MACHINE LEARNING / DATA SCIENCE / DATA ENGINEERING
April 7, 2022 @ Svenska Mässan

ABOUT THE CONFERENCE

GAIA organizes a one-day conference for people interested in artificial intelligence and all things data. The aim is to create an environment for learning, networking, and knowledge sharing among individuals, organizations, and academia around these common interests. The conference focuses on applied machine learning and real-world data science. It introduces diverse content from enthusiastic domain experts and typically covers what is happening within the field in Gothenburg as speakers often have local connections.

Our last conference was streamed online, and we’re currently putting together GAIA Conference 2022. See you there!

WHAT TO EXPECT

Inspiration and knowledge

Fascinating talks will be held by representatives from academia and many different industries. We expect to get inspired and learn about techniques, strategies, and tools commonly used by people in the field. We hope to leave the conference with a long list of new things to explore further!

Location

After two years at Lindholmen and one year online, we are excited to host the conference in person at a more central location. This year we’ll be meeting at Svenska Mässan, which allows us to invite even more attendees, partners, and startups with plenty of room left to mingle!

Food and drinks

Of course, food and drinks are included in the ticket price. We’ll provide you with breakfast, lunch, and fika with infinite amounts of coffee and tea so that you can stay sharp during the whole day. We recommend you plan for some extra time after the closing remarks as we finish the day with bubbles!

One great conference

We’re honored to have so many representatives from Gothenburg sharing their knowledge and thoughts. They’ll tell us more about what’s happening on the west coast, and you’ll have a chance to meet with other local enthusiasts with similar problems and interests.

SCHEDULE

Breakfast

08:00

Opening Remarks

by Jakob Andersson

Chairman | GAIA

09:00

AI Alignment and Our Momentous Imperative to Get It Right

by Olle Häggström

Professor of Mathematical Statistics | Chalmers University of Technology

09:15

Building the Next-Generation Digital Maps Using a Fusion of 3D Computer Vision and Deep Learning

by Martin Byröd

Engineering Manager | Apple

09:45

Break

10:15

Online Machine Learning With RiverML

by Max Halford

Data Scientist | Carbonfact

10:30

Trajectory Ensembling

by Louise Anderson-Conway

AI Resident | Google

11:00

Julia for AI and Data Science

by Kristoffer Carlsson, Fredrik Bagge Carlson

Software Engineer, Control Systems Specialist | Julia Computing

11:30

Lunch Break

12:00

From Nothing to Something: Klarna’s Journey With Recommendation Systems

by Anil Sharma

Data Science Manager | Klarna

13:30

How Spotify Analyzes Thousands of A/B Tests With Confidence

by Per Sillrén, Erik Stenberg

Senior Data Scientist, Data Scientist | Spotify

14:00

How AstraZeneca Scientists, Data Quality and AI IT Teams Came Together To Accelerate the COVID Vaccine

by John Kumar

Principal AI Platform Engineer | AstraZeneca

14:30

Break

15:00

Alert, Your Trip Will Be Crowded: Crowding Prediction at Västtrafik

by Erik Andersson, Björn Thalén

Product Manager BI and Analytics, Data Scientist | Västtrafik

15:30

Towards Zero Faster Using Deep Learning

by Erik Coelingh

Vice President, Product | Zenseact

15:55

Our Journey at Spotify Search Towards a More Data-Driven Product Development Culture

