FirstMark | 2023 MAD (ML/AI/Data) Landscape. Mad2023. AI Weirdness. AI and education: guidance for policy-makers - UNESCO Digital Library. Write With Transformer. ElevenLabs || Prime Voice AI. ISTE. Microsoft Designer - Stunning designs in a flash. By WOMBO. Lexica. Stable Diffusion Prompt Book | OpenArt. Deep Dream Generator. AI is for Everyone, Everywhere | EdSurge Guides. The term artificial intelligence was coined over 65 years ago. For decades, it resided almost exclusively within the realm of computer scientists and programmers. But in recent years, AI has become a central component of our everyday lives, acting as the backbone of familiar tech like music streaming services, navigation devices and delivery apps. Unsurprisingly, it now touches nearly every known field of academic study. Education is the conduit through which today’s students become tomorrow’s leaders.
And this guide serves as a toolkit for K-12 teachers who are preparing the next generation of AI users and developers. Featuring in-depth interviews with practitioners, infographics and project guidelines for classroom teachers, as well as a webinar on the importance of AI in education, it aims to provide schools with straightforward and practical ways to integrate computational thinking across their curricula. ElevenLabs || Prime Voice AI. ElevenLabs || Prime Voice AI. Demo – InferKit. Write With Transformer. ISTE. BlueWillow. By WOMBO. Microsoft Designer - Stunning designs in a flash. By WOMBO. Lexica. Gen-1 by Runway. Google Colab. Stable Diffusion Prompt Book | OpenArt. Overview of GAN Structure | Machine Learning | Google Developers. A generative adversarial network (GAN) has two parts: The generator learns to generate plausible data.
The generated instances become negative training examples for the discriminator.The discriminator learns to distinguish the generator's fake data from real data. The discriminator penalizes the generator for producing implausible results. When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it's fake: As training progresses, the generator gets closer to producing output that can fool the discriminator: Finally, if generator training goes well, the discriminator gets worse at telling the difference between real and fake.
It starts to classify fake data as real, and its accuracy decreases. Here's a picture of the whole system: Both the generator and the discriminator are neural networks. Let's explain the pieces of this system in greater detail. AI movie posters. Video Summarization | Cloudinary Labs. ISTE. Generate a ready-to-play lesson in seconds. (3) AI Workshop: Applications for Assessment. SkimIt.ai - Get an ai generated summary of any article delivered to your inbox. Gumroad.
Automated meeting summaries | AI-powered meeting notes - Vowel. The Expanding Dark Forest and Generative AI. Assumed Audience People who have heard of GPT-3 / ChatGPT, and are vaguely following the advances in machine learning, large language models, and image generators. Also people who care about making the web a flourishing social and intellectual space. It's like a dark forest that seems eerily devoid of human life – all the living creatures are hidden beneath the ground or up in trees. If they reveal themselves, they risk being attacked by automated predators. Humans who want to engage in informal, unoptimised, personal interactions have to hide in closed spaces like invite-only Slack channels, Discord groups, email newsletters, small-scale blogs, and .
That dark forest is about to expand . Over the last six months, we've seen a flood of LLM copywriting and content-generation products come out: , , , and are just a few. These models became competent copywriters much faster than people expected – too fast for us to fully process the implications. A Generated Web Passing the Reverse Turing Test. Future Tools - Find The Exact AI Tool For Your Needs. ChatGPT & Education - Google Slides. A college student made an app to detect AI-written text. GPTZero in action: The bot correctly detected AI-written text.
The writing sample that was submitted? ChatGPT's attempt at "an essay on the ethics of AI plagiarism that could pass a ChatGPT detector tool. " GPTZero.me/Screenshot by NPR hide caption toggle caption GPTZero.me/Screenshot by NPR GPTZero in action: The bot correctly detected AI-written text. Teachers worried about students turning in essays written by a popular artificial intelligence chatbot now have a new tool of their own.
Edward Tian, a 22-year-old senior at Princeton University, has built an app to detect whether text is written by ChatGPT, the viral chatbot that's sparked fears over its potential for unethical uses in academia. Edward Tian, a 22-year-old computer science student at Princeton, created an app that detects essays written by the impressive AI-powered language model known as ChatGPT. Edward Tian His motivation to create the bot was to fight what he sees as an increase in AI plagiarism. How GPTZero works. ChatGPT for Educators - Google Slides. Activity Resource Guides for Teaching Artificial Intelligence in K-12 – AI4K12. Artificial Intelligence and education eng. Four Conversations About Human-Centric AI. By Jeremy Roschelle September 14, 2022Comments Reflecting across many conversations in the past year, I've found there are four types of conversations about human-centered Artificial Intelligence (AI).
My own work has been focused on the need for policies regarding AI in education, and thus I’ve been involved in conversations about how teachers, students, and other educational participants should be in the loop when AI is designed, evaluated, implemented, and improved. I’ve been in many conversations about how surveillance and bias could harm teachers or students.
