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:

"The Materials Research Society is proud to announce the 2020 MRS Communications Lecture honorees, Drs. Chun-Teh Chen and Grace Gu from the University of California, Berkeley. The honor recognizes excellence in the field of materials research through work published in MRS Communications. Drs. Chen and Gu are recognized this year for their prospective paper on how researchers are harnessing artificial intelligence to accelerate the design and discovery of composite materials. Their work is featured in volume nine, issue two of MRS Communications. Composites are combinations of two or more base materials, whose collective properties exceed those possessed by either material alone. Composites are widely used as structural materials in the automotive and aerospace industries and can also be easily found in nature. Limitations in manufacturing methods have generally restricted the architecture these materials take on in real-world applications. Most commonly, they’re processed into multilayer sheets..."

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

Machine vision. Data wrangling. Reinforcement learning. What do these terms even mean? In AI 101, MIT researcher Brandon Leshchinskiy offers an introduction to artificial intelligence that’s designed specifically for those with little to no background in the subject. The workshop starts with a summary of key concepts in AI, followed by an interactive exercise where participants train their own algorithm. Finally, it closes with a summary of key takeaways and Q/A. All are welcome!

Learn the fundamentals and principal AI concepts about clustering, dimensionality reduction, reinforcement learning and deep learning to solve real-life problems.

Learn the fundamentals of machine learning to help you correctly apply various classification and regression machine learning algorithms to real-life problems.

A one-stop shop to get started on the key considerations about data for AI! Learn how crowdsourcing offers a viable means to leverage human intelligence at scale for data creation, enrichment and interpretation, demonstrating a great potential to improve both the performance of AI systems and their trustworthiness and increase the adoption of AI in general.

This course is a graduate introduction to natural language processing - the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.

This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems.

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:

"Antibiotic resistance is a growing problem worldwide—and in outer space. Spaceflight can promote biofilm formation and antimicrobial resistance development, and astronauts are especially vulnerable to infection due to the unique demands of spaceflight. To support future space travel, it is critical to understand exactly how spaceflight affects microbial diversity and virulence. To learn more, researchers recently used a machine learning algorithm to analyze sequencing data from the Microbial Tracking (MT)-1 mission, which sampled microbes at eight locations on the International Space Station during three flights. The model predicted the presence of hundreds of antibiotic resistance genes (ARGs) in the 226 bacterial strains isolated from the flights, including strains of the potentially very pathogenic bacterium Enterobacter bugandensis and the food poisoning-related bacterium Bacillus cereus..."

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

This is the GitHub repository for course CMP 414/765: Artificial Intelligence taught at The City University of New York, Lehman College, in Fall 2022

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Overview: Courses on Artificial Intelligence (AI) and Librarianship in ALA-accredited Masters of Library and Information (MLIS) degrees are rare. We have all been surprised by ChatGPT and similar Large Language Models. Generative AI is an important new area for librarianship. It is also developing so rapidly that no one can really keep up. Those trying to produce AI courses for the MLIS degree need all the help they can get. This book is a gesture of support. It consists of about 100,000 words on the topic, with a 4-500 item bibliography. It is the 2024 Second Edition of a 2023 book. It is about 100 pages longer than the first edition.

9. Brave New World - AI/ML

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities 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, city governments worldwide 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 video presentation was created by Ella Rechtenbach to provide instruction and insight into artificial intelligence and machine learning.

This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Materials are drawn from the Brains, Minds and Machines Summer Course offered annually at the Marine Biological Laboratory in Woods Hole, MA, taught by faculty affiliated with the Center for Brains, Minds and Machines headquartered at MIT. Elements of the summer course are integrated into the MIT course, 9.523 Aspects of a Computational Theory of Intelligence.
Contributors
This course includes the contributions of many instructors, guest speakers, and a team of iCub researchers. See the complete list of contributors.

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:

"_Clostridioides difficile_ infection (CDI), the most common hospital-acquired infection in the U.S., can cause severe diarrhea and even death, and more than 15% of infected individuals experience recurrent infection within 8 weeks. CDI is related to gut microbiome imbalance, but the factors that influence recurrence are not well understood. To identify potential predictors of recurrence, researchers sequenced and metabolically profiled the gut microbiomes of 53 patients with CDI over time. Compared to patients with no recurrence, patients with recurrent CDI had slower recovery of gut microbial diversity, and depletion of important anaerobic microbes, such as certain _Clostridium_ species. The patients with recurrent CDI also had delayed recovery of microbial metabolites in the gut, which was likely associated with dysfunction of the microbiome or of the host tissue..."

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

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

This course is a graduate level introduction to automatic discourse processing. The emphasis will be on methods and models that have applicability to natural language and speech processing.
The class will cover the following topics: discourse structure, models of coherence and cohesion, plan recognition algorithms, and text segmentation. We will study symbolic as well as machine learning methods for discourse analysis. We will also discuss the use of these methods in a variety of applications ranging from dialogue systems to automatic essay writing.
This subject qualifies as an Artificial Intelligence and Applications concentration subject.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.