On this Main with Knowledge episode, Eleni Verteouri, AI Tech Lead and Director at UBS, shares her invaluable insights on the transformative journey of AI in finance. With over a decade of expertise in mannequin improvement and a prestigious Forbes Cyprus 20 Girls in Tech Award 2024 recognition, Eleni has been on the forefront of shaping fashionable monetary applied sciences. On this podcast, we delve into her experience, exploring the evolution of AI, from conventional mathematical fashions to modern options. Be part of us as we uncover the important thing insights and study from Eleni’s outstanding profession.
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Key Insights from our Dialog with Eleni Verteouri
- The combination of AI in finance has developed from strict mathematical frameworks to modern and environment friendly purposes.
- Generative AI fashions supply new options to skinny knowledge issues, decreasing reliance on historic knowledge.
- Accountable AI requires a multifaceted strategy, together with schooling, discussions on moral requirements, and partnerships with startups.
- The way forward for AI consists of generative AI, issues round synthetic normal intelligence, and the mixing of AI into private life.
- Addressing biases in AI, significantly with LLMs, is difficult and requires modern options like AI brokers and human oversight.
- Empowering ladies in tech by means of expertise can result in a extra numerous and inclusive trade.
Let’s look into the small print of our dialog with Eleni Verteouri!
How did your journey into AI and expertise start?
I’ve at all times leaned in direction of technical topics, however it wasn’t clear what course to take. Throughout my first engineering diploma, I explored GPUs and distributed computing. At the moment, cloud computing wasn’t as prevalent, and finance appeared to demand excessive computational energy for complicated calculations. This curiosity led me to monetary arithmetic, which finally took me to Switzerland and into the banking sector. It was a pivotal second in my journey into expertise and AI.
What impressed you to pursue a Grasp’s in Quantitative Finance?
After finishing my engineering diploma with a concentrate on computer systems, I wished to delve deeper into modeling. I pursued quantitative finance, a mix of arithmetic and finance, at ETH Zurich and the College of Zurich. This schooling, mixed with my grasp thesis at an funding financial institution, paved the best way for my internship and subsequent roles within the trade.
How has AI developed within the banking trade over the previous decade?
AI has reworked the banking trade considerably. Initially, the main target was on conventional monetary theories and mathematical constraints. Over time, AI has been built-in into customized companies, prototype constructing by end-users, and danger estimation. It’s been a journey from working inside a strict framework to adopting AI for effectivity and innovation.
How do you strategy skinny knowledge issues, like these posed by the COVID-19 disaster?
Skinny knowledge issues have been historically tackled by people utilizing statistical fashions and rule-based approaches. Right this moment, we now have a extra sturdy knowledge infrastructure and governance, and generative fashions that don’t essentially require historic knowledge. They’ll generate artificial knowledge and help with metadata, providing new angles to handle these challenges.
Are you able to describe your present function as an AI Tech Lead and the influence of generative AI?
My function includes engaged on an inside generative AI platform designed to help numerous use circumstances, combine with different instruments, and foster inside collaboration. My focus is on security and safety, guaranteeing accountable AI and user-friendly knowledge science practices. I additionally contribute to setting improvement pointers and fostering tutorial partnerships.
What are the important thing focus areas for accountable AI in your work?
Training is essential, each for customers and builders, as roles are mixing with engineers working with AI and knowledge scientists driving adoption. Discussions about what “accountable” means are important, as are partnerships with startups filling gaps in protected and accountable AI adoption.
What traits do you foresee in AI and accountable AI within the coming years?
We’ll see a rethinking of enterprise and product views, governance, and the way we work. Generative AI, issues round synthetic normal intelligence, and the mixing of AI into private life are key traits. Security, safety, and sustainability will stay paramount.
What challenges do you anticipate as AI turns into extra prevalent in society?
Making certain that AI innovation advantages a good portion of the inhabitants is a problem. It’s about ensuring that new information and abilities are accessible and attainable for most individuals, which can require extra coordination and communication.
How do you tackle biases in AI, particularly with massive language fashions (LLMs)?
With conventional machine studying, it was simpler to establish and management biases. LLMs current a problem in explainability. Brokers might be a part of the answer, serving to simulate situations and offering a bandwidth of protected and acceptable operation. Human oversight stays a vital a part of the method.
What are your private dream initiatives or issues you’d prefer to see solved with AI?
I’m excited to see issues historically solved by AI being addressed with LLMs, similar to doc parsing and perception era. I’m additionally taken with AI’s potential to enhance psychological well being, present empathy, and assist neurodivergent people combine higher into society.
How do you stability the usage of AI in your private {and professional} workflows?
As a mannequin developer and AI product supervisor, I discover the boundaries between handbook work and AI help are blurring. I take advantage of AI for coding, troubleshooting, proofreading, and brainstorming. It has made me extra unbiased and environment friendly in constructing options.
What are your ideas on the function of girls in tech and learn how to encourage extra range?
Empowerment by means of expertise is an equalizer. It’s essential to evaluate people based mostly on capabilities reasonably than appearances. We must always guarantee inclusivity, present equal alternatives, and encourage ladies to be outspoken, assured, and assertive of their careers.
Summing Up
Eleni Verteouri’s journey and experience supply a novel perspective on the way forward for AI in finance. Her contributions, marked by her Forbes award and tutorial achievements, present a roadmap for the trade. As we navigate the complexities of AI, Eleni’s emphasis on accountable practices, schooling, and moral issues serves as a guiding mild. Keep tuned to Main with Knowledge for extra inspiring periods and insights into the world of AI and knowledge science.
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