To give female AI academics and others their well-deserved – and overdue – time in the highlight, TechCrunch is launching a series of interviews specializing in the extraordinary women who’re contributing to the AI ​​revolution. As the AI ​​boom continues, we are going to publish several articles throughout the 12 months, highlighting key work that always goes unnoticed. Read more profiles here.

Anna Korhonen is a professor of natural language processing (NLP) at the University of Adam Mickiewicz University in Poznań University of Cambridge. She is also senior research fellow at Churchill Collegemember of the Association of Computational Linguistics, and worker of the European Laboratory for Learning and Intelligent Systems.

Korhonen was previously an worker Alan Turing, holds a PhD in computer science and a master’s degree in computer science and linguistics. She researches NLP and how developing, adapting and applying computational techniques to satisfy the needs of artificial intelligence. He has special interests in responsible and “human-centered” NLP, which, as she says, “draws on an understanding of human cognitive, social and creative intelligence.”

Questions & Answers

Briefly speaking, how did you start in artificial intelligence? What drew you to the field?

I even have at all times been fascinated by the beauty and complexity of human intelligence, especially because it pertains to human language. However, my interest in STEM subjects and practical applications led me to review engineering and computer science. I selected to specialize in AI since it is a field that enables me to mix all of these interests.

What work in AI are you most proud of?

While learning easy methods to construct intelligent machines is fascinating and it will probably be easy to wander away in the world of language modeling, the ultimate reason we construct artificial intelligence is its practical potential. I’m most proud of the work where my fundamental research in natural language processing has led to the development of tools that may support social and global good. For example, tools that may help us higher understand how diseases like cancer and dementia develop and how they will be treated, or apps that may support education.

Much of my current research is driven by my mission to develop artificial intelligence that may improve people’s lives. Artificial intelligence has enormous positive potential for social and global good. A giant part of my job as an educator is to encourage the next generation of AI scientists and leaders to deal with realizing this potential.

How do you take care of the challenges of the male-dominated tech industry, and by extension, the male-dominated AI industry?

I’m lucky to work in the field of artificial intelligence, where we even have a major female population and well-established support networks. I find them extremely helpful in coping with skilled and personal challenges.

For me, the biggest problem is the way this male-dominated industry sets the agenda around AI. An ideal example is the current arms race to develop increasingly larger artificial intelligence models in any respect costs. This has a profound impact on the priorities of each academia and industry, in addition to wide-ranging socio-economic and environmental implications. Do we want larger models and what are their global costs and advantages? I believe we’d have asked these questions much earlier if we had a greater gender balance on the pitch.

What advice would you give to women wanting to start out working in the AI ​​industry?

Artificial intelligence desperately needs more women in any respect levels, but especially in leadership. The current leadership culture will not be necessarily attractive to women, but lively engagement can change that culture – and ultimately the culture of AI. Women aren’t at all times great at supporting one another. I would love to see a change of attitude in this regard: if we would like to realize a greater gender balance in this field, we want to actively network and help one another.

What are the most pressing issues facing artificial intelligence because it evolves?

Artificial intelligence has developed incredibly quickly: in lower than a decade, it has evolved from an instructional field to a worldwide phenomenon. During this time, most of the effort was dedicated to scaling massive data and computation. Little effort has been put into considering how this technology must be developed to best serve humanity. People have good reason to fret about the safety and credibility of artificial intelligence and its impact on jobs, democracy, the environment and other areas. We must urgently put human needs and security at the heart of AI development.

What issues should AI users remember of?

Current AI, even when it appears very fluid, ultimately lacks human knowledge of the world and the ability to grasp the complex social contexts and norms in which we operate. Even the best technology today makes mistakes, and our ability to stop or predict them is proscribed. Artificial intelligence could be a very great tool for a lot of tasks, but I would not trust it to teach my children or make essential decisions for me. We the people should remain in power.

What is the best method to construct AI responsibly?

Artificial intelligence developers are inclined to take into consideration ethics as an afterthought – after developing the technology. The best method to give it some thought is that any development begins. Questions like: “Do I have a diverse enough team to develop an equitable system?” or “Is my data truly free and representative of all user populations?” or “Are my techniques solid?” you actually should ask at the starting.

While we are able to solve some of this problem through education, we are able to only implement it through regulation. The recent development of national and global regulations around artificial intelligence is essential and must proceed to make sure that future technologies are safer and trustworthy.

How can investors higher promote responsible AI?

Artificial intelligence regulations are emerging and corporations will ultimately should adapt to them. We can see responsible AI as sustainable AI that is actually price investing in.