And I’ve seen wonderful things emerging that could really help teachers and students. The four types of conversations are: Opportunities and Risks. When we stay within only one or two of the four conversations, we limit progress towards human-centric AI. Likewise, building trust and engineering trustworthiness are absolutely key conversations we need to have for any field of human-centric AI. No entries found. Reading List: Machine Learning and Artificial Intelligence in Education – A Critical Perspective – CIRCLS. Edited by: Pati Ruiz and Aditi Mallavarapu The following reading list was compiled to provide an overview of the current state of Machine Learning (ML) and Artificial Intelligence (AI) in education, also referred to as AI in Education Research (AIED). Some articles are focused broadly on research themes in AI/ML as they are applied for teaching and learning scenarios, while others more specifically on the intersection of equity, bias, ethics, of using ML/AI methods and the mechanics of the methods themselves.
An additional reading section identifies relevant news articles, reports, and a podcast on these topics. Included under each article is a brief bulleted overview of what each article covers and how much technical background each article requires. Last Updated: 8/1/2022 Algorithmic Bias in Education Citation: Baker, R. This review focuses on solidifying the current understanding of the concrete impacts of algorithmic bias in education. News and Magazine Articles Reports Podcasts Books. What Is Cognitive Computing?
What is cognitive computing? Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM's cognitive computer system, Watson. Computers are faster than humans at processing and calculating, but they have yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way a human brain works. To accomplish this, cognitive computing makes use of artificial intelligence (AI) and other underlying technologies, including the following: Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis and pattern recognition to teach computing systems.
How cognitive computing works For example, by storing thousands of pictures of dogs in a database, an AI system can be taught how to identify pictures of dogs. Adaptive. ISTE Team Palm Beach. Top 10 Data Science And Machine Learning Tools For Non-Programmers. With the continuous generation of data, the need for Machine Learning and Data Science has increased exponentially.
This demand has pulled a lot of non-IT professionals into the field of Data Science. This blog on Data Science and Machine Learning For Non-Programmers is specifically dedicated to non-IT professionals who are trying to make a career in Data Science and Machine Learning without the experience of working on programming languages. To get in-depth knowledge of Artificial Intelligence and Machine Learning, you can enroll for live Machine Learning Engineer Master Program by Edureka with 24/7 support and lifetime access. Here’s a list of topics that will be covered in this blog: Introduction To Data Science And Machine Learning Data Science and Machine Learning have drawn professionals from all backgrounds. Data is the key to grow businesses, solve complex real-world problems and build effective models that will help in risk analysis, sales forecasting and so on. RapidMiner BigML. Enabling New Forms of Student Learning through Artificial Intelligence by World Bank EduTech Podcast.
The Global Education Policy Dashboard (GEPD), funded by a partnership between the World Bank, Bill & Melinda Gates Foundation, U.K.’s Foreign, Commonwealth and Development Office and government of Japan, provides policymakers with a system for measuring the drivers of learning outcomes in basic education around the world. As part of this initiative, the Edtech Readiness Index aims to help countries assess the readiness of their ecosystems in leveraging 'edtech' to promote learning for all. The Index not only includes device availability or connectivity but also institutional capacities, school management, educational resources, quality of the learning experience, and development of digital competencies which constitute an ‘ecosystem’ essential for an effective ‘edtech’ implementation. How Can AI Systems Support Teachers: 5 Big Ideas from the Learning Sciences. The learning sciences study the design and implementation of effective learning environments by drawing on a variety of perspectives across a range of physical, social, and technological spaces1.
Learning sciences focuses on human learning and helps individuals achieve their fullest potential and attain 21st-century skills. Because of this focus, the learning sciences should be foundational in the design and development of emerging technologies for teaching and learning. AI systems are an emerging technology that are starting to play a significant role in the redesign of learning environments. To increase our chances of creating successful AI systems for learning, they should be grounded in the learning sciences. Big Idea 1: Representation and Supports The learning sciences have found that enabling students to make connections across multiple representations (for example, graphs, writing, images, maps, blocks, etc.) contributes to knowledge construction. Big Idea 2: Collaboration. CIRCLS AI Report Nov2020. The Benefits and Limitations of Machine Learning in Education.
By Will McGuinness Hardly a day goes by where we don’t hear about the latest development in Artificial Intelligence and Machine Learning. In the 1990s, IBM trained Deep Blue to take on Kasparov in chess. In the mid-2010s Google developed a program, called AlphaGo, to play Go and challenge the world’s best. AlphaGo trained on the knowledge and insights of experts by studying thousands of professional games, amassing lifetimes of experience and wisdom in just a few short months. As machine learning has advanced in chess and Go, it would be reasonable to think we can rely on it for great advances in education as well. Reasoning Mind, the company I work for, has spent the last 18 years developing online math programs for pre-K through 8th-grade students, and while we have long recognized that computers can have great benefits in the class, we frequently must remind ourselves that there are limits to what they can do.
