In this roundtable session, we will explore the ethical considerations surrounding artificial intelligence (A.I.) in product development. As A.I. continues to advance and become more integrated into our daily lives, it is crucial to examine the potential ethical implications that arise from its use.

During this session, participants will engage in a collaborative discussion to address questions such as:
1) How can we ensure that A.I. algorithms are fair and unbiased?
2) What are the potential risks and consequences of relying heavily on A.I. in product development?
3) How can we balance innovation and progress with ethical considerations? Through sharing insights, experiences, and best practices, attendees will gain a deeper understanding of the ethical challenges associated with A.I. in product development.

In this presentation, we will introduce the Retrieval-Augmented Generation (RAG) architecture. This technique uses natural language capabilities of Large Language Model (LLM) to query a private database. You can literally ask question about your data, without the need to fine-tune an LLM. In the lab session we will design a RAG system in Python using Langchain, an open source framework. We will store private documents in a vector database (i.e. Elasticsearch) and ask questions using natural language thanks to the usage of an LLM (e.g. ChatGPT).

AI is often seen as just a buzzword. But it can become a real advantage if we let our strategy lead the way, not the other way around. To make AI work for us, we need clear goals, a focus on what will really impact our business, and a good grasp of what AI can and can't do. We also need to know our stuff, have solid basics like infrastructure, and measure how well things are going. Plus, we can't forget about bringing in people who know AI inside out.

On the flip side, just chasing the newest tech trends, copying competitors without thinking, ignoring the basics, changing plans too often because of AI, and getting caught up in the hype - these are mistakes we should avoid.

In this session, we'll talk about how to change our thinking to follow the successful approach, not the unsuccessful one. And we'll look at how to use AI the right way, thinking about ethics too. These will be our key lessons.

Software engineers are increasingly integrating machine learning (ML) into application development to create innovative solutions. Our in-depth workshop is designed for those eager to enhance their technical skills, covering machine learning and deep learning theories and practical applications. Attendees will learn through interactive lectures and hands-on activities, tackling topics like data preprocessing, feature selection, model building, and algorithm optimization. They'll also explore deep learning architectures such as neural networks.

By the end of the workshop, participants will be equipped to build ML models for their applications, making data-driven decisions and crafting intelligent software. This experience provides a foundation for anyone aiming to specialize in ML or improve their current applications' features. The workshop is an opportunity for both experienced engineers and novices to stay current with AI and ML technological advancements.

Every software engineer has stories of bug hunting or spending hours unraveling the root cause of a system failure. In this journey, we delve into a selection of these tales, aiming to extract key ideas, concepts, and hints for improvement. Additionally, we'll explore the impact of debugging and issue resolution on products, and take a glimpse into the future by discussing potential offerings from advancements in AI.