This talk proposes that the future of artificial intelligence is smart networks that have intelligence "baked in" in the form of Blockchain Distributed Ledgers for confirming authenticity and transferring value, and Deep Learning Algorithms for predictive identification. Smart networks are not a far-off possibility but already needed as deep learning systems are going online in connected apps for Autonomous Driving and Drone Delivery, and Human-Robot Interaction. Two high-impact contemporary emerging technologies for the future of AI are Blockchain Distributed Ledgers and Deep Learning Algorithms, and discusses their implications for the future of artificial intelligence.

1. Scientech
Indianapolis IN, January 8, 2018
Slides: http://slideshare.net/LaBlogga
The Future of Artificial Intelligence
Blockchain & Deep Learning
Melanie Swan
Philosophy, Purdue University
melanie@BlockchainStudies.org

2. 8 Jan 2018
Blockchain
Discussion Questions
1. Probability humans will extinct
ourselves by mistake by 2100? _____%
2. How much are automated algorithms
changing your workplace or everyday
life? _____%
3. Would you prefer a mortgage that
corresponds to your specific needs, or
is standard (for the same cost)?
4. Would you like to make a digital backup
of your mind?
1
?
??

3. 8 Jan 2018
Blockchain 2
Melanie Swan, Technology Theorist
 Philosophy Department, Purdue University,
Indiana, USA
 Founder, Institute for Blockchain Studies
 Singularity University Instructor; Institute for Ethics and
Emerging Technology Affiliate Scholar; EDGE invited
contributor; FQXi Advisor
Traditional Markets Background
Economics and Financial
Theory Leadership
New Economies research group
Source: http://www.melanieswan.com, http://blockchainstudies.org
https://www.facebook.com/groups/NewEconomies

4. 8 Jan 2018
Blockchain
Agenda
 Artificial Intelligence
 Blockchain Technology
 Deep Learning Algorithms
 Future of Artificial Intelligence
3

5. 8 Jan 2018
Blockchain 4
Considering blockchain and deep learning
together suggests the emergence of a new
class of global network computing system.
These systems are self-operating
computation graphs that make probabilistic
guesses about reality states of the world.
Future of AI Smart Network thesis

6. 8 Jan 2018
Blockchain
What are we running on networks?
5
Value (Money)
Intelligence (Brains)
Information
2010s-2020s
2050s(e)
1980s
Thought-
tokening
Value-
tokening

7. 8 Jan 2018
Blockchain
Future of AI: Smart Networks
6
Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html
Fundamental Eras of Network Computing

8. 8 Jan 2018
Blockchain
What is Artificial Intelligence?
 Artificial intelligence
(AI) is a computer
performing tasks
typically associated
with intelligent beings
-Encyclopedia Britannica
7
Source: https://www.britannica.com/technology/artificial-intelligence
Ke Jie vs. AlphaGo AI Go player, Future of
Go Summit, Wuzhen China, May 2017

9. 8 Jan 2018
Blockchain
“Creeping Frontier” of Technology
8
Source: https://www.britannica.com/technology/artificial-intelligence
 Achievements are quickly forgotten
 AI = “whatever we can’t do yet”
Innovation Frontier

10. 8 Jan 2018
Blockchain
Global Robotics Spending: $67 billion 2025e
9
Source: https://www.siemens.com/innovation/en/home/pictures-of-the-future/digitalization-and-software/autonomous-systems-facts-
and-forecasts.html

11. 8 Jan 2018
Blockchain
Global AI-specific Spending: $36 billion 2025e
10
Source: https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market
 Artificial Intelligence market analysis by Technology
 Deep Learning, Machine Learning, Natural Language
Processing, Machine Vision

12. 8 Jan 2018
Blockchain
Autonomous Driving - Waymo
 Nov 7, 2017: Waymo is
first to put fully self-
driving cars on US roads
without a safety driver
 Operating autonomous
minivans on public roads
in Arizona without a
human behind the wheel
since Oct 2017
 Soon to invite public
passengers for rides in
self-driving vehicles
11
Source: https://www.theverge.com/2017/11/7/16615290/waymo-self-driving-safety-driver-chandler-autonomous

