August 31st 2022
11.30 - 17.00
Why synthetic data?
Who is it for?
No privacy issues
The Nordic Synthetic Data Conference is designed for engineers, dev teams, and data scientists.
Organized for developers, by developers.
Get your ticket here
Meet our inspiring speakers and experts.
Synthetic Data Generation for AI Training
Dr. Ekaterina Sirazitdinova
Data Scientist @ NVIDIA
Ekaterina is a senior data scientist at NVIDIA specialised in solving computer vision and video analytics problems with AI. Her current focus includes deep learning inference optimisation on embedded devices.
With the help of advancements in simulation tools and generative models, more and more AI practitioners start utilising synthetic data as a possible alternative to real data. In this talk, Ekaterina will focus on NVIDIA’s approach to synthetic data generation and will showcase the underlying value of tools for realistic and accurate data creation across various industries and use cases.
How To Use Synthetic Data To Overcome Data Shortages
CEO & Founder @ ElectricityMap
He founded Electricity Maps with the mission to organise the world’s electricity data to drive the transition towards a truly decarbonised electricity system.
Electricity Maps is the leading resource for electricity CO2 data. They are used, for example, by Google to shift the timing of compute tasks running on their hyperscale data centers to times when low-carbon power sources, like wind and solar, are most plentiful.
Military-grade AI With Synthetic Data
Vice President of AI @ Terma Group
How do you get the right data for military-grade AI? Terma Group is building world class AI systems for mission critical systems in defense, space, and radar systems. An industry where the data topics are rarely collaborative, and where it can be very expensive to acquire the necessary data.
In his talk, Filip will talk about how to overcome these obstacles using synthetic data generation, and share key lessons on using synthetic data for military-grade AI.
Data generation with AI for GDPR in Cancer research
Laura Frølich, Lead Data Scientist & Emilie Lundblad, Managing director
@ Amesto NextBridge Denmark
Privacy is a very important consideration when working with medical data and classical anonymization techniques are not up to the task of sufficiently guaranteeing protection of sensitive information.
Amesto NextBridge worked with the Cancer Registry of Norway to explore generation of synthetic data based on real data from the SEER database to take the place of the real data. The generated data was evaluated both on similarities of characteristics to the real data, which should be as high as possible, and ability to reidentify individuals in the original data, which should be as low as possible. We will present the approach and results.
Driving AI disruption in traditional industries
Robert Luciani @ The AI Framework
Albert Bruhner @ HP
Join us as we walk through a use case on improving healthcare services by training AI models on synthetic data. In this short session we will draw on our experience of working in highly restricted enterprise environments to create AI models that are self-trained to allocate operational resources efficiently – improving service levels and operational costs simultaneously.
How To Fail With Federated Learning
Manager @ KPMG New Tech
The tools and methods for building excellent Artificial Intelligence solutions are already here. However, what is often lacking is the data required to train the AI models. This is especially true within healthcare and in smaller organisations like NGO’s where data is scarce.
One way to overcome this obstacle is using Synthetic data. An alternative to that is Federated Learning. In this talk we will cover what Federated Learning is and share our experience of how to get started with it.
Using new technologies is never easy and failures have therefore been inevitable during our Federated Learning journey. With failures come learnings and we will share some of ours so you can avoid making the same mistakes as us”
Closing the Gap Between Privacy and Utility
Chief Innovation Officer @ Rigshospitalet
Henning Langberg is CIO at the National Hospital Denmark and primary investigator at the international research project ‘Synthetic Health and Research Data’ (SHARED). SHARED is an active research project exploring opportunities and raising awareness of synthetic data in healthcare.
Henning will give us an insight into the great potential of synthetic data in healthcare innovation.
Denmark is home to some of the world’s best and most integrated health data. But to develop personalised and innovative healthcare solutions, the data has to be available to researchers, companies and start-ups – without compromising data privacy regulations. Synthetic data solves just that.
Schedule and agenda
15.40 How To Use Synthetic Data To Overcome Data Shortages – Olivier Corradi CEO & Founder @ ElectricityMaps
16.10 Wine & Networking
17.00 Thank you for today
Gartner predicts that synthetic data will surpass the use of real data in 2030, and by 2024 60% of the data used for the development of AI and analytics projects will be synthetically generated.
What is synthetic data?
Synthetic data is data that’s artificially manufactured rather than generated by real-world events.
It may be artificial, but synthetic data reflects real-world data, mathematically or statistically. Research demonstrates it can be as good or even better for training an AI model than data based on actual objects, events or people.
Benefits of synthetic data:
Real-world data isn’t just hard and expensive to source. It’s also prone to errors, inaccuracies and bias that can severely impact the quality of your machine learning model.
With synthetic data generation, you get increased confidence in data quality, variety and balance. From auto-completing missing values to automated labeling, it’s a way to dramatically increase the reliability and accuracy of your data and, in turn, the accuracy of your predictions.
Fueling the machine learning economy takes a huge amount of data. Few data scientists can access exactly the data they lack on the scale they need to test and train powerful predictive models. Synthetic data can close that gap.
Many data scientists supplement their real-world records with synthetic data, rapidly scaling up existing data – or just the relevant subsets of this data – to create more meaningful observations and trends.
- Finally, synthetic datasets can minimize privacy concerns. Instead of masking or anonymizing the original data, it is possible to use synthetic data to protect data privacy.
The event takes place in the inspiring setting of The National Gallery of Denmark.
Statens Museum for Kunst
1307 København K.
CEO & Founder @ Todai
Organiser of the conference
AI Consultant @ todai
Organiser of the conference
Head of DemandGen@ KPMG NewTech
Organisor of the conference