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AIStars Competition 2021

When: July 31st- Aug 7th, 2021

Deadline to Register: July 15th

AI will create the future. Who will create AI?

Artificial Intelligence is all around us and growing rapidly. The AI market is expected to be 190B in 2025 with AI in every industry and used in many jobs.

To help kids worldwide learn AI, we at AIClub are launching AIStars, for kids from ages 8-18 imagine the future with AI, build innovative projects, get guidance from expert mentors, and become part of a worldwide community of innovators. 

Why compete? You can have fun and build cool AI apps, learn from mentors and speakers, and meet other kids! You can use your project learnings in STEM fairs or other competitions, and showcase your project as an impressive accomplishment for college applications.

AIStars Competition Pitching Session 2021

AIStars Competition 2021 
Winner

AIStars Competition 2021 
Second Place

AIStars Competition 2021 
Third Place

Agenda (Pacific Standard Time)

30th July Friday
8:15 PM - 8:30 PM

30th July Friday
8:30 PM - 9:00 PM

31st July Saturday
9:30 AM - 9:45 AM

31st July Saturday
9:45 AM - 10:30 AM

31st July Saturday
10:30 AM - 10:45 AM

31st July Saturday
10:45 AM - 11:30 AM

31st July Saturday
11:30 AM - 11:45 AM

31st July Saturday
11:45 AM - 12:00 PM

31st July Saturday
12:00 PM - 12:45 PM

1st August Sunday
9:30 AM - 9:45 AM

1st August Sunday
9:45 AM - 10:30 AM

1st August Sunday
10:30 AM - 10:45 AM

1st August Sunday
10:45 AM - 11:30 AM

Welcome

Dr. Nisha Talagala (CEO AIClub)

Keynote - Experiences with Entrepreneurship
Dr. Amit Gupta (Head of Products - Pyxeda.ai)

Test Run Competition Chat Rooms
Sindhu

Training Session 1: Ideation - Deciding on a problem
Nisha Talagala

Speaker 1
Nithya Ruff

Design Thinking
Saloni Mohapatra

Break

Mixer
Get to know each other

Training Session 2: Introduction to AI
Sindhu Ghanta

Speaker 1
Dr Manjari Jain

Training Session 3: Pitching your Solution
Nisha Talagala

Speaker 2
Sarah Haq

Training Session 4: Website Building
AIClub

1st August Sunday
11:30 AM - 11:45 AM

1st August Sunday
11:45 AM - 12:00 PM

1st August Sunday
12:00 PM - 12:45 PM

Break

Speaker 3
Megan Branch

Training Session 5: Building an App
Sindhu Ghanta

Agenda (Indian Standard Time)

31st July Saturday
8:45 AM - 9:00 AM

31st July Saturday
9:00 AM - 9:30 AM

31st July Saturday
9:30 AM - 9:45 AM

31st July Saturday
9:45 AM - 10:30 AM

31st July Saturday
10:30 AM - 10:45 AM

31st July Saturday
10:45 AM - 11:30 AM

31st July Saturday
11:30 AM - 11:45 AM

31st July Saturday
11:45 AM - 12:00 PM

31st July Saturday
12:00 PM - 12:45 PM

1st August Sunday
9:30 AM - 9:45 AM

1st August Sunday
9:45 AM - 10:30 AM

1st August Sunday
10:30 AM - 10:45 AM

1st August Sunday
10:45 AM - 11:30 AM

1st August Sunday
11:30 AM - 11:45 AM

Welcome

Dr. Nisha Talagala (CEO AIClub)

Keynote - Experiences with Entrepreneurship
Dr. Amit Gupta (Head of Products - Pyxeda.ai)

Test Run Competition Chat Rooms
Sindhu Ghanta

Training Session 1: Ideation - Deciding on a problem
Nisha Talagala

Mixer
Get to know each other

Design Thinking
Saloni Mohapatra

Break

Bit Health and AI
Nana

Training Session 2: Introduction to AI
Yamuna Dulanjani

Speaker 1
Nithya Ruff (Video)