13. 8 Jan 2018
Blockchain
Autonomous Driving – 35 cities testing
12
Source: https://www.theverge.com/2017/10/23/16510696/self-driving-cars-map-testing-bloomberg-aspen,
https://avsincities.bloomberg.org/
 Live projects: San Francisco, Austin, Nashville,
Washington, Paris, Helsinki, and London (35 total)
 Impact studies: Los Angeles, Tel Aviv, Buenos Aires,
and Sao Paulo (18 total)

14. 8 Jan 2018
Blockchain
What is Real?
13
Source: https://futurism.com/soon-wont-able-difference-between-ai-human-voice/
 Voice Imitation and risk of personal
identity theft
 WaveNet: human and synthetic voice
indistinguishable
 Google DeepMind synthetic speech system,
Tacotron 2 (deep neural net)
 Lyrebird: create a digital copy of a voice
 Adobe DoCo: realistic altered speech
 Copy your voice, craft into synthetic speech
 Fake News
 Compound app: facial recognition, political
matching (Cambridge Associates), and
nervous system analysis

15. 8 Jan 2018
Blockchain
AI Superintelligence Problem
 Computer capabilities can grow faster than
human capabilities
 Therefore, one day computers might
become vastly more capable than humans
(i.e. superintelligent)
 And willfully or inadvertently present a
danger to humans
 Stuck on a goal: “paper-clip maximizers”
14
Source: https://www.cbsnews.com/news/cbsn-on-assignment-instagram-filtering-out-hate/, https://deepmind.com/applied/deepmind-
ethics-society/research/AI-morality-values/
“Pessimistic”
“Optimistic”

16. 8 Jan 2018
Blockchain
Global Existential Risk
15
Source: Sandberg, A. & Bostrom, N. (2008): “Global Catastrophic Risks Survey”, Technical Report #2008-1, Future of Humanity
Institute, Oxford University: pp. 1-5.
Percent chance of different types of disaster before 2100
Method: Informal
survey of
participants,
Global
Catastrophic
Risk Conference,
Oxford, July
2008

17. 8 Jan 2018
Blockchain
Standard AI Ethics Modules?
 Roboethics (how the machine behaves)
 Facebook AI bots create own language
 OpenAI self-play bot defeats top Dota2 player
 Instagram “nice” filter eliminates hate speech
 Time Well Spent: attention economy design
ethics contra addiction and web dark patterns
 Criminal justice algorithms discriminate
 Robotiquette (how the machine interacts)
16
Facebook
AI bots
OpenAI
Dota2
Victory
Source: Swan. M. In review. Toward a Social Theory of Dignity: Hegel’s Master-Slave Dialectic and Essential Difference in the
Human-Robot Relation. In Robots, Power, Relationships. Eds. J. Carpenter, F. Ferrando, A. Milligan. http://www.timewellspent.io/

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Blockchain 17
http://www.robotandhwang.com/attorneys
 Future of “work”
 “Work” = meaningful
engagement of
human capacities
Human-machine
collaboration

19. 8 Jan 2018
Blockchain
Technological Unemployment
 Challenge: facilitate an orderly transition to
Automation Economy
 Half (47%) of employment is at risk of automation in the
next two decades – Carl Frey, Oxford, 2015
 Why are there still so many jobs in a world that could be
automating more quickly? – David Autor, MIT, 2015
18
Source: Swan, M. (2017). Is Technological Unemployment Real? Abundance Economics. In Surviving the Machine Age: Intelligent
Technology and the Transformation of Human Work. Hughes & LaGrandeur, Eds. London: Palgrave Macmillan. 19-33.

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Blockchain
Our AI Future: high-impact emerging tech
19
Big Data &
Deep Learning
Blockchain CRISPR &
Bioprinting

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Blockchain 20
Top disruptors: Deep Learning & Blockchain
Source: https://www.ipe.com/reports/special-reports/securities-services/securities-services-blockchain-a-beginners-
guide/10014058.article

22. 8 Jan 2018
Blockchain
Job Growth Skills in Demand
1. Robotics/automation/data science/deep learning
2. Blockchain/Bitcoin
21
Source: https://www.computerworld.com/article/3235972/financial-it/blockchains-explosive-growth-pushes-job-
skills-demand-to-no-2-spot.html

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Blockchain
Agenda
 Artificial Intelligence
 Blockchain Technology
 Deep Learning Algorithms
 Future of Artificial Intelligence
22

24. 8 Jan 2018
Blockchain
Blockchain
23
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
 To inspire us to build
this world

25. 8 Jan 2018
Blockchain 24
Conceptual Definition:
Blockchain is a software protocol;
just as SMTP is a protocol for
sending email, blockchain is a
protocol for sending money
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
What is Blockchain/Distributed Ledger Tech?

