scholarly journals STEM Leadership and Training for Trailblazing Students in an Immersive Research Environment

2020 ◽  
Author(s):  
Marisel Villafañe-Delgado ◽  
Erik C. Johnson ◽  
Marisa Hughes ◽  
Martha Cervantes ◽  
William Gray-Roncal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Autonomous Systems ◽  
Training Models ◽  
Engineering Teams ◽  
Professional Development Training ◽  
Talented Students ◽  
Training And Support ◽  
And Training

Educating the workforce of tomorrow is an increasingly critical challenge for areas such as data science, machine learning, and artificial intelligence. These core skills may revolutionize progress in areas such as health care and precision medicine, autonomous systems and robotics, and neuroscience. Skills in data science and artificial intelligence are in high demand in industrial research and development, but we do not believe that traditional recruiting and training models in industry (e.g., internships, continuing education) are serving the needs of the diverse populations of students who will be required to revolutionize these fields. Our program, the Cohort-based Integrated Research Community for Undergraduate Innovation and Trailblazing (CIRCUIT), targets trailblazing, high-achieving students who face barriers in achieving their goals and becoming leaders in data science, machine learning, and artificial intelligence research. Traditional recruitment practices often miss these ambitious and talented students from nontraditional backgrounds, and these students are at a higher risk of not persisting in research careers. In the CIRCUIT program we recruit holistically, selecting students on the basis of their commitment, potential, and need. We designed a training and support model for our internship. This model consists of a compressed data science and machine learning curriculum, a series of professional development training workshops, and a team-based robotics challenge. These activities develop the skills these trailblazing students will need to contribute to the dynamic, team-based engineering teams of the future.

scholarly journals Autonomous Collision Avoidance at Sea: A Survey

2021 ◽  
Vol 8 ◽  
Author(s):  
Hans-Christoph Burmeister ◽  
Manfred Constapel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Collision Avoidance ◽  
Data Science ◽  
Autonomous Systems ◽  
Data Driven ◽  
Comprehensive Overview ◽  
Planning Methods ◽  
Survey Results

In this survey, results from an investigation on collision avoidance and path planning methods developed in recent research are provided. In particular, existing methods based on Artificial Intelligence, data-driven methods based on Machine Learning, and other Data Science approaches are investigated to provide a comprehensive overview of maritime collision avoidance techniques applicable to Maritime Autonomous Surface Ships. Relevant aspects of those methods and approaches are summarized and put into suitable perspectives. As autonomous systems are expected to operate alongside or in place of conventionally manned vessels, they must comply with the COLREGs for robust decision-support/-making. Thus, the survey specifically covers how COLREGs are addressed by the investigated methods and approaches. A conclusion regarding their utilization in industrial implementations is drawn.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Data Science: Big Data, Machine Learning, and Artificial Intelligence

2018 ◽  
Vol 15 (3) ◽  
pp. 497-498 ◽  
Author(s):  
Ruth C. Carlos ◽  
Charles E. Kahn ◽  
Safwan Halabi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Science

Artificial Intelligence, Machine Learning, and Data Science Technologies

2021 ◽  
Author(s):  
Neeraj Mohan ◽  
Ruchi Singla ◽  
Priyanka Kaushal ◽  
Seifedine Kadry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science

Artificial Intelligence, Machine Learning and Data Science as Iterations of Business Automation for Small Businesses

2020 ◽  
pp. 87-94
Author(s):  
Pooja Sharma ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Multinational Corporations ◽  
Small Businesses ◽  
Data Science ◽  
Small Data ◽  
Decision Systems ◽  
Business Operations ◽  
Business Automation ◽  
Machine Learning Tool

Artificial intelligence and machine learning, the two iterations of automation are based on the data, small or large. The larger the data, the more effective an AI or machine learning tool will be. The opposite holds the opposite iteration. With a larger pool of data, the large businesses and multinational corporations have effectively been building, developing and adopting refined AI and machine learning based decision systems. The contention of this chapter is to explore if the small businesses with small data in hands are well-off to use and adopt AI and machine learning based tools for their day to day business operations.

scholarly journals Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 193 ◽  
Author(s):  
Sebastian Raschka ◽  
Joshua Patterson ◽  
Corey Nolet
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Science ◽  
Gpu Computing ◽  
Graphics Processing Unit ◽  
General Purpose ◽  
Processing Unit ◽  
The Core ◽  
Critical Components ◽  
High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

Critical Digital Humanities

2019 ◽  
Author(s):  
James E. Dobson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Methods ◽  
Digital Humanities ◽  
Data Science ◽  
Computational Thinking ◽  
Major Question ◽  
Computational Tools ◽  
Multiple Dimensions ◽  
Selection For

This book seeks to develop an answer to the major question arising from the adoption of sophisticated data-science approaches within humanities research: are existing humanities methods compatible with computational thinking? Data-based and algorithmically powered methods present both new opportunities and new complications for humanists. This book takes as its founding assumption that the exploration and investigation of texts and data with sophisticated computational tools can serve the interpretative goals of humanists. At the same time, it assumes that these approaches cannot and will not obsolete other existing interpretive frameworks. Research involving computational methods, the book argues, should be subject to humanistic modes that deal with questions of power and infrastructure directed toward the field’s assumptions and practices. Arguing for a methodologically and ideologically self-aware critical digital humanities, the author contextualizes the digital humanities within the larger neo-liberalizing shifts of the contemporary university in order to resituate the field within a theoretically informed tradition of humanistic inquiry. Bringing the resources of critical theory to bear on computational methods enables humanists to construct an array of compelling and possible humanistic interpretations from multiple dimensions—from the ideological biases informing many commonly used algorithms to the complications of a historicist text mining, from examining the range of feature selection for sentiment analysis to the fantasies of human subjectless analysis activated by machine learning and artificial intelligence.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

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