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:

"Congenital heart defects affect approximately 1% of all babies born each year and account for almost 20% of all newborn deaths. Early diagnosis while still in the womb can greatly improve an affected baby’s chance of survival. Unfortunately, diagnosis relies exclusively on ultrasound imaging, where accurate readings aren’t guaranteed. Researchers in Japan are tackling this problem by enlisting the help of artificial intelligence. More importantly, they’re helping the doctors entrusted with patient care to understand how AI programs spot heart defects. Advancements in artificial intelligence have improved how congenital heart defects are diagnosed. Ultrasound videos of fetal hearts beating normally and others with structural defects can be studied with AI, which can then determine whether the fetal hearts in new videos are abnormal or not..."

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

This report from European Schoolnet Academy explores how professional development for teachers needs to change in the age of AI. From fostering an understanding of AI applications in education to equipping teachers with the requisite skills to leverage these technologies, our analysis aims to provide insights into how professional development can empower teachers to navigate the complexities of the changes brought about by AI.

Asking the question, “What is human-computer interaction - artificial intelligence? Students come to understand what artificial intelligence is in most everyday life, discussing the privacy, pros and cons of this topic and exploring with artificial intelligence activities online. This lesson plan includes using the Google Vision Kit to explore various pre-loaded facial recognition programs and advance programming students can access the Python code, manipulate the code and test the changes.

UNESCO has published the Beijing Consensus on Artificial Intelligence (AI) and Education, the first ever document to offer guidance and recommendations on how best to harness AI technologies for achieving the Education 2030 Agenda. It was adopted during the International Conference on Artificial Intelligence and Education, held in Beijing from 16 – 18 May 2019, by over 50 government ministers, international representatives from over 105 Member States and almost 100 representatives from UN agencies, academic institutions, civil society and the private sector.

This presentation is a part of the subjective assignment done for a MOOC course on Academic Writing in SWAYAM. This presentation gives a bird's eye view about how the technology is shaping the Banking industry in India and what are its advantages and limitations.

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.

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.

How does Artificial Intelligence impact Diversity?Explore what Artificial Intelligence in your own words and pictures.Español¿Cómo impacta la inteligencia artificial en la diversidad?Explore qué Inteligencia Artificial en sus propias palabras e imágenes.

Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline.

This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications.

Andrew Marcinek is an innovative K-12 leader, author, and national speaker. His work is focused on the intersection of instructional design and Technology.  This article, published in Medium in March 2024, discusses best practices for K-12 Technology Directors when leading an Artificial Intelligence implementation for their district.