MIT OpenCourseWare’s Post

In 2024 schools have been asked to address the new Australian Gen AI Framework. Make that journey less confusing and less effort and get resources free for you to use. Join us at a Webinar on Thursday Feb 1st at 3:30 https://lnkd.in/gaKsMHwn or reach out to me directly. The new Australian Framework for AI in Schools a great contribution with 6 thoughtful guiding principles. However, the implementation of such a framework can be a daunting task, potentially shifting focus away from your core mission of teaching and learning. That's where we come in. Over three months of collaboration with the Catholic Diocese of Wollongong, our team has developed a model that simplifies the adoption of this framework, making it easier for your school to integrate AI into your current policy and strategy. With our resources, you can quickly formulate your 2024 AI plan, saving weeks of work on developing policies, teacher guidance, and professional learning strategies. If want the AI framework quick start but you cant make it just DM me on linked-in. #aieducation, Bede Ritchie, Leon Furze, Matthew Deeble, Dr Nick Jackson, Jim Hayden, Louka Parry, Stuart Mitchell, Chris Harte

After tinkering with AI, I see a tsunami of the middle class being forced to retire early or learn a new trade/skill. The next generation of people entering the marketplace will have to be more creative and entrepreneurial than they have been in the last several generations. Our educators would be best advised to help the next generation of newcomers to study formal logic early on in their primary school education. This will allow the next generations to have a toolbox to navigate the data deluge of good versus bad actors on the digital highway.

The Ministry of Education and Science of Ukraine and the Ministry of Digital Transformation of Ukraine recently released draft AI guidance for schools! Some may wonder why Ukraine cares about creating AI guidance during this time of war. I once asked them a similar question. They told me that innovation in education shouldn't stop. The war will end, and when it does, they want to know that they have done their best to keep their kids ready for their future. Thinking about that answer continues to inspire me. ❤️ I'm honored to have them as longtime partners of Code.org and now participants in TeachAI. They specifically referenced TeachAI materials and the AI Guidance for Schools Toolkit, including the Sample Considerations for Existing Policies section. Congrats to Mykhailo Fedorov, Oksana Pasichnyk, Valeriya Ionan, Dmytro Zavgorodnii, and all the others who worked on this, including all of the teachers who provided input. Attached is a Google-translated version of their PDF. If you can read Ukrainian or want to try, check out the link in the comments. #TeachAI, #Ukraine

Completed Introduction to artificial intelligence at great learning

DAY 10 OF 20 DAYS CHALLENGE WITH Africa Agility Foundation So far, I've been introduced to the two major types of Machine Learning Algorithm, supervised and unsupervised learning. Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Supervised learning is the types of machine learning in which machines are trained using well "labelled" training data, and on basis of that data, machines predict the output. The labelled data means some input data is already tagged with the correct output. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept as a student learns in the supervision of the teacher. Thanks to my tutor Onyekachi (Osisiogu) Emenike , she did an amazing job. #20DaysChallengeinDSMLwithAA #10of20DaysinDSMLwithAA #GITBOOTCAMP #GirlsInTech #AfricanGirlsinTechBootcamp #gitbootcampcohort7 #AfricaAgility #africaagilty #datascientist #machinelearning

certificate of completion in artificial intelligence

🌟 Calling all LLM and knowledge graph practitioners! 🌐 Time is flying, and we're just 3 weeks away from the kickoff of my Knowledge Graphs and Large Language Models course, in collaboration with the DATAVERSITY Training Center! 🚀⏰ We already have several sign ups, but there's room for some more, so If you haven't yet secured your spot, or if you know someone eager to delve into the world of Knowledge Graphs and Large Language Models, this is the perfect opportunity. For more details and registration options you can visit https://lnkd.in/gKzwieKY. As a reminder, you can use code PANOS for a 20% discount on the registration price. Also, if you want to take a sneak peek into the course and get the chance to ask more details about its content, join me for a free online 1-hour webinar on July 2nd at 18:00 CEST, where we’ll explore the different roles LLMs play in KG development, and discuss their competence and trustworthiness in each of them. To attend, just accept the event at https://lnkd.in/g8U8xHT7,  and share with anyone who might be interested. Looking forward to seeing you all there! 🚀 #artificialintelligence #generativeai #largelanguagemodels #knowledgegraphs #datamodeling #datascience

