Thank you very much. That's a very interesting question.
I think we are now embracing new technologies, and AI is part of that. There are several things we have been doing.
One is looking at the historical data, because we have invested a lot in data collection over the past decades. It's about using the existing data wisely and developing new methods and measures to find out something we didn't know. That's very interesting.
For example, we are looking at human factors related to oil spills. We look from the top layer of decision-making and policy-making to the bottom—the operators and the crews. What are the human mistakes, such as incomplete training, incomplete knowledge or lack of experience? What are the errors and mistakes made that lead to a disaster and an emergency response? During the response, how do people react, and are there human factors contributing to the consequences? I think we collected several hundred cases and looked at the human factors. The interesting finding is this: Human factors contribute 70% to 80% to all of those man-made disasters, especially related to ships and oil spills. That's one thing we are doing.
We're also using the growing data we're collecting from the air to the sea. We try to predict what's going to happen. What should we do in an emergency, if there's an oil spill incident? Consider the BP oil spill in the Gulf of Mexico. If an oil spill of similar magnitude happened, unfortunately, in the Arctic, that would be a huge disaster. How can we react? How could we be prepared for that? That's what we have been helping professional responders with, in terms of training and support. It's also about how the communities could be prepared. They gain some basic knowledge, in order to understand what a spill is, how you can report it, how you can protect yourself, etc.
For all of those things collectively, you're talking about a huge amount of data. It's also very diverse data, even in the format.... I think that's one of the key challenges. We have a lot of data from different agencies and organizations. They all have, probably, different formats and deposits. How do we consolidate data in a more efficient and timely way and use it to support decision-making for responders, in order to save time, lives and costs? That's what we have been doing. I'm developing some tools for that.