by Claire Detilleux

Senior Data Scientist | Spotify

16:20

Closing Remarks

by Josef Lindman Hörnlund

Board Member | GAIA

16:45

Quantum Machine Learning vs. Machine Learning for Quantum Computing

by Mats Granath

Docent in Theoretical Physics | University of Gothenburg

09:45

Break

10:15

Moving Monocl’s Spark Workflows to Kubernetes

by Henrik Alburg

Data Science Manager | Monocl

10:30

It All Starts With a Consistent Labeling Guideline

by Tommy Johansson

Perception Expert | Annotell

11:00

Privacy Aspects of High-Performance Video Analytics

by Erik Landolsi

VP Future Labs | Irisity

11:30

Lunch Break

12:00

Predicting Properties of Materials With Machine Learning

by Magnus Röding

Adjunct Associate Professor | Chalmers University of Technology

13:30

Effective ML Systems Development

by Leonard Aukea

Head of Machine Learning Engineering & Operations | Volvo Cars

14:00

Topic Classification for Survey Analysis with the Peltarion Platform

by Francesca Carminati

Data Scientist | Peltarion

14:30

Break

15:00

What Is the Place of Democracy in an Intelligent Device World?

by Anass Sedrati

Business Technologist | Bintess

15:30

Deploying Deep Learning to Prevent Online Hate

by Sebastian Nabrink

Co-Founder | Oterlu AI

15:55

Oompa Loompa and How to Save Lives Using Speech to Text

by Niclas Johansson

Machine Learning Engineer | Tenfifty

16:20

SPEAKERS

Mats Granath

Docent in Theoretical Physics @ University of Gothenburg

Quantum Machine Learning vs. Machine Learning for Quantum Computing

Machine learning and quantum computing are two expanding technologies with tremendous potential. What if they are combined, into quantum machine learning (QML)? I will give an overview of QML, pointing out that quantum mechanics makes it difficult to “quantize” classical ML algorithms such as artificial neural networks. Instead, existing QML algorithms are typically hybrid algorithms, part classical, part quantum. An alternative to making ML quantum is to use ML as part of the software required to operate a quantum computer. I will give some examples from my own research of both types of algorithms, using a hybrid QML approach to address a logistics problem and using deep reinforcement learning for compiling quantum code and for quantum error correction.

Biography

Mats Granath is docent in theoretical physics employed at the University of Gothenburg (GU). He has an MSc in Engineering Physics from Chalmers, a PhD from GU, and postdoc stays at UCLA and Nordita. Recent research has focused on quantum computing, quantum machine learning, and deep learning applications to both physics and engineering problems. He is a project PI at the Wallenberg Centre for Quantum Technology (WACQT), the director of the master’s program Complex adaptive systems at GU and Chalmers, and the coordinator at GU for the Swedish National Infrastructure for Computing (SNIC).

 

 

Olle Häggström

Professor of Mathematical Statistics @ Chalmers University of Technology

AI Alignment and Our Momentous Imperative to Get It Right

We are standing at the hinge of history, where actions taken today can lead towards a long and brilliant future for humanity, or to our extinction. Foremost among the rapidly developing technologies that we need to get right is AI. Already in 1951, Alan Turing warned that “once the machine thinking method had started, it would not take long to outstrip our feeble powers”, and that “at some stage therefore we should have to expect the machines to take control.” If and when that happens, our future hinges on what these machines’ goals and incentives are, and in particular whether these are compatible with and give sufficient priority to human flourishing. The still small but rapidly growing research area of AI Alignment aims at solving the momentous task of making sure that the first AIs with power to transform our world have goals that in this sense are aligned with ours.

Biography

Olle Häggström is a professor of mathematical statistics at Chalmers University of Technology and a member of the Royal Swedish Academy of Sciences. His foremost research qualifications are in probability theory, but in recent years he has turned his attention to issues in global risk and AI safety. He has worked on AI policy on both the national and the EU level, as well as in the World Economic Forum. Most recent among his five books are Here Be Dragons (2016) and Tänkande maskiner (2021).

John Kumar

Principal AI Platform Engineer @ AstraZeneca

How AstraZeneca Scientists, Data Quality and AI IT Teams Came Together To Accelerate the COVID Vaccine

In late 2020, against the backdrop of lockdowns and a global pandemic, AstraZeneca was striving to deliver a vaccine at cost. As the vaccine trials were drawing to a close, there was a need to accelerate the analysis of free-text reporting by doctors as part of the trial. There was a need to stand up both the infrastructure and a robust NLP model in a highly regulated, high stakes environment. Ultimately, the team managed to transform the COVID-19 vaccine reporting process from days to hours. In addition to laying the foundation for an enterprise machine learning platform, this work was a small part of releasing the vaccine, which has saved an estimated million lives, prevented 50 million infections, and delivered 2 billion doses.