Day12 of #20DayslearningChallenge It seems we are moving from fire to frying pan was the first thought that pooped my mind when we delved into Machine learning😭😭 But I've determined not to go back at all💪 . So let me share on types of machine learning There are 3 main types of machine learning algorithm,each with their special characteristics and applications 1. Supervised Machine Learning: in supervised learning,a model gets trained on a "labelled dataset". It learn to map points between inputs and correct outputs. Supervised machine learning is literally an image classification. Supervised learning is grouped into two  1. Classification: it deals with predicting categorical target variables which represents discrete classes or labels . 2. Regression: Regression deals with predicting continuous target variables,which represents numerical values. It maps the input features to a continuous numerical value. Supervised learning process includes Data collection Data preprocessing Model selection Training Validation Testing Advantages of supervised Machine learning 1. Supervised learning models can have high accuracy as they are trained on labelled data 2. The process of decision making in supervised learning models is often interpret able 3. It can often be used in pre-trained models which saves time and resources when developing new models from scratch.  Thanks to Africa Agility Foundation for this great knowledge #Day12#20DayslearningChallenge#Datascienceandmachinelearning#AfricaAgility#GITBootCampCohort7

𝐖𝐡𝐢𝐥𝐞 𝐭𝐡𝐞 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐬𝐜𝐨𝐫𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐔𝐏𝐒𝐂 𝐩𝐫𝐞𝐥𝐢𝐦𝐬 𝐭𝐡𝐢𝐬 𝐭𝐢𝐦𝐞 𝐰𝐚𝐬 70/200, 𝐨𝐧𝐞 𝐚𝐬𝐩𝐢𝐫𝐚𝐧𝐭 𝐦𝐚𝐧𝐚𝐠𝐞𝐝 𝐭𝐨 𝐬𝐜𝐨𝐫𝐞 𝐚 𝐰𝐡𝐨𝐩𝐩𝐢𝐧𝐠 170/200, 𝐜𝐫𝐚𝐜𝐤𝐢𝐧𝐠 𝐀𝐈𝐑 1 🤯 That too in 𝐥𝐞𝐬𝐬 𝐭𝐡𝐚𝐧 10 𝐦𝐢𝐧𝐮𝐭𝐞𝐬! Are you kidding me⁉️ Yes, I'm talking about none other than the #PadhAI app - your AI-Powered Guide for UPSC Preparation! And, in today's edition of The Vision Debugged - AI Newsletter, Sandra & I try to dissect this amazing innovation that managed to ace one of the toughest competitive exams, not just in India but across the world too. 🕵️ Learn about the impressive launch strategy that broke the headlines 🕵️ Explore the various cool features that PadhAI has to offer 🕵️ Discover interesting strategies that go behind creating & launching innovative products in today's AI age 🕵️ How PadhAI stacks up against other evolving AI in education For details, check out today's edition 👉 https://lnkd.in/gPw-Zmut PS: Also check out the edition for cool PM, AI & Data Science resources, pertinent in the field of tech & AI PPS: Found this edition exciting? Consider subscribing to our FREE newsletter for your 2x/week dose of cutting-edge AI content... Now trusted by 650+ enthusiastic readers: https://lnkd.in/gwUNCBdJ #padhai #aiineducation #PadhAIvsUPSC #upsc2024 #futureoflearning #edtech #ainewsletter

AI and the Typical Houses Test: Fifth graders outperform the computer 💅 💻 👀 Recently, our Pumice team visited the Jože Moškrič Primary School in Ljubljana to test a lesson we designed on the topic of typical Slovene traditional houses. 🐾 The lesson went like this: students were asked to sort some pictures of typical traditional houses, of which only one type was easily recognisable, into four groups. They did excellently. 🙌 And then... We looked at how the same task would be solved by a computer: 🐾 Given the same instruction but without being presented with examples of the four house types, the computer only clearly recognised two house types, while the other two groups were mixtures of different examples. 🐾 It was only after it was shown examples of the four types of houses that the computer was able to successfully perform the task. Fun fact: it still did worse than the students (admittedly, it was only by one mistake, and in only one try, but still). 🙃   🙋♂️ 🙋♀️ 💬 What followed was a fascinating discussion with the students: when the computer (correctly) classifies a house as Alpine, can we say that it knows that the house is Alpine? Can the computer really know anything in the same sense as a person? Does a dictionary know how to say a word in a different language? How are computers different? Where do they get their data? Can they think? Is a computer in fact intelligent, or is #ai an illusion❓ How many adults would be able to answer these questions, if you randomly stopped them in the street? What do you think? Should AI be taught in schools? Let us know in the comments 😊 Find out more about our Pumice project here 👉 https://pumice.si/ #dataanalytics #datascience #machinelearning #education