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Introduction of machine learning at Reflen

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Introduction of machine learning at Reflen

  1. 1. Introduction of Machine Learning By Homer Quan
  2. 2. Our understanding of today’s ML
  3. 3. OPTIMAZATION DATA UNDERSTANDING Two groups of problems
  4. 4. Breakthrough so far Image recognition Voice recognition Voice synthesizer Video 2. Structured Behavior understanding 1. Raw data understanding (in timeline)
  5. 5. Underneath… • CNN, RNN, LSTM, GAN, VAE • GPU, TPU, FPGA • Facebook, Google, Amazon, Apple Practice and technologies in Training method, hardware, and data
  6. 6. Alchemy or electricity? Mostly an engineering problem
  7. 7. What is emerging Reinforcement Learning
  8. 8. How about Language It’s far more complex… Source: PBS The Brain with David Eagleman “6 times reverse data to thalamus than input to visual cortex” • Internal model matters • Memory(knowledge) matters
  9. 9. Opportunity “Bicycles for the mind” steve Jobs used to describe computers. We believe, today, A.I. system is an e-bike for human mind. 
 
 First you need a good bike, then a good motor and battery, finally a good design to put them together.
  10. 10. We build algorithm, software and platform to support Augmented Intelligence. Reflen is an e-bike to accelerate Augmented Intelligence
  11. 11. How we do it Composite Learn by mimicking
  12. 12. Where are we
  13. 13. Roadmap MVP 1st Stage 2nd Stage Feature
 RT Sensing RT Engagement Knowledge management Product
 Convospot 
 (Personal and Education) Feature
 Behavior understanding 
 Intention prediction Interactive mimic learning Product
 Convospot
 Cognospot 
 (Small business and Enterprise) Feature
 Mobile, IoT SDK 
 Optimized decision
 Task allocation
 Plugins ecosystem Privacy protected learning
  14. 14. Thanks Reflen stands for reflective learning. Q&A

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