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.
Publicly available generative AI (GenAI) tools are rapidly emerging, and the release of iterative versions is outpacing the adaptation of national regulatory frameworks. The absence of national regulations on GenAI in most countries leaves the data privacy of users unprotected and educational institutions largely unprepared to validate the tools.
UNESCO’s first global guidance on GenAI in education aims to support countries to implement immediate actions, plan long-term policies and develop human capacity to ensure a human-centred vision of these new technologies.
The Guidance presents an assessment of potential risks GenAI could pose to core humanistic values that promote human agency, inclusion, equity, gender equality, and linguistic and cultural diversities, as well as plural opinions and expressions.
It proposes key steps for governmental agencies to regulate the use of GenAI tools including mandating the protection of data privacy and considering an age limit for their use. It outlines requirements for GenAI providers to enable their ethical and effective use in education.
The Guidance stresses the need for educational institutions to validate GenAI systems on their ethical and pedagogical appropriateness for education. It calls on the international community to reflect on their long-term implications for knowledge, teaching, learning and assessment.
The publication offers concrete recommendations for policy-makers and educational institutions on how the uses of GenAI tools can be designed to protect human agency and genuinely benefit learners, teachers and researchers.
The projects in this guide use a student-driven approach to learning. Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from machine learning models to video games—to demonstrate their learning.
Project 1: Programming with Machine Learning
Project 2: AI-Powered Players in Video Games
Project 3: Using AI for Robotic Motion Planning
Project 4: Machine Learning as a Service
Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education
ISTE and GM have partnered to create Hands-On AI Projects for the Classroom guides to provide educators with a variety of activities to teach students about AI across various grade levels and subject areas. Each guide includes background information for teachers and student-driven project ideas that relate to subject-area standards. The hands-on activities in the guides range from “unplugged” projects to explore the basic concepts of how AI works to creating chatbots and simple video games with AI, allowing students to work directly with innovative AI technologies and demonstrate their learning.
ISTE and GM have partnered to create Hands-On AI Projects for the Classroom guides to provide educators with a variety of activities to teach students about AI across various grade levels and subject areas. Each guide includes background information for teachers and student-driven project ideas that relate to subject-area standards. The hands-on activities in the guides range from “unplugged” projects to explore the basic concepts of how AI works to creating chatbots and simple video games with AI, allowing students to work directly with innovative AI technologies and demonstrate their learning.
This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society.
Instead of simply learning about AI through videos or lectures, the students completing these projects are active participants in their AI exploration. In the process, students work directly with innovative AI technologies, participate in “unplugged” activities that further their understanding of how AI technologies work, and create various authentic products—from presentations to designing an AI robot—to demonstrate their learning.
• Project 1: What AI Does Well and Does Not Do Well
• Project 2: Training Data and Machine Learning
• Project 3: Senses vs. Sensors
• Project 4: Navigation and AI
Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education
ISTE and GM have partnered to create Hands-On AI Projects for the Classroom guides to provide educators with a variety of activities to teach students about AI across various grade levels and subject areas. Each guide includes background information for teachers and student-driven project ideas that relate to subject-area standards. The hands-on activities in the guides range from “unplugged” projects to explore the basic concepts of how AI works to creating chatbots and simple video games with AI, allowing students to work directly with innovative AI technologies and demonstrate their learning.
ISTE and GM have partnered to create Hands-On AI Projects for the Classroom guides to provide educators with a variety of activities to teach students about AI across various grade levels and subject areas. Each guide includes background information for teachers and student-driven project ideas that relate to subject-area standards. The hands-on activities in the guides range from “unplugged” projects to explore the basic concepts of how AI works to creating chatbots and simple video games with AI, allowing students to work directly with innovative AI technologies and demonstrate their learning.
This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society. Several key approaches were taken into consideration in the design of these projects. Understanding these approaches will support both your understanding and implementation of the projects in this guide, as well as your own work to design further activities that integrate AI education into your curriculum.
Project 1: AI Chatbots
Project 2: Developing a Critical Eye
Project 3: Using AI to Solve Environmental Problems
Project 4: Laws for AI
Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education
In this guide, students’ exploration of AI is framed within the context of ethical considerations and aligned with standards and concepts, and depths of understanding that would be appropriate across various subject areas and grade levels in K–12. Depending on the level of your students and the amount of time you have available, you might complete an entire project, pick and choose from the listed activities, or you might take students’ learning further by taking advantage of the additional extensions and resources provided for you. For students with no previous experience with AI education, exposure to the guided learning activities alone will create an understanding of their world that they likely did not previously have. And for those with some background in computer science or AI, the complete projects and resources will still challenge their thinking and expose them to new AI technologies and applications across various fields of study.
