Kamshat Saduakassova

#Interspeech2026 #Interspeech2026KoalaCompetition

@KatrinaL2899 @Cohere_Labs Katrina, thank you so much 🫠🥹 you didnt forget about me 😇

@Kemmi92 @Cohere_Labs Hi Kamshat, yes you can find the recordings here: sites.google.com/cohere.com/coh…

Be sure to check out ML Summer School! I will be teaching a ML math refresher on July 2. I will go over the math that powers machine learning algorithms! Calculus, vector calculus, and linear algebra concepts will be covered. 🌟

@krisgligoric Hello, Kristina! Will there be recording available after for those who can’t attend in-person and can’t get passwords in-person?

Excited to co-organize the Bridging Human and LLM Annotations for Social Science tutorial at #ic2s2 tomorrow! Join us at 1:30pm CEST to learn how to combine Qualtrics+Prolific, MTurk, & LLMs for automated annotation and valid estimates. Code&materials: github.com/kristinagligor…

Our fine-tuned model on Kazakh audio dataset🙌🥳: huggingface.co/BilalS96/commo… @BilalS700

robotics. This course is an invaluable resource for anyone keen to explore the cutting edge of AI, and I highly recommend it.

balance between technical depth and broader business insights, making it highly accessible while remaining deeply informative. As I continue my work on ASR systems and voice assistants, I look forward to applying these learnings and exploring the exciting intersection of NLP and

e something I plan to revisit in the future. Applying the theoretical knowledge I’ve gained through this MOOC to real-world scenarios is a priority as I continue to build my skills in NLP. The course concluded with an insightful lecture by Professor Dawn Song on the challenges of

s. While I found the theoretical components of the course immensely enriching, my full-time job left me with limited time to engage with the hands-on lab assignments. These labs, designed to provide practical experience with frameworks like AutoGen and exercises in AI safety, are

My Journey Through UC Berkeley’s LLM Agents MOOC: A Linguist’s Perspective on Transitioning into NLP This year, I embarked on a transformative journey into the world of Large Language Model (LLM) agents through UC Berkeley’s LLM Agents MOOC. As a linguist transitioning into the

also be seen as part of the robotics domain, as they enable human-machine interaction through speech. This connection sparked my curiosity to delve deeper into robotics, which could potentially become my next goal as I continue to expand my understanding of AI and its application

research area, this lecture opened my eyes to its potential. It explored the development of generalist robots capable of performing diverse tasks and introduced innovative technologies such as RoboCasa and MimicGen. Reflecting on the lecture, I realized that ASR systems could

further enhanced my understanding of integrating multimodal data, which is essential for creating intelligent and versatile agents. Jim Fan’s lecture on NVIDIA’s Project GR00T stood out as a particularly fascinating session. While I consider robotics to be a highly challenging

systems. The Agentic AI Frameworks & AutoGen lecture by Chi Wang delved into the orchestration of agent communication, offering practical insights directly applicable to building sophisticated voice assistants. Jerry Liu’s discussion on Building a Multimodal Knowledge Assistant

and software development, offering a broad perspective on their applications. The LLM Reasoning class by Denny Zhou was a game-changer in understanding how large language models process and generate human-like text, which is crucial for developing NLP applications like ASR

topics like the history and reasoning of LLMs to agentic AI frameworks such as AutoGen and DSPy, the lectures provided a comprehensive exploration of this burgeoning field. The curriculum highlighted the transformative potential of LLM agents in enterprise workflows, robotics,

of cutting-edge AI technologies and their practical applications. Led by Professor Dawn Song and the talented team at Berkeley’s Center for Responsible Decentralized Intelligence (RDI), the course combined advanced theoretical insights with practical knowledge. From foundational

field of Natural Language Processing (NLP), the course presented a unique challenge but aligned perfectly with my ongoing work developing an ASR (Automatic Speech Recognition) system and a voice assistant. It offered an invaluable opportunity to deepen my understanding of

@BilalS700 @JPBrebner @southpkcommons Got it 🫠

@JPBrebner @southpkcommons @Kemmi92

The @southpkcommons Founder Fellowship application closes in just a little over 12 days...