To present AI-focused girls lecturers and others their well-deserved — and overdue — time within the highlight, Trendster is launching a sequence of interviews specializing in exceptional girls who’ve contributed to the AI revolution. We’ll publish items all year long because the AI increase continues, highlighting key work that always goes unrecognized. Learn extra profiles right here.

Emilia Gómez is a principal investigator on the European Fee’s Joint Analysis Centre and scientific coordinator of AI Watch, the EC initiative to watch the developments, uptake and impression of AI in Europe. Her crew contributes with scientific and technical data to EC AI insurance policies, together with the not too long ago proposed AI Act.

Gómez’s analysis is grounded within the computational music discipline, the place she contributes to the understanding of the way in which people describe music and the strategies wherein it’s modeled digitally. Ranging from the music area, Gómez’s investigates the impression of AI on human conduct — particularly the results on jobs, selections and little one cognitive and socioemotional growth.

Q&A

Briefly, how did you get your begin in AI? What attracted you to the sphere?

I began my analysis in AI, particularly in machine studying, as a developer of algorithms for the automated description of music audio alerts by way of melody, tonality, similarity, model or emotion, that are exploited in numerous functions from music platforms to training. I began to analysis the right way to design novel machine studying approaches coping with completely different computational duties within the music discipline, and on the relevance of the info pipeline together with knowledge set creation and annotation. What I preferred on the time from machine studying was its modelling capabilities and the shift from knowledge-driven to data-driven algorithm design — e.g. as an alternative of designing descriptors based mostly on our data of acoustics and music, we had been now utilizing our know-how to design datasets, architectures and coaching and analysis procedures.

From my expertise as a machine studying researcher, and seeing my algorithms “in motion” in numerous domains, from music platforms to symphonic music concert events, I spotted the massive impression that these algorithms have on individuals (e.g. listeners, musicians) and directed my analysis towards AI analysis fairly than growth, particularly on finding out the impression of AI on human behaviour and the right way to consider techniques by way of facets corresponding to equity, human oversight or transparency. That is my crew’s present analysis subject on the Joint Analysis Centre.

What work are you most happy with (within the AI discipline)?

On the tutorial and technical aspect, I’m happy with my contributions to music-specific machine studying architectures on the Music Know-how Group in Barcelona, which have superior the state-of-the-art within the discipline, because it’s mirrored in my quotation data. As an example, throughout my Ph.D. I proposed a data-driven algorithm to extract tonality from audio alerts (e.g. if a musical piece is in C main or D minor) which has grow to be a key reference within the discipline, and later I co-designed machine studying strategies for the automated description of music alerts by way of melody (e.g. used to seek for songs by buzzing), tempo or for the modelling of feelings in music. Most of those algorithms are at present built-in into Essentia, an open-source library for audio and music evaluation, description and synthesis and have been exploited in lots of recommender techniques.

I’m notably happy with Banda Sonora Important (LifeSoundTrack), a undertaking awarded by Pink Cross Award for Humanitarian Applied sciences, the place we developed a personalised music recommender tailored to senior Alzheimer sufferers. There’s additionally PHENICX, a big European Union (EU)-funded undertaking I coordinated on the usage of music; and AI to create enriched symphonic music experiences.

I like the music computing neighborhood and I used to be completely satisfied to grow to be the primary feminine president of the Worldwide Society for Music Info Retrieval, to which I’ve been contributing all my profession, with a particular curiosity in growing variety within the discipline.

At present, in my position on the Fee, which I joined in 2018 as lead scientist, I present scientific and technical assist to AI insurance policies developed within the EU, notably the AI Act. From this latest work, which is much less seen by way of publications, I’m happy with my humble technical contributions to the AI Act — I say “humble” as chances are you’ll guess there are numerous individuals concerned right here! For instance, there’s quite a lot of work I contributed to on the harmonization or translation between authorized and technical phrases (e.g. proposing definitions grounded in current literature) and on assessing the sensible implementation of authorized necessities, corresponding to transparency or technical documentation for high-risk AI techniques, general-prpose AI fashions and generative AI.

I’m additionally fairly happy with my crew’s work in supporting the EU AI legal responsibility directive, the place we studied, amongst others, specific traits that make AI techniques inherently dangerous, corresponding to lack of causality, opacity, unpredictability or their self- and continuous-learning capabilities, and assessed related difficulties introduced on the subject of proving causation.

How do you navigate the challenges of the male-dominated tech business, and, by extension, the male-dominated AI business?

It’s not solely tech — I’m additionally navigating a male-dominated AI analysis and coverage discipline! I don’t have a way or a technique, because it’s the one setting I do know. I don’t understand how it could be to work in a various or a female-dominated working setting. “Wouldn’t it’s good?,” just like the Seashore Boy’s tune goes. I truthfully attempt to keep away from frustration and have enjoyable on this difficult state of affairs, working in a world dominated by very assertive guys and having fun with collaborating with wonderful girls within the discipline.

What recommendation would you give to girls looking for to enter the AI discipline?

I might inform them two issues:

You’re a lot wanted — please enter our discipline, as there’s an pressing want for variety of visions, approaches and concepts. As an example, in line with the divinAI undertaking — a undertaking I co-founded on monitoring variety within the AI discipline — solely 23% of writer names on the Worldwide Convention on Machine Studying and 29% on the Worldwide Joint Convention on AI in 2023 had been feminine, no matter their gender id.

You aren’t alone — there are numerous girls, nonbinary colleagues and male allies within the discipline, though we might not be so seen or acknowledged. Search for them and get their mentoring and assist! On this context, there are numerous affinity teams current within the analysis discipline. As an example, after I turned president of the Worldwide Society for Music Info Retrieval, I used to be very energetic within the Ladies in Music Info Retrieval initiative, a pioneer in variety efforts in music computing with a really profitable mentoring program.

What are a number of the most urgent points dealing with AI because it evolves?

In my view, researchers ought to dedicate as many efforts to AI growth as to AI analysis, as there’s now an absence of stability. The analysis neighborhood is so busy advancing the state-of-the-art by way of AI capabilities and efficiency and so excited to see their algorithms utilized in the true world that they overlook to do correct evaluations, impression evaluation and exterior audits. The extra clever AI techniques are, the extra clever their evaluations ought to be. The AI analysis discipline is under-studied, and that is the reason for many incidents that give AI a nasty status, e.g. gender or racial biases current in knowledge units or algorithms.

What are some points AI customers ought to pay attention to?

Residents utilizing AI-powered instruments, like chatbots, ought to know that AI is just not magic. Synthetic intelligence is a product of human intelligence. They need to be taught in regards to the working ideas and limitations of AI algorithms to have the ability to problem them and use them in a accountable approach. It’s additionally necessary for residents to be told in regards to the high quality of AI merchandise, how they’re assessed or licensed, in order that they know which of them they will belief.

What’s one of the best ways to responsibly construct AI?

For my part, one of the best ways to develop AI merchandise (with an excellent social and environmental impression and in a accountable approach) is to spend the wanted assets on analysis, evaluation of social impression and mitigation of dangers — as an illustration, to elementary rights — earlier than putting an AI system out there. That is for the good thing about companies and belief on merchandise, but in addition of society.

Accountable AI or reliable AI is a option to construct algorithms the place facets corresponding to transparency, equity, human oversight or social and environmental well-being have to be addressed from the very starting of the AI design course of. On this sense, the AI Act not solely units the bar for regulating synthetic intelligence worldwide, nevertheless it additionally displays the European emphasis on trustworthiness and transparency — enabling innovation whereas defending residents’ rights. This I really feel will improve citizen belief within the product and know-how.