mEducation Alliance SymposiumHal Speed & Marco Zennaro

2021.09.29https://ai4k12.org/news/presentations-and-papers/

Preparing for the Age of Artificial Intelligence

AI4K12 Guidelines (Not Standards) Providing:

● Guidance to standards writers (e.g. CSTA), teachers, educators, curriculum developers, professional development providers

● Recommendations, not requirements

● Addresses a diversity of learners and implementations

● Meant to be revised—a living document

Guidelines define:

● What students should know (Enduring Understanding - Knowledge)● What students should do (Learning Objective - Skill)

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Big Idea #1: Perception

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Computers perceive the world using sensors

Perception is the extraction of meaning from sensory signals using knowledge.

● Human senses vs. computer sensors● Types of perception: vision, speech,

recognition, etc.● How perception works: algorithms

Big Idea #2: Representation and Reasoning

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Agents maintain representations of the world and use them for reasoning

● Types of representations● Families of algorithms and the work they do● Representation supports reasoning:

algorithms operate on representations

Big Idea #3: Learning

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Computers can learn from data

● Nature of learning● Fundamentals of neural networks● Datasets

Big Idea #4: Natural Interaction

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Intelligent agents require many kinds of knowledge to interact

naturally with humans

● Natural language understanding

● Common sense reasoning● Affective computing &

interaction (e.g. with robots or speech agents)

● Consciousness and philosophy of mind

Big Idea #5: Societal Impact

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Artificial Intelligence can impact society in both positive and negative ways

● Ethics of AI making decisions about people

○ Fairness, bias, transparency, explainability, accountability

● Economic impacts of AI● Cultural impacts of AI

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AI4K12 Resource Directory

Includes:● Books and Reports (Adults)● Children’s Books● Competitions● Curriculum Materials● Demos

● Online Professional Development Courses● Online Courses for K-12 Students● Reference Sources & Tutorials● Resource Directories● Software Tools & IDEs● Videos

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https://ai4k12.org/resources/list-of-resources/

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https://code.org/ai

Additional Resource Lists

https://aiforteachers.org/ https://raise.mit.edu/resources.html

https://www.actua.ca/ai/ https://tinyml.seas.harvard.edu/4K12

jupyter notebook/Colab

Edge Impulse

Seeed Studio Codecraft

Google Teachable Machine

Microsoft Lobe

ML for Kids

Code.org

mBlock + Teachable Machine +Microsoft Cognitive Services

Nano 33

Wio Terminal

Pico4ML

micro:bit

others

Arduino IDE

MicrosoftMakeCode

MicroPython/ CircuitPython

Scratch + Teachable Machine

Machine Learning Workflow for tinyML

tinyML4D Global Academic Network

Good Best

training material

Reliableequipment

HumanNetworking

https://tinyml.seas.harvard.edu/4D/

tinyML4D Global Academic Network https://tinyml.seas.harvard.edu/4D/AcademicNetwork

tinyML4D Roadmap September 17 with Prof. Vijay Janapa Reddi of Harvard University on "Why the future of ML is Tiny and Bright.”

October 1 will be lead by Prof. Marcelo Rovai of Universidade Federal de Itajubá who will guide us through setting up the software tools needed for the ICTP workshop.

October 8 will be about our TinyML network, including some case studies from network members.

ICTP workshop will be held from October 18 to 22.http://indico.ictp.it/event/9622/