Artificial Intelligence Emerges

2020 ◽  
pp. 75-92
Author(s):  
Chris Bleakley
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Program ◽  
Human Intelligence ◽  
Board Game ◽  
Mathematics Textbook ◽  
The Future ◽  
The Right ◽  
Intelligent Computer

Chapter 5 delves into the origins of artificial intelligence (AI). By the end of the 1940s, a few visionaries realised that computers were more than mere automatic calculators. They believed that computers running the right algorithms could perform tasks previously thought to require human intelligence. Christopher Strachey completed the first artificially intelligent computer program in 1952. The program played the board game Checkers. Arthur Samuel of IBM extended and improved on Strachey’s program by including machine learning - the ability of a program to learn from experience. A team from Carnegie Melon University developed the first computer program that could perform algebra. The program eventually reproduced 38 of the 52 proofs in a classic mathematics textbook. Flushed by these successes, serious scientists made wildly optimistic pronouncements about the future of AI. In the event, project after project failed to deliver and the first “AI winter” set in.

Artificial Intelligence in Medicine

2021 ◽  
pp. 49-68
Author(s):  
Gia Merlo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
New Technologies ◽  
New Technology ◽  
Technological System ◽  
Medical Professionals ◽  
Human Intelligence ◽  
Connected Health ◽  
The Future

Disruptive forces are challenging the future of medicine. One of the key forces bringing change is the development of artificial intelligence (AI). AI is a technological system designed to perform tasks that are commonly associated with human intelligence and ability. Machine learning is a subset of AI, and deep learning is an aspect of machine learning. AI can be categorized as either applied or generalized. Machine learning is key to applied AI; it is dynamic and can become more accurate through processing different results. Other new technologies include blockchain, which allows for the storage of all of patients’ records to create a connected health ecosystem. Medical professionals ought to be willing to accept new technology, while also developing the skills that technology will not be able to replicate.

scholarly journals Artificial Intelligence Driven Crypto Currencies

2020 ◽  
Vol 9 (2) ◽  
pp. 107-118
Author(s):  
Apoorva Ganapathy ◽  
Md. Redwanuzzaman ◽  
Md. Mahbubur Rahaman ◽  
Wahiduzzaman Khan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Technological Development ◽  
Third Parties ◽  
Human Intelligence ◽  
Human Intervention ◽  
Specific Data ◽  
Development Framework ◽  
The Future

Artificial intelligence-driven cryptocurrencies are cryptocurrencies created by Artificial intelligence using the traditional human cryptocurrency development framework without human intervention. An AI explores the data from each different stream and arriving at the framework which can host these cryptocurrencies following the standards of legality. Cryptography is the encryption of specific data to conceal it and keep it a secret from unwanted third parties. Cryptocurrencies are encrypted currencies with unique keys as developed by developers. Artificial intelligence is an advanced machine programmed to simulate and emulate human intelligence by carrying tasks and reaching conclusions with little or no human intervention. This work considered the use of AI through machine learning and deep learning in the development of cryptocurrencies. The AI machine will set all the parameters and structure of the cryptocurrency. This will include how data is added, removed, and verified on the stream. Blockchain is an open ledger of a cryptocurrency's transactions. It stores files in the system, arranged in blocks, and connected on a list called chains. The article considers how AI-driven cryptocurrency will run using the blockchain network and its impact on it. Artificial intelligence and cryptocurrency are technological very essential technological development currently. The effect of the combination of both technologies would be enormous in the future as both technologies will develop each other remarkably.  

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

scholarly journals Machine Learning in Healthcare Communication

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 220-239
Author(s):  
Sarkar Siddique ◽  
James C. L. Chow
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Therapy ◽  
Medical Imaging ◽  
Health Education ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
Dialogue Management ◽  
Topical Review ◽  
Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

scholarly journals Are Artificial Intelligence (AI) And Machine Learning (ML) Having An Effective Role In Helping Humanity Address The New Coronavirus Pandemic?

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Outbreaks ◽  
Hospital Capacity ◽  
Data Engineering ◽  
Clinical Presentations ◽  
The Face ◽  
Effective Role ◽  
Using Data ◽  
The Right

COVID-19 has become a pandemic affecting the most of countries in the world. One of the most difficult decisions doctors face during the Covid-19 epidemic is determining which patients will stay in hospital, and which are safe to recover at home. In the face of overcrowded hospital capacity and an entirely new disease with little data-based evidence for diagnosis and treatment, the old rules for determining which patients should be admitted have proven ineffective. But machine learning can help make the right decision early, save lives and lower healthcare costs. So, there is therefore an urgent and imperative need to collect data describing clinical presentations, risks, epidemiology and outcomes. On the other side, artificial intelligence(AI) and machine learning(ML) are considered a strong firewall against outbreaks of diseases and epidemics due to its ability to quickly detect, examine and diagnose these diseases and epidemics.AI is being used as a tool to support the fight against the epidemic that swept the entire world since the beginning of 2020.. This paper presents the potential for using data engineering, ML and AI to confront the Coronavirus, predict the evolution of disease outbreaks, and conduct research in order to develop a vaccine or effective treatment that protects humanity from these deadly diseases.