Training Session 3: Pitching your Solution
Nisha Talagala

Speaker 2
Dr. Manjari Jain

Training Session 4: Website Building
Amit Gupta

Break

1st August Sunday
11:45 AM - 12:00 PM

1st August Sunday
12:00 PM - 12:45 PM

Speaker 3
Sarah Haq

Training Session 5: Building an App
Yamuna Dulanjani

Here is the AIClub competition Rubric (Scoring guide)

Rubric.PNG
Rubric

Agenda and Timeline

19th April 

15th July 

Registration Ends

Registration Starts

31st July and 1st August

Live Event Days

2nd-6th August

Teams Collaborate

with mentor guidance

7th August

Pitch/Demo Day

8th August

Winners Announced

Competiton Schedule

The competition will feature inspiring guest speakers and hands-on learning sessions to help you brainstorm, design, and build your AI-powered app!

After the kick-off, each team will have one week to build your app, with guidance from expert mentors.

Competition will conclude with Pitch Presentations and Prize Awards

Learning Sessions

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Ideation: Deciding on a problem
Learn how to brainstorm, research and decide on a problem
July  31st
Artificial Intelligence_38.png
Introduction to AI 
Learn what Artificial Intelligence is and how to build one
July 31st
Creative Design_3.png
Design Thinking 
Learn how to design your solution and make it user-friendly
July 31st
Creative Design_45.png
Building a Website
Learn how to build a website for your solution
August 1st
Artificial Intelligence_41.png
Building an App
Learn how to build an app for your solution
August 1s
Creative Design_7.png
Pitching your solution
Learn how to pitch your solution to the judges
August 1st

Prizes

Each team will submit a pitch deck describing their idea and prototype. Each team will also have an opportunity to present their idea to our international panel of expert judges. 

More information about the judges and the judging rubric will be made available shortly.

FIRST PRIZE

For every team member, a $100 gift card and a full year of courses from AIClub

Mentoring from Silicon Valley experts to continue your app!

