Build with AI Brunei

Name: Build with AI Brunei
Start: 2024-05-05T16:00:00+08:00
End: 2024-05-05T20:00:00+08:00

GDG Brunei Darussalam

Build with AI events are community-led technical workshops or hackathons hosted by Google Developer Groups (GDG). Events aim to help developers learn and apply their skills on GenAI trends and products from Google.

May 5, 8:00 AM – 12:00 PM

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Key Themes

AIBuild with AICareer DevelopmentCertification PrepCertification Study Group

About this event

Generative AI is transforming how we interact with technology.
Build with AI events are community-led technical workshops or hackathons hosted by Google Developer Groups (GDG). Events aim to help developers learn and apply their skills on GenAI trends and products from Google. 
Developers from all segments (including Backend, Android & Flutter devs) and all skill levels can benefit.
We have a wide range of codelabs and material already available, with more coming soon and GDG communities may be able to offer Google Cloud credits and Cloud Skills Boost access to their attendees in certain countries.

Click here for InfoPack!

When

Sunday, May 5, 2024
8:00 AM – 12:00 PM UTC

Agenda

Opening Remarks	Join Google Developer Groups' "Build with AI" event series to harness the potential of Generative AI in your applications. Whether a seasoned developer or novice, explore and network while building real-world projects with Google's guidance. These community-led workshops offer resources and opportunities for developers of all skill levels.
Powering Gemini AI to build Multiplatform app using KMP	Gemini is a suite of generative AI models from Google that can support a range of digital products and services. With Gemini AI available through an open API, we can build an amazing app on top of it. Therefore, in this session, I'll share how to optimize the Gemini AI API to develop a multi-platform app using KMP with a book summarization use case.
Advancing Ethical AI: Strategies for Ensuring Fairness, Transparency, & Accountability in AI System	This topic explores ethical considerations in AI development and deployment, focusing on fairness, transparency, and accountability. It addresses algorithmic bias, data privacy, interpretability, and regulatory frameworks. Through case studies, it highlights organizations promoting ethical AI, aiming to foster collaboration for AI systems benefiting society with minimized harm.
No Code, No Problem: Building Image Analysis Apps with Gemini and FlutterFlow	Explore the integration of Gemini's AI capabilities with FlutterFlow, a mobile app development tool, to swiftly and effortlessly build an image analysis application. Learn step-by-step techniques for leveraging Gemini's power within FlutterFlow, enabling rapid development of intuitive and efficient solutions for image analysis tasks. **The participant will need to create FlutterFlow account. (Free)
Limited Data, No Problem: The Power of Synthetic Data	Synthetic data offers a breakthrough solution in regulated industries like healthcare and finance, addressing privacy and compliance challenges. This session provides insights on its generation techniques, benefits, and real-world applications, helping organizations navigate data scarcity while fostering innovation in analytics and AI within strict regulatory frameworks.
STEAM EDUCATION for Global Competency with Gemini	Utilizing Gemini via STEAM Education can address the UN's 17 Sustainable Development Goals by integrating hands-on learning experiences. From local to global levels, students can engage in projects that promote sustainability, innovation, and problem-solving. They can tackle issues such as climate change, poverty, and inequality, fostering a sense of global citizenship and empowerment.
Be Aware of Gen AI: Ethics, Biases, and Disruptive Innovation	In this session, we'll examine the dual nature of innovation, exploring how various generative AI applications can both enhance and disrupt careers and workplaces. Delving into the strengths and weaknesses of these technologies, we'll also scrutinize the societal impact arising from the inherent biases within generative AI models.