scholarly journals EXPLORING THE POTENTIALS OF ARTIFICIAL INTELLGIENCE IN THE JUDICIARY

2020 ◽  
Vol 5 (8) ◽  
Author(s):  
Oloruntoba Samson Abiodun ◽  
Akinode John Lekan
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Legal Service ◽  
Business Analytics ◽  
Ethical Challenges ◽  
Related Case ◽  
Legal Professionals ◽  
And Robotics ◽  
Applications Of Ai

In recent years, there has been massive progress in Artificial Intelligence (AI) with the development of machine learning, deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in the judiciary with many of the legal service activities currently being delivered by lawyers predicted to be taken over by AI in the coming years. This paper explores the potentials and efficiency of Artificial intelligence (AI) in justice delivery. The paper has two objectives: first to highlight the main applications of AI in justice administrations through some examples of AI tools recently developed; second, to assess the ethical challenges of AI in the judiciary. Artificial Intelligence algorithms are starting to support lawyers, for instance, through artificial intelligence search tools, or to support justice administrations with predictive technologies and business analytics based on the computation of Big Data. Using the concept of Artificial Intelligence (AI), Legal knowledgebased tools may accelerate the service delivery of legal professionals from typical searching of related case journals to extraction of precise information in a customized manner.

scholarly journals Artificial intelligence-enabled healthcare delivery

2018 ◽  
Vol 112 (1) ◽  
pp. 22-28 ◽  
Author(s):  
Sandeep Reddy ◽  
John Fox ◽  
Maulik P Purohit
Keyword(s):  
Artificial Intelligence ◽  
Health System ◽  
Language Processing ◽  
Patient Monitoring ◽  
Deep Neural Networks ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Realistic Assessment ◽  
Healthcare Interventions ◽  
And Robotics

Summary In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare with many of the health service activities currently being delivered by clinicians and administrators predicted to be taken over by AI in the coming years. However, there has also been exceptional hype about the abilities of AI with a mistaken notion that AI will replace human clinicians altogether. These perspectives are inaccurate, and if a balanced perspective of the limitations and promise of AI is taken, one can gauge which parts of the health system AI can be integrated to make a meaningful impact. The four main areas where AI would have the most influence would be: patient administration, clinical decision support, patient monitoring and healthcare interventions. This health system where AI plays a central role could be termed an AI-enabled or AI-augmented health system. In this article, we discuss how this system can be developed based on a realistic assessment of current AI technologies and predicted developments.

scholarly journals Human Gaze Assisted Artificial Intelligence: A Review

2020 ◽  
Author(s):  
Ruohan Zhang ◽  
Akanksha Saran ◽  
Bo Liu ◽  
Yifeng Zhu ◽  
Sihang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cognitive State ◽  
Data Sets ◽  
Common Goal ◽  
Research Areas ◽  
Future Challenges ◽  
Potential Applications ◽  
High Level ◽  
And Robotics

Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.

Hey Siri, tell me a story: Digital storytelling and AI authorship

2020 ◽  
Vol 26 (4) ◽  
pp. 808-823
Author(s):  
Sarah Thorne
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
News Media ◽  
Future Directions ◽  
Daily Lives ◽  
Short Films ◽  
The Future ◽  
Commercial Applications ◽  
New Applications ◽  
Applications Of Ai

Surveying narrative applications of artificial intelligence in film, games and interactive fiction, this article imagines the future of artificial intelligence (AI) authorship and explores trends that seek to replace human authors with algorithmically generated narrative. While experimental works that draw on text generation and natural language processing have a rich history, this article focuses on commercial applications of AI narrative and looks to future applications of this technology. Video games have incorporated AI and procedural generation for many years, but more recently, new applications of this technology have emerged in other media. Director Oscar Sharp and artist Ross Goodwin, for example, generated significant media buzz about two short films that they produced which were written by their AI screenwriter. It’s No Game (2017), in particular, offers an apt commentary on the possibility of replacing striking screenwriters with AI authors. Increasingly, AI agents and virtual assistants like Siri, Cortana, Alexa and Google Assistant are incorporated into our daily lives. As concerns about their eavesdropping circulate in news media, it is clear that these companions are learning a lot about us, which raises concerns about how our data might be employed in the future. This article explores current applications of AI for storytelling and future directions of this technology to offer insight into issues that have and will continue to arise as AI storytelling advances.

scholarly journals Use of Artificial Intelligence in Nononcologic Interventional Radiology: Current State and Future Directions

