In June 2020, NYP AI was born. Our vision - To spread awareness of AI among polytechnic students.
Progress (The ups...)
Fast forward 2 years: We've grown to a 100+ member interest group, hosted over 10 school-wide events and ultimately found our place as SIT student's go to for Artificial Intelligence events.
We have our annual flagship NYP AI Summer Camp event, a week long programme introducing students to the up & coming AI concepts such as NLP Transformers and Stock prediction with LSTMs.
More recently, we've delved more into out-of-the-box algorithms, touching upon Tensorflow Magenta, OpenAI's CLIP model, and Huggingface's Transformers, just to name a few.
As champions of open sourcing, all our content (slides and codes) are freely available online. Our Github repository contains all our event source codes, and the content slides for each of our events can be found in the respective readme.md files. Below are the URLs of our Slides (In a Google Drive folder) and our Github Repo.
NYP-AIOur blog is constantly updated to feature our latest initiatives & events. For students who are unsure whether Artificial Intelligence is for them, they can read our blog posts to learn more about our past events. Our blog gives prospective members a glimpse into all our events - such as the concepts we've covered and the way we teach these concepts. We even have a "Projects" page on our website, dedicated to showcasing some of our projects from past events - which will hopefully pique the interests of curious students.
And the downs...
But it didn't always go so smooth. At the beginning, we were struggling to make a place for ourselves. Our overemphasis on coding proved a double-edged sword as some students (understandably) found coding AI algorithms too daunting. No doubt Machine Learning requires quite a bit of coding, be it during data preprocessing or model building. However, if done right, breaking into this subject can be an enjoyable and smooth one.
Learning how to teach
We spent the next 2 years "perfecting" the craft of teaching AI, and although we've yet to reach the peak, what we can say for sure is that we're better than our version 2 years ago.
Here are 3 "mindset shifts" we've come to realize, powerful ones which we sure can benefit other interest groups.
Our first mindset shift: Upholding the relevancy of our events.
Our first few events always ended up with a trained model in a python notebook. No websites, no applications - just a .ipynb notebook.
Over time, we realized there were better ways to do this. By creating an application that integrates these AI models, we would accurately mirror real-life deployments of AI. You'll probably never interact with production AI in a python notebook. Instead, you'll interact with AI on your phone (Siri, Facial Recognition) or on websites (Recommender systems, Chatbots) instead. Cutting a long story short - We'll be teaching students how to apply AI in a real-world setting.
Furthermore, by showing students how to integrate their trained models into their own applications, we teach them how to use AI in their own personal/school projects. During our past events, we taught students how to save their AI models onto pickle files and deploy them onto their Flask websites. Additionally, we covered how to "AI Supercharge" their applications using AI REST APIs - granting them access to the latest & greatest models, trained by the biggest research labs. After all, why reinvent the wheel when there are already pre-trained models freely available online?
Finally, by having an application as a deliverable, it serves as a cool end-product, a representation of them having successfully applied AI. It's this sense of accomplishment which we hope will spur students to delve deeper into AI.
Our second mindset shift: Concepts over code.
Coding proved to be a high barrier to entry for students looking to break into AI. Our initial efforts of wanting to provide an in-depth view of AI backfired when we realized that some students could not catch up with the coding exercises.
We decided to shift away from our "build from scratch" mentality which led to a lot of (unnecessary) code writing. Instead, we shifted to using interesting Github packages and projects (Huggingface models, CLIP model, Tensorflow Magenta, etc..), drastically reducing the amount of code written while maintaining the integrity of our events. Using these packages, we could easily illustrate AI concepts with highly abstracted pieces of code, ensuring that our attendees wouldn't get "burnt out" from having to type out long lines of code.
More recently, we've come to embrace no-code solutions. Highly-visual tools such as "Teachable Machine" were excellent in teaching students how to train their first Image classification model. The simple drag-and-drop interface made learning a very painless and interesting experience for first-timers. Other forms of no-code solutions include drag-and-drop model builders such as "Microsoft Cognitive Toolkit" which allow us to teach data preprocessing and hyperparameter tuning easily.
To put it simply, we came to realize that coding was merely a means to an end; A way for students to gain a better understanding and appreciation of AI.
Our third mindset shift: Publicity 👀
An event is only as good as the number of participants it benefits.
Despite hosting a variety of events, we realized that NYP AI still wasn't very well-known within NYP SIT. Despite many students taking an interest in AI, a substantial number of them had never attended our events.
We realized that more had to be done on our end to publicize NYP AI and our events to students. Knowing this, we utilized social media (Instagram), enlisted the help of our teachers and academic club (SIT Club), and used word-of-mouth to increase our mindshare. We organized inter-SIG events such as "Tech in SIT", a collaboration between all SIT SIGs to host a week-long worth of events. We organized school-wide puzzles during "Solve IT", a puzzle campaign aimed at teaching students new concepts through gamified puzzles. With the support of our school & teachers, we used NYP open houses & orientations to publicize NYP AI (SIGs in general). Moreover, we decided to introduce AI concepts in a highly beginner-friendly way (As explained in our second mindset shift), which greatly expanded our target audience.
Our efforts were not in vain. With a more "offensive" publicity strategy, we've garnered more average participants for our events. We've managed to reach out to other NYP schools such as NYP School of Engineering. Given the interdisciplinary nature of AI, we're looking to expand NYP AI to other NYP schools in the future, bringing room for more all-rounded collaborations.
Epilogue
We've made it far, but not without the passion & commitment of the NYP AI leadership team, comprised of six - Jun Cheng, Tony, Dylan, Jing Kai, Nuzul and me. We're proud of what NYP AI has become and we're excited for what NYP AI will be as we hand over the torch to our next leadership team.
~ Sincerely
The NYP AI Team (20-22)