Biography

Dr. Rohan John Kumar holds a PhD from the University of Sydney in Pharmaceutical Science. After working with Royal Medal winner Andrew Holmes in one of Australia’s best chemistry labs, John emigrated to Sweden in 2017. He took up a position at AstraZeneca, initially supporting the development and production deployment of computational chemistry models before taking on challenges related to medical image classification. As part of this work, he developed a machine learning platform and is currently Principal AI Engineer in AstraZeneca’s AI Platform Team.

Louise Anderson-Conway

AI Resident @ Google

Trajectory Ensembling

Traditionally, only the last checkpoint of a training run is used for final model prediction, but so much more data is accumulated during training. In this talk, we will discuss ways of utilizing data from the entire training trajectory in the context of image classification to improve final model performance, at a fraction of the cost of traditional ensembles.

Biography

Louise Anderson-Conway is an AI resident at Google, where she researches model ensembles in the Perception group. She got her M.Sc. from Chalmers University in 2011, a Ph.D. in theoretical physics from Chalmers in 2015 and spent time as a postdoc at both Imperial College London and Stanford University before joining Google in 2020.

 

 

Max Halford

Data Scientist @ Carbonfact

Online Machine Learning With RiverML

A growing number of data teams have to deal with real-time data feeds. Handling these feeds is challenging. Part of the reason comes down to habits: data processing is usually done in a batch fashion. This is very much the case for machine learning, which involves inference and learning. Both of these can be done online. But how? What design patterns does this require? What software components are necessary? How does this look in practice from day to day? We’ll try to refine these questions and answer them during this talk. In particular, we’ll focus on River, which is a Python package for online machine learning. We’ll also discuss the higher-level tools necessary to deploy an online machine learning model into production.

Biography

Max is a data scientist currently working at Carbonfact. He holds a PhD in machine learning applied to query optimization in database systems. He develops and researches online machine learning algorithms in his spare time. Max is fond of open-source software and maintains a blog where he discusses some of the things he is working on.

Claire Detilleux

Senior Data Scientist @ Spotify

Our Journey at Spotify Search Towards a More Data-Driven Product Development Culture

At Spotify, we aim to build and improve our product in a data-informed way. To do that, teams are encouraged to generate and test hypotheses by running experiments and gathering evidence for what works and what doesn’t. In the Search team, on our journey towards this goal, we have learned that, besides having the ambition, we need at least two more things:

  1. An experimentation platform that allows us to run experiments at scale and generate accurate results
  2. A product development culture with evidence-based hypothesis testing at its core

The talk would be about the key drivers that enabled our journey in the Search team at Spotify towards building a more data-driven culture for product development.

Biography

Claire Detilleux is a senior data scientist and has been at Spotify Stockholm for three years. She moved to Sweden from Paris, where she worked as a data scientist in other tech companies. In the Search team at Spotify, she works with the product team to study user behavior and help improve the search experience. She has also been dedicated in the last year to helping the Search team to integrate experimentation into their culture and processes to evaluate product changes.

Martin Byröd

Engineering Manager @ Apple

Building the Next Generation Digital Maps Using a Fusion of 3D Computer Vision and Deep Learning

The past decade has seen a revolution in the ability of machines to extract high-level, semantic meaning from raw sensory data. This revolution has primarily been brought about by deep learning. In combination with exponential growth in the availability of compute and the rapidly decreasing cost of sensing, this has been an explosive change to the field of digital mapping. The 3D Vision team of Apple Maps capitalizes on these trends to push the boundaries of mapping. By using state-of-the-art technologies from machine learning and computer vision, we can redefine the modern digital map and how it is built. In the talk, we will walk you through our end-to-end philosophy for large-scale mapping in the age of deep learning. We take a look under the hood with examples and visualizations to explain how our approach has led to many breakthrough products like Look Around, Visual Localization for Geo-Referenced AR, and the new 3D Apple Maps.