2021 ◽  
Author(s):  
Rohil Malpani ◽  
Christopher W. Petty ◽  
Neha Bhatt ◽  
Lawrence H. Staib ◽  
Julius Chapiro
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Language Processing ◽  
Vascular Imaging ◽  
Future Directions ◽  
Current State ◽  
The Future ◽  
Procedural Planning ◽  
Applications Of Ai

AbstractThe future of radiology is disproportionately linked to the applications of artificial intelligence (AI). Recent exponential advancements in AI are already beginning to augment the clinical practice of radiology. Driven by a paucity of review articles in the area, this article aims to discuss applications of AI in nononcologic IR across procedural planning, execution, and follow-up along with a discussion on the future directions of the field. Applications in vascular imaging, radiomics, touchless software interactions, robotics, natural language processing, postprocedural outcome prediction, device navigation, and image acquisition are included. Familiarity with AI study analysis will help open the current “black box” of AI research and help bridge the gap between the research laboratory and clinical practice.

scholarly journals Chatbots Employing Deep Learning for Big Data

2019 ◽  
Vol 8 (11) ◽  
pp. 1005-1010
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Heterogeneous Data ◽  
Instructive Feedback ◽  
Technical Specifications

With the evolution of artificial intelligence to deep learning, the age of perspicacious machines has pioneered that can even mimic as a human. A Conversational software agent is one of the best-suited examples of such intuitive machines which are also commonly known as chatbot actuated with natural language processing. The paper enlisted some existing popular chatbots along with their details, technical specifications, and functionalities. Research shows that most of the customers have experienced penurious service. Also, the inception of meaningful cum instructive feedback endure a demanding and exigent assignment as enactment for chatbots builtout reckon mostly upon templates and hand-written rules. Current chatbot models lack in generating required responses and thus contradict the quality conversation. So involving deep learning amongst these models can overcome this lack and can fill up the paucity with deep neural networks. Some of the deep Neural networks utilized for this till now are Stacked Auto-Encoder, sparse auto-encoders, predictive sparse and denoising auto-encoders. But these DNN are unable to handle big data involving large amounts of heterogeneous data. While Tensor Auto Encoder which overcomes this drawback is time-consuming. This paper has proposed the Chatbot to handle the big data in a manageable time.

scholarly journals The Problem of Meaning in AI and Robotics: Still with Us after All These Years

Philosophies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 14 ◽  
Author(s):  
Tom Froese ◽  
Shigeru Taguchi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Cognitive Robotics ◽  
Macroscopic Scale ◽  
Enactive Approach ◽  
The Mind ◽  
Synthetic Approaches ◽  
And Robotics ◽  
Made In

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches.

scholarly journals Artificial Intelligence and its Application in Various Fields

2020 ◽  
Vol 9 (5) ◽  
pp. 1336-1339
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Cyber Security ◽  
Main Idea ◽  
Human Beings ◽  
Military Planning ◽  
Actual Meaning ◽  
And Robotics ◽  
Applications Of Ai ◽  
The Way

The term ‘AI’ is not a new term but the actual meaning of ai is still hidden. Artificial intelligence is a branch of computer science that aims to create machines which are as intelligent as human beings. AI mainly focus on some questions like knowledge required while thinking, the way knowledge can be presented and the way knowledge can be used in other field’s viz. Robotics. Scope of AI is much wider than our thinking. It is not limited to only one or two areas rather in coming future everything will be directly or indirectly linked to AI. Much research has been done on artificial intelligence which has shown that by the end of 2020 many works which was not possible by human beings will be efficiently and accurately can be carried out by the help of robots. Robotics is a branch of engineering that deals with formation, designing, manufacturing, operation of robots. Artificial intelligence is being applied to many areas which are capable to solve many problems like in robotics, e-commerce, domestic chores, medical treatment, gaming, mathematics, military planning etc. The main idea behind the merging of artificial intelligence and robotics is to optimize the level of autonomy through learning. In the coming future we can surely overcome the disadvantages of robots like misuse of it with the help of facial recognition. Or we can use AI in other fields like in cyber security to prevent the systems from being hacked. The applications of AI and how we can implement other applications in coming time are discussed adding to it how we can overcome the disadvantages of using robots in regular life are also discussed.

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

AI Magazine ◽  
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.

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

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.

scholarly journals The use of artificial intelligence and robotics in regional anaesthesia

Anaesthesia ◽  
2021 ◽  
Vol 76 (S1) ◽  
pp. 171-181 ◽  
Author(s):  
M. McKendrick ◽  
S. Yang ◽  
G. A. McLeod
Keyword(s):  
Artificial Intelligence ◽  
Regional Anaesthesia ◽  
And Robotics
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