1. Artificial Intelligence In Finance
Keynote at R/Finance Conference, Chicago
(based on my invited presentation at MIT TechReview EmTech Digital, San Francisco, March 2018)
Hi, I’m LI Deng, Chief AI officer of Citadel, a
Specialist in AI and machine learning, information theory and statistics, speech, NLP, and now finance.
Before I start, I would like to thank Will and MIT Tech Review team for inviting me here to share my and company’s perspectives on AI in Finance.
Li Deng, Chief AI Officer
May 2018

2. Why I Joined Citadel The Opportunity for AI in Investment Management
I joined Citadel last summer from Microsoft, as its Chief Artificial Intelligence Officer because:
Finance is a dynamic industry
Not only an exciting new field for me, but it presents greater analytical challenges than I faced earlier
Citadel is one of the most technology-savvy firms in not just the financial services industry but the world
Before joining, I was very impressed by the firm’s emphasis on driving technology innovation and its tremendous passion for addressing and solving the most challenging problems facing financial markets
We are constantly reevaluating and improving our technology in order to stay ahead of the competition.
Citadel’s meritocracy and flat, entrepreneurial structure encourages people to build things and gives them resources to do it. Employees can have a very quick impact, which is great for technology innovation.
Joining Citadel gives me the opportunity to make a greater and faster impact on the industry and society
The unique challenges of financial markets present a new frontier for AI and I believe that this technology can make a greater impact in this space than in any other modern industry
Citadel has a strong track record of attracting top technology talent, including people like me who come from the technology industry.
Background on Citadel:
Founded in 1990 and one of the largest multistrategy hedge fund businesses in the world, managing over $28 billion in assets.
Citadel is among the one per cent of hedge fund firms that have been in existence for more than 25 years.
Our investor base includes some of the largest US and global institutional investors, including pension, endowments, foundations and sovereign wealth funds.
We are a global company with more than 1800 team members based in offices across North America, Europe and Asia. My team is based in Seattle.

3. Role of Investment Firms
Social Benefits of Investment Management + + =
Talent
Technology
Data
Investment Firm
Who Benefits:
Governments
Universities
Hospitals
Museums
Research
Retirees
Before I get into the opportunity for AI in financial services, I would like to touch briefly on the social benefits of financial services, and of investment management firms in particular:
Capital markets are vital to individuals and institutions alike.
Without the capital markets, entrepreneurs wouldn’t have access to the funding they need to grow and succeed and individuals would have no reliable way to save for education and retirement.
Investment firms like Citadel play a key role in this landscape by managing capital for institutions that provide vital services to society. You can see some examples here on this slide.
Investment firms and other market participants also play a role in distilling data generated by the world’s economic activity into prices, which in turn helps money and goods flow efficiently to where they are needed most in society.
The vast amounts of data available in the capital markets creates an ideal environment for AI to make an impact.

4. Outline of the Main Topics1
Will AI transform the financial markets?
Speech
Computer vision
NLP
Finance 2
Three technical challenges unique to financial investment industry 3
Other constraints in applying AI to financial investment management
In the remaining time, I would like to cover three closely related topics:
First, will AI transform the financial markets? The answer is of course positive (otherwise I would not accept to speak here on the topic of AI in Finance). But I would like to provide some rationale behind the answer, from the perspective of high successful AI (deep learning) in other industries which I had first-hand experience in my past career.
Transitioning from speech/NLP to finance industries, in term of the past, present and future of AI, leads to the second topic of Technical Challenges that are unique to the finance industry.
This is followed by the third topic of other less technical constraints and challenges in applying AI to finance, investment in particular.

5. Will AI Transform the Financial Markets?
Learning From Other Industries
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)
Modern AI or deep learning is advanced technology increasingly relevant to finance.
As I am sure all of us in the audience know, with MIT Tech Review credited for rapid dissemination of such progresses, deep learning has disrupted speech, vision, NLP, and robotics industries over the past decade.
Due to the time limit, I will have time only to focus on speech industry here and in next few slides.
The connection between speech and finance industries, on surface, seems obvious.
- For one thing, since 90’s, a number of speech recognition experts specialized in shallow statistical machine learning (e.g. HMMs) have moved away from speech industry to become well known leaders in hedge fund industry.
More technically, both speech signals and the financial market data are in a similar form of non-stationary time series, from which deeper information is extracted for the purpose of predicting linguistic symbols or of forecasting future stock values.
However, beyond these superficial similarities, much larger technical differences stand out between finance industry (which has not yet been significantly impacted by AI) and speech/vision/NLP (which are towards maturing due to deep learning).
Cut below
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[Li will change this slide to remove CV & NLP; not enough time]
AI and deep learning is advanced technology increasingly relevant to finance
 provide the potential to unlock large positive benefits for society
Immense amounts of data and resources available in the financial industry and capital markets
 big data essential for AI and deep learning
 unlike other industries, most financial/market data public or easily obtainable
 despite data availability, not all of it is being used or used to max effectiveness
Deep learning and AI piece together massive, diverse data sets
 in ways that they can be beneficially incorporated into financial markets
 big data ((un)supervised): hallmark of deep learning and modern AI

