Review 2021 - Agenda Europe

Coffee Roundtables – grab your real coffee and share experiences virtually with your peers to explore the new and old challenges. Just like pre-show breakfast in a regular conference you’ll join a “round table” with fellow attendees and see where the conversation takes you.

Kick-starter: Share the impact the pandemic has had on your working environment, interaction with colleagues, management of projects and processes. Do you see digital transformation in your organization being accelerated as a result and what lasting effects do you think it will have on your career and working environment once the pandemic is over?

1. Introduction to Climate/Sustainable Finance & Its Link with AI (Pedro Baiz)
   

   The Financial Stability Board (FSB) after working on regulations to address the 2008/9 financial crisis, moved to the next biggest risk: Climate Change. The talk will provide a comprehensive overview of what constitutes Climate/Sustainable Finance. Standards and potential upcoming regulations, such as TCFD (Task Force on Climate related Financial Disclosures) among many others (e.g. SASB, GRI, IRFS, etc) will be covered. Finally, the role of AI and on this emerging field will be discussed.
   

2. Predictive Analytics for Climate Risk Assessment (Christian Spindler)
   

   Climate change is a systemic risk which impacts on all business sectors. It increases uncertainty and investment risk and endangers entire business models. Professional investors and asset managers are taking climate change more and more into account. Corporates are starting to quantify climate risk in their mid- and long-term strategies. Predictive analytics turns out to be key in making quantified assessments in mostly unexplored terrain: How are extreme weather risks impacting on production sites and physical assets of the firm? How is the upcoming carbon taxation impacting on the company now and in future? And how vulnerable is the global supply chain of the company against business interruption risks? This presentation explores methods and tools for climate risk quantification.
   

3. Sustainable Federated Learning for Predicting Climate Changes (Sharmistha Chatterjee) 
   

   The talk unveils the art of building sustainable federated machine learning models by considering different aspects of ethical AI. The talk will highlight various techniques of incorporating fairness in private federated learning while ensuring the sustainability of future smart ecosystems. By the end of the talk, the audience will get the know-how of sustainable federated learning, how to build a scalable distributed architecture, and important KPIs to focus on when designing such a system.

1. Turning Data Science into CO2 savings (Daniel Rohr)

“Can I drive more efficiently?” was the question to be answered at the beginning of all data science efforts at Tracks. This is a typical real-life question that is difficult to solve with ML methods. The answer to this question has sparked a number of follow up business questions that we are tackling with Tracks’ complex AI system. In this session you will learn how to turn ML into saved CO2.

2. Finding Sensitive Intervention Points by Mapping the Global Fossil Fuel Supply Chain (Jannes Klaas)

Over 84% of the worlds energy comes from fossil fuels. But oil, gas and coal are also a major transport good. By identifying the networked structure of the global fossil fuel supply chain we can identify sensitive intervention points. We present our work in creating an asset level network of the global fossil fuel supply chain. We will highlight some early research avenues enabled by this dataset, including the use of new modelling techniques.

3. Applying Machine Learning to Solve the Ocean-Bound Plastic Crisis (Bosse Rothe)

11 million tonnes of plastic are estimated to end up in our oceans every year. Cleanhub developed a digital solution that can scale plastic collection in high-impact countries. To deliver digital evidence about how much plastic was collected, all collection partners use CLEANHUB’s software to track and trace the entire recovery process. Detecting anomalies is key to mitigate fraudulent behaviours. We will discuss the problem, opportunities and the role of machine learning.

1. Data Can Feed the World. But Do We Have the Right Data? (Sharavani Basu and Sébastien Foucaud)

Amid the current health and economic crisis, another chronic but fast deteriorating one is the food crisis! Not to sound alarmist, but shifting climatic patterns, trade & geopolitical instability, rising population among others has brought millions to the brink of poverty and starvation. As the productivity gains through mechanization and chemistry-based solutions in an intensive mode is fast plateauing or even reversing due to resistance build-ups, Digital Agriculture & Precision Farming, powered with advanced Analytics and Data Science seems to hold much promise in building sustainable systems with reduced environmental impact. Challenges to use data in agriculture is an experience shared across industries: aggregating data at large scales can be difficult, but identifying the right data is where the real challenge lies! We will present different use cases of novel pest outbreak forecasts, where despite abundant data availability, new approach for data acquisition is urgently needed.

