Running Machine Learning in your organization is more than clicking through an AI tutorial on your laptop or building a Proof-of-Concept. In this presentation we will talk about the journey of a company becoming AI-driven. This includes how to become data-driven, how to combine narrow AI-models to achieve complex tasks and last but not least how to deploy AI algorithms continuously based on the principles of Continuous Integration and Continuous Delivery (CI/CD). All this will be illustrated with several ThoughtWorks project examples.

The humanitarian sector is fighting stagnancy when it comes to technology’s influence. The technology that is currently being talked about a lot in the technology sector sometimes doesn’t work in these high resource-constrained environments. The sector however still seems inundated with data, but the challenge is harnessing the same. What is the kind of money and skills needed to support the social sector, when it comes to ensuring that data can make a difference to the social ecosystem? The solutions vary from being tech based to policy based ones and more. This talk would outline some key problems in the current humanitarian data ecosystem and some suggestions/solutions that we arrived at through extensive research and interviews with some key influencers in this space.

AI is math. Though we do not have to proof something when creating a deep neuro network, we are using statistical methods to learn the patterns from the data. Unsurprisingly, we've done some unseen assumption behind the model that will never lead to an intelligence could understand and generalize about the world. Today we are going to discuss about the limitations of machine intelligence based on some real world projects, from image recognition to natural language processing, about both data and models.