من مهندس طائرات إلى أخصائي بيانات الذكاء الاصطناعي – مقابلة مع Gosia Los-Brzozowska

يتم كتابة جميع محتوانا ومراجعته والموافقة عليه من قبل البشر.

كارولينا سبرشينسكا

رئيسة قسم العلامة التجارية لصاحب العمل

من مهندسة طائرات إلى عالمة بيانات في مجال الذكاء الاصطناعي، فإن مسيرة غوشيا لوس-بجوزوفسكا ليست نموذجية بأي حال. في مكتب Spyrosoft في بياويستوك, she’s turned a knack for data into a force for innovation, working on everything from generative AI to predictive maintenance. In this candid chat with Karolina Spryszynska, Head of Employer Branding, Gosia shares her path, the challenges she’s tackled, and what it means to be among the Top 100 Women in Data Science.

غوشيا، كيف بدا مسارك نحو منصب عالم بيانات الذكاء الاصطناعي؟

Well, it all started in high school; I wondered what kind of studies would be interesting for me. Once, I went to a mechanics lecture and understood nothing; all the Greek letters, formulas…But despite that, I felt like something was leading me that way, so I decided to follow that path. After university, I started working as an aircraft engineer in the design and manufacturing areas. My tasks included redesigning, evaluating field experiences, and approving manufacturing processes. Every decision had to be made based on data. This is where my adventure began as a Data Specialist.

اختيار الأكثر إثارة للاهتمام ليس بالمهمة السهلة! في Spyrosoft AI, we work on a variety of projects, from cost optimisation to autonomous vehicles. Recently, the hottest topic has definitely been gen AI. Our projects aim to develop data driven AI solutions that leverage advanced computational methods to enhance decision-making and improve operational efficiencies.

My first professional experience with generative AI was a thrilling dive into the future of software testing. At Spyrosoft, I was developing an intelligent assistant for generating test scripts for QML applications using machine learning.

Imagine this: A tool that not only understands the intricacies of QML but also autonomously crafts precise, efficient test scripts. Our goal was to develop an AI that could read through the QML code, comprehend its structure and functionality, and then generate comprehensive test scripts, all with minimal human intervention. It was a thrill – working with cutting-edge AI models, brainstorming and conducting experiments. It’s the perfect playground.

هل واجهت أي تحديات في مشاريعك الأخيرة؟ أخبرنا ما كانت تتعلق به.

Challenges are the daily bread in AI. I haven’t worked on the project without them. Automating and optimizing business processes is crucial to overcoming these challenges. Most often, we face meeting financial constraints, which requires close budget monitoring, automation per-custom needs, often of specific if not unique use, and finally, managing client expectations. That’s the GIGO effect – Garbage In, Garbage Out. No magic. You need data, computing power and highly qualified people to incorporate AI.

على أي حال… هناك حل لكل تحدٍ. والنتائج مُرضية للغاية عندما تدعمها بنية تحتية قوية لإدارة البيانات.

ما هي بعض المخاطر والمزالق الشائعة في تخطيط مشروع علم البيانات مع البنية التحتية لإدارة البيانات؟

هناك طرفة:
– ما المهم عند شراء شقة؟
– ثلاثة أشياء فقط: الموقع، الموقع، الموقع.

If it goes about planning a data science project or any project, I could rephrase the joke and say: Communication, communication, communication. Adapting communication techniques is crucial for resolving the problem and ensuring all involved parties understand the importance of data quality. Moreover, even with technologies such as ChatGPT, some time is needed to research, develop, test, and deploy.

I led a predictive maintenance project for a newly introduced aircraft fleet, ultimately, a few thousand units. The goal was to rank the engines in terms of overhaul need, thereby preventing unexpected failures and minimising downtime through comprehensive data analysis. This project used a Cumulative Damage Model (CDM) learning from design, production, operational and environmental data. The tool provided not only valuable insights into engines’ health, but also contributed to more efficient and cost-effective maintenance practices. Additionally, the project helped enhance human decision making by integrating advanced technologies to optimize maintenance workflows.

لقد تم اختيارك أيضاً ضمن أفضل 100 امرأة في علوم البيانات هذا العام؛ تهانينا! ماذا يعني ذلك لك؟

شكرًا لك على تقدير ذلك! أن يُعترف بك كواحد من أفضل 100 امرأة في علوم البيانات this year is a significant honour. It means a lot because it reflects my hard work and dedication, and it proves it’s not only my feeling. It is incredible to be included among talented and fantastic women, which also demonstrates the inclusive culture in IT.