The Future of ML in Biology: CRISPR for Health and Climate
Jennifer A. Doudna, Innovative Genomics Institute, Howard Hughes Medical Institute and University of California Berkeley & UCSF/Gladstone Institutes Machine learning will have profound impacts on biological research in ways that are just beginning to be imagined. The intersection of ML and CRISPR provides exciting examples of the opportunities and challenges in fields ranging from healthcare to climate change. CRISPR-Cas programmable proteins can edit specific DNA sequences in cells and organisms, generating new biological insights as well as approved therapeutics and improved crops. I will discuss how ML may accelerate and perhaps fundamentally alter our use of CRISPR genome editing in both humans and microbes.
AI and Marginalized Languages
Abstract: During the past year or so, we have seen rapidly growing interest and excitement in large-scale language models and their applications to various domains beyond traditional problems in natural language processing and machine learning. These large-scale language models are not anymore just a proof of concept but have been productized and are being served as actual products to millions of users all over the world. Although it is indeed an exciting development, these language models have been trained on a large corpus that may not be representative of all the languages in the world and may focus disproportionately on better-served languages, such as English and European languages. This raises both questions and concerns about the potential for these language models to exacerbate the issue of digital divide as well as inequality and inequity in information access. As a main venue of publication as well as discussion in the field of artificial intelligence, ICML is thus hosting a panel discussion to discuss the issue at the intersection of artificial intelligence, with particular focus on recent large-scale language models, and marginalized languages, in 2023.
ML in Korea
We warmly invite everyone intrigued by Machine Learning and Deep Learning research in Korea to our upcoming social event. Our aim is to bridge gaps between various universities and corporations, fostering dialogue and collaboration. We firmly believe casual conversations can kindle innovative ideas and research possibilities. Join us for group programs where attendees can share their research and interests, and enjoy light-hearted quiz events designed to stimulate engagement and discourse.
How to Know Your True Market Value as an AI Researcher
How to Know your Market Value in AI
Join us for an interactive session where you can get the tools and data necessary to optimally negotiate offers in the current economy and how to determine your current market value.
Some of the topics we'll cover are:
- Market data for AI researchers at different levels of their career
- How this market has influenced negotiations for different industries
- How to get over your fears of negotiating, especially regarding the above
- How to decide which company / offer is right for you (it's not always about comp!)
- How to negotiate without counter offers and without knowing "market value"
- How to respond to pushback from recruiters and other guilt tripping / lowballing / pressure tactics
- How to avoid having an offer rescinded
- How to negotiate deadline of an offer
- Walking through a timeline of the negotiation process for a new offer
The world of machine learning is vast and continually expanding, and one of the most exciting frontiers is the intersection with information theory. As the two fields continue to intermingle and influence each other, it's essential to cultivate a community of scholars and practitioners who share a passion for these disciplines. With this in mind, we organize the social session at the International Conference on Machine Learning (ICML) entitled "Information Theory at ICML: A Confluence of Minds". The primary objective of this social session is to provide a platform for the vibrant community of scholars, researchers, and practitioners coming from the information theory community attending ICML. This gathering aims to foster interactions, collaborations, and friendly networking among like-minded individuals, facilitating discussions about the latest research and trends at the intersection of machine learning and information theory.
Black in AI
AI is advancing faster than our understanding of its impact on society. This fireside chat is a moment of reflection to explore the effects of AI. From economical implications to developmental issues and environmental impacts, our goal is to nurture a rich discussion with researchers and practitioners. Featuring speakers from long-time players and emerging AI institutes, we invite you to this open conversation featuring hot opinions and plenty of questions worth thinking further.
ML Safety Social
As ML systems become more capable and integrated into society, it is becoming increasingly important that they are reliable, beneficial, and aligned with our objectives. We welcome participants interested in or working on ML safety topics for a semi-structured social meetup. Meet peers who may have read your papers or whose papers you've read and build new friendships and collaboration opportunities!