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Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial Markets

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I will first survey how deep learning has disrupted speech and language processing industries since 2009. Then I will draw connections between the techniques for modeling speech and language and those for financial markets. Finally, I will address three unique technical challenges to financial investment.

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Li Deng at AI Frontiers : From Modeling Speech/Language to Modeling Financial Markets

  1. 1. AI Frontiers Conference, San Jose Convention Center, Nov. 9-11, 2018 From Modeling Speech/Language to Modeling Financial Markets Li Deng, Chief AI Officer November 9, 2018
  2. 2. Outline Of The Main Topics 1Will AI transform the financial markets?  Speech  Computer vision  NLP  Robotics  …  …  Finance 2Three technical challenges unique to financial investment industry 3Other constraints in applying AI to financial investment management
  3. 3. Will AI Transform The Financial Markets? What can we learn from successful AI applications in other industries:  AI disrupting speech industry (2009-present) – (Small) similarities to finance industry – (Large) differences from finance industry AI disrupting computer vision industry (2012-present) AI disrupting NLP (2014-present) Learning From Other Industries
  4. 4. Launch of Deep Learning in Speech at NIPS in 2009 Disrupting The Speech Industry
  5. 5. Disrupting The Speech Industry Deep Learning practically solved the speech recognition problem by 2012 By John Markoff Tianjin, China, October 25, 2012 Voice recognition and translation program translated speech in English given by Richard Rashid, Microsoft’s top scientist, into Mandarin Chinese. https://www.youtube.com/watch?v=xpoFSoTnBpU&t=911s
  6. 6. Disrupting The Speech Industry: Going Deeper After little improvement for 10+ years by the research community… …MSR reduced error from ~23% to <13% (and under 7% for Rick Rashid’s S2S demo in 2012)
  7. 7. Disrupting The Speech Research in Academia “This joint paper from the major speech recognition laboratories was the first major industrial application of deep learning.”
  8. 8. Common Deep-Learning Architectures for Speech Recognition Deep Recurrent Neural Networks Deep Convolutional Neural Networks
  9. 9. Components of Speech Recognition System Separate Speech Recognition Models Unified by End2End Deep Learning Training Data Applying Constraints Search Recognized Words Representation Speech Signal Acoustic Models Language ModelsLexical Models
  10. 10. Components of Quant Trading Strategy
  11. 11. Deep Learning in Natural Language Processing: Understanding
  12. 12. Deep Learning in NLP: Dialogue Systems
  13. 13. Deep Learning in NLP: Machine Translation
  14. 14. Deep Learning in NLP: Question Answering
  15. 15. Deep Learning in NLP: Sentiment Analysis
  16. 16. Deep Learning in NLP: Image to Text
  17. 17. Three Challenges Unique To Investment Management 1 Very low signal-to-noise ratio 2 Strong nonstationarity with adversarial nature 3 Heterogeneity of big (alternative) data
  18. 18. Three Challenges Unique To Investment Management 1. Very low signal-to-noise ratio The technology used to combat noise shares characteristics with the technology used to handle small data in training large AI systems, including:  Ability to exploit structure in data  Reliance on prior knowledge  Use of data simulation/augmentation  Smart model regularization  Etc. AI problems outside finance generally have lower noise levels, for example:  Speech  Machine translation  Language understanding  Image/video classification & detection  Medical diagnosis
  19. 19. Three Challenges Unique To Investment Management 2. Strong non-stationarity with adversarial nature
  20. 20. Three Challenges Unique To Investment Management 2. Strong non-stationarity with adversarial nature Contrast: nonstationary signals with no adversarial nature
  21. 21. Three Challenges Unique To Investment Management 3. Heterogeneity of big (alternative) data
  22. 22. Additional Constraints Applying To AI In Investment Management What still needs to be done to ensure success? Data Access Respect for Privacy Scarcity of Talent Tailored Algorithms
  23. 23.  This document and the information it contains is strictly confidential and may not be disclosed to any persons other than those for whom it is intended, nor should this document or the information it contains be copied, distributed, or redistributed, in whole or in part, without the prior written consent of Citadel.  All trademarks, service marks and logos used in this document are trademarks or service marks or registered trademarks or service marks of Citadel. Thank You !