While working on our daily tasks in agile teams, we quite often have this feeling where we are working on multiple tasks all day long and at the end of day when we review our work, we realize we haven’t accomplished anything concrete. The main reason for this is, our work environments are filled with distractions from having unnecessary and unproductive meetings to people constantly checking their messages on phones, emails and slack channels. As a result, we feel demotivated, less productive and burnt out by the end of the day. Burnout is a serious issue and is attributed to a lot of mental and health issues. I was one such person who after working around the clock and helping people 24/7 ended up getting admitted to the hospital. How do we prevent this repetitive cycle from happening on a daily basis? This is where mindfulness and productivity hacks can help.

In this talk, I will share my real life experiences from experimenting with different mindfulness and productivity hacks which ended up helping me get more focus, regain my mental and physical health and most of all made me fall in love with technology all over again. Some of these hacks include doing deep work, different tips and tricks to prioritize tasks, deal with emails, make meetings more productive, effective engagement with remote teams and much more. Come join my journey and become the best version of yourself.

Artificial neural networks are one of the many approaches to Machine Learning (ML). It is based on the neural network of the human body where each neuron keeps learning and interconnects with other neurons to perform different actions based on different responses. We are currently surrounded by several neural networks used in various fields to solve different kinds of problems. This being the case, for most of us building and training a neural network still remains a mystery.

Come attend this session, where I demystify neural networks and show how easy it is to build your own neural network in under 5 minutes using Brain.js. I will first cover the basics of AI, ML and Deep learning. Then, I will walk you through a step by step process of building a neural network in a live demo session. Discuss how this is applied to the field of software development and testing. This session will prove to you that, there is no need to be a data scientist to train your own models. Anyone just interested to learn about AI can get started on this journey. Both technical and non-technical people will be able to build their own models after this session.

This session will prove to you that, there is no need to be a data scientist to train your own models. Anyone just interested to learn about AI can get started on this journey using the power of open source frameworks. In this case it is Brain.js.

A recent study conducted by Gartner, shows that by 2020, AI will be pervasive in almost all software products and services. The major highlight of the study was how our skills as engineers would have to adapt accordingly. The current roles in companies are going to change significantly and we need to be prepared for it. There are areas of healthcare, finance, retail, customer analytics, and manufacturing that have already started seeing positive effects of using AI, many of which we would have never thought possible just a few years ago. At the same time, we have downsides of using AI, such as how it can easily impact our data and privacy if we do not pay attention to how the AI models are trained. The working of AI is a black box-- we do not control or understand how the algorithm forms different relationships and makes decisions -- we just provide different training datasets and monitor the learning/progress. We are trying to make predictions on future values based on learning from past examples, or trying to discover different patterns from datasets. This being the case how do we approach these systems?

In this talk, I will discuss the basics of how AI based systems work, what are the key factors to consider when developing and testing these systems and how to come up with an enterprise wide strategy. I will provide insights into what skill sets would be needed by developers and testers to co-exist in the world of AI. Finally, I will address some common questions and concerns surrounding AI and also show what are the tools available for doing AI based development and testing. This talk will help anyone get a basic understanding of developing and testing AI based systems and make us aware of the key factors to consider to ensure these systems do not have a negative impact on humans.

Key Takeaways-

• Learn with live demos how AI model training happens
• Learn how AI is used in software development and testing
• Learn what AI based tool and frameworks are available for software development and testing
• Learn key factors to ensure these systems are safe for humans