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1
Wh
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No
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What is intelligence? and why discovery of the correct answer will completely change finance
Chi Lee
_
Wh
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No
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What is intelligence?
Engineer build it from scratch to
understand the principles
Reverse Engineer study what’s there already
and test it
2 scientific approaches
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Wh
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Engineering intelligence, e.g.
Chess
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Deep Blue (IBM custom server)
vs Kasparov (World Champion) 1996 Lost 2-4
Deeper Blue (IBM upgraded)
vs Kasparov 1997 Won 31
2-21
2
Deep Fritz (Windows software)
vs Kramnik (World Champion) 2006 Won 4-2
Since 2007 a Windows PC is basically unbeatable
Wh
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No
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But
Is there a difference between looking intelligent and being intelligent?
Yes there is
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Wh
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the Chinese Room argument (Searle, 1980)
中国人
Englishman
中国
Very long, complicated (hypothetical) rulebook on putting together Chinese pictorials given other Chinese pictorials. Written in English.
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Looking Intelligent is Weak artificial intelligence
Being Intelligent is Strong artificial intelligence
(Searle)
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What about Reverse Engineering?
Human Brain
“Hardware”
Uses 20W of power to run
and does 100 x 1012 “calculations” per second
which implies 2 x 1019 calculations per kWh
or 21 trillion calculations per calorie
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Wh
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2E+19
2030
follow arrows around
less than two decades away
How close is computing hardware?
Source: Koomey, Jonathan G., et al. "Implications of historical trends in the electrical efficiency of computing." Annals of the History of Computing, IEEE 33.3 (2011): 46-54.
Cal
cula
tio
ns
per
kW
h (
log 1
0)
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Human Brain
Understanding the “Software”
Single neuron model 1943 McCulloch and Pitts
Neural network (bunch of neurons) 1958 Rosenblatt
Multi-layer neural networks 1974 Werbos
Deep learning 2007 Hinton
for the very first time, we are seeing elements of Strong artificial intelligence
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BTW
Intelligence is required for consciousness Consciousness is not required for intelligence
Human consciousness (better description is spirit or soul) is a deeply human property
Purpose, faith, creativity, feelings and desires are all seated in consciousness
True “artificial consciousness” is likely long way away
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Deep learning and some related technologies (Strong artificial intelligence)
change everything
Automated Driving
Driverless cars, planes and boats
Automated Visual and Verbal Translation
No language barriers
Humanoid robots (finally)
No manual labour
many, many others … education, healthcare, government …
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know this very well and positioning ahead
most of the economy and the world do not understand yet … field of finance has barely started
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Acquisition date Company Business Country Price Used as or integrated with
24-Oct-14 Revolv Home Automation US Nest Labs
23-Oct-14 Dark Blue Labs Artificial Intelligence UK £tens of millions Google DeepMind
23-Oct-14 Vision Factory Artificial Intelligence UK £tens of millions Google DeepMind
17-Aug-14 Jetpac Artificial intelligence, image recognition US —
20-Jun-14 Dropcam Home Monitoring US $555m Nest Labs
16-May-14 Quest Visual Augmented Reality US — Code Project, Google Translate
14-Apr-14 Titan Aerospace High-altitude UAVs USA — Project Loon
26-Jan-14 DeepMind Technologies Artificial Intelligence UK $500m Google X
13-Jan-14 Nest Labs, Inc Home automation USA $3.2bn Google
10-Dec-13 Boston Dynamics Robotics USA — Google X
7-Dec-13 Bot & Dolly Robotic cameras USA — Google X
6-Dec-13 Holomni Robotic wheels USA — Google X
5-Dec-13 Meka Robotics Robots USA — Google X
4-Dec-13 Redwood Robotics Robotic arms USA — Google X
3-Dec-13 Industrial Perception Robotic arms, computer vision USA — Google X
2-Dec-13 SCHAFT, Inc. Robotics, humanoid robots JPN — Google X
2-Oct-13 Flutter Gesture recognition technology USA $40m Google, Android, Google X
How we know that Google knows
Acquisitions made by Google related to artificial intelligence in the previous 12 months
Source: Wikipedia
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Software can now excel at playing computer games by tracking their game score and learning to play by (literally) looking at the screen and pressing the controls using something called Deep Reinforcement Learning Software could feasibly look at Bloomberg screens and browse the internet and learn to trade by issuing trades like a trader within 10 years from now, and likely do it better than any human trader within 20 years
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Sellside
Fully automated trading (no Traders) for all liquid securities Structurers will need to be proficient in artificial intelligence Salespeople remain critical to the business and sales-traders remain key to trading illiquid securities
Future
Why? Because creativity in keeping clients happy comes from human consciousness and not from (just) intelligence
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Buyside
Fully automated investing in liquid strategies Asset managers will need to be proficient at artificial intelligence Private equity and illiquid investments remain fully people driven (except analysts will have some roles automated) Client Directors and Specialists remain critical to the business for interacting with clients
Future
Why? Because creativity in keeping clients happy comes from human consciousness and not from (just) intelligence
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Chi Lee
Portfolio Manager
Chi Lee was previously at GAM as Portfolio Manager and Head of Investment Risk. He was responsible for developing, launching and lead managing all alternative risk premia strategies at GAM until he left in November 2014. He successfully grew the strategy from an initial USD50m seed investment in 2012 to USD1bn in institutional AUM within two years as a result of developing and using new, cutting edge quantitative techniques.
Prior to joining GAM in January 2008, he originated and structured credit derivatives on the fixed income trading floor with Morgan Stanley, and before that, he was head of quantitative research with KGR Capital.
He was awarded a BA with First Class Honours in Computer Science from Cambridge University and an MBA with Honors in Finance from the Wharton School, where he was a Fulbright Scholar. He is also a Certified FRM with the Global Association of Risk Professionals.
Wh
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No
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