Highlighting Women in AI: Exploring Anna Korhonen’s Work at the Crossroads of Linguistics and AI

To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch has been publishing a series of interviews focused on remarkable women who’ve contributed to the AI revolution. We’re publishing these pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

In the spotlight today: Anna Korhonen is a professor of natural language processing (NLP) at the University of Cambridge. She’s also a senior research fellow at Churchill College, a fellow at the Association for Computational Linguistics, and a fellow at the European Laboratory for Learning and Intelligent Systems.

Korhonen previously served as a fellow at the Alan Turing Institute and she has a PhD in computer science and master’s degrees in both computer science and linguistics. She researches NLP and how to develop, adapt and apply computational techniques to meet the needs of AI. She has a particular interest in responsible and “human-centric” NLP that — in her own words — “draws on the understanding of human cognitive, social and creative intelligence.”

Briefly, how did you get your start in AI? What attracted you to the field?

I was always captivated by the intricacies and allure of human intelligence, especially how it connects to language. Yet, my passion for STEM studies and their real-world application guided me towards engineering and computer science. I opted for specialization in AI, a domain that perfectly blends all these areas of interest.

Which AI work you take utmost pride in?

Despite the lure in the science of crafting intelligent machines and the immense curiosity in language modeling, the ultimate purpose of building AI is its practical implications. The work I’m proudest of is when my basic research in natural language processing fostered the development of useful tools. These tools are designed to aid in social welfare and global goodness such as understanding critical diseases like cancer or dementia, hunting for ideal treatments, and assisting in education.

A majority of my ongoing research is motivated by the mission to create AI that enhances human life quality. With its potential for social and global benefit, AI is a powerful tool. A significant part of my responsibilities as an educator includes motivating the forthcoming generation of AI scientists and leaders to focus on harnessing this potential.

How do you manage the challenges present in a male-controlled tech industry, and more so, in the male-controlled AI sector?

Thankfully, I operate in a segment of the AI domain where there’s a notable number of female professionals and available support systems. These have greatly assisted me in dealing with both career and personal issues.

As for me, the primary issue lies in how the male-controlled sector sets the AI agenda. The ongoing competition to produce bigger AI models regardless of the price is a prime example. This strongly affects the priorities of academia and the business world, and has far-reaching socio-economic and environmental outcomes. Do we require larger models, and what are their worldwide costs and advantages? I believe we would have posed these questions much sooner if there was an improved gender equilibrium in the industry.

What guidance would you offer to women aspiring to find placement in the AI industry?

AI urgently needs increased female representation, particularly in leadership roles. The prevailing leadership dynamic might not seem appealing to women, but with active participation, it can be reshaped – ultimately leading to a transformation of AI’s cultural ethos. It’s no secret that women aren’t always the best at mutually supporting each other. I would love to see a shift in mindsets here: Pursuing active networking and mutual assistance is crucial if we’re to attain more gender balance in this sector.

Are there any pressing challenges AI faces as it progresses?

AI has experienced impressive acceleration: transitioning from a purely academic discipline to an international marvel in below a decade. In this period, a significant amount of work has been channeled towards data and computation scaling. Only a minimal amount of thought has been given to how this technology should be engineered to ideally benefit human kind. It’s understandable for individuals to voice concerns about AI’s safety, reliability, and its effects on employment, democracy, the environment, among others. Situating human needs and safety at the core of AI’s evolution is a matter requiring immediate attention.

What potential complications should AI users be alert to?

Current AI, despite demonstrating a high level of fluency, still does not possess the ability to understand the world as deeply as humans do. It cannot navigate the intricate socio-cultural norms that we, as humans, rely on. Even the most advanced technologies today are prone to errors, and our capacity to foresee or correct these errors is often limited. Though AI can be a beneficial tool for several tasks, I would not trust it for crucial tasks such as educating my children or making decisive decisions. Humans need to retain control.

What is the optimal approach to building AI responsibly?

AI developers generally consider ethical implications as an afterthought, i.e., post the development of the technology. However, it’s crucial to think about these aspects prior to initiating any developmental work. Inquiries like, “Is my team diverse enough to build a just system?”, “Is the data I’m using truly free and reflective of the user population?” or “Are the techniques I’m using reliable?” should ideally be asked at the beginning of the process.

While education can help solve some of these issues, regulation is the key to enforcement. The recent emergence of national and global regulations on AI is a significant forward step. It’s imperative to continue this path to ensure that future technologies will be safer and more reliable.

How can investors better push for responsible AI?

AI regulations are emerging and companies will ultimately need to comply. We can think of responsible AI as sustainable AI truly worth investing in.

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