Artificial Intelligence Theory And Practice Pdf
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A fun comparison of machine learning performance with two key signal processing algorithms — the Fast Fourier Transform and the Least Mean Squares prediction.
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History Of Machine Learning Pdf
This chapter will map the ethical and legal challenges posed by artificial intelligence AI in healthcare and suggest directions for resolving them. Section 1 will briefly clarify what AI is and Section 2 will give an idea of the trends and strategies in the United States US and Europe, thereby tailoring the discussion to the ethical and legal debate of AI-driven healthcare.
This will be followed in Section 3 by a discussion of four primary ethical challenges, namely, 1 informed consent to use, 2 safety and transparency, 3 algorithmic fairness and biases, and 4 data privacy. Section 4 will then analyze five legal challenges in the US and Europe: 1 safety and effectiveness, 2 liability, 3 data protection and privacy, 4 cybersecurity, and 5 intellectual property law. Finally, Section 5 will summarize the major conclusions and especially emphasize the importance of building an AI-driven healthcare system that is successful and promotes trust and the motto Health AIs for All of Us.
Economic forecasters have predicted explosive growth in the AI health market in the coming years; according to one analysis, the market size will increase more than fold between and . With this growth comes many challenges, and it is crucial that AI is implemented in the healthcare system ethically and legally.
This chapter will map the ethical and legal challenges posed by AI in healthcare and suggest directions for resolving them. We will begin by briefly clarifying what AI is and giving an overview of the trends and strategies concerning ethics and law of AI in healthcare in the United States US and Europe. This will be followed by an analysis of the ethical challenges of AI in healthcare. We will discuss four primary challenges: 1 informed consent to use, 2 safety and transparency, 3 algorithmic fairness and biases, and 4 data privacy.
We then shift to five legal challenges in the US and Europe, namely, 1 safety and effectiveness, 2 liability, 3 data protection and privacy, 4 cybersecurity, and 5 intellectual property law. To realize the tremendous potential of AI to transform healthcare for the better, stakeholders in the AI field, including AI makers, clinicians, patients, ethicists, and legislators, must be engaged in the ethical and legal debate on how AI is successfully implemented in practice Table Deep learning, a subset of ML, employs artificial neural networks with multiple layers to identify patterns in very large datasets  , p.
One of the reports also stressed the need to improve fairness, transparency, and accountability-by-design as well as building ethical AI  , pp. One of the key takeaways from the summit breakout discussions was that the Trump Administration aims to remove regulatory barriers to AI innovations  , pp. Only recently, in January , the White House published draft guidance for the regulation of AI applications.
It contains 10 principles that agencies should consider when formulating approaches to AI applications: 1 public trust in AI, 2 public participation, 3 scientific integrity and information quality, 4 risk assessment and management, 5 benefits and costs, 6 flexibility, 7 fairness and nondiscrimination, 8 disclosure and transparency, 9 safety and security, and 10 interagency coordination .
In February , the White House also published an annual report on the American AI Initiative, summarizing the progress made since Trump signed the executive order. This report, for example, highlights that the US led historic efforts on the development of the Organization for Economic Co-operation and Development OECD Principles of AI that were signed by over 40 countries in May to promote innovative and trustworthy AI and respect democratic values and human rights  , .
This committee shall also study and assess, inter alia, how to incorporate ethical standards in the development and implementation of AI [Sec. There are also legal developments related to AI at state and local levels .
AIs are already in clinical use in the US. In particular, AI shows great promise in the areas of diagnostics and imaging. For example, in January , Arterys received clearance from the US FDA for its medical imaging platform as the first ML application to be used in clinical practice  , .
It was initially cleared for cardiac magnetic resonance image analysis, but Arterys has meanwhile also received clearance from the FDA for other substantially equivalent devices . IDx-DR is the first FDA-authorized AI diagnostic system that provides an autonomous screening decision without the need for a human being to interpret the image or results additionally  , .
In April , the FDA permitted marketing of this AI-based device to detect more than a mild level of the eye condition diabetic retinopathy in adult patients ages 22 and older diagnosed with diabetes  , . OsteoDetect uses ML techniques to analyze two-dimensional X-ray images to identify and highlight this type of fracture  , . In this Communication, the Commission  , pp. The Commission  , p. The European Commission  , p.
