Posts by Collection

projects

Mole Project Permalink

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Created a robotic mole plush toy that talks and takes voice commands for mole day.

Blind Assist Permalink

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Created a headset and program to be used by visually impaired pedestrians.

Covid Detection Kaggle Permalink

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Created a model to view lung X-rays to localize lung opacities and use them to make COVID-19 diagnoses.

Pong Bot Permalink

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Created a pong playing robot for Columbia Art of Engineering project.

Charizzma Permalink

Published:

Charizzma is an AI conversational advice-giver that helps you respond smoothly when you need it most. Specifically, we used real-time transcription, keyword extraction, and speech synthesis to deliver advice in real time.

LangShell Permalink

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Our shell tool, called LangShell, is a GPT-3 based shell assistant with a memory. You can tell LangShell facts about yourself, and you will never forget them (unless you want it to). We built LangShell using Python, the OpenAI GPT-3 API, and the Huggingface MiniLM-L6-v sentence similarity API. It is based on a fork of Shell GPT, and we plan to release it as a Pull request on the original repo.

LLM Trajectory Analysis Permalink

Published:

Analyzed trajectories of language model embeddings projected onto a 3-sphere using UMAP. Won the special Nomic bounty at the NYU Generative AI Betaworks Hackathon.

publications

Machine Learning and Proactive Network Maintenance: Transforming Today’s Plant Operations

Published in Fall Technical Forum: SCTE, NCTA, CableLabs, 2021

This paper discusses uses machine learning to classify types radio frequency impairments in cable modems.

Recommended citation: Berkan Ottlik, Brady Volpe. (2021). "Machine Learning and Proactive Network Maintenance: Transforming Today's Plant Operations" 2021 Fall Technical Forum: SCTE, NCTA, CableLabs. https://www.nctatechnicalpapers.com/Paper/2021/2021-machine-learning-and-proactive-network-maintenance-transforming-today-s-plant-operations/

The Effect of Model Capacity on the Emergence of In-Context Learning

Published in ICLR 2024 Workshop on Understanding of Foundation Models (ME-FoMo), 2024

This paper investigates the relationship between model capacity and the emergence of in-context learning under a simplified statistical framework in the transformer model.

Recommended citation: Berkan Ottlik, Narutatsu Ri, Daniel Hsu, Clayton Sanford. (2024). "The Effect of Model Capacity on the Emergence of In-Context Learning" ICLR 2024 Workshop on Understanding of Foundation Models (ME-FoMo). https://openreview.net/pdf?id=YZM9g0Mi9a

A Sequential Lightbulb Problem

Published in Presented at the Fall Fourier Talks, University of Maryland, 2024

This paper studies an online version of the light bulb problem and gives simple lower-bounds and develops algorithms that allude to a space-runtime tradeoff.

Recommended citation: Noah Bergam, Berkan Ottlik, Arman Özcan. (2024). "A Sequential Lightbulb Problem". https://berkan.xyz/files/lightbulb.pdf