Applications of Artificial Intelligence in Ophthalmology: General Overview

With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.

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CAPformer: Pedestrian Crossing Action Prediction Using Transformer

Sensors ◽

10.3390/s21175694 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5694

Author(s):

Javier Lorenzo ◽

Ignacio Parra Alonso ◽

Rubén Izquierdo ◽

Augusto Luis Ballardini ◽

Álvaro Hernández Saz ◽

...

Keyword(s):

Language Processing ◽

Autonomous Vehicles ◽

State Of The Art ◽

Image Data ◽

Vehicle Speed ◽

Action Prediction ◽

Kinematic Data ◽

Proposed Model ◽

Temporal Models ◽

Pedestrian Crossing

Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.

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Autonomous Vehicles – a New Challenge to Human Rights?

Przegląd Prawniczy Uniwersytetu im Adama Mickiewicza ◽

10.14746/ppuam.2019.9.04 ◽

2019 ◽

Vol 9 ◽

Author(s):

Dominika Iwan

Keyword(s):

Artificial Intelligence ◽

Human Rights ◽

Computer Science ◽

Autonomous Vehicles ◽

New Technologies ◽

Autonomous Vehicle ◽

Human Beings ◽

Human Rights Law ◽

Human Control ◽

And Robotics

New technologies, as autonomous vehicles are, disrupt the way people exist, and con-sequently with human rights. Research devoted to artificial intelligence and robotics moves freely and the destination, for the time being, is unknown. This is the reason why special attention should be paid to the ethics of these branches of computer science in order to prevent the creation of a crisis point, when human beings are no longer neces-sary.. The aim of this paper is to examine whether such development is a new challenge to human rights law and what happens when an autonomous vehicle drives an autono-mous human being. The paper also mentions the desirable level of human control over the machine so that human dignity, from which human rights originate, is preserved.

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PeTaL (Periodic Table of Life) and Physiomimetics

Designs ◽

10.3390/designs3030043 ◽

2019 ◽

Vol 3 (3) ◽

pp. 43

Author(s):

Shyam ◽

Friend ◽

Whiteaker ◽

Bense ◽

Dowdall ◽

...

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Periodic Table ◽

Design Tool ◽

Open Source Framework ◽

Evolutionary Success ◽

Computer Vision Applications ◽

Ontological Model ◽

Future Work ◽

Human Space Exploration

The Periodic Table of Life (PeTaL) is a system design tool and open source framework that uses artificial intelligence (AI) to aid in the systematic inquiry of nature for its application to human systems. This paper defines PeTaL’s architecture and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields has been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. An objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence, topology optimization and conventional modeling in problem solving. Specifically, specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools that support PeTaL include machine learning, natural language processing and computer vision. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life. Also discussed is the formation of V.I.N.E. (Virtual Interchange for Nature-inspired Exploration), a virtual collaborative aimed at generating data, research and applications centered on nature. Details of implementation will be presented in subsequent publications. Recommendations for future work are also presented.

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Chatbot Interaction with Artificial Intelligence: human data augmentation with T5 and language transformer ensemble for text classification

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03439-8 ◽

2021 ◽

Author(s):

Jordan J. Bird ◽

Anikó Ekárt ◽

Diego R. Faria

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Text Classification ◽

Classification Accuracy ◽

Intelligent System ◽

Human Interaction ◽

Training Data ◽

Fine Tuning ◽

Large Set ◽

Human Beings

AbstractIn this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP). Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. “Robot, can we have a conversation?”) and allows for better accessibility to AI by non-technical users.

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Scope of Artificial Intelligence in Law

10.20944/preprints201806.0474.v1 ◽

2018 ◽

Author(s):

Jyoti Dabass ◽

Bhupender Singh Dabass

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Natural Language Processing ◽

Language Processing ◽

Machine Learning Techniques ◽

Law Firms ◽

Legal Services ◽

Human Beings ◽

Learning Techniques

Over the years, artificial intelligence (AI) is spreading its roots in different areas by utilizing the concept of making the computers learn and handle complex tasks that previously require substantial laborious tasks by human beings. With better accuracy and speed, AI is helping lawyers to streamline work processing. New legal AI software tools like Catalyst, Ross intelligence, and Matlab along with natural language processing provide effective quarrel resolution, better legal clearness, and superior admittance to justice and fresh challenges to conventional law firms providing legal services using leveraged cohort correlate model. This paper discusses current applications of legal AI and suggests deep learning and machine learning techniques that can be applied in future to simplify the cumbersome legal tasks.

