Jack Madrid, president and CEO of the Information Technology and Business Process Association of the Philippines (IBPAP), puts it this way: build expertise in your chosen field, then use AI as a co-pilot to become better and faster at what you do.
At first glance, this advice is good but seemingly superficial. You already know that and the more important question is how do you actually get it to do that? Well, there is a curriculum designed to teach you precisely how to train your own AI to be the perfect co-pilot.
In response to strong demand, we are running the NVIDIA AI Developer Bootcamp, an NVIDIA-certified workshop, in partnership with De La Salle University, Manila.
It runs three days and stays hands-on throughout:
1. Deep learning fundamentals. The core techniques and tools, worked through rather than lectured.
2. Data types and model architectures. Practice with the kinds you will actually meet at work, not textbook cases.
3. Transfer learning. Building a model by starting from one that has already learned the general patterns and adapting it to your own data, which is how most working models get built today.
Participants who complete all three days receive an NVIDIA certification.
When: 9 to 11 October 2026, Friday to Sunday, 9 AM to 6 PM
Where: De La Salle University, Manila
1. A co-pilot needs someone qualified in the other seat. It assumes a person who can tell a good output from a plausible one, and that judgement comes from the domain. Someone with no accounting background cannot supervise an accounting model, because the errors look like correct answers. The dangerous outputs are the confident, well-formatted ones, and only a domain expert reads past the formatting. For a student, that judgement is what a degree is building; knowing what good code looks like is what makes reviewing generated code possible.
2. The larger benefit is coverage. The visible use is applying the tool to the task you already do and finishing sooner. The bigger change is doing work previously skipped for lack of time: checking every record rather than a sample, every contract rather than the flagged ones.
3. The word lifelong matters. Madrid framed learning agility as a lifelong responsibility, which rules out treating this as one course. A tool learned this year will have moved by next year; what carries over is the habit of testing each new tool against work you already know the answer to.
A reasonable first move: take one task you can already judge instantly, run it through a model, and count the errors. That number tells you how much supervision the rest of your work will need.