scholarly journals The AI Bookie — Place Your Bets: Will Machine Learning Outgrow Human Labeling?

AI Magazine ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 123-126
Author(s):  
Mike Schaekermann ◽  
Christopher Homan ◽  
Lora Aroyo ◽  
Praveen Paritosh ◽  
Kurt Bollacker ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Community ◽  
Online Forum ◽  
The Future ◽  
The Media ◽  
The Creation

The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions and bets about the future of artificial intelligence. Although it is easy to make a prediction about the future, this forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when a prediction comes due. The bets will be documented online and regularly in this publication in The AI Bookie. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated or too specific to an institution or individual. The goal is not to continue to feed the media frenzy and pundit predictions about artificial intelligence, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. Place your bets! Please go to ai.sciencebets.org.

The AI Bookie

AI Magazine ◽  
2018 ◽  
Vol 39 (4) ◽  
pp. 84-87
Author(s):  
Kurt Bollacker ◽  
Praveen Paritosh ◽  
Chris Welty
Keyword(s):  
Scientific Community ◽  
Online Forum ◽  
The Future ◽  
On Line ◽  
The Media ◽  
The Creation

The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions and bets about the future of AI. While it is easy to make a prediction about the future, this forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when they come due. The bets will be documented on line, and regularly in this publication in The AI Bookie. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated, or too specific to an institution or individual. The goal is not to continue to feed the media frenzy and pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community

scholarly journals The AI Bookie

AI Magazine ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 79-82
Author(s):  
Chris Welty ◽  
Lora Aroyo ◽  
Eric Horvitz
Keyword(s):  
Scientific Community ◽  
Online Forum ◽  
The Future ◽  
The Media ◽  
The Creation

The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions, in the form of bets, about the future of AI. While it is easy to make broad, generalized, or off-the-cuff predictions about the future, it is more difficult to develop predictions that are carefully thought out, concrete, and measurable. This forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when the bets come due. The bets will be documented both online and regularly in this column. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated or too specific to an individual or institution. The goal is not to continue to feed the media frenzy and outsized pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. For detailed guidelines and to place bets, visit sciencebets.org.

AI Bookie

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 79-84
Author(s):  
Don Sofge ◽  
William Lawless ◽  
Ranjeev Mittu
Keyword(s):  
Scientific Community ◽  
Online Forum ◽  
The Future ◽  
On Line ◽  
The Media ◽  
The Creation

The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions and bets about the future of AI. While it is easy to make a prediction about the future, this forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when they come due. The bets will be documented on line, and regularly in this publication in The AI Bookie. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated, or too specific to an institution or individual. The goal is not to continue to feed the media frenzy and pundit predictions about AI, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. Place your bets! Please go to ai.sciencebets.org

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 In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

Internet of Things and Nature-Inspired Intelligent Techniques for the Future of Biomedical Engineering

2018 ◽  
pp. 263-282
Author(s):  
Gur Emre Guraksin
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Biomedical Engineering ◽  
Scientific Community ◽  
Engineering Applications ◽  
Related Literature ◽  
Research Fields ◽  
The Future ◽  
Autonomous Machines ◽  
General Network

Along with the rise of artificial intelligence (AI), there are many different research fields gaining importance. Because of the growing amount of data and needs for immediate access to information for dealing with the problems, different types of research fields take place within the scientific community. Internet of things (IoT) is one of them, and it enables devices to communicate with each other in order to form a general network of physical, working devices. The objective of this chapter in this manner is to provide a general discussion of using nature-inspired techniques of AI to form the future of biomedical engineering over IoT. Because it is often thought that the medical services of the future will be based on autonomous machines supported with AI and IoT, discussing such a topic by considering biomedical engineering applications will be good for the related literature.

scholarly journals Advancing Post-Genome Data and System Integration through Machine Learning

2002 ◽  
Vol 3 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Francisco Azuaje
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Intelligent Systems ◽  
Biological Data ◽  
Markup Languages ◽  
Public And Private ◽  
Genome Data ◽  
The Creation ◽  
Biological Data Integration

Research on biological data integration has traditionally focused on the development of systems for the maintenance and interconnection of databases. In the next few years, public and private biotechnology organisations will expand their actions to promote the creation of a post-genome semantic web. It has commonly been accepted that artificial intelligence and data mining techniques may support the interpretation of huge amounts of integrated data. But at the same time, these research disciplines are contributing to the creation of content markup languages and sophisticated programs able to exploit the constraints and preferences of user domains. This paper discusses a number of issues on intelligent systems for the integration of bioinformatic resources.

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 Automating Cyber Attacks

2020 ◽  
Author(s):  
Ben Buchanan ◽  
John Bansemer ◽  
Dakota Cary ◽  
Jack Lucas ◽  
Micah Musser
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Attacks ◽  
Learning Systems ◽  
Depth Analysis ◽  
The Future

Based on an in-depth analysis of artificial intelligence and machine learning systems, the authors consider the future of applying such systems to cyber attacks, and what strategies attackers are likely or less likely to use. As nuanced, complex, and overhyped as machine learning is, they argue, it remains too important to ignore.

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