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Pages
Raza Hashmi
Product leadership + ML research
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Posts
I Designed a Trolley Problem That Puts You on the Tracks
Published:
For over half a century, a single, deceptively simple scenario has served as philosophy’s favorite moral laboratory: the trolley problem. We all know the setup, originally conceived by Philippa Foot in 1967 and later refined by Judith Jarvis Thomson. A runaway trolley is about to kill five people. You can pull a lever to divert it, but doing so will kill one person on a side track.
Building a Sandbox for Human Behavior
Published:
It all started with a simple walk to class. Back at university, there were two distinct pathways to the lecture hall. Both were the exact same distance, had the same view, and took the same amount of time. Yet, I noticed something strange: people rarely picked a path at random. One route always seemed to pull more people than the other.
My AI-Powered Imposter Syndrome
Published:
AI is a wonderful, powerful tool. For someone like me, with a mind constantly buzzing with ideas, possibilities, and new cases, it acts as a “know-it-all buddy” to bounce those ideas off of. It’s an incredible accelerator. But, as you might have guessed, a know-it-all buddy can be dangerously wrong and, worse, doesn’t know when it’s wrong.
Can an AI Learn the Art of Valet Parking?
Published:
Valet parking is a classic test of human multitasking. It’s not just about driving; it’s a high-pressure logistical puzzle that combines spatial reasoning, memory, and strategic decision-making against a ticking clock. This complexity makes it a fascinating and challenging problem to solve with Artificial Intelligence.
portfolio
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publications
Explainable AI: Object Recognition With Help From Background
Published in The International Conference on Learning Representations (ICLR), CSS Workshop, 2022
This paper explores how and when backgrounds help a model with object classification task
Recommended citation: Raza Hashmi, Qiang Li. (2022). "Object Recognition With Help From Background." ICLR CSS Workshop.(1) https://sites.google.com/view/cssiclr2022/home
talks
Adversarial Label Learning: Training a Predictive Network with a Label-based Adversary
Published:
The idea was born out of my fascination with Generative Adversarial Networks (GANs). The concept of two networks locked in a competitive dance—a Generator creating fakes and a Discriminator spotting them—was revolutionary. It transformed what we could do with AI-generated art, music, and more. But I wondered: could this adversarial dynamic be used for something other than generating data?
I Gave an LLM a Holographic Brain — Here’s What Actually Worked (and What Didn’t)
Published:
We have a memory problem in AI. If you’ve built applications with Large Language Models, you’ve hit the wall: the context window. The moment your conversation exceeds the token limit, the model forgets. The beginning of the chat falls off a cliff, and the AI loses who you are. The industry’s standard fix is RAG (Retrieval Augmented Generation). We chop up data, embed it, and stuff it into a vector database like Pinecone or Chroma. It works. But RAG has a hidden cost: linear growth. To store 1 million facts, you need 1 million vectors. Your database grows with every interaction.
teaching
work
Work Experience
For nearly a decade, I was deeply involved in building innovative, user-centric products.