At the same time, the Commission also published a Communication on a European strategy for data  and a Report on the liability implications and safety of AI, the Internet of Things IoT , and robotics . There are also already AI health applications in Europe, and more are in the pipeline.
Another example is Ultromics . Corti  is a software developed by a Danish company that leverages ML to help emergency dispatchers make decisions. Corti can detect out-of-hospital cardiac arrests i. As the prior section suggests, the use of AI in the clinical practice of healthcare has huge potential to transform it for the better, but it also raises ethical challenges we now address. Health AI applications, such as imaging, diagnostics, and surgery, will transform the patient—clinician relationship.
But how will the use of AI to assist with the care of patients interface with the principles of informed consent? This is a pressing question that has not received enough attention in the ethical debate, even though informed consent will be one of the most immediate challenges in integrating AI into clinical practice there is a separate question about informed consent to train AI we will not focus on here; . There is a need to examine under what circumstances if at all the principles of informed consent should be deployed in the clinical AI space.
To what extent do clinicians have a responsibility to educate the patient around the complexities of AI, including the form s of ML used by the system, the kind of data inputs, and the possibility of biases or other shortcomings in the data that is being used? Under what circumstances must a clinician notify the patient that AI is being used at all? This lack of knowledge might be worrisome for medical professionals . How much transparency is needed?
What about cases where the patient may be reluctant to allow the use of certain categories of data e. How can we properly balance the privacy of patients with the safety and effectiveness of AI? AI health apps and chatbots are also increasingly being used, ranging from diet guidance to health assessments to the help to improve medication adherence and analysis of data collected by wearable sensors  , pp.
Such apps raise questions for bioethicists about user agreements and their relationship to informed consent. In contrast to the traditional informed consent process, a user agreement is a contract that an individual agrees to without a face-to-face dialog  , p.
Most people do not take the time to understand user agreements, routinely ignoring them  , p. Moreover, frequent updates of the software make it even more difficult for individuals to follow what terms of service they have agreed to . What information should be given to individuals using such apps and chatbots?
Tackling these questions is tricky, and they become even more difficult to answer when information from patient-facing AI health apps or chatbots is fed back into clinical decision-making. Safety is one of the biggest challenges for AI in healthcare. MSK has stated that errors only occurred as part of the system testing and thus no incorrect treatment recommendation has been given to a real patient . This real-life example has put the field in a negative light.
It also shows that it is of uttermost importance that AIs are safe and effective. But how do we ensure that AIs keep their promises? To realize the potential of AI, stakeholders, particularly AI developers, need to make sure two key things: 1 the reliability and validity of the datasets and 2 transparency.
First, the used datasets need to be reliable and valid. The better the training data labeled data is, the better the AI will perform . In addition, the algorithms often need further refinement to generate accurate results. Another big issue is data sharing: In cases where the AI needs to be extremely confident e. However, there are also cases e. In general, it always depends on the particular AI and its tasks how much data will be required. Second, in the service of safety and patient confidence some amount of transparency must be ensured.
Third party or governmental auditing may represent a possible solution. Moreover, AI developers should be sufficiently transparent, for example, about the kind of data used and any shortcomings of the software e. Finally, transparency creates trust among stakeholders, particularly clinicians and patients, which is the key to a successful implementation of AI in clinical practice.
It will be a challenge to determine how transparency can be achieved in this context. Even if one could streamline the model into a simpler mathematical relationship linking symptoms and diagnosis, that process might still have sophisticated transformations beyond the skills of clinicians and especially patients to understand. However, any ML system or human-trained algorithm will only be as trustworthy, effective, and fair as the data that it is trained with.
AI also bears a risk for biases and thus discrimination. It is therefore vital that AI makers are aware of this risk and minimize potential biases at every stage in the process of product development. Several real-world examples have demonstrated that algorithms can exhibit biases that can result in injustice with regard to ethnic origins and skin color or gender  ,  ,  ,  , .
Biases can also occur regarding other features such as age or disabilities. The explanations for such biases differ and may be multifaceted.
