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A growing guide for ML researchers featuring videos, links, and conversations on research skills, reading and writing papers, navigating careers in ML, mentorship and more.Β
π¬Doing Research
π¬Doing Research
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Sara Hooker - The changing spaces of research breakthroughs
This conversation explores the evolving landscape of research, particularly in AI, emphasizing the importance of mentorship, collaboration, and the need for a more inclusive approach to research. Sara Hooker, Head of Cohere Labs, discusses the historical context of research, the impact of the printing press, and the challenges faced by independent researchers. Key principles for success in research are shared, along with insights on navigating career paths in academia and industry. The importance of community building and the role of usable artifacts in research are also highlighted.Β
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A panel conversation on research question ideation, hosted by Marzieh Fadaee, with panelists Stella Birderman, Mostafa Dehghani, Eugene Cheah
This session focuses on the process of research ideation in machine learning, featuring a panel of experts who share their strategies for generating research ideas, the importance of collaboration, and the common pitfalls to avoid. The discussion emphasizes the need for researchers to think critically about existing literature, explore under-researched areas, and communicate their ideas effectively. The panelists also highlight the significance of personal motivation and the value of working with others in the research community.
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Your journey into research: lessons to live by with Sara Hooker
This conversation explores the journey of becoming a machine learning researcher, discussing the challenges and opportunities within the research landscape. It emphasizes the importance of collaboration, mentorship, and choosing meaningful research topics.Β Sara Hooker, Head of Cohere Labs, share insights on best practices for conducting research, handling failure, and managing time effectively. Networking and building relationships within the research community are highlighted as key components for success in the field.
π Reading, Writing and Presenting Papers
π Reading, Writing and Presenting Papers
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Journey of a Research Paper with Ahmet ΓstΓΌn and Marzieh Fadaee
This session focuses on demystifying the journey of becoming a machine learning researcher, providing practical tips on experimental management, writing research papers, structuring papers, seeking feedback, and effectively presenting research. The speakers emphasize the importance of clarity, organization, and storytelling in research, along with the value of community support and engagement.
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Scientific Communication with Marzieh Fadaee, Jay Alammar and Shayne Longpre
This conversation explores the nuances of scientific communication in AI research, emphasizing the importance of writing, visual elements, and storytelling. The speakers discuss strategies for effective collaboration, adapting communication for different audiences, and the role of visualizations in enhancing understanding. They also share insights on oral presentation skills and handling questions during talks, while highlighting influential figures in the field of AI communication.
Writing papers:Β
Writing papers:Β
How to write a research paper [from Peyton Jones / MSR]
Philip Resnikβs writing advice
Must-read style advice
A short guide on typesetting math
Have an organized clean bibliography
Rebuttals: How we write rebuttals by Devi Parikh
π« Education
π« Education
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Paths to Graduate Studies, with Bryan M. Li, Winnie Xu, Gbemileke Onilude and Emma Strubell
This conversation explores the diverse journeys of individuals into machine learning research, emphasizing the importance of mentorship, the decision-making process for graduate school, and strategies for building a strong research profile. Panelists share their experiences with the Cohere Labs Research Scholar Program, discuss how to choose research topics, and provide insights into navigating the graduate school application process. The discussion also addresses common concerns and questions from aspiring researchers.
π’ Careers
π’ Careers
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Applying to Research Roles in Industry, with Ahmet ΓstΓΌn, Laura Ruis, and Cate Ciugureanu
This conversation explores the nuances of applying for research roles in the industry, contrasting it with academia. The panelists discuss their motivations for choosing industry, the essential skills required for machine learning research roles, and the importance of communication. They delve into the differences between industry and academia, provide tips on structuring applications, and share insights on interview preparation. The discussion also highlights common mistakes applicants make and offers advice on how to navigate failures during the application process. Finally, the panelists share their final thoughts on pursuing a career in research, emphasizing the importance of passion and networking.
π’ Mentorship
π’ Mentorship
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Mentorship in ML Research, with Pablo Samuel Castro, Esra'a Saleh and Viraat Aryabumi.Β
This conversation explores the multifaceted nature of mentorship in research, particularly in the field of machine learning. Panelists share their personal experiences as both mentors and mentees, emphasizing the importance of seeking guidance, being prepared for mentorship interactions, and the organic nature of mentorship relationships. They discuss the value of informal mentorship, the timing for seeking mentorship, and the benefits of becoming a mentor. The discussion culminates in practical advice for early career researchers and the significance of kindness and openness in professional relationships.
π§βπ¬ Life as a Researcher
π§βπ¬ Life as a Researcher
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When life happens: How to navigate challenging times in research? with Marzieh Fadaee, Giuseppe Castellucci and Sowmya Vajjala
This panel discussion focuses on the challenges faced by researchers in AI and NLP, particularly in navigating cultural transitions, balancing life in academia versus industry, and adapting to the rapid changes brought by large language models. The panelists share their personal experiences and insights on how to thrive in these environments, emphasizing the importance of community, mentorship, and adaptability in research careers.
π Subfields
π Subfields
πΌ Other Resources
πΌ Other Resources
LLMs
LLMs
Cohere LLM University to learn more about LLMs and Transformers!
Andrej Karpathy Youtube Channel to learn how to build LLMs from first principles
Deep Learning
Deep Learning
Fast.ai contains courses on Deep Learning and other AI related topics
π GOLD - Great Old List of Dataset
π GOLD - Great Old List of Dataset
We invited members of the community to contribute to GOLD: Great Old List of Datasets.Β Β
We are happy to share GOLD publicly with our open science community as a comprehensive (and ever-growing) list of NLP datasets in all languages. Our hope is that you will make use of it to inform your own research. As a collaborative document, we continue to invite you to propose edits and additions to the document to support your own work and that of fellow community members.
π’ Want to add a resource?
π’ Want to add a resource?
This guide is always growing, and your input helps make it stronger. We welcome blog posts, articles, videos, toolkits, podcasts, and other helpful resources β Submit the form below to share your resource!
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