26. 8 Jan 2018
Blockchain 25
Technical Definition:
Blockchain is the tamper-resistant
distributed ledger software underlying
cryptocurrencies such as Bitcoin, for
recording and transferring data and assets
such as financial transactions and real
estate titles, via the Internet without needing
a third-party intermediary
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
What is Blockchain/Distributed Ledger Tech?

27. 8 Jan 2018
Blockchain
How does Bitcoin work?
Use eWallet app to submit transaction
26
Source: https://www.youtube.com/watch?v=t5JGQXCTe3c
Scan recipient’s address
and submit transaction
$ appears in recipient’s eWallet
Wallet has keys not money
Creates PKI Signature address pairs A new PKI hashed signature for each transaction

28. 8 Jan 2018
Blockchain
P2P network confirms & records transaction
27
Source: https://www.youtube.com/watch?v=t5JGQXCTe3c
Transaction computationally confirmed
Ledger account balances updated
Peer nodes maintain distributed ledger
Transactions submitted to mempool, and miners assemble
new batch (block) of transactions each 10 min
Each block includes a cryptographic hash of the last
block, chaining the blocks, hence “Blockchain”

29. 8 Jan 2018
Blockchain
How robust is the Bitcoin p2p network?
28
p2p: peer to peer; Source: https://bitnodes.21.co, https://github.com/bitcoin/bitcoin
 11,678 global nodes run full Bitcoind (1/18); 160 gb
Run the software yourself:

30. 8 Jan 2018
Blockchain
What is Bitcoin mining?
29
 Mining is the accounting function to record
transactions, fee-based
 Mining ASICs “discover new blocks”
 Mining software makes nonce guesses to win the
right to record a new block (“discover a block”)
 At the rate of 2^32 (4 billion) hashes (guesses)/second
 One machine at random guesses the 32-bit nonce
 Winning machine confirms and records the
transactions, and collects the rewards
 All nodes confirm the transactions and append the
new block to their copy of the distributed ledger
 “Wasteful” effort deters malicious players
Sample
code:
Run the software yourself:
Fast because ASICs
represent the hashing
algorithm as hardware

31. 8 Jan 2018
Blockchain
Distributed Networks
30
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
Decentralized
(based on hubs)
Centralized Distributed
(based on peers)
 Radical implication: every node is a peer who can
provide services to other peers

32. 8 Jan 2018
Blockchain
P2P Network Nodes provide services
31
Source: http://www.amazon.com/Bitcoin-Blueprint-New-World-Currency/dp/1491920491
Centralized bank tracks
payments between clients
“Classic”
Banking
Peer
Banking
 Nodes deliver services to others, for a small fee
 Transaction ledger hosting (~11,960 Bitcoind nodes)
 Transaction confirmation and logging (mining)
 News services (“decentralized Reddit”: Steemit, Yours)
 Banking services (payment channels (netting offsets))
 Direct peer-to-peer digital clearing = no central bank needed
Network nodes store transaction
record settled by many individuals

33. 8 Jan 2018
Blockchain
Public and Private Distributed Ledgers
32
Source: Adapted from https://www.linkedin.com/pulse/making-blockchain-safe-government-merged-mining-chains-tori-adams
 Private: approved users
(“permissioned”)
 Identity known, for enterprise
 Approved credentials
 Controlled access
 Public: open to anyone
(“permissionless”)
 Identity unknown, for individuals
 Ex: Zcash zero-knowledge proofs
 Open access
Transactions logged
on public Blockchains
Transactions logged
on private Blockchains
Any user Financial Inst, Industry
Consortia, Gov’t Agency
Examples:
Bitcoin
Ethereum
Examples:
R3
Hyperledger