SECOND PRIZE

For every team member, a $50 gift card and a full year of courses from AIClub

THIRD PRIZE

For every team member, a $25 gift card and. a full year of courses from AIClub

  • How does data help AI?
    Previously we talked a little bit about how AI's need a lot of data to learn patterns from. In this video we'll talk about how this data looks. Data can be in many forms, it can be numbers, images, videos, audio, etc. We will focus on data that can be stored in tables. You might have seen tables and softwares like Excel or Google Sheets. It essentially consists of some rows and some columns. What you see on this slide is that rows are numbered 1,2, 3 and so on, while the columns are labeled A, B, C etc. This is the basic structure of tables as you'll see in Google Sheets. It consists of several rows and columns. Let's take an example dataset. In this dataset, note that the columns are, number of countries visited, number of years in school, height and, who am I. You see how the two words feature and label appear right about in yellow color. These are really important 2 words. You'll be using them quite a lot when you build an AI. First thing to note is that there is a single column called label and rest everything is features. Label refers to what you would like your AI to predict and features represent information that you will provide your AI for it to make predictions. For example, in this data set, the features are `number of countries visited`, `number of years` in school and `height` and the label is `Who am I`. This means that you will give your AI information about how many years someone went to school, how many countries someone visited and their height. The AI will take this information and it is supposed to predict if this person is an adult or a child. Another word that you come across is sample. Every row here is called a sample. On this slide, for example, there are three samples,, which are provided to the AI to learn from. A really good question here is, what is a good number of samples? How many samples do you need to provide your AI, before it can learn to do predictions?. In the real world, there are sometimes millions of samples. It really depends on the kind of prediction that you're trying to make. The number of features which is the number of columns can also be like hundreds, or 1000s in the real world. For a simple example like this, probably a few 100 samples are enough for the AI to learn the pattern. But what happens if you don't provide enough samples?, what if I provide only these three samples?. The AI will have a really hard time to recognize patterns. So it may not do that great a job, if you don't provide enough samples. The process of building an AI can be divided into 2 stages. The first stage is called training. Training is the process by which AI learns patterns from data. For example, if you give it three features and one label column, it will try to recognize patterns in your features that will help it to predict the label. The output or result of training is something called a model. You can think about the model as the brain. This brain has learnt a specific task to recognize certain patterns in data and is now capable of making some predictions. Here is another example of providing data to an AI. This is a table which contains only two columns. The label column is the feeling, and the feature is the sentence. The AI will get this information as an input, and it is supposed to predict if someone is happy or sad. You can provide a bunch of these examples as data to your algorithm, and the algorithm will recognize patterns in these sentences and create a model. And once a model is created, it is capable of making predictions on new data that comes in. Now you can use this model in a second stage called the prediction stage where you can provide it with any English sentence or even a paragraph, and the AI is going to predict if that sentence is happy or sad. One thing to note is that an AI can do a very specific job, and how well it does that job depends on the quality of data that you provide. If you train an AI to detect if someone is happy or sad, then that's the only thing that AI can do. What will happen if you provided data about how many years someone went to school to an AI that has learnt to predict mood?. The AI has no idea about that kind of data, it will be super confused and do nothing. So AI's are very specific problem solvers, they learn to solve a specific problem. We have introduced a lot of new terms in this video. To reiterate, the new words that you have heard are training, which is the stage where you help your AI to learn patterns, prediction stage, which is also popularly called inference which is where your AI answers new questions. Lets talk about one last new term called AI Service. Going back to the example that we saw, where you can enter a sentence and it's going to predict happy or sad, a corresponding AI service would work something like this. You type in a sentence in english, lets say in a box and the AI service will take this as an input and predict if you're happy or sad. You actually interact with a lot of such AI services in your daily life, like Siri, google home, google translate etc. these are all AI services, where you have a model that has already trained on a lot of examples and is available for you to use. In this course, we are going to be creating such AI services, and depending on what kind of data you train your AI model with, the service is going to do a very specific job for you. So whenever we say AI service, think about it as the inference stage or the prediction stage, where you expect the service to accept new data and provide a prediction.

Judges

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Christine Santos - CEO EPIC Generation

Christine Santos is a Children’s TV Producer, Educator, Kids’ Life Coach and Video Journalist.
She has a BA in Psychology and is a Certified Common Sense Media Educator and WISDOM Coach™️. 
Prior to Media and Broadcasting, she had an extensive career in Human Resources in various industries.  In 2019, she left the corporate world to pursue her passion in empowering kids, and established EPIC Generation, a media production and leadership training academy for kids. 
She is currently working on some film projects and is nominated for Excellence Award in Media in the upcoming Golden Balangay Awards in Canada. 

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Angela Lau
Entrepreneur

Angela Lau is an experienced product professional and entrepreneur. She constantly thinks about using data science and machine learning to build products that solve real-world problems and delight users. As a builder at heart and engineer by training, Angela loves to roll up her sleeves and be hands-on to solve problems. She was a former startup founder, software engineer, product manager at major technology companies, such as Apple and Oracle. She is currently working on a computer vision AI project for occupational ergonomic assessments.

 

Angela graduated from the University of Southern California’s Viterbi School of Engineering. She also holds an MBA degree at the Haas School of Business, University of California Berkeley.

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Deepika Sikri
Sr Director of Engineering Sleep
Number Labs

Deepika Sikri - is the Sr. Director of Engineering at  Sleepnumber Labs. She has 19 years of experience in software engineering, solving problem and making software development “hum”. In her current role, she is engineering the next generation IoT and AI in connected healthcare space.  She has forged a signature style of enquiry which blends hard data and rigorous analysis with concrete solutions and on-the-ground action. A strong believer in the limitless power of science and technology, she walks her talk by educating, coaching and mentoring young minds and believes this is the best gift to growing society. She coaches middle schoolers on STEM and Robotics and has been speaker in multiple conferences.  

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Danika Gupta - Student Judge

Danika is a rising 8th grader at the Harker School in San Jose CA. She has been building AI and code projects for several years with her friends and teams.

Partners

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