Dr. Gianluca Demartini is Associate Professor at the Faculty of Engineering, Architecture and Information Technology, University of Queensland. His research interests are Human Computation, Information Retrieval and Semantic Web. He obtained his PhD in Computer Science at the Leibniz University of Hanover.
Dr. Demartini, first of all, please tell us how voice recognition and artificial intelligence works?
The field of artificial intelligence deals with the design and development of methods that can solve computational problems using data as evidence from which to learn how to do it. Examples include image and natural language understanding, language translation, chatbots, search engines, and voice recognition. These methods benefit from the availability of very large amounts of data (e.g., images we tag on social media platforms) to learn, for example, how objects look like. Face recognition models work well because of the vast amount of annotated images being available for them to learn how to recognise people depicted in images and videos.
In lay person’s terms would you explain Hybrid Human-Machine Information Systems and its applications.
The idea of building hybrid human-machine information systems is about using digital content and data by getting the best out of the ability of computing machines to efficiently process very large amounts of data together with the human ability to understand images, videos, and natural language content. The goal is to combine these two in order to build systems that can process and understand data both very efficiently and very effectively.
One of the studies you’re associated with is understanding trustworthy and untrustworthy workers. How does this technology work?
While the majority of these human workers are reliable and accurate, some of them display adversarial behaviours and try to attack the system………………
We have been looking at how crowdsourcing workers perform tasks in online crowdsourcing platforms like Amazon MTurk and in online crowdsourcing projects like Wikipedia. While the majority of these human workers are reliable and accurate, some of them display adversarial behaviors and try to attack the system (as, for example, by vandalizing Wikipedia articles).
In our research, we study how their online behaviors (that is, for example, the time they spend on a task, the clicks they do, the text they type) can be used as a signal to build artificial intelligence systems able to detect untrustworthy contributors.
With all the automation that’s happening, do you think a good part of the workforce will become redundant in stages?
The future of work will require us to change the way in which we do our job. As our current jobs get more and more automated………………………….
The future of work will require us to change the way in which we do our job. As our current jobs get more and more automated, we will need to do something else. Thus, there will be more and more a request for continuous up-skilling. I expect that getting an education once in a lifetime (that is, go to uni for 3-5 years and become an accountant) might be not enough in the future, but, rather, we will need to periodically take a break from work and learn something new every 5-10 years to make the next step in our career.
If this redundancy does take place, what are your thoughts on a minimum guaranteed income?
The way we work has surely been transforming over time. We used to have many people employed to do physical work in fields and factories but many of these tasks are now done by machines. The same will soon happen with office jobs that are currently done by people like lawyers, accountants, managers, etc. If these people are to be made redundant, I don’t think that a minimum guaranteed income would be a solution that can scale, and that can be accepted for this type of workers.
On the other hand, also thanks to innovation and technology, we are seeing higher concentration of capital and increased inequality in society. Redistribution of capitals by means, for example, of a minimum guaranteed income can be a tool to address such increasing unfairness. Overall, I believe that investing in continuous education and up-skilling could be a better tool to address future potential redundancies.
Please tell us about your growing up years and how you came to follow the professional direction you did.
I grew up and went to school in a small town in north-east Italy. I was lucky to have a few great teachers in high school that made me understand what I really liked. Thanks to them, I chose to study computer science in university where I became really passionate about those topics and I decided to do a PhD and become a researcher in this field. After completing my bachelor and masters studies close to home in Italy, I lived to Germany, Spain, Switzerland, and UK before moving to Australia.
The possibility to be embedded into different cultures and ways of living across the EU has really shaped my thinking and my approach to all the things I do.
Were there any individuals who inspired you?
I was lucky to have many mentors throughout my life who inspired me and guided me in my career path. My professional choices started to be clear at high school when my computer science teacher instilled in me the passion for computational thinking. While at university, many lecturers were really passionate about their topic and were very good in fostering excitement.
Our readers are mainly the youth in different parts of the world who look up to achievers such as you for inspiration. A word of advice for them?
If you like what you are doing, hard work will not be painful…….
Follow your passion. Whatever career or life objective you want to achieve, it is going to be hard work. If you like what you are doing, hard work will not be painful but, rather, it will be an enjoyable way to spend your time. If you follow your passion and do what you enjoy doing, it will be much easier and much more likely that you will achieve your goals.
Photo: From the Archive of Dr. Gianluca Demartini
Title photo: Shutterstock
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