Machine Learning & Artificial Intelligence Stage
Technical and Automation focus
Artificial intelligence (AI) is no longer the future. For businesses, it is the here and now. And it is not used only in companies like Apple, Amazon, Tesla, Netflix, Nest and so on. Implementation can be found in almost every industry. Recent real-time examples are in the finance and legal sector.
Artificial Intelligence by it self is disruptive. It refers to set of computer science techniques that enable systems to perform tasks normally requiring human intelligence and interaction. Boosted with Machine Learning, Deep learning adaptation and learning algorithms., companies now can automise defined and undefined process and design new products and services.
On the Machine Learning and Artificial Intelligence Stage this year we will not focus on the futuristic aspect of Machine Learning and Artificial Intelligence, but more on practical implementation functionalities, like Artificial Narrow Intelligence (ANI), Robotic Process Automation (RPA), chatbots, neural networks and deep learning.
All presentations last 18 minutes + 2 min for quick Q&A.
Speakers are the ones who make events unforgettable, exciting and insightful.
Here are some of the awesome speakers on the ML / AI Stage
Machine Learning Lead
Shani got her PhD in neuroscience from NYU in 2008, with a focus on computational vision. At Spotify, she leads a machine learning team in the quest to make audio ads interactive, relevant, and measurably effective. This effort spans many areas of ML and AI, from algorithms to infrastructure. Her favorite algorithm is division, she is amused by superstitious pigeons, and she recommends fiction as a good way to expand your priors.
Jim Aldon D´Souza
Autonomous Driving Engineer
Jim is a research engineer working with autonomous driving systems at TomTom. His primary interests lie in the perception, localization, and mapping aspects of self-driving technology.
Jim has a master's in electrical engineering from the Technical University of Denmark, and a bachelor's from National Institute of Technology Karnataka, India. His master's thesis addresses the localization issues faced by a planetary rover when navigating in a symmetrical and featureless environment such as the one on the surface of Mars. It was concluded in 2016 at the German Research Center for Artificial Intelligence in Bremen, Germany.
Senior Data Scientist
A leading data scientist for optimizing infrastructure, Ritesh Agrawal at Uber leads the Infrastructure Analytics and Science (IAS) team. The team focuses on scaling data infrastructure for Uber’s growing business needs now and foreseeable in the future. Before Uber, Ritesh specialized in predictive and ranking models at Netflix, AT&T Labs and Yellow Pages where he built scalable machine learning infrastructure with technologies such as Docker, Hadoop, Spark, and more. A Ph.D. scholar from Pennsylvania State University (State College) in Environmental Earth Science, Ritesh’s thesis focused on computational tools and technologies such as concept map ontologies.