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.

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. 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. 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. Launch of Deep Learning
in Speech at NIPS in 2009
Disrupting The
Speech Industry

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. 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. 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. Common Deep-Learning Architectures for Speech Recognition
Deep
Recurrent
Neural
Networks
Deep
Convolutional
Neural
Networks

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. Components of Quant Trading Strategy

11. Deep Learning in Natural Language Processing: Understanding

12. Deep Learning in NLP: Dialogue Systems

13. Deep Learning in NLP: Machine Translation

14. Deep Learning in NLP: Question Answering

15. Deep Learning in NLP: Sentiment Analysis

16. Deep Learning in NLP: Image to Text

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. 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. Three Challenges Unique To Investment Management
2. Strong non-stationarity with adversarial nature

20. Three Challenges Unique To Investment Management
2. Strong non-stationarity with adversarial nature
Contrast: nonstationary signals
with no adversarial nature

21. Three Challenges Unique To Investment Management
3. Heterogeneity of big (alternative) data

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

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