2. Challenges & Best Practices: How to Handle Weather Data in Forecasting Models with Success (Christian Schneider)

Weather happens all the time with an undeniable influence on behavior, such as consumption, and therefore also on commercial success. Though one can´t change the weather, there are many options to utilize weather for one’s advantage. However, weather is not just weather. What are the special characteristics of weather compared to other data? Which machine learning models are most suitable? Christian Schneider talks about challenges and opportunities using weather in scalable forecast solutions.

 3. ESG in the News: Text Mining for Sustainability Signals (Gerhard Rolletschek)

Many current ESG rankings lead to absurd results. Greenwashing occurs when companies can rank high in ESG scores by streamlining their reports and figures instead of truly reducing their ecological footprint. Using text mining based on predicate-argument-structures and using a few-shot learning             approach, we show how ESG-relevant signals in multiple dimensions can be extracted from unstructured news sources and how this can lead to a more balanced and fair assessment of ESG activities.

Meet the speakers, fellow attendees, sponsors, moderators – randomly for a quick chat, just like in real life. If you are a match you can exchange contact details with one click. If not, you simply move on to the next contact.

1. ESA Earth Observation Data Generation, Management and Access (Nicolaus Hanowski)
ESA is operating the most productive and sophisticated fleet of Earth Observation satellites in the world. Understanding the data content, the way it is processed and made accessible to everyone should facilitate new applications. New missions are relied for generating entirely new Earth Observation data sets and provide new opportunities for new applications. The presentation will illustrate easy data access options. The potential of “predictive” Earth Observation will be explained.

2. Why Open EO Data Should be Accessible for Everyone and How They Could Help Solve Our Global Issues (Niklas Jordan)
Earth observation data from public space agencies, such as the ESA, is available to everyone free of charge, but not everyone can access it. Technical and professional requirements make it challenging to access this treasure trove of data. In my presentation, I will discuss how, in addition to science and professional users, civil society, such as journalists, teachers, NGOs, and almost all citizens, can benefit from this data to solve our time’s major global problems.

3. Convolutional Neural Networks for Damage Assessment and Disaster Relief (Thomas Chen)
Natural disasters ravage the world’s cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we have conducted research training multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. Participants in this session will learn about why interpretability is important and the ramifications of AI for disaster management. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change.

Europe mastered the corona crisis, but a bigger one is already on the list: the climate crisis. The EU set ambitious goals for their climate change programme, but still the question remains how to reach them. Hopes in ClimateTech are thus huge and AI is to expected to play a key role. However, AI is also a driver to CO2 emissions: it consumes a lot of energy and it is also used e.g. to detect new oil deposits. How helpful is AI really and is Europe prepared to lead in and with AI the climate movement? Finally, what political and legal changes, technical infrastructure, economic conditions and so on are required to strengthen the climate tech community?

Keynote 1: Why It’s Time to Quit Your Big Tech Job and Work on Climate (Eugene Kirpichov)

Most climate-concerned engineers are still unaware that they can directly work on climate solutions, because of the outdated idea that climate is about activism and non-profits. This may have been true a few years ago, but could not be further from the truth today. I quit Google AI last year to work on climate, and I will try to convince you, too. I’ll give an overview of the commercial climate tech ecosystem, the role ML plays in it, and how to find a climate job as an ML practitioner.

Keynote 2: Tackling Climate Change with Machine Learning (Nikola Milojevic-Dupont) 

Collaborations between researchers and academics are essential to scale up solutions and enable GHG emissions reduction at scale. I will give an overview of existing academic research at the intersection of climate change and machine learning. I will also present Climate Change AI, an organization of volunteers facilitating impactful applications of machine learning to climate change, highlighting resources that can be helpful for companies aiming to work in this space.

Join the moderators of the day for a wrap up of content, discussions, findings and an outlook.