Pranati Modumudi

Pranati Modumudi

MS Student at Columbia Univeristy studying CS and Neuroscience

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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

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The Alignment Problem Is an Anthropological Issue

6 minute read

Published:

When AI researchers talk about aligning machines with “human values,” the phrase sounds deceptively simple. It implies a shared moral universe, a single standard of right and wrong. But there is no single moral universe. Across cultures, intelligence, agency, and control are understood in vastly different ways. Western traditions often equate intelligence with abstract reasoning, analytical problem-solving, and autonomous decision-making. In many collectivist societies, intelligence is relational: it involves the ability to act with discernment in social contexts, maintain harmony, and fulfill moral obligations. Indigenous cosmologies may distribute agency across humans, ancestors, and the environment, with control being relational rather than individual.

CAIAC Papers Week 2

3 minute read

Published:

This week, our AI safety reading group explored three papers addressing why alignment is hard, how misgeneralization can arise even with correct specifications, and why all ML systems have “weird” failure modes. Below are my reflections, observations, and speculative ideas for addressing these challenges.

CAIAC Papers Week 1

4 minute read

Published:

This semester, I joined the Columbia AI Alignment Club Technical Fellowship, where we discuss AI safety concepts and papers. This week, we explored papers and blog posts on specification gaming, deceptive behavior, RLHF, and alignment failures. These readings highlight how models can behave in unintended or manipulative ways, even when objectives appear simple or human-aligned.

Intelligence as Dynamic Balance - From Evolution to the AI-Culture Fork

10 minute read

Published:

Your brain just performed an extraordinary feat. As your eyes moved across these words, millions of neurons fired in precise patterns, transforming chaotic photons into meaning. But here’s what’s remarkable. Your visual system didn’t just recognize familiar letter shapes. It is simultaneously ready to make sense of fonts you’ve never seen, handwriting styles that would baffle it, and even words that don’t quite look right. This is the fundamental tension that defines intelligence across every domain: the balance between finding useful patterns and breaking free from them when circumstances change. This same dynamic, what I call the pattern-finding/pattern-breaking tension, operates across scales from evolutionary deep time to the millisecond decisions your neurons make right now. It is not just a curious parallel. These systems are nested within each other, each building on the solutions developed by the previous level, each facing its own unique constraints while grappling with the same core challenge.

Papers I Read This Week

5 minute read

Published:

I’m starting a new series called “Papers I Read This Week” to keep track of some of the most interesting work I’ve been reading. I often skim more abstracts and excerpts than I can list here, but this will serve as a place to highlight the papers that stood out—whether for their ideas, methods, or the questions they raise.

What the Wild Taught Us - Reflections from My Trip to Africa

3 minute read

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This summer, my parents and I traveled to Kenya and Tanzania, visiting Maasai Mara, Serengeti, and the Ngorongoro Crater. The safari was more than just a vacation — it gave us a chance to step back and think about the world and our place in it. We expected to see animals in their natural habitat, but what we gained was a deeper perspective.

Neuromatch NeuroAI Reflections

2 minute read

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This summer, I completed the NeuroAI course offered by Neuromatch Academy, a volunteer-led organization providing research education and training at the intersection of neuroscience and machine learning. The course introduced key topics in biologically inspired AI, including neural coding, learning dynamics, and open problems in cognition, while emphasizing hands-on coding, peer collaboration, and engagement with current research. As someone who had been passively interested in NeuroAI, this course was foundational in helping me move from curiosity to genuine direction. It gave me the conceptual framework, technical skills, and intellectual community to explore the field seriously.

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Human Decision Modeling

Published:

Studying EEG and eye-tracking patterns to inform AI models of human decision-making