Biography

Martin Byröd is head of R&D for the 3D Vision team at Apple Maps, where he leads research and development for processing large-scale 3D sensor data (imagery and lidar) for mapping and beyond. Martin obtained his PhD in Mathematics from Lund University in June 2010 in the areas of Applied Mathematics and Computer Vision. After graduation, he joined the Swedish startup C3 Technologies, where he worked on large-scale image-based 3D reconstruction. The past ten years have been spent at Apple, where he has led core R&D for products such as Flyover 3D, Look Around, and Visual Localization.

Fredrik Bagge Carlson

Control Systems Specialist @ Julia Computing

Julia for AI and Data Science

Julia is a programming language with a set of features and a package ecosystem that makes it especially useful for AI/ML and data science. While the language is relatively young (measured in programming language years) the productivity it brings means that there is already a number of mature packages that push the state-of-the-art forward in domains such as differentiable programming, simulation, and optimization.

In this talk, we will give a high-level overview of Julia with an emphasis on features, tools, and packages that makes it suitable for a typical AI/ML and data science workflow. We will also give some examples of ongoing research done in Julia, such as differentiable programming and automatic surrogatization for increased productivity in simulation and model-based workflows.

 Biography

Fredrik received his MSc and PhD from Dept. Automatic Control at Lund University. He has a background in robotics and an interest in developing software tools for control, identification, and simulation, which he is doing as a part of the simulation team at Julia Computing.

Tommy Johansson

Perception Expert @ Annotell

It All Starts With a Consistent Labeling Guideline

The road to reliable ground truth starts before any labeling is even made. In perception software development, when training a network, the specifications of the wanted function need to be translated to the labeling guideline. The function and its desired behavior need to be described in the labeling guideline to get the expected behavior from the model in the end.

During annotation, ambiguous situations frequently pop up; these must be dealt with, preferably before starting the labeling work. A guideline shall also be understood and interpreted in the same way by hundreds of persons, usually with different cultural backgrounds and education.

How can we achieve a solid guideline that captures all function needs and has no room for ambiguities, and how can we assure that the rules/requirements are fulfilled? This presentation will show a structured way to minimize the risk of ambiguities and inconsistency in the guidelines and the tools available at Annotell to help you.

Biography

Tommy has worked with AD/ADAS in a range of different OEMs and suppliers of AD/ADAS systems since 2006. In 2021, he joined Annotell because of the realization that the future of AD/ADAS will depend on the capabilities of deep learning and its safety argumentation.

At Annotell, he works as a Perception Expert, guiding their customers on achieving the ground truth they need with the quality they need. He is also managing a new team called Perception Research with the goal to identify and drive Annotell’s unique position in making safe perception possible.

Sebastian Nabrink

Co-Founder @ Oterlu AI

Deploying Deep Learning to Prevent Online Hate

How do you deliver Natural Language Processing (NLP) services using deep learning when resources are scarce, and requirements on latency are demanding? That is the daily struggle at Oterlu AI, a Gothenburg-based tech startup setting out to tackle online hate. Listen to co-founder Sebastian Nabrink as he shares their experience with this thus far.

Biography

Sebastian has a background as both a developer and data scientist with experience working at startups and larger corporations. At Oterlu AI, he is involved in everything from building models to setting up cloud infrastructure.

Francesca Carminati

Data Scientist @ Peltarion

Topic Classification for Survey Analysis with the Peltarion Platform

This presentation will go through the main steps and challenges of a real-life project, from the data collection to the end model. In this case, my team and I worked with a customer that analyses employee surveys to help the management identify problematic areas within their company. Our final solution involved fine-tuning a multilingual transformer model and individual threshold tuning for each label. The model was built, trained, and deployed on the Peltarion Platform.

