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Economics of A.I. Robin Hanson George Mason University. A Fog of Future Possibilities. To Deal With: Focus on big, robust, sharp changes Combine expert knowledge from several fields Beware: experts in A with newspaper level knowledge of B. Types of Views. A.I.-Econ View Combos. Econ. - PowerPoint PPT Presentation
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Economics of A.I.Economics of A.I.
Robin Hanson
George Mason University
A Fog of Future Possibilities
To Deal With:• Focus on big, robust,
sharp changes• Combine expert
knowledge from several fields
• Beware: experts in A with newspaper level knowledge of B.
Types of Views
A.I.-Econ View Combos
A.I.A.I.
Amateur
Amateur
Expert
Expert
Social Science Exists!!
Once Express Tech in Econ Terms, Can Say
• Tech’s social implications– Given weak regulation – Given particular strong regulation
• Likely regulations, given political climate– Likely political climates
• Social support of tech innovations
• Tech innovations given social support
✔✔✔
✖
✔✔✔✔✔
✔✔✔
✔✔✔
Econ Terms
• Utility Functions – what folks want– Like outputs, e.g. ,products, services– Dislike give up inputs, e.g., effort, time
• Production Functions - outputs from inputs
• Endowments – base inputs, e.g. abilities
• Info - who knows how much about what
• Deals - trades allowed, contracts enforced
Economics of Robots
• Staple of fiction – ancient legends to TV now• If have more of X, do you want Y more (complement)
or less (substitute)?• Machine as Substitute to human labor
– Predicts wages fall to machine cost– Ricardo 1821, most science fiction
• Automation as Complement to human labor– Predicts wages rise as automation costs fall– Wicksell 1923, modern economics consensus
• So are robots a substitute or complement?
Robots Substitute On Task, But Tasks Are Complements
Hum
an A
dvan
tage
Useful Mental Tasks
SubstituteFor Task
HumansDo These
MachinesDo These
Tasks AreComplements
A Rising Tide
Hum
an A
dvan
tage
Useful Mental Tasks
A Rising Tide
Hum
an A
dvan
tage
Useful Mental Tasks
A Simple Robot Growth Model
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,0 ,
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Switch Between Growth Modes
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Exogenous Tech Endogenous Tech
Learning by doing:
Pre-Robot
Post-Robot
100x Faster Growth Isn’t Crazy
000 DT LateTech All Endog.00148. DT LateTech Genl. Endog.261.27.222.7 DT LateTech Exogenous
9.143.157.17Time nguct DoubliEarly Prod35.35.35.)(- FallriceComputer PEarly 01.015.025.)( Growth Tech GeneralEarly 015.015.02.)( Growth Annual Population015.015.025.)( Share Human005.01.005.)( ShareComputer 64.5.25.)(Share Capital31.2.1.)( ShareLabor
P
A
H
gg
g
Slow FastMid
Error9.6%
2.0%
1.7%
1.E-8
1.E-7
1.E-6
1.E-5
1.E-4
1.E-3
1.E-2
1.E-1
1.E+0
1.E+1
1.E+2
1.E+01.E+11.E+21.E+31.E+41.E+51.E+61.E+71.E+81.E+9
Years before 2050
Annu
al G
row
th R
ate
World Product Growth Rate
Could It Happen Again?
Brains
Hunting
Farming
Industry
Growth Mode Statistics
Forecasting The Next Mode
2039 2040 2041 2042 2043 2044 2045 2046 20476.1% 6.1% 6.6% 8.0% 14.0% 40.5% 147.3% 476.2% 1023.2%
Sample growth rate transition
DateRate
Industry Shares of US GDP
Construction
Communications
Wholesale trade
Retail trade
ManufacturingFinance, insurance, real
estate
Services
Government Transportation
Electric, gas, trash
Mining
Farming, forestry, f ishing
Factor Shares of Income
Labor
Human Capital
Other Non-Human Capital
Stock Dividends Natural Resources
Cheap For Robots
• Immortality (even so, most can’t afford)• Travel – transmit to new body• Nature – don’t need ecosystems• Wildly differing bodies• Widely varying mental abilities – e.g., run speed• Survive in severe environments
– Backups insure against severe events
• Labor – work gets less tool intensive
Copies Become Cheap
• Malthusian population explosion– Makes rapid economic growth
• Robot wages fall to hardware cost– And hardware costs fall fast too– Human wages may fall to robot wages
• Depends on human-robot task landscape shape• Not matter if robots slave or free
– Only Draconian population or wage laws could stop
Humans Eclipsed
• Wages well below human subsistence– Though some humans may find servant jobs
• But rich if held non-wage assets– Investments double as fast as economy!