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Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽

10.1609/aimag.v40i3.4981 ◽

2019 ◽

Vol 40 (3) ◽

pp. 67-78

Author(s):

Guy Barash ◽

Mauricio Castillo-Effen ◽

Niyati Chhaya ◽

Peter Clark ◽

Huáscar Espinoza ◽

...

Keyword(s):

Artificial Intelligence ◽

Language Processing ◽

Cyber Security ◽

Question Answering ◽

Intent Recognition ◽

Affective Content ◽

Learning Plan ◽

Dialog System ◽

Affective Content Analysis ◽

Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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Autonomous Vehicles in the Mobility System

The Oxford Handbook of Mobile Communication and Society ◽

10.1093/oxfordhb/9780190864385.013.33 ◽

2020 ◽

pp. 500-514

Author(s):

Thilo von Pape

Keyword(s):

Artificial Intelligence ◽

Mobile Phones ◽

Mobile Communication ◽

Autonomous Vehicles ◽

Communication Research ◽

Collective Control ◽

System P ◽

Mobility System ◽

And Control ◽

Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

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Welcome to the marriage of human capability and artificial intelligence

Strategic HR Review ◽

10.1108/shr-11-2019-0085 ◽

2019 ◽

Vol 19 (1) ◽

pp. 10-14

Author(s):

Ryan Scott ◽

Malcolm Le Lievre

Keyword(s):

Artificial Intelligence ◽

Team Building ◽

Workplace Change ◽

Content Type ◽

The Future ◽

Human Capability ◽

Mind Set ◽

Future Work ◽

Crucial Part ◽

Practical Implications

Purpose The purpose of this paper is to explore insights methodology and technology by using behavioral to create a mind-set change in the way people work, especially in the age of artificial intelligence (AI). Design/methodology/approach The approach is to examine how AI is driving workplace change, introduce the idea that most organizations have untapped analytics, add the idea of what we know future work will look like and look at how greater, data-driven human behavioral insights will help prepare future human-to-human work and inform people’s work with and alongside AI. Findings Human (behavioral) intelligence will be an increasingly crucial part of behaviorally smart organizations, from hiring to placement to adaptation to team building, compliance and more. These human capability insights will, among other things, better prepare people and organizations for changing work roles, including working with and alongside AI and similar tech innovation. Research limitations/implications No doubt researchers across the private, public and nonprofit sectors will want to further study the nexus of human capability, behavioral insights technology and AI, but it is clear that such work is already underway and can prove even more valuable if adopted on a broader, deeper level. Practical implications Much “people data” inside organizations is currently not being harvested. Validated, scalable processes exist to mine that data and leverage it to help organizations of all types and sizes be ready for the future, particularly in regard to the marriage of human capability and AI. Social implications In terms of human capability and AI, individuals, teams, organizations, customers and other stakeholders will all benefit. The investment of time and other resources is minimal, but must include C-suite buy in. Originality/value Much exists on the softer aspects of the marriage of human capability and AI and other workplace advancements. What has been lacking – until now – is a 1) practical, 2) validated and 3) scalable behavioral insights tech form that quantifiably informs how people and AI will work in the future, especially side by side.

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Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

Human Systems Management ◽

10.3233/hsm-211179 ◽

2021 ◽

pp. 1-13

Author(s):

Lamiae Benhayoun ◽

Daniel Lang

Keyword(s):

Artificial Intelligence ◽

Natural Language Processing ◽

Text Mining ◽

Natural Language ◽

Language Processing ◽

Academic Training ◽

Market Requirements ◽

Job Advertisements ◽

The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

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