#0064 – Høydimensjonale data
Expørt: Valeriya Naumova
Head of Machine Intelligence
fra Simula Metropolitan Center for Digital Engineering
Med lørner Silvija Seres
Kunstig intelligens reiser spørsmål om arbeidsmåter i fremtiden. Hvordan vil det forme fremtiden for telekommunikasjon? Og hvilke ferdigheter trengs for å være relevant i AIs tidsalder? I denne episoden av #LØRN snakker Silvija med VP i Telenor Research, Leva Martinkenaite, om politikk, fremtidens ferdigheter og AI-racet mellom Europa vs. Canada og USA.
Noen kjappe med ekspørt Valeriya Naumova
Simula Metropolitan Center for Digital Engineering
Head of Machine Intelligence Department, Senior Research Scientist
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. The ultimate goal is to apply the developed techniques to address challenging problems in various real-life applications, such as biomedical signal and image analysis.
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. This overall problem has been broken down into sub-problems, which deal with specific aspects of an intelligent machine.
Machine learning in one of the fundamental concepts of AI, since it studies algorithms that improve automatically with experience.
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?
Big data spans both the public and private sectors – from advertising, education and massive industries such as healthcare and banking, to guest services and entertainment.
As an example, 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.
I also try to demonstrate the intuition and motivation behind methods and techniques. You need to understand the fundamentals in order to apply studied methods to other problems or design another method to a specific problem.
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. It is now time for us to start developing and promoting our research in ML/AI so that we are not just “ML users” but also empower ML/AI with the available infrastructure and data sets we have here in Norway.
Can you recommend any good material to read/view on AI?Andrew Ng’s Machine Learning course on Coursera provides a good introduction, as does his introductory course on deep learning. If online courses are too slow, the best consolidated resource is probably the Deep Learning book by Goodfellow, Bengio and Courville. I would then suggest delving more deeply into a specific sub-field, like computer vision or speech and natural language processing. This field is very competitive and moves quickly, so it also helps to stay updated by following machine learning researchers on Twitter and the Reddit Machine Learning community.
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.”
Dette lørner du:
Report by Teknologirådet “Kunstig intelligens for Norge”.
Deep Learning book by Goodfellow, Bengio, and Courville
AI-teknologi gjør i stadig større grad avgjørende beslutninger på våre vegne. Dette gjelder innenfor en rekke områder, med alt fra autonome kjøretøy til kliniske diagnosesystemer.
AI kan sammenliknes med en sosial ingeniør som kan analysere atferden din og gjøre tilfredsstillende konklusjoner deretter. Hadde du vært komfortabel med at du fikk svar på spørsmålet ditt før du i det hele tatt hadde rukket å stille det?