conference day two
8:30 am - 8:45 am Welcome - Tea and coffee
8:45 am - 9:00 am Opening address by the chairperson
9:00 am - 9:30 am MythBusters AI
MythBusters AI:
• Common and prevailing myths surrounding AI
9:30 am - 10:00 am Layered approximation approach for developing Deep Neural Network
• Real business challenges when designing & building your Deep Neural Network model
• Testing and Hyper-Parameter tuning of the models
• Layered Approximation design as a possible solution
10:00 am - 10:30 am AI/ Data first strategy: Building your AI business case and demonstrating ROI
• Why an “AI/Data First” strategy is critical for a sustainable, competitive advantage
• Key considerations and building blocks
10:30 am - 11:00 am Building intelligence for facilities management through AI & IoT
• Automating functionalities of Facility Management via hardware & software
• Using AI for predictive maintenance, analytics, usage insights
• How AI & IoT will help to manage assets more effectively, become more operationally efficient, reduce costs, and increase the throughput
11:00 am - 11:30 am Networking break
11:30 am - 12:00 pm AI staff education - reinforcement and unsupervised learning
11:30 am - 12:00 pm Smart cities for smart businesses
11:30 am - 12:00 pm Data augmentation
12:00 pm - 12:30 pm Panel Discussion | Smart business automation
12:30 pm - 1:00 pm Networking lunch break
2:00 pm - 2:30 pm New cognitive models - generative models (GANs and VAEs)
2:30 pm - 3:00 pm Humans & machines side by side – machine translation engine ensures translation quality
• Quality translation without reference translation
• Proper machine learning engine at Unbabel
3:00 pm - 3:30 pm Networking break
8:00 am - 8:30 am Tools to innovate and test ML approaches
• AutoML and Keras
• Eclipse and Eric
• Tensorflow and H2O.ai
4:00 pm - 4:15 pm Closing statements by the chairperson
4:15 pm - 5:45 pm CODE ME RIGHT: DATA PREPARATION FLOW
Companies now spend 80% of their time on preparing data, getting it ready for algorithms to mull over it. You can cut costs significantly by putting a process in place for how to prepare your data and how to deal with parallel data.
• How to structure your data science projects as part of your AI strategy
• Create a data preparation flow that follows your business goals
• Integrate your departmental processes and your data flow