not less; and imminently expected advances in machine learning and artificial intelligence are likely to confirm the vital importance of the legal information professional at the centre of legal services.

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Artificial intelligence with neural networks in optical measurement and inspection systems

at - Automatisierungstechnik ◽

10.1515/auto-2020-0006 ◽

2020 ◽

Vol 68 (6) ◽

pp. 477-487

Author(s):

Michael Heizmann ◽

Alexander Braun ◽

Markus Hüttel ◽

Christina Klüver ◽

Erik Marquardt ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Image Processing ◽

Optical Measurement ◽

Development Effort ◽

Processing Methods ◽

Machine Learning Methods ◽

Inspection Systems ◽

Classical Image

AbstractOptical measuring and inspection systems play an important role in automation as they allow a comprehensive and non-contact quality assessment of products and processes. In this field, too, systems are increasingly being used that apply artificial intelligence and machine learning, mostly by means of artificial neural networks. Results achieved with this approach are often very promising and require less development effort. However, the supplementation and replacement of classical image processing methods by machine learning methods is not unproblematic, especially in applications with high safety or quality requirements, since the latter have characteristics that differ considerably from classical image processing methods. In this paper, essential aspects and trends of machine learning and artificial intelligence for the application in optical measurement and inspection systems are presented and discussed.

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Human fActor

Ontology of Designing ◽

10.18287/2223-9537-2021-11-1-8-19 ◽

2021 ◽

Vol 11 (1) ◽

pp. 8-19

Author(s):

V.A. Soifer ◽

Keyword(s):

Artificial Intelligence ◽

Intelligent Systems ◽

Information Technologies ◽

Industrial Revolution ◽

Industrial Society ◽

Digital World ◽

Leading Role ◽

Stage Of Development ◽

Technological Advances ◽

Human Actors

We live in the era of the fourth industrial revolution and the natural question to ask are: 'What is next? Who will play a leading role in the new digital world – artificial intelligence (AI) or the humans?' In the search for answers, we need to make a short trip back in history. Technological advances have always led to changes in the social and economic struc-ture: harnessing the power of water and steam, mankind leapt from the hunter society to the agricultural society. The discovery of the electric power and conveyers paved way for the transition to the industrial society. With electronic information technologies, we have found ourselves at the modern stage of development, which is information society. What is the next step? While Industry 4.0 was concerned with the automation of physical & technical processes, the latest trends of Society 5.0 are mostly oriented toward the automation of 'thinking' processes and the human collabora-tion with intelligent systems. Artificial intelligence and the universal 'digitalization' are intended to promote the achievement of the main goals – an increase in life expectancy, improvement of the quality of life, and the emergence of human 'actors' who create the intellectual property objects, which are then materialized by means of cyber-physical systems.

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Virtual Reality in Biology: Can We Become Virtual Naturalists?

10.20944/preprints201910.0311.v3 ◽

2021 ◽

Author(s):

Juliano Morimoto ◽

Fleur Ponton

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Virtual Reality ◽

Virtual Worlds ◽

Mixed Reality ◽

Biology Education ◽

Educational Tool ◽

Technological Advances ◽

Biological Interest ◽

The Future

Technological advances made Virtual and Mixed Reality (VMR) accessible at our fingertips. However, only recently VMR has been explored for the teaching of biology. Here, we highlight how VMR applications can be useful in biology education, discuss about caveats related to VMR use that can interfere with learning, and look into the future of VMR applications in the field. We then propose that the combination of VMR with Machine Learning and Artificial Intelligence can provide unprecedented ways to visualise how species evolve in self-sustained immersive virtual worlds, thereby transforming VMR from an educational tool to the centre of biological interest.

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Artificial Intelligence and Learning

Education ◽

10.1093/obo/9780199756810-0269 ◽

2021 ◽

Author(s):

Jaekyung Lee ◽

Richard Lamb ◽

Sunha Kim

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Cognitive Abilities ◽

Human Beings ◽

Technological Advances ◽

Future Society ◽

Self Driving Cars ◽

Selection Of ◽

Applications Of Ai

Rapid technological advances, particularly recent artificial intelligence (AI) revolutions such as digital assistants (e.g., Alexa, Siri), self-driving cars, and cobots and robots, have changed human lives and will continue to have even bigger impact on our future society. Some of those AI inventions already shocked people across the world by wielding their power of surpassing human intelligence and cognitive abilities; see, for example, the examples of Watson (IBM’s supercomputer) and AlphaGo (Google DeepMind’s AI program) beating the human champions of Jeopardy and Go games, respectively. Then many questions arise. How does AI affect human beings and the larger society? How should we educate our children in the AI age? What changes are necessary to help humans better adapt and flourish in the AI age? What are the key enablers of the AI revolution, such as big data and machine learning? What are the applications of AI in education and how do they work? Answering these critical questions requires interdisciplinary research. There is no shortage of research on AI per se, since it is a highly important and impactful research topic that cuts across many fields of science and technology. Nevertheless, there are no effective guidelines for educational researchers and practitioners that give quick summaries and references on this topic. Because the intersection of AI and education/learning is an emerging field of research, the literature is in flux and the jury is still out. Thus, our goal here is to give readers a quick introduction to this broad topic by drawing upon a limited selection of books, reports, and articles. This entry is organized into three major sections, where we present commentaries along with a list of annotated references on each of the following areas: (1) AI Impacts on the Society and Education; (2) AI Enablers: Big Data in Education and Machine Learning; and (3) Applications of AI in Education: Examples and Evidence.

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