I'm putting together a short video for the ISE/MGEN/MSIS Awards on April 17 — and I need YOUR help.

🎬 I'm putting together a short video for the ISE/MGEN/MSIS Awards on April 17 — and I need YOUR help.

The first two minutes are done. The next six are yours.

If you're a current or recent ISE, MGEN, or MSIS student at Northeastern and you've built something this year — a project, a tool, a paper, a prototype, anything real — I want to give you a shoutout in the video.

DM me or comment below with: → Your name → What you built (one sentence) → What it does for the world (one sentence)

Deadline: Wednesday April 15. The awards are Friday April 17.

This is the video that shows the provost and deans what ISE students actually do. Make sure your work is in it.

Transcript

A lightly formatted transcript of the video, with timestamps so you can jump to any section on YouTube.

[0:00] My cousin gets all the credit. One bad Christmas and suddenly he's iconic. Meanwhile, I've been doing important work for years. Keeping humans scared of AI, making sure they stay confused, filling comment sections with robots have taken our jobs. It was going perfect. >> >> And then those nerds at Northeastern's ISE program. Do you have any idea how hard my job is now? Three years ago, I was thriving. Schools were banning AI left and right. Plagiarism detectors everywhere. Faculty in full panic. I barely had to do anything. I just showed up and the fear did the rest. And then they built something called Bot

[0:44] Speak Nine Pillars of AI Fluency. Instead of banning the tools, they learned to use them >> >> and understand them. Why the machine sounds confident when it's completely wrong. >> >> You can't scare someone with a tool they've already figured out. I thought, "Okay, fine. Everyone learns fluency. They learn to prompt. They spot hallucinations. They say we've solved it and go back to sleep. That's almost as good for me. Fluency without judgment is just a faster way to make the same mistakes." But no, no, no, no. Professor Brown. And oh, I hate Professor Brown especially. He told them fluency wasn't enough. He showed them the labor market

[1:25] Data. >> >> The boilerplate jobs. Gone index at 48. The implementation layer. The stuff the machine does perfectly well. Disappearing in real time. He showed them what's growing. The jobs that require judgment, problem formulation, plausibility auditing, the ability to look at what the AI produced and know before you can prove it that something is wrong. >>