This session aims to connect and advocate for physicists in data science. We will be discussing cutting-edge data science applications in physics research, data science education approaches in the physics curriculum, as well as how physicists with AI/ML experience can transfer their skill sets to careers in industry.
This panel will focus on the latest developments in the fight to slow climate change with an emphasis on the application of AI and ML, the implications of energy policy on deep learning and how GANs can help visualize climate change and it's impact, as well as high impact opportunities to get involved.
In 2018, the Global Slavery Index found that there were 40.3 M people in modern slavery. The United Nations demands companies to take immediate measures and state clear policies. The Future Society is an independent nonprofit think-and-do tank curating an up-to-date repository of >16K of those statements.
This workshop targets teachers, parents or anyone interested in teaching AI for kids. We'll present some methods and continue with a discussion about methods, ressources and challenges when teaching AI for kids. Ever wondered where to start when teaching AI to kids with no prior coding experience? Have you already taught AI principles for kids in or out off school and want to share your experiences? We want to present two different methods/resources - more information to follow - and host a discussion to share ressources, learn from each others experiences and discuss common problems. Everyone is welcome, from the experienced AI teacher to any parent planning to teach their own children.
Join Shalini Kantayya, Meredith Broussard, and Deb Raji for a screening of the film Coded Bias. Panelists will discuss the societal implications of the biases embedded within AI algorithms, examples of AI systems with disparate impact across industries and communities, and what can be done about it.
Bring your AI to a Codenames competition! Every contestant will submit an AI-powered bot, which will need to be able to give 1-word clues as well as interpret their teammate's. Will the winner take a word embeddings approach? A reinforcement learning approach? Something off the wall? Join us to find out!
Deep learning has become the industry standard in Computer Vision and NLP, however, in the time series domain many companies still prefer simple models. The session will go over LSTMs and other RNNs and include practical examples of how these models perform in different industries such as retail and healthcare.
Join this live keynote interview for a discussion with Suzana Ilić, computational linguist and founder of MLT (Machine Learning Tokyo). Sam and Suzana will explore her work applying NLP techniques to accelerating biomedical research at Causaly, as well as her experiences building the popular MLT community.
Why is machine learning hard in the agricultural domain, how does addressing these challenges broaden our understanding of machine learning generally, and what challenges exist around adoption within the global agricultural community? This session will feature speakers from both academia and industry.
In these sessions, we invite any and all from expert to beginner to share their ML experience, knowledge, and projects. The goal is to provoke interesting project-related discussions to help the presenting and audience learn and progress their knowledge and skills. With thanks to our confirmed presenters: Madhusoodhana Chari, Nick Doiron and Nicholas Teague.
In these sessions, we invite any and all from expert to beginner to share their ML experience, knowledge, and projects. The goal is to provoke interesting project-related discussions to help the presenting and audience learn and progress their knowledge and skills. Any and all ML topic areas are welcome.
This panel will bring together AI/ML practitioners and instructors from various specializations to discuss their journey in creating artificial intelligence/machine learning courses for the community. We'll discuss how to identify the knowledge or skill gap to be addressed in your course, the steps to refine the scope and timeline of that course, useful tools and resources, best practices, and more.
Let us face the facts, startups don't have the money like tech giants. So, having a dedicated ML engineering team is completely out of the box. This is a story of difficulties I faced during the first few years of my career and how I overcome those. This is not much of a technical talk. If you want to hear about deep learning architecture or how I came to some aha moment because I am able to achieve higher accuracy than existing state of the art models, or how ml can help solve estimating climate change, then this talk is not for you. This is more casual tongue in cheek story of problems I encountered for past few years, pivot from struggling to build generalised ai to business impact oriented problems.