• Robot-Human war unlikely if integrated– E.g., lefties don’t war against righties
Human Level Robots Require
• Sensors/Actuators (arms/eyes) ~now
• Processors <~2040
• Software ??
– Direct code it? Hard!
– Learn from brain organization? Eventually?
– Simulate particular human brain? Can Do!
Needed To Emulate Brains
1. Model each brain cell type
2. Scan – freeze, slice, 2D scan
3. Computer (very parallel task)
Simulating Dominos
• Wave speed, energy are robust
• Only a few details matter
• Brains seem similar
Emulations Feel Human
• Most emulations are of few best humans– First mover advantage to show quality
• Remember human life, retain human tendencies– love, gossip, argue, sing, violate, play, work, innovate
• More alienated worlds - as farms, factories were– Office work in virtual reality, – Physical work in android bodies
• Many humans won’t believe conscious, “is me”– But same social implications, few dozen is plenty
More Implications
• Copies rent bodies, or own on loan– Evicted if can’t pay!!
• To recoup training investment, copy cabal limit copy wage– Security to prevent bootleg copies
• Fast growth discourages transport, encourages local production
• Laws hold copies co-responsible
• <1cm robots seem feasible– Mind/body sped up with size reduction– 0.2in. tall => Subjective year/day in 24hr/4min
• One skyscraper holds billions - is megacity– City radius now is hour travel distance =>
10sec
Time Line
• IP rights secure?• Regulation fears?• Second mover better? • Secrets leak fast?• What products drive?
• Rights of Emulation• Govt. Slow or Speed?• What tech ready last?
– Computer –> gradual– Sim/Model –> sudden
For-Profit ResearchAnd Development
Sell, Maintain, UseNon-Profit Research
World Product, 10K BC-2K AD
0.001
0.010
0.100
1.000
10.000
100.000
1,000.000
-10000 -8000 -6000 -4000 -2000 0 2000
Year
Wo
rld
Pro
du
ct (
su
bs
iste
nce
bill
ion
s) Singularities!
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
100,000,000,000
1,000,000,000,000
-2000000 -1500000 -1000000 -500000 0 500000
Year
Wor
ld P
rodu
ct (s
ubsi
sten
ce h
uman
s)
World Product, 2 Million BC+
0.001
0.01
0.1
1
10
-600 -500 -400 -300 -200 -100 0
Re
lati
ve
Bra
in S
ize
Millions of Years
Bigger Brains
Modeling Exponential Sequences
iii
i
aai
aii
tii
tMtYtY
tYtYctM/1
1
/
))()(()(
)()( 2)(
)(/)(
)()(:
1 /
11
1
1
iiii
iiiii
ii
dYdY
dMdMd
ExponentialModes
CES Combined
Doubling Time Factor
1st Date Mode Biggest
World Product Factor
Method: Min2))log())((log( j
jj YtY
Outline
• Economic capacity drives tech, wartech
• World long-term econ trend mostly steady– Now doubles in ~15 years
• So far have seen 2-4 “singularities” when– World econ growth rate increased x150-250– In much less than a previous doubling time
• Next?: by 2100, takes 5 yr, double monthly
• Candidate driver: whole brain emulation
Emulation Supply & Demand
Quantity
PriceNow
Value
Cost
Quantity
PriceLater?
Net
CostValue
Kinds of Costs
• Design• Build• Maintain• Repair• Security
• Material• Energy• Capital• Labor• Time Delay
• Any brain• Per brain• Per copy• Scale• Scope
Kinds of Strategy• Lockin• Standards
• Price Discrim.• Bundle
• Lease/Buy• Differentiate
Kinds of Quality
• Speed
• Weight
• Portability
• Reliability
• Durability
• Power needs
• Environs handle
• Security
• Nearness to human behavior, feeling
• Improve: memory, interface range, bandwidth