Biography

Francesca is a data scientist at Peltarion. She has an MSc in Mathematics and studied in Pavia, Milan, and Graz. Before Peltarion, she was part of the R&D department of STMicroelectronics where she used machine learning techniques for industrial applications. At Peltarion, she develops end-to-end solutions for customers in various industries, focusing on deep learning for natural language processing and computer vision.

Erik Stenberg

Data Scientist @ Spotify

How Spotify Analyzes Thousands of A/B Tests With Confidence

Confidence (spotify-confidence on PyPi) is an open-source Python library that has been used to analyze experiments at Spotify for more than four years. It is now also an integrated part of Spotify’s internal experimentation platform, where it’s used to automatically analyze thousands of experiments every year. In this talk, we will discuss its benefits, demonstrate how to use it, and talk about what lies ahead.

Biography

Erik Stenberg is a Data Scientist working on the Experimentation Platform team at Spotify in Stockholm, but with roots in Lerum outside of Göteborg. He got his master’s in statistics from Uppsala university in 2019, and wrote his thesis on variance reduction methods in sequential A/B testing at Spotify.

 

Anil Sharma

Data Science Manager @ Klarna

From Nothing to Something: Klarna’s Journey With Recommendation Systems

This talk will be about Klarna’s journey from zero recommendation models to our current state of five use cases in one year. I will discuss challenges, wins, learnings and give a sneak peek of where we are headed.

Biography

Anil is a Data Science Manager at Klarna, working with recommendation systems to help customers find products and services to enrich their lives. Before that, he was a data scientist working with recommendations in the H&M Group, delivering fashion recommendations to hundreds of millions of customers. Before becoming a data scientist, Anil was a research scientist with a PhD in molecular and cellular neurobiology. His experience with collecting and analyzing data and breaking down and tackling unsolved problems has served him well as a data scientist and keeps him informed in his way of working.

Erik Andersson

Product Manager BI and Analytics @ Västtrafik

Alert, Your Trip Will Be Crowded: Crowding Prediction at Västtrafik

When the pandemic hit the world, Västtrafik wanted a precise way of informing travelers on what trips were likely to be crowded so travelers could plan their trips safely. Since Västtrafik is working in a large geographical area and has a heterogeneous population landscape, creating warnings based only on the time of day wasn’t considered good enough. We will present a use case of machine learning in production based on data from the automatic passenger counting system. The focus will be on all those details that make ML solutions useful in real life. How do you communicate the results? How do you maintain it? What engine does the model use? We will also present results on how it was received by the travelers and how it is continuously evaluated and evolving today.

Biography

Erik Andersson is a product owner at Västtrafik and is responsible for the BI and Analytics team. Erik has been at Västtrafik for six years, and during that time, he has mainly been working with vehicle and passenger data. Before that, he was an ITS consultant working with road and vehicle systems. Erik has studied Civil Engineering at Chalmers Univerity in Gothenburg, focusing on traffic-related IT systems.

Per Sillrén

Senior Data Scientist @ Spotify

How Spotify Analyzes Thousands of A/B Tests With Confidence

Confidence (spotify-confidence on PyPi) is an open-source Python library that has been used to analyze experiments at Spotify for more than four years. It is now also an integrated part of Spotify’s internal experimentation platform, where it’s used to automatically analyze thousands of experiments every year. In this talk, we will discuss its benefits, demonstrate how to use it, and talk about what lies ahead.

Biography

Per Sillrén is a Senior Data Scientist and has been at Spotify in Gothenburg for almost five years. He works with product teams to understand user behaviour and to make sure product changes are evaluated with relevant metrics and through scientific methods before being rolled out. He has a PhD in applied physics from Chalmers and also spent a few years implementing ML algorithms in ad-tech before joining Spotify.

