Artificial Intelligence Mathematics And Logarithms Pdf
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- Word Vectorizing and Statistical Meaning of TF-IDF
- Well-log correlation using a back-propagation neural network
- Michael Rabbat
A statistical way of comparing two or more techniques, typically an incumbent against a new rival.
Word Vectorizing and Statistical Meaning of TF-IDF
You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong.
We are living in the automation society that automates more tasks and automates to a larger extent than before [ 1 ]. The boost to automation originates from the introduction of artificial intelligence in many human tasks. A typical example is represented by the so-called home automation provided by systems like Alexa, Cortana, Google Assistant, Apple Home, etc.
Such systems allow to automate and repeat human tasks as switching on and off the house light, scheduling a music playlist, switching on and off the heating in our house, etc. Automation is a benefit until these automated actions are carried out in harmony with what we want, but when the automated actions go further, suggesting or carrying out unwanted actions or causing damage to things and people, questions of responsibility arise which have been unexplored up until now.
When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. A typical example is the case of a self-driving car or an airplane using AI [ 3 ]; it should be asked: If the automation system of the car or the airplane autopilot causes an accident, who is responsible?
The Greek philosopher and polymath Aristotle gives an answer to this problem [ 4 ]. Since Aristotle, there are at least two traditional conditions for attributing responsibility for an action, the so-called control condition and the epistemic condition. Aristotle argued in the Nicomachean Ethics that the action must have its origin in the agent and that one must not be ignorant of what one is doing [ 5 ]. AI technologies do not meet traditional criteria for full moral action and hence preconditions for responsibility such as freedom and consciousness, and therefore, they also cannot be held responsible [ 6 ].
If this assumption holds, then our only option is to make humans responsible for what the AI technology does. It is a resolution that somehow anticipates the current situation that involves the professional responsibility of the radiologist. In fact, the resolution refers to the role of robotics in surgery, but the similarities of the relationship between robots and the surgeon with the relationship between artificial intelligence and radiologist are so strong that they are superimposable.
We can therefore state that the resolution correctly predicted the current situation. In the paragraph dedicated to medical robots, the resolution of the European parliament underlines: the importance of appropriate education, training and preparation for health professionals, such as doctors and care assistants; the need to define the minimum professional requirements to use a robot; the respect of the principle of the supervised autonomy of robots; the need for training for users to allow them to familiarize themselves with the technological requirements in this field; the risk of self-diagnosis of patients using mobile robot; and, consequently, the need for doctors to be trained in dealing with self-diagnosed cases.
The use of such technologies should not diminish or harm the doctor—patient relationship. Radiologists must be trained on the use of AI since they are responsible for the actions of machines.
In order to use artificial intelligence safely, as a support to the activity of the radiologist, it is necessary that it be trustworthy and validated in clinical practice [ 9 ]. In , the European Commission established the High-Level Experts Group on Artificial Intelligence with the general objective to support the implementation of the European Strategy on Artificial Intelligence, including the elaboration of recommendations on future-related policy development and on ethical, legal and societal issues related to AI, including socio-economic challenges.
Based on fundamental rights and ethical principles, the guidelines list seven key requirements that AI systems should meet in order to be trustworthy: 1 the human agency and oversight, ensuring that an AI system does not undermine human autonomy; 2 the technical robustness and safety; 3 privacy and data governance; 4 transparency; 5 diversity, non-discrimination and fairness; 6 societal and environmental well-being; and 7 accountability [ 10 ].
Radiologists are familiar with digital imaging and informatics, and those with an imaging informatics special interest are frequently involved in AI algorithms development and clinical validation. In radiology, the clinical use of computer-aided diagnosis CAD is well known; multiple studies have shown advantages, limitations and risks of image interpretation in a CAD paradigm, as first reader, second reader, or in concurrent reading [ 12 , 13 , 14 , 15 ].
In all cases, the final responsibility is in the hands of the radiologist, and a debate is still open about including CAD results into the radiology report and informing patients that the diagnosis is supported by automated software. However, a clear distinction between CAD and AI must be traced, since CAD is designed to perform specific tasks on the basis of a training set and AI power and promise is that useful features can exist that are not currently known or are beyond the limit of human detection.
In practice, AI brings new features from images that radiologists, as human beings, cannot detect or quantify. A typical example is radiomics, where a texture analysis can generate hundreds of features that a human being cannot generate and interpret [ 16 ]. In clinical practice, radiologists will be asked to monitor AI system outputs and validate AI interpretations; so they risk to carry the ultimate responsibility of validating what they cannot understand. Automation bias is the tendency for humans to favor machine-generated decisions, ignoring contrary data or conflicting human decisions.
Automation bias leads to errors of omission and commission, where omission errors occur when a human fails to notice, or disregards, the failure of the AI tool. High decision flow rates, where decisions are swiftly made on radiology examinations, and the radiologist is reading examinations rapidly, predispose to omission errors. This is compounded by AI decisions made based on features that are too subtle for humans to detect. The typical feature of the act performed by the radiologist, as a professional, is the autonomy of decisions on the provision of service and the technical tools to be used, and the personality of the service [ 18 ].
It is clear that these peculiarities must be harmonized with the automation of AI. Much discussion has been raised in the media about the introduction of artificial intelligence in radiological practice, suggesting that radiologists could become useless or even disappear as a specialty [ 19 , 20 , 21 ].
