In this episode of #LØRN Silvija speaks with the Head of Machine Intelligence from Simula Metropolitan Center for Digital Engineering, Leva Martinkenaite. The Simula Metropolitan Center for Digital Engineering is a newly established research institute aimed at areas such as networks and communication, machine learning and IT management. In the episode, Leva explains who we should trust most out of an AI-system or an expert, how an algorithm can give accurate predictions of glucose levels, as well as how they work towards strengthening artificial intelligence and machine learning.
Who are you and how did you become interested in AI?
I head up the Machine Intelligence Department at a newly established research institute, Simula Metropolitan Center for Digital Engineering, which is a joint venture between Simula Research Lab and Oslo Metropolitan University. I became fascinated by machine learning and data-driven modelling during my PhD, when I worked on developing an algorithm that would provide an accurate short-term prediction of blood glucose/sugar levels in diabetes patients from current and previously observed data.
What is your role at work?
I primarily focus on research, but I also actively help promote formal education in ML/AI. My research focuses on developing new methodologies and numerical methods for the analysis of complex systems and learning from high-dimensional data in science and industry.
What are the most important concepts in AI?
The overall goal of AI is to create a technology that allows machines to function in an intelligent way – in other words, making them capable of thinking, acting, and learning like humans.
Why is this exciting?
AI technologies are increasingly making far-reaching decisions on our behalf in a number of fields, from self-driving cars to clinical diagnostic systems.
What do you think are the most interesting controversies?
AIl progress has raised various controversial topics that we need to address, including:
• Should AI development be heavily regulated?
• Should humanoid robots have rights?
• Will AI kill jobs?
• Can we combat AI cultural insensitivities?
What is your own favourite example of AI?
Can you name any other good examples of big data, nationally or internationally?
I am fascinated by a project we are working on with Norwegian Cancer Registry, where we analyse screening data and provide personalised predictions about when next to perform screening, and identify women at risk of cervical cancer based on their screening history and additional personal information.
How do you usually explain how it works, in simple terms?
I always start with the simplest concepts, since they are essential for understanding more complex and advanced concepts. It is also important to clearly explain how a machine reasons and how this differs to human reasoning.
Is there anything unique about what we do in AI here in Norway?
Norway has pioneered the digitalisation of various industries and improved energy consumption, etc. as a result. Moreover, Norway has uniquely well-preserved data sets, such as medical registries that could be used for training ML algorithms to provide more personalised advice on treatment options.
Do you have a favourite big data quote?
Eliezer Yudkowsky’s quote: “By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.”
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Spørsmål:Det er mye skepsis rundt teknologiske endringer, og da særskilt helsebehandlende endringer. Men utvikling, forskning og feiling før det feires er gjort av tverrfaglige team. Vi går til legen når vi er syk og opereres av kirurgens hender. Med risiko for menneskelig feil, legger vi oss med tillitt under kniven. Hva må ligge til rette for at vi skal føle liknende menneskelig tillitt ved å la oss bli behandlet av kunstig intelligente helseverktøy? Vil de noen gang oppleves like tillitsfulle?