26-28 November, 2019, Vilnius
Conference Starts in:
Wolfram Research Europe Ltd., UK
As Director of Technical Services, Communication and Strategy at Wolfram Research Europe, Jon McLoone is central to driving the company’s technical business strategy and leading the consulting solutions team. Described as “The Computation Company”, the Wolfram group are world leaders in integrated technology for computation, data science and AI including machine learning. With over 25 years of experience working with Wolfram Technologies, Jon has helped in directing software development, system design, technical marketing, corporate policy, business strategies and much more. Jon is also Co-founder and Director of Development for computerbasedmath.org, an organisation dedicated to a fundamental reform of maths education.
You can AI like an Expert
The talk will discuss Wolfram’s progress towards an entirely automated workflow for machine learning that makes it possible for anyone who can code to make use of AI methods.
The talk will assume no knowledge of machine learning and will introduce key concepts before showing how to use the Wolfram Language to create advanced predictors from scratch, in seconds. Use will be made of the Wolfram Language functions Predict and Classify which automate the process of data encoding, feature extraction, method selection, training, end decoding. The net result is a single function that produces a ready-to-use classifier or predictor, directly from raw data.
Examples will be shown from computer vision such as content recognition, scene recogniton and segmenting images into components relevant for autonomous driving applications. The speaker will attempt to train a predictor using live camera images to be able to recognize the hand gestures of the game Rock, Paper, Scissors.
The talk will explain how symbolic computation is the they key ingredient needed to make such automation possible. Symbolic computation provides a unified representation of structured data such as images, sounds, documents, databases, networks as well as the machine learning models themselves.
The talk will go on to show that symbolic representation also helps in automating the transition from research experiments to the production deployment of AI services.