34. 8 Jan 2018
Blockchain
Blockchain Applications Areas
33
Source: http://www.blockchaintechnologies.com
Smart Property
Cryptographic
Asset Registries
Smart Contracts
IP Registration
Money, Payments,
Financial Clearing
Identity
Confirmation
 Impacting all industries
because allows secure
value transfer in four
application areas

35. 8 Jan 2018
Blockchain
Agenda
 Artificial Intelligence
 Blockchain Technology
 Deep Learning Algorithms
 Future of Artificial Intelligence
34

36. 8 Jan 2018
Blockchain
 Global Data Volume: 40 EB 2020e
 Scientific, governmental, corporate, and personal
Big Data…is not Smart Data
Source: http://www.oyster-ims.com/media/resources/dealing-information-growth-dark-data-six-practical-steps/
35
35

37. 8 Jan 2018
Blockchain
Big Data requires Deep Learning
36
 Older algorithms cannot keep up with the growth in
data, need new data science methods
Source: http://blog.algorithmia.com/introduction-to-deep-learning-2016

38. 8 Jan 2018
Blockchain
Broader Computer Science Context
37
Source: Machine Learning Guide, 9. Deep Learning
 Within the Computer Science discipline, in the field of
Artificial Intelligence, Deep Learning is a class of
Machine Learning algorithms, that are in the form of a
Neural Network

39. 8 Jan 2018
Blockchain 38
Conceptual Definition:
Deep learning is a computer program that can
identify what something is
Technical Definition:
Deep learning is a class of machine learning
algorithms in the form of a neural network that
uses a cascade of layers (tiers) of processing
units to extract features from data and make
predictive guesses about new data
Source: Swan, M., (2017)., Philosophy of Deep Learning, https://www.slideshare.net/lablogga/deep-learning-explained
What is Deep Learning?

40. 8 Jan 2018
Blockchain
Deep Learning & AI
 System is “dumb” (i.e. mechanical)
 “Learns” with big data (lots of input examples) and trial-and-error
guesses to adjust weights and bias to identify key features
 Creates a predictive system to identity new examples
 AI argument: big enough data is what makes a
difference (“simple” algorithms run over large data sets)
39
Input: Big Data (e.g.;
many examples)
Method: Trial-and-error
guesses to adjust node weights
Output: system identifies
new examples

41. 8 Jan 2018
Blockchain
Sample task: is that a Car?
 Create an image recognition system that determines
which features are relevant (at increasingly higher levels
of abstraction) and correctly identifies new examples
40
Source: Jann LeCun, http://www.pamitc.org/cvpr15/files/lecun-20150610-cvpr-keynote.pdf

42. 8 Jan 2018
Blockchain
Supervised and Unsupervised Learning
 Supervised (classify
labeled data)
 Unsupervised (find
patterns in unlabeled
data)
41
Source: https://www.slideshare.net/ThomasDaSilvaPaula/an-introduction-to-machine-learning-and-a-little-bit-of-deep-learning

43. 8 Jan 2018
Blockchain
Early success in Supervised Learning (2011)
 YouTube: user-classified data
perfect for Supervised Learning
42
Source: Google Brain: Le, QV, Dean, Jeff, Ng, Andrew, et al. 2012. Building high-level features using large scale unsupervised
learning. https://arxiv.org/abs/1112.6209

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Blockchain
Machine learning: human threshold
43
Source: Mary Meeker, Internet Trends, 2017, http://www.kpcb.com/internet-trends
 All apps voice-activated and conversational?

45. 8 Jan 2018
Blockchain
2 main kinds of Deep Learning neural nets
44
Source: Yann LeCun, CVPR 2015 keynote (Computer Vision ), "What's wrong with Deep Learning" http://t.co/nPFlPZzMEJ
 Convolutional Neural Nets
 Image recognition
 Convolve: roll up to higher
levels of abstraction in feature
sets
 Recurrent Neural Nets
 Speech, text, audio recognition
 Recur: iterate over sequential
inputs with a memory function
 LSTM (Long Short-Term
Memory) remembers
sequences and avoids
gradient vanishing

