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After we last spoke with you five years ago — in our very first Writer Highlight! — you were within the early stages of your PhD program in Japan. What have you ever been as much as?
It appears like without end since we did the last writer highlight! I began writing for TDS in 2019. I used to be preparing to start out my PhD, did so in 2020, and I finished it in 2024. I have to admit that writing for TDS helped me get through the isolation of being a PhD student during COVID.
I moved to the U.S. in mid-2024, right after defending my thesis, and worked for six months as an outreach and education coordinator before returning to academia for a one-year postdoc. I finally moved to Scotland in October of last 12 months.
Within the five years since that Q&A, we’ve witnessed the arrival of LLMs and agents, amongst other innovations. How has the rise of on a regular basis AI tools affected your work — and life typically?
The rise in popularity of LLMs modified the world and not only my life. As an individual mainly in academia, I’ve all the time read the papers and talked to the researchers who worked on these technologies. I worked with them and discussed their ideas. I all the time find it interesting how research grows outside of research labs — how researchers don’t know the way a technology will probably be used once everyone has access to it.
The sudden, explosive popularity of generative AI made me more aware of the importance of sharing research because it develops, moderately than only when it matures.
I do imagine LLMs might be used to make a whole lot of people’s lives easier, but they might be misused to cause harm. Finding the balance on a private level, on an expert level, and on a community level is a challenge that any emerging technology faces at first.
Your interest in quantum technology began long before the sector began to generate serious buzz previously couple of years. What drew you to this area in the primary place?
My interest in quantum tech began somewhere around 2018! I used to be doing my master’s and dealing as a teaching assistant for a quantum physics class. I enjoyed the category greatly, and the professor did an important job explaining things I never understood before.
After I was considering pursuing a PhD, the sector of quantum computing was just beginning to bloom: IBM had shared its intention to make its devices public and released Qiskit. It was exciting, complex, and mentally difficult (the three things that attract me to any field). It had the mathematics, the potential, and the coding. I asked the professor I used to be working with if he knew anyone willing to tackle a PhD student with no quantum background to do a PhD, and to my surprise, he did. The person he introduced me to turned out to be my PhD supervisor.
I like software and math, and quantum combines these two with the potential for nice applications. Today, I’m a researcher within the Quantum Software Lab on the University of Edinburgh, in Scotland. I’m working on the bridge between data science and quantum computing, in addition to on quantum machine learning and the applications of quantum computing.
Your public writing on TDS has shifted previously 12 months or two to focus almost exclusively on quantum. Why is it essential for data and ML professionals to study this technology?
Since “quantum” is a buzzword, misinformation about it has exploded. As someone in the sector, I hate seeing people being misled by false information. I do see the potential of quantum, and I see how briskly it’s developing. I believe the one reason it’s improving so quickly is the involvement of individuals outside academia. I imagine data scientists are essential to the event of quantum computing, and quantum computing has the potential to alter the best way we take into consideration data science and machine learning.
I personally imagine that data scientists should care about quantum computing because most of the core tasks they already work on (resembling optimization, sampling, and large-scale linear algebra) are precisely the sorts of problems quantum algorithms aim to hurry up or handle in a different way. Quantum approaches, resembling the Quantum Approximate Optimization Algorithm and Quantum Machine Learning, have the potential to enhance performance in areas resembling model training, complex simulations, and decision-making under uncertainty.
Realistically, today’s hardware continues to be limited, however the long-term impact could reshape how difficult data problems are solved. So it’s a likelihood not only to be ready for the subsequent big step in tech, but in addition to be a part of shaping that technology.
What’s your experience been like as a public writer within the age of ChatGPT, Gemini, and the remainder? What motivates you to jot down nowadays?
That’s an important query! I like generative AI; it shows how far we, as humans, have been in a position to take technology. But it surely is, in spite of everything, a machine; it’s an algorithm that finds patterns: it has no soul, no experience.
I proceed to jot down and skim posts by authors I like because teaching or transferring knowledge is a human thing. ChatGPT can offer you the fundamentals of a subject, but someone who has been through the educational process can inform you more, as they are going to consider the obstacles they faced and the challenges they overcame. They’ll relate to the readers greater than AI can — and that, for me, may be very essential.