 

 

Magnus Röding

Predicting Properties of Materials With ML

Porous materials and their mass transport properties (for example fluids flowing in the pores, or diffusion of particles) are essential for the functionality of products in many industries e.g. pharma, hygiene, wound care, packaging, and energy. Understanding the microstructural geometry and how it relates to mass transport properties is crucial for designing better materials. We combine e.g. image analysis, statistics, numerical simulations, and machine learning to characterize materials and to predict, understand, and optimize their properties. In this talk, we will focus on the usefulness of machine learning in this area. We discuss a number of cases where the incorporation of machine learning techniques leads to valuable new tools and insights: for semantic segmentation of imaging data, accelerated analysis of data from other physical/chemical measurement techniques, and optimizing the design of materials structures.

 Biography

Magnus Röding has an MSc in Engineering Physics and a PhD in Mathematical Statistics, both from Chalmers University of Technology. After a two-year stint at the University of South Australia, he joined Research Institutes of Sweden (RISE) in 2015 as a scientist and also leads the RISE Materials & AI area. Further, he is Adjunct Associate Professor in Mathematical Statistics at the Department of Mathematical Sciences, Chalmers, and collaborates on a daily basis both with academia and numerous companies. His expertise is in statistical modeling, image analysis, numerical and stochastic simulation techniques, and machine learning.

Kristoffer Carlsson

Software Engineer @ Julia Computing

Julia for AI and Data Science

Julia is a programming language with a set of features and a package ecosystem that makes it especially useful for AI/ML and data science. While the language is relatively young (measured in programming language years) the productivity it brings means that there is already a number of mature packages that push the state-of-the-art forward in domains such as differentiable programming, simulation, and optimization.

In this talk, we will give a high-level overview of Julia with an emphasis on features, tools, and packages that makes it suitable for a typical AI/ML and data science workflow. We will also give some examples of ongoing research done in Julia, such as differentiable programming and automatic surrogatization for increased productivity in simulation and model-based workflows.

 Biography

Kristoffer is a Chalmers University of Technology alumn who works at Julia Computing to develop the Julia language and the surrounding tooling. He is the current release manager and also works on the Julia package manager, the Julia debugger, and various other Julia tools.

Erik Landolsi

VP Future Labs @ Irisity

Privacy Aspects of High-Performance Video Analytics

Irisity develops systems for security and safety using video analytics, triggering real-time alerts based on configured rules. For many applications, data protection and personal privacy are the main customer concerns. One example is detecting patients falling to the ground in nursing homes while maintaining absolute privacy for the monitored individuals. One of the privacy aspects is around the training data. Our tech stack relies heavily on deep learning, and it is hard to get real-world training data from sensitive environments such as hospitals in large quantities. A second aspect is privacy in live systems. Our system includes features for anonymization, where all detected humans can be replaced with a low-resolution pixelized version, hiding the person’s identity. A final aspect of privacy and data protection is to support video processing at the edge, as many of our customers are reluctant to upload video data to cloud systems. We found using GPUs at the edge to be impractical in many cases, and spent significant efforts on making our model inference run highly optimized on edge CPUs. This talk describes the details on how we address these three types of challenges, with examples, results, and a few lessons learned.

Biography

Erik did his PhD in Linköping in 2008, focusing on computer vision and machine learning. After a few years of initial industrial experience, he founded the consulting agency Visionists in 2012, specializing in computer vision solutions for customers in various industries. In 2019, Visionists had grown to a strong technical team of 13 employees. Around then, Visionists was acquired by Irisity, where Erik spent his first few years as CTO. Erik currently holds the role of VP Future Labs at Irisity, with a responsibility for the most research-oriented and forward-looking activities of the company.

Niclas Johansson

Machine Learning Engineer @ Tenfifty

Oompa Loompa and How to Save Lives Using Speech to Text

I will talk about how we at Tenfifty, together with The Swedish Maritime Administration, use AI to help the rescue leaders working at the Joint Rescue Co-ordination Centre (JRCC) save lives around the Swedish coastline. The project named Heimdall is set out to detect emergency messages sent using VHF around the Swedish coast. I will talk about how open source can outperform existing solutions and how big of an impact noisy audio and the specific domain have on existing models.