46. 8 Jan 2018
Blockchain
3 Key Technical Principles of Deep Learning
45
Reduce combinatoric
dimensionality
Core computational unit
(input-processing-output)
Levers: weights and bias
Squash values into
Sigmoidal S-curve
-Binary values (Y/N, 0/1)
-Probability values (0 to 1)
-Tanh values 9(-1) to 1)
Loss FunctionPerceptron StructureSigmoid Function
“Dumb” system learns by
adjusting parameters and
checking against outcome
Loss function
optimizes efficiency
of solution
Non-linear formulation
as a logistic regression
problem means
greater mathematical
manipulation
What
Why

47. 8 Jan 2018
Blockchain
How does the neural net actually learn?
 System varies the
weights and biases
to see if a better
outcome is obtained
 Repeat until the net
correctly classifies
the data
46
Source: http://neuralnetworksanddeeplearning.com/chap2.html
 Structural system based on cascading layers of
neurons with variable parameters: weight and bias

48. 8 Jan 2018
Blockchain
Backpropagation
 Problem: Inefficient to test the combinatorial
explosion of all possible parameter variations
 Solution: Backpropagation (1986 Nature paper)
 Backpropagation of errors and gradient descent are
an optimization method used to calculate the error
contribution of each neuron after a batch of data is
processed
47
Source: http://neuralnetworksanddeeplearning.com/chap2.html

49. 8 Jan 2018
Blockchain
Agenda
 Artificial Intelligence
 Blockchain Technology
 Deep Learning Algorithms
 Future of Artificial Intelligence
48

50. 8 Jan 2018
Blockchain
Future of Artificial Intelligence
49
Source: https://www.slideshare.net/lablogga/deep-learning-explained
 Blockchain & Deep Learning
 Robust self-operating computational
systems
 Probabilistic guesses about reality
states of the world; state engines
 New forms of automation
technology that might orchestrate
entire classes of human activity

51. 8 Jan 2018
Blockchain
Future of AI: Smart Networks
50
Source: Expanded from Mark Sigal, http://radar.oreilly.com/2011/10/post-pc-revolution.html
Fundamental Eras of Network Computing
 Network computing to bring about next-gen AI
 Future of AI: intelligence “baked in” to smart networks
 Blockchains to confirm authenticity and transfer value
 Deep Learning algorithms for predictive identification

52. 8 Jan 2018
Blockchain
Next Phase: Deep Learning Chains
 Put Deep Learning systems on the Internet
 Need blockchain security for registration and audit-tracking
 Blockchain P2P nodes provide deep learning network services:
security (facial recognition), identification, authorization
 Application: Autonomous Driving and Drone Delivery,
Human-Social Robotics
 Deep Learning (CNNs): identify what things are
 Blockchain: secure automation technology
 Track arbitrarily-many units, audit, upgrade
 Legal liability, accountability, remuneration
51

53. 8 Jan 2018
Blockchain
Deep Learning Chains
Application: Big Health Data
52
Source: https://www.illumina.com/science/technology/next-generation-sequencing.html
 Need big health data to understand biological
mechanisms of disease and prevention
Population
7.5 bn
people
worldwide

54. 8 Jan 2018
Blockchain
Application: Leapfrog Technology
To enable human potential
 Financial Inclusion
 2 bn under-banked, 1.1 bn without ID
 70% lack access to land registries
 Health Inclusion
 400 mn no access to health services
 Does not make sense to build out
brick-and-mortar bank branches
and medical clinics to every last
mile in a world of digital services
 eWallet banking and deep learning
medical diagnostic apps
53
Source: Pricewaterhouse Coopers. 2016. The un(der)banked is FinTech's largest opportunity. DeNovo Q2 2016 FinTech ReCap
and Funding ReView., Heider, Caroline, and Connelly, April. 2016. Why Land Administration Matters for Development. World Bank.
http://www.who.int/mediacentre/news/releases/2015/uhc-report/en/
Digital health wallet

55. 8 Jan 2018
Blockchain 54
Considering blockchain and deep learning
together suggests the emergence of a new
class of global network computing system.
These systems are self-operating
computation graphs that make probabilistic
guesses about reality states of the world.
Future of AI Smart Network thesis

56. Scientech
Indianapolis IN, January 8, 2018
Slides: http://slideshare.net/LaBlogga
The Future of Artificial Intelligence
Blockchain & Deep Learning
Melanie Swan
Philosophy, Purdue University
melanie@BlockchainStudies.org