Machine learning (neural networks, bayesian networks, CRFs, SVMs, genetic/evolutionary algorithms, decision trees,…)
Data mining
Data science
Computer vision, natural language processing, image and voice recognition
Predictive analytics
Parallel programming
Cognitive computing
Neural networks
Knowledge acquisition, representation, networks
Tools & Services

Machine learning libraries and frameworks (TensorFlow, Caffe, Cognitive Toolkit, MXNet etc…)
Tools for data collection and analysis
ML services (IBM Watson, Amazon Lex, Amazon Rekognition, Amazon SageMaker, Saffron, Azure Machine Learning, Google Cloud ML, HPE Haven,…)
Frameworks and computing platforms including CUDA, OpenCL,…
The emphasis will be hands-on introductions, conceptual decisions, live demos, or comparisons of different techniques. Presentations that are based solely on promoting a product will not be allowed.

Production

Distributed machine learning
Integration with software stacks and software pipelines
Applying AI technologies to what’s already there
Designing AI products from scratch
UI/UX
Supporting infrastructure
Misc

Smart anything (home, city, metering, traffic control, autonomous driving, crime prediction, fraud detection, chatbots…)
Intelligent agents/systems
Ethics (decision making, data collection, etc.), privacy
Security, data safety etc.
Computational creativity
Affective computing
Managing rollouts
Building datasets