scholarly journals AI Verification: Mechanisms to Ensure AI Arms Control Compliance

2021 ◽  
Author(s):  
Matthew Mittelsteadt
Keyword(s):  
Artificial Intelligence ◽  
Arms Control ◽  
Critical Questions ◽  
Starting Point ◽  
Military Systems ◽  
Technical Details

The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.

Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 170-176
Author(s):  
Anggit Dwi Hartanto ◽  
Aji Surya Mandala ◽  
Dimas Rio P.L. ◽  
Sidiq Aminudin ◽  
Andika Yudirianto
Keyword(s):  
Artificial Intelligence ◽  
Greedy Algorithm ◽  
Dijkstra Algorithm ◽  
Testing Phase ◽  
A Value ◽  
Route Problem ◽  
Starting Point ◽  
The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

scholarly journals Misconceptions in the health technology industry that are delaying the translation of artificial intelligence technology into relevant clinical applications

2021 ◽  
Vol 54 (4) ◽  
pp. 243-245
Author(s):  
Fabíola Macruz
Keyword(s):  
Artificial Intelligence ◽  
Human Performance ◽  
Health Technology ◽  
Medical Community ◽  
Technology Industry ◽  
Diagnosis And Prognosis ◽  
Starting Point ◽  
Causative Factors ◽  
The Status ◽  
Artificial Intelligence Technology

Abstract There is great optimism that artificial intelligence (AI), as it disrupts the medical world, will provide considerable improvements in all areas of health care, from diagnosis to treatment. In addition, there is considerable evidence that AI algorithms have surpassed human performance in various tasks, such as analyzing medical images, as well as correlating symptoms and biomarkers with the diagnosis and prognosis of diseases. However, the mismatch between the performance of AI-based software and its clinical usefulness is still a major obstacle to its widespread acceptance and use by the medical community. In this article, three fundamental concepts observed in the health technology industry are highlighted as possible causative factors for this gap and might serve as a starting point for further evaluation of the structure of AI companies and of the status quo.

scholarly journals Optical nanoscopy

2020 ◽  
Vol 43 (8) ◽  
pp. 385-455
Author(s):  
A. Diaspro ◽  
P. Bianchini
Keyword(s):  
Artificial Intelligence ◽  
Spatial Resolution ◽  
Scanning Probe Microscopy ◽  
Molecular Level ◽  
Super Resolution ◽  
Optical Microscope ◽  
Scanning Probe ◽  
Living Systems ◽  
Starting Point ◽  
Basic Concepts

Abstract This article deals with the developments of optical microscopy towards nanoscopy. Basic concepts of the methods implemented to obtain spatial super-resolution are described, along with concepts related to the study of biological systems at the molecular level. Fluorescence as a mechanism of contrast and spatial resolution will be the starting point to developing a multi-messenger optical microscope tunable down to the nanoscale in living systems. Moreover, the integration of optical nanoscopy with scanning probe microscopy and the charming possibility of using artificial intelligence approaches will be shortly outlined.

scholarly journals Mood and force in defeasible arguments

2021 ◽  
pp. 1-26
Author(s):  
Ryan Phillip Quandt ◽  
John Licato
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Word Meaning ◽  
The Other ◽  
Argumentation Schemes ◽  
Easy Task ◽  
Critical Questions ◽  
Grammatical Mood ◽  
The Moment ◽  
Precise Theory

Argumentation schemes bring artificial intelligence into day to day conversation. Interpreting the force of an utterance, be it an assertion, command, or question, remains a task for achieving this goal. But it is not an easy task. An interpretation of force depends on a speaker’s use of words for a hearer at the moment of utterance. Ascribing force relies on grammatical mood, though not in a straightforward or regular way. We face a dilemma: on one hand, deciding force requires an understanding of the speaker’s words; on the other hand, word meaning may shift given the force in which the words are spoken. A precise theory of how mood and force relate helps us handle this dilemma, which, if met, expands the use of argumentation schemes in language processing. Yet, as our analysis shows, force is an inconstant variable, one that contributes to a scheme’s defeasibility. We propose using critical questions to help us decide the force of utterances.

scholarly journals Law and software analysis: A step closer to resolving investment disputes through artificial intelligence?

