Recently, I read the latest greeting from Andrew Ng in The Batch (Issue #308), where he shared a tip about getting more practice building with AI. His advice really resonated with me, especially as someone exploring computational linguistics research while balancing schoolwork and robotics competitions.
Andrew Ng’s Key Advice
In his post, Andrew Ng emphasized:
If you find yourself with only limited time to build, reduce the scope of your project until you can build something in whatever time you do have.
He shared how he often cuts down an idea into the smallest possible component he can build in an hour or two, rather than waiting for a free weekend or months to tackle the entire project. He illustrated this with his example of creating an audience simulator for practicing public speaking. Instead of building a complex multi-person AI-powered simulation, he started by creating a simple 2D avatar with limited animations that could be expanded later.
Implications for Computational Linguistics Research
Reading this made me think about how I often approach my own computational linguistics projects. Here are a few reflections:
1. Start Small with Linguistic Tasks
In computational linguistics, tasks can feel overwhelming. For example, creating a full sentiment analysis pipeline for multiple languages, building a neural machine translation system, or training large language models are all massive goals.
Andrew Ng’s advice reminds me that it’s okay — and often smarter — to start with a small, well-defined subtask:
- Instead of building a multilingual parser, start by training a simple POS tagger on a small dataset.
- Instead of designing a robust speech recognition system, start by building a phoneme classifier for a single speaker dataset.
- Instead of developing an entire chatbot pipeline, start by implementing a rule-based intent recognizer for a specific question type.
2. Build Prototypes to Test Feasibility
His example of building a minimal audience simulator prototype to get feedback also applies to NLP. For instance, if I want to work on dialect detection on Twitch chat data (something I’ve thought about), I could first build a prototype classifier distinguishing only two dialects or language varieties. Even if it uses basic logistic regression with TF-IDF features, it tests feasibility and lets me get feedback from mentors or peers before expanding.
3. Overcome Perfection Paralysis
As a student, I sometimes hold back on starting a project because I feel I don’t have time to make it perfect. Andrew Ng’s advice to reduce the project scope until you can build something right away is a mindset shift. Even a basic script that tokenizes Twitch messages or parses sentence structures is progress.
4. Practicing Broad Skills by Hacking Small Projects
He also mentioned that building many small projects helps practice a wide range of skills. In computational linguistics, that could mean:
- Practicing different Python NLP libraries (NLTK, spaCy, Hugging Face)
- Trying out rule-based vs. machine learning vs. deep learning approaches
- Exploring new datasets and annotation schemes
Final Thoughts
I really appreciate Andrew Ng’s practical mindset for builders. His advice feels especially relevant to computational linguistics, where small wins accumulate into larger research contributions. Instead of feeling blocked by the scale of a project, I want to keep practicing the art of scoping down and just building something small but meaningful.
If you’re also working on computational linguistics or NLP projects as a student, I hope this inspires you to pick a tiny subtask today and start building.
Let me know if you want me to share a future post listing some small NLP project ideas that I’m working on this summer.
— Andrew
CompLing Insights 