This could lead to a lack of motivation for young doctors to pursue a career in radiology, with a real imminent risk of not having enough radiologist specialists. An additional risk for young doctors in training is the reduction in training opportunities. In other words, the greater the automatic tasks performed by artificial intelligence, the less those performed by the radiologist. The paradox of this situation would be the need to implement ever greater AI tasks to compensate for the progressive lack of radiologists.
A contingent problem with the introduction of AI and of no less importance is transparency toward patients. They must be informed that the diagnosis was obtained with the help of the AI. Quality measures should relate to software robustness and data security, as well as constant updating of software and hardware, avoiding equipment obsolescence.
Particular attention should also be paid to image processing, guaranteeing its integrity during the analysis process with neural networks, thus avoiding a modification of the raw data. Artificial intelligence is entering the radiological discipline very quickly and will soon be in clinical use. The European Society of Radiology stated that the most likely and immediate impact of AI will be on the management of radiology workflows, improving and automating acquisition protocols, appropriateness with clinical decision support systems , structured reporting, up to the ability to interpret the big data of image biobanks connected to tissue biobanks, with the development of radiogenomics [ 22 ].
Are there solutions? In a recent article published by Thomas PS et al. The authors propose a framework for designing machine learning algorithms and show how it can be used to construct algorithms that provide their users with the ability to easily place limits on the probability that the algorithm will produce any specified undesirable behavior [ 23 ].
Perhaps the solution is to create an ethical AI, subject to a constant action control, as indeed happens for the human conscience: an AI subjected to a vicarious civil liability, written in the software and for which the producers must guarantee the users, so that they can use AI reasonably and with a human-controlled automation. Coeckelbergh M Artificial intelligence, responsibility attribution, and a relational justification of explainability. Sci Eng Ethics.
Med Ref Serv Q 37 1 — Front Psychol 1 10 Am Sociol 46 1 — J Med Ethics 11 3 — Philos Technol 31 2 — Winfield AFT, Jirotka M Ethical governance is essential to building trust in robotics and artificial intelligence systems. Clin Radiol 74 5 — Insights Imaging 9 5 — Doi K Computer-aided diagnosis in medical imaging: historical review, current status and future potential.
Eur J Radiol 82 8 — Clin Radiol 72 6 — Katzen J, Dodelzon K A review of computer aided detection in mammography. Clin Imaging — Can Assoc Radiol J 70 4 — Medical professionalism from the perspective of evidence-based medicine. Med Health Care Philos 20 1 — Insights Imaging.
Morgenstern M Automation and anxiety. Accessed 7 Aug Mukherjee S A. I Versus M. New Yorker. Science — Download references. Correspondence to Emanuele Neri. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Neri, E. Artificial intelligence: Who is responsible for the diagnosis?. Radiol med , — Download citation. Received : 13 December Accepted : 16 January Published : 31 January Issue Date : June Search SpringerLink Search.
Artificial intelligence: Who is responsible for the diagnosis? Download PDF. Introduction We are living in the automation society that automates more tasks and automates to a larger extent than before [ 1 ].
Who or what is the agent of responsibility? Key point 1 Using AI the radiologist is responsible for the diagnosis. A help from the laws On February 16th, , the European Parliament approved a resolution with recommendations to the Commission on Civil Law Rules on Robotics [ 7 , 8 ]. Key point 2 Radiologists must be trained on the use of AI since they are responsible for the actions of machines.
The need for a trustworthy AI In order to use artificial intelligence safely, as a support to the activity of the radiologist, it is necessary that it be trustworthy and validated in clinical practice [ 9 ]. Key point 4 Radiologist responsibility is at risk of validating the unknown black box. Key point 5 Radiologist decision may be biased by the AI automation.
Well-log correlation using a back-propagation neural network
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You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. We are living in the automation society that automates more tasks and automates to a larger extent than before [ 1 ]. The boost to automation originates from the introduction of artificial intelligence in many human tasks. A typical example is represented by the so-called home automation provided by systems like Alexa, Cortana, Google Assistant, Apple Home, etc.
We present a back-propagation neural network with an input layer in the form of a tapped delay line wich can be trained effectively on one or several well logs to recognize a particular geological marker. Subsequently, the neural network proposes locations of this marker on other wells in the field. Another neural network, similar in architecture to the first one, performs the same task for secondary markers using, in addition to the well logs, a depth reference function to the first marker. This method is shown to have better performance and better discrimination than standard cross-correlation techniques. It lends itself well for an interactive implementation on a workstation.
It is a statistical assumption and it has a purpose. What is its purpose? When we use eigenvalues in PCA algorithm to reduce dimension, we select most useful created features to explain target value.
Log interpretation is usually a significant job for asset development with relative high uncertainties and low efficiency. Incorrect results are associated with several negative effects. Remarkable efforts have been made to develop machine learning method to solve this problem. However, with only a single algorithm, the performance may be limited. To maximize prediction accuracy, we present another option using stacking machine learning technique, within which different types of base machine learning algorithms function together through a mega - classifier. Compared with the best performed single algorithm, stacking algorithm performs better, achieving These results suggest that this strategy could lead to better prediction and improve the prediction accuracy.
About Blog Location. Download PDF. Some layers have more than one input. Mathematics of Deep Learning. Deep Learning in the Wolfram Language 1.
Sign in. The purpose of this post is to explain the mathematics of some critical parts of the paper as well as to give some insights.