Carina Peng

My time as a research engineering intern at Google DeepMind has come to a close! As someone whose world revolves around networked, computer, and distributed systems, I never imagined I'd land on my dream team – the model partitioning team! This summer, I worked on PartIR's automatic model partitioning capabilities(https://lnkd.in/egp5Ygvy), diving deeper into distributed computing and exploring applications in reinforcement learning. It was definitely a challenging project, but I picked up so many invaluable skills, from software engineering to research in an industry setting. I’d like to give a huge thank you to Sami Alabed, Masha Samsikova, and the entire model partitioning team for making me feel so welcome and helping me successfully complete my project! I’m also thrilled that my final presentation was well received (and even got some laughs!), helping many at GDM understand a complex topic. Outside of my project, I had the chance to meet so many fascinating people across different teams and, of course, the other interns! A big thanks to the GDM Academy organizers, Charlotte Colson and Claudia Pope, for their incredible support in running such a fantastic program while I was balancing my thesis. And to Lucia Lopez Rivilla and Ioana Mihăilescu, former GDM Academy interns, for helping me find my way around the company! This summer wasn’t without its challenges, though. I finally addressed the anxiety issues I’d been struggling with since my teenage years, as they were impacting my studies and relationships. Unfortunately, I wasn’t able to attend my final year exams as I had hoped. However, I'm so grateful to UCL's Computer Science team and Faculty of Engineering for giving me the flexibility to extend my studies by a year, without penalty, and extending my scholarship. I couldn’t be more thankful for their support! All in all, this experience has solidified my determination to pursue research. In my third year, I discovered my passion for networked systems, and during my time at AMD, I knew I wanted to work on distributed systems. Now, after my time at GDM, I’m confident that distributed and networked systems are my future – and with the added experience of AI, I feel more prepared to navigate the machine learning-filled computer science landscape. Here’s to a (hopefully) restful but equally exciting year ahead! See you soon, Morgane 🦆

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Ever wondered why LLMs struggle with math? In this piece, I take a look under the hood of LLMs and math-related AI study tools, to uncover the reasons LLMs are not well-suited for math, what innovators are doing to get ahead of the problem, and some exciting companies in the space. I explore several approaches to improve LLM math performance: process supervision, integration with symbolic computing systems (i.e. ChatGPT + Wolfram|Alpha LLC), and novel ways of capturing unique and structured data ( Thinkverse (formerly Learnie AI), Photomath, Thetawise, among many others). One startup in Korea — QANDA(Mathpresso) — has built its own math-specific LLM that can outperform many of the incumbents. If you are building AI to promote math learning and understanding, please reach out! Very grateful for the opportunity to go deeper in the EdTech space at Reach Capital this summer before kicking off my MBA/MA in Education at Stanford in the fall! Shoutout to Tony Wan and James Kim for the support on this piece! https://lnkd.in/g8xk_hqJ

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