Biography

Niclas is a former software engineering student from Chalmers, working as a data scientist at Tenfifty in Gothenburg since 2021. He works with applied machine learning to solve real-world problems for customers every day. Niclas co-authored the thesis “Automatic Emergency Detection in Naval VHF Transmissions” at Tenfifty and has since worked with integrating the theory into practice.

Erik Coelingh

Vice President, Product @ Zenseact

Towards Zero Faster Using Deep Learning

Description to be announced

Björn Thalén

Data Scientist @ Västtrafik

Alert, Your Trip Will Be Crowded: Crowding Prediction at Västtrafik

When the pandemic hit the world, Västtrafik wanted a precise way of informing travelers on what trips were likely to be crowded so travelers could plan their trips safely. Since Västtrafik is working in a large geographical area and has a heterogeneous population landscape, creating warnings based only on the time of day wasn’t considered good enough. We will present a use case of machine learning in production based on data from the automatic passenger counting system. The focus will be on all those details that make ML solutions useful in real life. How do you communicate the results? How do you maintain it? What engine does the model use? We will also present results on how it was received by the travelers and how it is continuously evaluated and evolving today.

Biography

Björn Thalén is a data science consultant at B3Indes, a company helping organizations to create value from data. At one of his clients, Västtrafik, Björn has been the lead developer for an AI model for crowd predictions. His academic background is in applied mathematics and history of science. In his professional life, he has focused on mathematical optimization and transportation planning systems, helping some of the world’s leading transportation companies to optimize their planning. Björn is a coordinator for the EURO Practitioners’ Forum, a European network of industry mathematicians. He got his MSc at Linköping University and has lived in Gothenburg since 2010.

Anass Sedrati

Business Technologist @ Bintess

What Is the Place of Democracy in an Intelligent Device World?

Democracy is one of the most appreciated and encouraged concepts throughout the world. One of its pillars is equality, suggesting that all humans are equal in choice, vote, and decision-making. In the future (and already now), artificial intelligence will empower countless devices to make more and more decisions based on data and machine learning. An important question emerging from this new shift will be if the concept of democracy will need to be revisited with the strong upcoming of AI?

Democracy as a concept was created to regulate human governance, given that they were the only beings reaching the intelligence level required to discuss decisions and share power. But with the advent of artificial forms of intelligence, will their existence be considered in future decision-making? Will intelligent devices have the same weight as humans or citizens with the same level of knowledge? What gives legitimacy to humankind to hold the ultimate power over other intelligent forms?

Biography

Anass Sedrati is a PhD candidate in IoT security and governance, and a Wikipedia administrator.

Henrik Alburg

Data Science Manager @ Monocl

Moving Monocl’s Spark Workflows to Kubernetes

Monocl collects data from various public sources within life sciences, spanning publications, clinical trials, meeting presentations, and research grants. We extract and disambiguate all the names from these sources, allowing us to figure out who has done what, making them searchable on our platform. Doing this name disambiguation can be both complicated and resource-intensive, so we use Apache Spark to distribute the load to a cluster.

From the start, we managed our very own Spark clusters using Ansible and Bash, running Spark in standalone mode. But as the data team, the weekly commitment, and the number of sources has grown, we needed a new, more scalable solution. Looking at the different alternatives, we decided to go with Spark on Kubernetes and use Argo Workflows as our workflow orchestration tool. This talk is about why those choices were made and how it all turned out.

Biography

Henrik Alburg leads the data science and engineering teams at Monocl. He is working on everything ranging from the infrastructure of running all data pipelines to model optimization for all Monocl’s sources. Before that, Henrik worked with consultancy within search and data science. He holds a Master’s in Computer Science from Chalmers University of Technology.

Leonard Aukea

Head of Machine Learning Engineering & Operations @ Volvo Cars

Effective ML Systems Development

CONTACT

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Questions or comments

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