Keynote lecture on 'Artificial Intelligence and Antitrust' delivered during the FSR C&M, CMPF and FCP Annual Scientific Seminar on 'Competition, Regulation and Pluralism in the Online World'
Artificial Intelligence and Antitrust (Hal Varian)
1. Artificial Intelligence and Antitrust
Hal Varian
Florence Italy
March 22-23, 2018
Competition, Regulation and Pluralism in the Online World
The views in this presentation are those of the author and do
not represent the views of his employer or any other party.
2. Outline
● Machine learning and AI
● What ML can do
● Data and other factors of production
● Online competition
● Leaders, laggards, superstars
● Algorithmic collusion
5. Machine learning and Artificial Intelligence
● AI has been around a long time (in the lab)
● ML has been around for a long time---in classroom and in practice
● Last 5 years saw tremendous advances in AI methods for ML
○ Deep learning: layered neural networks
○ Applications in image recognition, voice recognition, translation
○ Progress is due to better algorithms, hardware, data, expertise
● What does it take to use ML and AI?
○ Hardware and software are easy via cloud computing
○ Data is often easy to acquire
○ Expertise is scarce but growing rapidly due to online courses
6. What can ML do? Examples from Kaggle
● Home Prices; Improve accuracy of Zillow’s home price prediction: $1,200,000
● Traffic to Wikipedia Pages; Forecast traffic to Wikipedia pages: $25,000
● Personalized Medicine; Predict effect of genetic variants $15,000
● Taxi Trip Duration: Predict total ride duration of taxi trips in New York: $30,000
● Product Search Relevance: Relevance of search results: $40,000
● Clustering Questions: Question pairs that have the same intent: $25,000
● Cervical cancer screening: Effectiveness of treatments $100,000
● Click Prediction: Can you predict where each user will click: $25,000
● Inventory Demand: Minimize returns of bakery goods: $25,000
● Video understanding: classify what is happening in YT videos $100,000
9. Economic characteristics of data
Is data the new oil?
● Like oil, data must be refined before it becomes useful
○ It has to be turned into information, knowledge, action
● Unlike oil, data is non-rival
○ Partly excludable (intellectual property, privacy regulation)
○ Excludable, non-rival goods are “club goods” (tennis club, swimming club)
○ Ownership for private goods, access for club goods
● Rights, permissions, licensing, regulation, contracts
○ Example: who has access to autonomous vehicle data?
○ Pluralism is generally good, see airlines
● Data portability
○ Google Takeout
○ Facebook account
○ But where are the Take-Ins?
10. Advice to students (Feb 2008)
“If you are looking for a career where your services will be in high
demand, you should find something where you provide a scarce,
complementary service to something that is getting ubiquitous and
cheap. So what’s getting ubiquitous and cheap? Data. And what is
complementary to data? Analysis. So my recommendation is to take
lots of courses about how to manipulate and analyze data: databases,
machine learning, econometrics, statistics, visualization, and so on.”
A noted pundit
11. Advice to students (Feb 2008)
“If you are looking for a career where your services will be in high
demand, you should find something where you provide a scarce,
complementary service to something that is getting ubiquitous and
cheap. So what’s getting ubiquitous and cheap? Data. And what is
complementary to data? Analysis. So my recommendation is to take
lots of courses about how to manipulate and analyze data: databases,
machine learning, econometrics, statistics, visualization, and so on.”
Hal Varian
12. Where does the data come from?
● A by-product of operations. Data warehouses.
● Web scraping. Bots, spiders, Common Crawl
● Offering a service. GOOG411, ReCaptcha, QuickDraw
● Hiring humans to label data. Mechanical Turk, India, Philippines, etc.
● Buying data from provider. Mail lists, credit reports, data brokers
● Sharing data. 9.5 million labeled images, 4.5 million labeled YouTube videos
● Data from governments and NGOs. Economic, transport, agricultural, scientific
● Data from cloud providers. Google Patents Public Dataset, AWS Public
Datasets.
● Computer generated data. Synthetic images, AlphaGo 0, back translation
13. How important is data?
● Imagenet challenge
● 20M labeled images
● 1.2M images for training
● 150,000 images for testing
● 1000 object categories
● Roughly constant training
size since 2010
● Dramatic improvement due to
hardware, algorithms,
expertise
● Now beats humans
Imagenet 2017
14. Diminishing returns to data: Stanford dog dataset
● Of course, more data is
better
● But there are diminishing
returns, like any other factor
of production
● Stanford dog breed images
○ 120 breeds of dogs
○ 20,580 images
● Goal: recognize breeds
● Accuracy goes up but at a
decreasing rate
Stanford Dog Dataset
15. Cognitive assistance
It used to be that being a ...
● ...cashier required knowing how to make change
● ...writer required knowing how to spell
● ...taxi driver meant knowing city streets
● ...a hospitality worker in an international you know a bit of foreign languages
● ...gardener, you needed to recognize plants
● ...veterinarian how to recognize dog breeds
Where there is a skills gap, you can bring the worker’s skills up to the requirement, or
bring the job down to workers’ competencies. Cognitive assistances helps people get
jobs, by reducing the tasks they need to master.
● In 1880 machines offered physical assistance to workers
● In 2018 machines offer cognitive assistance to workers
21. Competition in the cloud
Providers: Amazon Web Services, Microsoft Azure, Google Cloud
Platform, Adobe, VMware, IBM Cloud, Rackspace. Red Hat, Oracle,
SAP, etc. (60 more)
22. Dimensions of competition
● Pricing: posted and negotiated
○ Bertrand pricing due to low marginal cost and rapid technological progress
○ Reserved or interruptible use
● Standard services: images, transcripts, voice, translation (see demos)
● Software: databases, prediction API, statistical tools (patents database)
● Consulting: direct and 3rd party
● Portability: containers, dockers avoid lock-in
○ Multihoming is common
○ For example, Snap uses both Google and Amazon
● Make or buy decision?
○ Expertise is in demand
○ New grads want to work with great teams (as with any new technology)
○ Skills will diffuse, specializations develop, domain knowledge is valuable
○ Cost of maintaining infrastructure v specialization in specific domain
27. VC funding is robust
● But fewer firms are going public.
● The US stock markets have half
the number of firms listed as
they had 20 years ago.
● In VC there are four times as
many acquisitions as IPOs
● The average listed firm spends
more on R&D than on capital
Kathleen Kahle and Rene Stulz,
“Is the US Public Corporation in Trouble”?
Sand Hill Econometrics
29. Algorithmic collusion
● Law
○ OECD on Algorithms and Collusion
○ Stucke and Ezrachi (2016), Virtual Competition
○ Gal and Elkin-Koren (2017), “Algorithmic Consumers”, HJLT
● Economics
○ Repeated games (1970s) and the folk theorem
○ Axelrod (1981): Evolution of cooperation in prisoner's dilemma
■ Tournament of strategies and tit-for-tat
■ Evolution of cooperation
■ Noisy prisoners’ dilemma
○ Rapid response equilibrium
■ Gas stations with posted prices
■ Airline price signaling
30. Computation? Or communication?
Strategies for collusion are simple, communication is key issue
● NASDAQ odd-eighth pricing (1994)
● Spectrum auction signalling by bids (1996) [483] bidding
Considerations
● Is communication open or closed?
● Is it implicit or explicit?
● Facebook AI “own language”
● Google AI neural cryptography
32. Summary
● AI is here to stay
● Hardware, software, data, expertise are inputs to production and all have
diminishing returns.
● Variety of applications
● Online competition is robust
● Algorithmic collusion has more to do with communication than
computation
● Adversarial AI