The benefits: For more, see: How Can AI Systems Support Teachers: 5 Big Ideas from the Learning Sciences. Engage AI Institute. Day of AI. AI Ethics Guide EN. AIGDSE 1120. AIGDCS 0820 red. AIGDEL 0820 red. AIGDK5 1120. AI and education: guidance for policy-makers - UNESCO Digital Library. AI is for Everyone, Everywhere | EdSurge Guides. AI and the Future of Teaching and Learning: Defining Artificial Intelligence | by Office of Ed Tech | Apr, 2022 | Medium.
Educational technology is evolving to include artificial intelligence.Artificial intelligence will bring “human-like” features and agency into future technologies.Policy will have an important role in guiding the uses of artificial intelligence in education to realize benefits while limiting risks. Educators, students, and parents and caregivers use technology daily and it has become essential to teaching and learning. Yet, familiarity with educational technology obscures a transformation occurring behind the scenes: almost all forms of technology used in education are beginning to incorporate artificial intelligence (AI) systems. More than half of school leaders already see the role of AI increasing in their school districts (Figure 1). Within five years, AI will change the capabilities of teaching and learning tools. “the theory and development of computer systems able to perform tasks normally requiring human intelligence” [1] Let’s start with a simple example.
. [3] Noble, S. International Forum on AI and the Futures of Education, developing competencies for the AI Era, 7-8 December 2020: synthesis report - UNESCO Digital Library. Teachable Machine. RAISE - resources. (1) gender shades. (1) Joy Buolamwini — AI, Ain't I A Woman? Presented by Organizational Transformation. (1) Compassion through Computation: Fighting Algorithmic Bias | Joy Buolamwini. "The Coded Gaze": Joy Buolamwini Poetically Explains Bias in AI - Frontlines @ epic.org. EPIC Advisor Joy Buolamwini is a computer scientist and poet who masterfully uses poetry and research to illustrate the social implications of artificial intelligence. She founded the Algorithmic Justice League in order to create a world with more equitable and accountable technology. In a talk given at the World Economic Forum last year, Ms.
Buolamwini discussed a term she coined, “the coded gaze” – which describes the priorities, preferences, and the biases of those who create code. During her talk she showed several examples of this by using facial recognition algorithms from top tech companies to see if they could accurately identify famous black women throughout history. Repeatedly, the algorithms mistook the faces of Sojourner Truth, Michelle Obama, Oprah Winfrey, and others, for men. In fact, these algorithms were only able to identify the faces of white men with complete accuracy.
For more information on this, and other topics, please visit www.epic.org. Algorithm Literacy Project | Understanding algorithms. RAISE - resources. Resources for educators | KCJ. Kids Code Jeunesse Coding for Kids| KCJ. K-12 AI curricula: a mapping of government-endorsed AI curricula - UNESCO Digital Library. Artificial intelligence in education. Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the Education 2030 Agenda, while ensuring that the application of AI in educational contexts is guided by the core principles of inclusion and equity.
UNESCO’s mandate calls inherently for a human-centred approach to AI. It aims to shift the conversation to include AI’s role in addressing current inequalities regarding access to knowledge, research and the diversity of cultural expressions and to ensure AI does not widen the technological divides within and between countries. Artificial intelligence and inclusion, compendium of promising initiatives: Mobile Learning Week 2020 - UNESCO Digital Library. Artificial Intelligence for Children. Children and youth are surrounded by AI in many of the products they use in their daily lives, from social media to education technology, video games, smart toys and speakers. AI determines the videos children watch online, their curriculum as they learn, and the way they play and interact with others. This toolkit, produced by a diverse team of youth, technologists, academics and business leaders, is designed to help companies develop trustworthy artificial intelligence (AI) for children and youth and to help parents, guardians, children and youth responsibly buy and safely use AI products.
AI can be used to educate and empower children and youth and have a positive impact on society. But children and youth can be especially vulnerable to the potential risks posed by AI, including bias, cybersecurity and lack of accessibility. AI must be designed inclusively to respect the rights of the child user. AI technology must be created so that it is both innovative and responsible. IBM AI — MindSpark. AI and the Future of Teaching and Learning: New Interactions, New Choices | by Office of Ed Tech | Apr, 2022 | Medium. Artificial intelligence in education. RAISE - resources. Teachable Machine. Artificial Intelligence in Education: A Reading Guide Focused on Promoting Equity and Accountability in AI – CIRCLS. AI Experiments - Experiments with Google. Google Cloud Big Data and Machine Learning Fundamentals | Google Cloud Skills Boost. The social life of Artificial Intelligence in education.
Artificial intelligence | TED Talks. Artificial Intelligence. List of Resources – AI4K12.