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UID:pretalx-euroscipy-2026-RBUGDR@pretalx.com
DTSTART;TZID=CET:20260721T152000
DTEND;TZID=CET:20260721T154000
DESCRIPTION:Recent advances in AI have shifted industries’ attention towa
 rd integrating LLM-based systems. Even though LLMs can solve a wide range 
 of business problems\, they came with a significant complexity overhead. A
 t same time\, many real-world business applications involve well-defined o
 bjectives\, predictable inputs\, and clear evaluation criteria. \n\nToday\
 , we are increasingly seeing a default pattern: for almost any NLP use cas
 e\, teams prompt GPT-like models and pay the bill at the end of the month.
  However\, this approach often introduces unnecessary complexity\, costs\,
  and operational risk. Many business and research problems exist in constr
 ained environments that can be solved with simpler techniques\, achieving 
 the same or higher success rates.\n\nThis talk defends that fine-tuned BER
 T-based models remain a strong and often superior choice for targeted busi
 ness use cases that require NLP-based solutions. I propose to present a re
 al\, in-production use case where a simple transformer-based classifier de
 monstrates a more favourable performance-cost trade-off than LLM-based app
 roaches\, driven by lower latency\, reduced operational complexity\, easie
 r fine-tuning\, and significantly lower maintenance costs.\n\nThe goal of 
 this presentation is not to reject LLMs\, but to promote a pragmatic\, out
 come-driven approach to NLP\, where “boring” solutions often deliver t
 he most value.
DTSTAMP:20260603T201621Z
LOCATION:Room 2.41 (First Floor\, Turing)
SUMMARY:Boring AI Works: When BERT Beats Billion-Parameter Models - Diogo R
 odrigues
URL:https://pretalx.com/euroscipy-2026/talk/RBUGDR/
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