About this Event
At this meetup, we will hear first hand experiences from two exceptional researchers and one outstanding Engineering Manager from Google, Lightricks, and the Technion who have all harnessed Generative AI for their applications and projects. We'll see incredible demos and have the chance to discuss the lessons they’ve learned in their respective journeys.
Agenda:
- 16:30 - 17:00 : Networking & food and drinks.
- 17:00 - 17:20: Personalizing Generative AI Models: How to Teach Them About You. Eyal Ziv - Researcher @Lightricks.
- 17:20 - 17:40: Why is Deep Learning Training so Sensitive?
Prof. Daniel Soudry - Associate Professor @ the Technion.
17:40 - 18:00: AI for Good: Saving Lives With Data.Adi Mano - Engineer Manager @Google
The talks will be delivered in Hebrew.
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More info:
Personalizing Generative AI Models: How to Teach Them About You
Abstract: Recent months saw an explosion of progress and interest in diffusion models, which revolutionized the field of visual generative AI. We will explore the building blocks of diffusion models and then dive in on a few methods for teaching diffusion models additional concepts they aren't familiar with, such as how YOU look. We'll compare them and their respective shortcomings and how these affect their use in the industry.
Bio: Eyal Ziv is a senior Machine Learning researcher at Lightricks. Currently, he is developing new features for photo and video editing in Lightricks apps, including novel view synthesis and 3D reconstruction, as well as background removal. Before Lightricks, Eyal has worked on developing models and methods for detecting acute findings in X-Rays and CT scans. Having spent more than 12 years in the industry, Eyal's research spans defense, healthcare, and mobile apps.
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Why is Deep Learning Training so Sensitive?
Abstract: Training deep learning models typically works well in a very narrow regime. For example, small changes to hyper-parameters, sample presentation order, image translations, or data formats can severely degrade performance — because of the implicit inductive bias of the training algorithm and the model. I will discuss how we can understand such phenomena and reduce these issues.
Bio: Daniel Soudry is an associate professor and Schmidt Career Advancement Chair in AI in the Electrical and Computer Engineering Department at the Technion, working in the areas of machine learning and neural networks. His recent works focus on resource efficiency and implicit bias in neural networks. He did his post-doc in the Department of Statistics and the Center for Theoretical Neuroscience at Columbia University, and his Ph.D. in the Electrical Engineering Department at the Technion. He is the recipient of the Gruss Lipper fellowship, the Goldberg Award, the ERC starting grant, and Intel's Rising Star Faculty Award.
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AI for Good: Saving Lives With Data
Abstract: At Google, our mission is “to organize the world’s information and make it universally accessible and useful.” During a crisis, access to information could mean the difference between life and death, so our goal is to help people get the information they need - when they need it the most - to stay safe and informed. We aim to work alongside on-the-ground authorities to help them spread timely, reliable, actionable information about a crisis. We help detect and predict natural disasters, such as flooding and fires, using AI and other innovative software methodologies. We want to reach people faster with critical information and to research additional types of crises where surfacing alerts would be useful.
Bio: Adi is a Software Engineering Manager at Google Israel. She has been with Google for 15 years, and for the last 7 years she has been part of the Crisis Response team, trying to make critical information more accessible in times of disaster through Google products. Prior to Google, Adi worked at Intel for five years. Adi holds a Master's degree in Computer Science and BioInformatics and an MBA from the Technion. Adi lives in Haifa with her husband and 3 children.
Event Venue & Nearby Stays
Tech&Talk Matam, Haifa, Israel
USD 0.00