13. Brave New World - Emerging Threats: Cybersecurity

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

This research resource suggests you some best sites of dataset collection which some of you might have known earlier. In the research field of machine learning, it is always tough to find the related dataset and if found it is always hard to filter some. Now that technologies have imporved, this article suggests some well known resources to collect your dataset related to your research. 

15. Brave New World: Ethical Considerations and Risks

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more accessible with technology and more connected.

Artificial Intelligence (AI), and the algorithmic judgment at its core, is developing at breakneck speed. This version of the popular Ethics for Engineers course focuses on the ethics issues involved in the latest developments of computer science.

In an effort to build the capacity of the students and faculty on the topics of bias and fairness in machine learning (ML) and appropriate use of ML, the MIT CITE team developed capacity-building activities and material. This material covers content through four modules that an be integrated into existing courses over a one to two week period.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Diarrhea is common among calves and causes huge losses to the global cattle industry. Fecal microbiota transplantation (FMT) is one promising approach to prevent and treat calf diarrhea. However, achieving success with FMT is difficult because of farm management differences, a lack of good donors, and the difficulty of recipient selection. To guide more effective FMT, a recent study investigated factors related to FMT success or failure in 20 donor–recipient pairs. The overall success rate for diarrhea improvement was 70%. Selenomonas bacteria were found in both donors and recipients when FMT was successful, suggesting that Selenomonas may be a biomarker of donor–recipient compatibility, and Sporobacter was identified as a potential biomarker for good donor selection. Pairs of correlations between specific microbial taxa and metabolites were also linked to success, and low levels of pre-FMT glycerol 3-phosphate, dihydroxyacetone phosphate, and isoamylamine were predicted to facilitate good results..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course about financial technology, or FinTech, is for students wishing to explore the ways in which new technologies are disrupting the financial services industry—driving material change in business models, products, applications and customer user interface. Amongst the significant technological trends affecting financial services into the 2020’s, the class will explore AI, deep learning, blockchain technology and open APIs. Students will gain an understanding of the key technologies, market structure, participants, regulation and the dynamics of change being brought about by FinTech.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"A new, fully automated approach could help spot faulty electric motors before they leave the production floor. Based on a popular machine-learning algorithm known as an autoencoder, this technique could prove invaluable to the numerous industries that produce electric motors, as well as those that rely on them. An autoencoder is an algorithm that distills, or encodes, input data down to a few key elements. It then decodes that information to reproduce the original data as closely as possible. At first glance, it might look like a simple cut-and-paste operation. But there’s more than meets the eye. The algorithm actually learns to pick out patterns that are fundamental to the structure of the original data set. For that reason, the tool is incredibly useful for cleaning up noisy data. Trained on a sufficiently large data set, an autoencoder can look at a muddled image and output a fair restoration. That ability, it turns out, is also valuable for telling a good electric motor from a bad one..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Environment-derived gut microbes can contribute to host health. In soil invertebrates, the gut microbiome is gradually assembled from the specific soil microecological region that the host inhabits. However, the effects of environmental stress on soil invertebrate microbiomes remain unknown. To learn more, a new study sought to characterize the gut bacterial taxa of the soil invertebrate Folsomia candida in the presence or absence of environmental concentrations of soil pollutants. Sequencing revealed that exposure to the fungicide azoxystrobin (AO), the antibiotic oxytetracycline (OTC), or their mixture (AO) increased Gammaproteobacteria abundance. The Gammaproteobacteria response was closely associated with F. candida physiological and functional indicators, such as the locomotion (HAA) index and oxidative stress..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This course reviews the key genomic technologies and computational approaches that are driving advances in prognostics, diagnostics, and treatment. Throughout the semester, emphasis will return to issues surrounding the context of genomics in medicine including: what does a physician need to know? what sorts of questions will s/he likely encounter from patients? how should s/he respond? Lecturers will guide the student through real world patient-doctor interactions. Outcome considerations and socioeconomic implications of personalized medicine are also discussed. The first part of the course introduces key basic concepts of molecular biology, computational biology, and genomics. Continuing in the informatics applications portion of the course, lecturers begin each lecture block with a scenario, in order to set the stage and engage the student by showing: why is this important to know? how will the information presented be brought to bear on medical practice? The final section presents the ethical, legal, and social issues surrounding genomic medicine. A vision of how genomic medicine relates to preventative care and public health is presented in a discussion forum with the students where the following questions are explored: what is your level of preparedness now? what challenges must be met by the healthcare industry to get to where it needs to be?
Lecturers
Dr. Atul J. Butte
Dr. Steven A. Greenberg
Dr. Alvin Thong-Juak Kho
Dr. Peter Park
Dr. Marco F. Ramoni
Dr. Alberto A. Riva
Dr. Zoltan Szallasi
Dr. Jeffrey Mark Drazen
Dr. Todd Golub
Dr. Joel Hirschhorn
Dr. Greg Tucker-Kellogg
Dr. Scott Weiss

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Machine Learning in Action". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Setting up Jupyter Notebooks". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Most current knowledge about gut microbiomes has been obtained from studies on mammals, while the microbiomes of fish, the most diverse group of vertebrates (~33,000 species), are less well understood. Specifically, the major influencing factors and unique features of fish gut microbiomes remain unclear. To bridge this knowledge gap, a recent study analyzed the gut contents of 227 fish representing 85 different freshwater fish (FWF) and saltwater fish (SWF) species. rRNA sequencing revealed that Proteobacteria and Firmicutes were the two most abundant phyla, indicating a different composition from the typical vertebrate microbiome, which is composed mainly of Firmicutes and Bacteroidetes. Habitat (freshwater versus saltwater) more strongly influenced the host microbiome than host taxonomy or trophic level and the microbiome taxonomic and functional profiles were better indicators of a fish’s habitat than of its taxonomy..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

With a focus on the creation of functional prototypes and practicing real magical crafts, this class combines theatrical illusion, game design, sleight of hand, machine learning, camouflage, and neuroscience to explore how ideas from ancient magic and modern stage illusion can inform cutting edge technology.

6.0002 is the continuation of 6.0001 Introduction to Computer Science and Programming in Python and is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response

This is MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), and we'll try to explain everything else along the way! Experience in Python is helpful but not necessary.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.
This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling.

This lesson centers around the How AI Works: What is Machine Learning? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson students are introduced to a form of artificial intelligence called machine learning and how they can use the Problem Solving Process to help train a robot to solve problems. They participate in three machine learning activities where a robot - AI Bot - is learning how to detect patterns in fish.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

Course DescriptionMachine Learning is the study of how to build computer systems that learn from experience. This course will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include concept learning, neural networks, genetic algorithms, reinforcement learning, instance-based learning, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems.