The Developer Conference for Synthetic Data
Featuring speakers and researchers in AI, ML, and data science from startups, companies, and research institutions.
Join us for the online conference on Wednesday, Feb 8, 2023, and be part of what comes next in data and AI. Each session is delivered by tech innovators and business leaders using the latest advancements in synthetic data to solve some of the world’s greatest challenges with data.
Why Synthetic Data?
Private – mathematically provable privacy makes it easy to share data.
Scalable – unlimited amounts of AI generated data.
Speakers & Sessions
Welcome to all attendees, an overview of the event program and speaker sessions, and an overview of the synthetic data space within AI.
Keynote: In Search of Data…
What brought us to this point in the generative AI and synthetic data market, the needs that emerged to drive this sector, and what could this space look like in the next decade?
How Foundation Models can help unlock Multi-Modal Synthetic Data
In this presentation, we will discuss how foundation models can help unlock multi-modal synthetic data by learning to generate synthetic data that is representative of the real data distribution in multiple modalities, such as text, audio, and images. By training a foundation model on a diverse and representative dataset, it can learn to generate synthetic data that is more realistic and diverse, which can be useful for a variety of applications.
Panel: Large Language Models and new opportunities in Generative AI
Breaking the Data Bottleneck
How companies without large data engineering teams can leverage synthetic data to address regulatory, data privacy concerns and secure sharing of data across trust boundaries. The talk will include real challenges (e.g, building fraud detection models compliant with stringent data localization guidelines) faced by Swiggy and approaches to address them.
Leveraging Privacy-Enhancing Technologies with Large Foundation Models
How privacy-enhancing technologies (PETs) like synthetic data and federated learning are helping advance the science and safe application of foundation models.
Panel: How Synthetic Data is Evolving Information Security
The intersection and interplay of data science, data privacy, and information security.
Genomics Innovation in the Age of Generative AI
How synthetic data enables responsible medical and life science research and product development, including NLP technology's increasingly vital role, such as conversational AI.
Synthetic data for training large NLP models
A Computer’s Vision for the future of computer vision
ML for computer vision has unlocked fantastic capabilities for developers of end-to-end applications. However, image collection, curation, and labeling are often prohibitively expensive. We explore how advances in text-to-image models and zero-shot object identification enable developers to rapidly build and drive business value.
Prepare for turbulence: The relationship between Generative and Physics based synthetic data
Synthetic data revolutionizing clinical trial data collaboration and research
Bootstrapping NLP applications with Large Language Models
Accelerating 3D Synthetic Data Generation for Perception
Models power AI applications. Training a vision AI model requires mountains of data; this isn’t palpable for many enterprises. In most cases, the data simply doesn’t exist or is restricted. Synthetic data can help overcome the lack of data in most cases, but like any technology, it needs to be implemented properly.
Interview: How LLMs Can Generate Value for Organizations
Digging into various use cases and specific examples of applied synthetic simulation.
Join the community and discussion
There may also be opportunities to participate in related in-person meetups or activities. Details regarding those plans will be announced at a later date.
You can use synthetic data to create unlimited amounts of data quickly and cheaply, tackle class imbalances in biased datasets, and even combat privacy attacks on data. These capabilities enable safe data sharing, unlocking new opportunities for building open data ecosystems and exchanges that anyone can use. The possibilities are endless, with applications stretching across industries, including life sciences, finance, robotics, Web3, and beyond.