6. Disrupting the Speech Industry
Launch of Deep Learning
in Speech
was at NIPS in
Let me briefly reflect on the path of modern AI (deep learning) in disrupting speech recognition industry, giving rise to today’s prominent products of Microsoft’s Cortana, Amazon’s Alexa/Echo, Google Assistant, and Apple’s Siri.
After many years of slow progress in speech recognition using (shallow) machine learning (HMM-GMMs), the launch of deep learning into this 40 year-old field started at NIPS-2009.
I was fortunate to co-organize this event with Prof. Geoff Hinton (my consultant), and with his graduate students (my interns) working closely with my speech recognition group at Microsoft Research in Redmond in coming 1.5 years.
[two students, one at Microsoft and Amazon leaving the same day as I; another at Google Brain after turning down my best offer at MSR contributing to the “high pay” of deep learning fresh Ph.D. in media]
We at Microsoft took then academic idea of deep learning with promising results in a very small phone recognition task to several stages of increasingly larger industry scales of very large vocabulary conversational speech recognition.
Cut below
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Invitee 1: give me one week to decide …,…
Not worth my time to fly to Vancouver for this…
Invitee 2: A crazy idea… Waveforms for ASR are not like pixels for image recognition. It is more like using photons!!!

7. 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.
After two+ years of intense work at Microsoft, with Hinton twice visiting Redmond in 2009 and 2010 working side-by-side with me on deep learning, and with two of his students interning with me, speech recognition error rate was cut by about half.
Then, in the fall of 2012, a public demo in China was carried out, with 3000 people in the audience, voice recognition and translation program successfully translated speech in English given by Richard Rashid, into Mandarin Chinese with virtually no error.
This impressive event was reported in this NY Times’ full-page article (John Markoff interviewed me at Microsoft). Words quickly spread out about this very first industry-scale success of deep learning.

8. Disrupting the Speech Industry
Deep Learning for Speech Named Best Breakthrough Technology by MIT Tech Review in 2013
Not long after the Markoff’s article in NY Times, Deep Learning for Speech recognition and translation (as well as object recognition) was Named Best Breakthrough Technology by MIT Tech Review in 2013.
---
Interesting stories about how speech recog connects to CV (Alex Net)

9. Disrupting the Speech/Vision/NLP Industry by Deep Learning
The success has no controversy
The success of deep learning (modern AI) in speech first, followed by computer vision, NLP, and more recently by board games and robotics, is universal and has no controversy.
I am grateful to Microsoft for encouraging me to spend time in documenting technical details of how deep learning has been successfully applied in these tasks.

10. Disrupting the Speech/NLP Industry
Separate Speech Recognition Models Unified by End2End Deep Learning
Training Data
Applying Constraints
Search
Recognized Words
Representation
Speech Signal
Acoustic Models
Language Models
Lexical Models
Now let me return to the connections between speech models to finance models.
The main success of deep learning during , attributed to the collaborations between Microsoft and U. Toronto, lies in the use of DNNs to unify several (but not all) major components in the full modeling and recognition process. (Andrew Ng later in Baidu unified all components, and threw away lexical models).
We believe this type of success in speech may inspire future successes of deep learning in finance, at least at a high, strategic level.

11. Three Challenges Unique to Investment Management1
Very low signal-to-noise ratio 2
Strong nonstationarity with adversarial nature 3
Heterogeneity of big (alternative) data
However, as alluded earlier, drastic technical differences stand out between finance and speech/vision/NLP. Let me now address three significant challenges that are unique to financial investment management, one by one.

12. Three Challenges Unique to Investment Management
1. Very low signal-to-noise ratio
AI problems outside finance generally have lower noise levels, for example:
Speech
Machine translation
Language understanding
Image/video classification & detection
Medical diagnosis
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
Examples: speech, machine translation, language understanding image/video detection and prediction, medical diagnosis
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.

13. Three Challenges Unique to Investment Management
2. Strong non-stationarity with adversarial nature
The adversarial nature in financial market is very different from that in other applications such as playing board-games and fighting robots
Financial academia have yet to propose an effective model for addressing nonstationarity in financial markets due to adversarial competition
Recent literature in AI and robotics is shedding some light - recent papers by Google-DeepMind, Berkeley and Open AI

14. Three Challenges Unique to Investment Management
3. Heterogeneity of big (alternative) data
There has been an immense proliferation of data in recent years: Market price data, fundamental data, and huge sets of “alternative” data including text, image, voice, and multimedia
JP Morgan recently compiled comprehensive sources of such alternative data useful for forecasting financial market
Many other useful data are proprietary to private individuals and data owners (e.g. click streams from search engines, user data from cell phone usages, product ordering from Alexa at home, etc.)

15. Structural 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
Access to data is incomplete given that a lot of information is proprietary or private
Need more sharing of data for training deep learning models, as long as this does not violate fiduciary duties of finance firms to their clients and it does not harm privacy of individuals
Scarcity of talent capable of bridging the gap between AI research and finance (need to cultivate a talent base that understands both technology and the financial markets)
Need for advanced algorithm development tailored to the financial market

16. Q&A