Relevance of Artificial Intelligence in Modern Healthcare

2022 ◽  
pp. 67-88
Author(s):  
Dhanabalan Thangam ◽  
Anil B. Malali ◽  
Gopalakrishanan Subramaniyan ◽  
Sudha Mariappan ◽  
Sumathy Mohan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Infection Rate ◽  
Human Intelligence ◽  
Virus Spread ◽  
Advanced Technologies ◽  
The People

Artificial intelligence (AI) and machine learning (ML) are playing a major role in addressing and understanding better the COVID-19 crisis in recent days. These technologies are simulating human intelligence into the machines and consume large amounts of data for identifying and understanding the patterns and insights quickly than a human and preparing us with new kinds of technologies for preventing and fighting with COVID-19 and other pandemics. It helps a lot to notice the people who got infected by the virus and to forecast the infection rate in the upcoming days with the earlier data. Healthcare and medical sectors are in requirement of advanced technologies for taking accurate decision to manage this virus spread. AI-enabled technologies are working in a talented way to do things intelligently like human intelligence. Thus, the AI-enabled technologies are employed for attaining accurate health results by examining, forecasting, and checking present infected and possibly future cases.

scholarly journals The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

2019 ◽  
Vol 63 (1) ◽  
pp. 1471-1475
Author(s):  
Lorenzo Barberis Canonico ◽  
Christopher Flathmann ◽  
Nathan McNeese
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Game Theory ◽  
Human Factors ◽  
Cognitive Science ◽  
Collective Intelligence ◽  
Human Intelligence ◽  
Prediction Markets ◽  
New Model ◽  
Large Groups

There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence.

scholarly journals Transparency and Fairness in Machine Learning Applications

2018 ◽  
Vol 4 (5) ◽  
pp. 443-463
Author(s):  
Jim Shook ◽  
Robyn Smith ◽  
Alex Antonio
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reading Comprehension ◽  
New Technology ◽  
Human Intervention ◽  
Daily Work ◽  
Comprehension Test ◽  
The Future ◽  
Machine Learning Applications ◽  
Self Driving Cars

Businesses and consumers increasingly use artificial intelligence (“AI”)— and specifically machine learning (“ML”) applications—in their daily work. ML is often used as a tool to help people perform their jobs more efficiently, but increasingly it is becoming a technology that may eventually replace humans in performing certain functions. An AI recently beat humans in a reading comprehension test, and there is an ongoing race to replace human drivers with self-driving cars and trucks. Tomorrow there is the potential for much more—as AI is even learning to build its own AI. As the use of AI technologies continues to expand, and especially as machines begin to act more autonomously with less human intervention, important questions arise about how we can best integrate this new technology into our society, particularly within our legal and compliance frameworks. The questions raised are different from those that we have already addressed with other technologies because AI is different. Most previous technologies functioned as a tool, operated by a person, and for legal purposes we could usually hold that person responsible for actions that resulted from using that tool. For example, an employee who used a computer to send a discriminatory or defamatory email could not have done so without the computer, but the employee would still be held responsible for creating the email. While AI can function as merely a tool, it can also be designed to act after making its own decisions, and in the future, will act even more autonomously. As AI becomes more autonomous, it will be more difficult to determine who—or what—is making decisions and taking actions, and determining the basis and responsibility for those actions. These are the challenges that must be overcome to ensure AI’s integration for legal and compliance purposes.

scholarly journals EXERCISE OF MACHINE LEARNING USING SOME PYTHON TOOLS AND TECHNIQUES

2018 ◽  
Vol 6 ◽  
pp. 1062-1070
Author(s):  
Ertan Mustafa Geldiev ◽  
Nayden Valkov Nenkov ◽  
Mariana Mateeva Petrova
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Computer Program ◽  
Predictive Analytics ◽  
Search Model ◽  
Accurate Model ◽  
Different Types ◽  
The Right ◽  
Tools And Techniques ◽  
Complex Computer

One of the goals of predictive analytics training using Python tools is to create a "Model" from classified examples that classifies new examples from a Dataset. The purpose of different strategies and experiments is to create a more accurate prediction model. The goals we set out in the study are to achieve successive steps to find an accurate model for a dataset and preserving it for its subsequent use using the python instruments. Once we have found the right model, we save it and load it later, to classify if we have "phishing" in our case. In the case that the path we reach to the discovery of the search model, we can ask ourselves how much we can automate everything and whether a computer program can be written to automatically go through the unified steps and to find the right model? Due to the fact that the steps for finding the exact model are often unified and repetitive for different types of data, we have offered a hypothetical algorithm that could write a complex computer program searching for a model, for example when we have a classification task. This algorithm is rather directional and does not claim to be all-encompassing. The research explores some features of Python Scientific Python Packages like Numpy, Pandas, Matplotlib, Scipy and scycit-learn to create a more accurate model. The Dataset used for the research was downloaded free from the UCI Machine Learning Repository (UCI Machine Learning Repository, 2017).

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