They can, for example, result from the datasets themselves which are not representative , from how data scientists and ML systems choose and analyze the data, from the context in which the AI is used  , etc. In the health sector, where phenotype- and sometimes genotype-related information are involved, biased AI could, for instance, lead to false diagnoses and render treatments ineffective for some subpopulations and thus jeopardize their safety.
For example, imagine an AI-based clinical decision support CDS software that helps clinicians to find the best treatment for patients with skin cancer. However, the algorithm was predominantly trained on Caucasian patients. Thus the AI software will likely give less accurate or even inaccurate recommendations for subpopulations for which the training data was underinclusive such as African American.
Some of these biases may be resolved due to increased data availability and attempts to better collect data from minority populations and better specify for which populations the algorithm is or is not appropriately used. However, a remaining problem is that a variety of algorithms are sophisticated and nontransparent.
In addition, as we have seen in the policing context, some companies developing software will resist disclosure and claim trade secrecy in their work  , .
It may therefore likely be left to nongovernmental organizations to collect the data and show the biases . However, does this view really hold true? Some argue that what matters is not how the AI reaches its decision but that it is accurate, at least in terms of diagnosis . A related problem has to do with where AI will be deployed.
AI developed for top-notch experts in resource-rich settings will not necessarily recommend treatments that are accurate, safe, and fair in low-resource settings  Minssen, Gerke, Aboy, Price, and Cohen, . One solution would be not to deploy the technology in such settings. More thought must be given to regulatory obligations and resource support to make sure that this technology does improve not only the lives of the people living in high-income countries but also of those people living in low- and middle-income countries.
However, patients were not properly informed about the processing of their data as part of the test  , . Although the Streams app does not use AI, this real-life example has highlighted the potential for harm to privacy rights when developing technological solutions  , p.
History Of Machine Learning Pdf
Practical Deep Reinforcement Learning Pdf. Silver et al. Nature, While we cover the basics of deep learning backpropagation, convolutional neural networks, recurrent neural networks, transformers, etc , we expect these lectures to be mostly review. There are certain concepts you should be aware of before wading into the depths of deep reinforcement learning. Expected return is can be defined in few ways.
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Artificial intelligence - theory and practice · 1. Introduction. · 2. Symbolic Reasoning. · 3. Representation and Logic. · 4. Search. · 5. Learning. · 6. Advanced.
Artificial intelligence - theory and practice
Business intelligence supports managers in enterprises to make informed business decisions in various levels and domains such as in healthcare. These technologies can handle large structured and unstructured data big data in the healthcare industry. Because of the complex nature of healthcare data and the significant impact of healthcare data analysis, it is important to understand both the theories and practices of business intelligence in healthcare. Theory and Practice of Business Intelligence in Healthcare is a collection of innovative research that introduces data mining, modeling, and analytic techniques to health and healthcare data; articulates the value of big volumes of data to health and healthcare; evaluates business intelligence tools; and explores business intelligence use and applications in healthcare. While highlighting topics including digital health, operations intelligence, and patient empowerment, this book is ideally designed for healthcare professionals, IT consultants, hospital directors, data management staff, data analysts, hospital administrators, executives, managers, academicians, students, and researchers seeking current research on the digitization of health records and health systems integration.
This chapter will map the ethical and legal challenges posed by artificial intelligence AI in healthcare and suggest directions for resolving them. Section 1 will briefly clarify what AI is and Section 2 will give an idea of the trends and strategies in the United States US and Europe, thereby tailoring the discussion to the ethical and legal debate of AI-driven healthcare. This will be followed in Section 3 by a discussion of four primary ethical challenges, namely, 1 informed consent to use, 2 safety and transparency, 3 algorithmic fairness and biases, and 4 data privacy.
Machine Learning Tutorial Pdf
Beard , T. The following lecture materials are included as a resource for instructors. The slides closely follow the book. We welcome suggestions on how these slides might be improved. The following files are included to help students with the project outlined in the book. We have found that if students start with these files, that they can generally do the project in about 3 hours per chapter. Full solutions to the project are available to instructors upon request.
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This book is written for undergraduate engineers and those who teach them. Causal Inference with Machine Learning. Reading and Writing It seems stupidly obvious, but, if you have a problem typing, you will have a problem learning to code. Dimensioning 7. The book covers a broad array of topics not usually included in introductory machine learning texts.
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