Project 1: Fair's Fair
Project 2: Who is in Control?
Project 3: The Trade-offs of AI Technology
Project 4: AI and the 21st Century Worker
Visit the ISTE website with all the free practical guides for engaging students in AI creation: https://www.iste.org/areas-of-focus/AI-in-education.
ISTE and GM have partnered to create Hands-On AI Projects for the Classroom guides to provide educators with a variety of activities to teach students about AI across various grade levels and subject areas. Each guide includes background information for teachers and student-driven project ideas that relate to subject-area standards. The hands-on activities in the guides range from “unplugged” projects to explore the basic concepts of how AI works to creating chatbots and simple video games with AI, allowing students to work directly with innovative AI technologies and demonstrate their learning.
Understanding protein functions is crucial to unlocking the value of genomic data for biomedical research and innovation. Delivering personalised health and precision medicine requires a detailed understanding of the consequences of sequence variants in proteins and their impact on phenotype. The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Artificial intelligence and machine learning hold a large repertoire of algorithms and methodologies to discover and infer prediction models. Coupled with the new big data technologies for interactive analytics and data transformation, the AI/ML methods represent valuable assets that could enhance the discovery of protein functions.
This tutorial will help you understanding how to use Spark and Interactive Analytics to make sense of protein data and build machine learning models to infer their functions.
Who is this course for?
Scientists and bioinformaticians with an interest in protein functions, machine learning and modelling.
This lesson is intended for classrooms that want to show the entire How AI Works video series in a single day. It is not intended to be taught in sequence with the other lessons in this unit, which introduces each video one day at a time.
Students follow along with each video by matching vocabulary from the video, then answering a reflection question about each video. The lesson plan and slides are very sparse and open-ended to allow for improvisation and customization to fit your classroom.
How to Train Your Robot was originally developed as an extension of the AI + Ethics for Middle School curriculum. In its current form, How to Train Your Robot provides a full, one-week of activities that allow middle school students to explore artificial intelligence and ethics through hands-on activities.
In this course, students participate in a range of hot-topic discussions and hands-on, creative activities to learn about how artificial intelligence is impacting society today. Students will design robot companions to solve real-world problems and use machine learning to make them intelligent. At the end of the course, you will have designed your very own robot companion to share with the world.
Bryan Ryder, Beth LaPensee, and Chirag Parekh (ITHAKA) present 'ITHAKA & JSTOR's Ethical Exploration of Generative AI' during the AI & Implementation session at the Fantastic Futures ai4LAM 2023 annual conference. This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.
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Glen Horton (University of Cincinnati) presents 'Learning as we grow: challenges and wins in implementing an AI-generated subject indexing tool for repositories' during the AI & Implementation session at the Fantastic Futures ai4LAM 2023 annual conference. This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.
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Jessica Kincaid and Jeremiah Colonna-Romano (University of Alabama) present 'Implementing and Assessing AI Tools in Archival Metadata Workflows' during the Short Talk and Demo session at the Fantastic Futures ai4LAM 2023 annual conference. This item belongs to: movies/fantastic-futures-annual-international-conference-2023-ai-for-libraries-archives-and-museums-02.
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Este libro es una introducción al area de la Inteligencia Artificial y presenta algunas de las aplicaciones que puede tener en la vida real en diversos campos de aplicación, El libro esta compuesto de ocho capítulos los cuales abarcan los antededentes, algunos conceptos importantes para la resolución de problemas como es la representación de conocimiento, el planteamiento de los problemas. Asimismo se menciona la teoría de agentes por un lado y por otro lo que es el aprendizaje computacional. Otra area que se aborta es la computación evolutivo y los algoritmos bioinspirados para la resolución de problemas, dandole enfasis a los problemas de optimizacion. Por ultimo se menciona una nueva tendencia en el area de las ciencias computacionales como es el uso de las GPUs para trabajar de una manera mas rapida al realizar el procesamiento en paralelo.
To improve the students’ understanding of AI, Aspen Digital developed a lesson plan to teach students how to recognize and critically interpret how writing choices impact readers’ perceptions of how AI works, what it can be used to do, and who is using it.