2021 ◽  
Vol 59 (2) ◽  
pp. 123-140
Author(s):  
Milena Galetin ◽  
Anica Milovanović
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Legal Issues ◽  
The Other ◽  
Judicial Discretion ◽  
Applicable Law ◽  
Software Analysis ◽  
Starting Point ◽  
Decision Making Processes ◽  
Larger Sample

Considering the possibility of using artificial intelligence in resolving legal disputes is becoming increasingly popular. The authors examine whether soft ware analysis can be applied to resolve a specific issue in investment disputes - to determine the applicable law to the substance of the dispute and highlight the application of artificial intelligence in the area of law, especially in predicting the outcome of a dispute. The starting point is a sample of 50 arbitral awards and the results of previously conducted research. It has been confirmed that soft ware analysis can be useful in decision-making processes, but not to the extent that arbitrators could exclusively rely on it. On the other hand, the development of an algorithm that would predict applicable law for different legal issues required a much larger sample. We also believe that the existence of different legal and factual circumstances in each case, as well as the personality of the arbitrator and arbitral/judicial discretion are limitations of the application of artificial intelligence in this area.

scholarly journals Legal AI from a Privacy Point of View: Data Protection and Transparency in Focus

2021 ◽  
pp. 181-202
Author(s):  
Cecilia Magnusson Sjöberg
Keyword(s):  
Artificial Intelligence ◽  
Data Processing ◽  
Success Factors ◽  
Personal Data ◽  
Point Of View ◽  
The Public ◽  
Critical Success ◽  
Starting Point ◽  
Access Rights ◽  
Digital Human

A major starting point is that transparency is a condition for privacy in the context of personal data processing, especially when based on artificial intelligence (AI) methods. A major keyword here is openness, which however is not equivalent to transparency. This is explained by the fact that an organization may very well be governed by principles of openness but still not provide transparency due to insufficient access rights and lacking implementation of those rights. Given these hypotheses, the chapter investigates and illuminates ways forward in recognition of algorithms, machine learning, and big data as critical success factors of personal data processing based on AI—that is, if privacy is to be preserved. In these circumstances, autonomy of technology calls for attention and needs to be challenged from a variety of perspectives. Not least, a legal approach to digital human sciences appears to be a resource to examine further. This applies, for instance, when data subjects in the public as well as in the private sphere are exposed to AI for better or for worse. Providing what may be referred to as a legal shield between user and application might be one remedy to shortcomings in this context.

scholarly journals RadImageNet: A Large-scale Radiologic Dataset for Enhancing Deep Learning Transfer Learning Research

2021 ◽  
Author(s):  
Yang Yang ◽  
Xueyan Mei ◽  
Philip Robson ◽  
Brett Marinelli ◽  
Mingqian Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Transfer Learning ◽  
Large Scale ◽  
Medical Images ◽  
Bone Age ◽  
Natural World ◽  
X Rays ◽  
Starting Point ◽  
Size Diversity

Abstract Most current medical imaging Artificial Intelligence (AI) relies upon transfer learning using convolutional neural networks (CNNs) created using ImageNet, a large database of natural world images, including cats, dogs, and vehicles. Size, diversity, and similarity of the source data determine the success of the transfer learning on the target data. ImageNet is large and diverse, but there is a significant dissimilarity between its natural world images and medical images, leading Cheplygina to pose the question, “Why do we still use images of cats to help Artificial Intelligence interpret CAT scans?”. We present an equally large and diversified database, RadImageNet, consisting of 5 million annotated medical images consisting of CT, MRI, and ultrasound of musculoskeletal, neurologic, oncologic, gastrointestinal, endocrine, and pulmonary pathologies over 450,000 patients. The database is unprecedented in scale and breadth in the medical imaging field, constituting a more appropriate basis for medical imaging transfer learning applications. We found that RadImageNet transfer learning outperformed ImageNet in multiple independent applications, including improvements for bone age prediction from hand and wrist x-rays by 1.75 months (p<0.0001), pneumonia detection in ICU chest x-rays by 0.85% (p<0.0001), ACL tear detection on MRI by 10.72% (p<0.0001), SARS-CoV-2 detection on chest CT by 0.25% (p<0.0001) and hemorrhage detection on head CT by 0.13% (p<0.0001). The results indicate that our pre-trained models that are open-sourced on public domains will be a better starting point for transfer learning in radiologic imaging AI applications, including applications involving medical imaging modalities or anatomies not included in the RadImageNet database.

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