Day 1 of Singularity University Exec Program

1. SingularityU
Executive Program
Day 1 - Technology foundations

2. Networks,
Computing & sensor
Brad Templeton

3. Exponential
computing
What makes revolution?
early adopters: open hackable
platforms with a culture of innovation
free (Internet): innovative apps do not
need financial justification to be
developed
the stupid network

4. OSI model
Transports
Networks
Data link
Physical

5. –Marc Andreessen
“Software is eating the world”

6. Software &
hardware
Software lets you visualise the
physical; takes you above hardware
& infrastructure
This flexibility allows you to delay
decisions (!)

7. Bandwidth
abundance
Google Loon
Facebook Drones
Expect more connectivity

8. Cool new things &
devices
Contact lens that measure concentration of sugar
Google Glass (immediate computing)
Oculus (turned down $1.1B deal without having
product)
Magic leap (600M seed round)
HoloLens
CastAR

9. Ambient
intelligence
Cheaper, low power, smaller
Sensors
Computing
Networks

10. Networking
Problems with multiple wireless low-power
standards (Zigbee, BLE & etc)
Seems that BLE is winning the race
Nest —> Machine - Machine - Human network
Probably Google bought them for 3B not
because of thermostat, but rather to have
gateway to talk to other smart home
devices!

11. Sensors
applications
Supply chain management (huge!)
Pipes, factories, trucks & etc

12. Wearables
Problem: people buy it, but dont wear it
When people are healthy, they dont care
about wearable health devices
Intel Edison & Baby Jumper
application

13. Killer app
Wearables & sensors are waiting for
killer app
Probably it is in the field of
context: putting smaller devices into
environment & sensing what is
happening there

14. Quantum
Computing
Specific problems to solve:
factoring big numbers —> huge
threat to cryptography (public key)
optimisation problems

15. Bitcoin &
blockchain
Global & outside of government control
No central bank to meddle with it or fix it
Senders & recipients are anonymous (or
maybe not)
Transactions are irrevocable
Quicker/slower than other instruments

16. Bitcoin & open
hackable culture
Bitcoin lets anybody innovate in
Money, Title, Transactions
Ethereum platform
Research: smart contracts

17. Summary

18. Artificial
Intelligence
Neil Jacobstein

19. Agenda
What is AI?
What is already accomplished
What is under the hood
Applications
Implications
Action items & recommendations

20. What is AI?
Pattern recognition
to
Solve practical problems

21. DeepMind Tech
Input: raw pixels
Output: value function estimation of future
rewards
Acquired by Google in 2014
Plays Atari with deep reinforcement learning
Video: learns from game & comes to intelligent
strategy that developers have not envisioned

22. 3 myths
You cant just sprinkle a little bit of AI
Garbage In - Garbage Out (you have to clean &
shape data before using it)
Smart algorithms are not the same as human
level intelligence or sentience
—> start with the problem, not AI algorithm.
Framing the problem is THE important step

23. AI comes with
tradeoffs
One side: job disruption, human
identity, risk amplification
Other side: faster, cheaper, better
problem solving

24. 4 key areas
Verification - meets the spec
Validation - spec meets the
problem/situation
Security
Control

25. Nice movies about
AI
Ex_Machina
Imitation game
Chappi

26. AI
Symbols - computer are very good at
Perceptions - just become good at
Concepts - just starting —> why? —>
cant make decisions without data,
need to have mechanical models
about reality

27. Where we are
Vicarious —> next generation AI
algorithm that passed Captcha test
Future states of cow —> AI imagination
Human brain had no upgrade fo 50K
years
Siri & Google Now - long way to
understand our intentions & wishes

28. More on where we
are
VIV - spin-off from Siri
Intelligence become a utility
IBM watson

29. Development
vectors
Deep learning algorithm - champion now
A_PIE - raging Pose illumnation
Expression
Face 2 algorithm —> 99% identifying
faces in different poses, emotions & etc.
Better than human
But there are mistakes

30. AI value added
Augment human skill
Expands range of possibilities

31. Application
oriented AI
Machine Intelligence Landscape
Gecko conference
Attensisty - sentiment analysis
TalentBin - talent search engine for entire web
Brigtherion
Narrative Science
BitSight
Gaggle - solving business challenges through predictive analytics
Experfy - hire data scientists (the problem: you expose your data)
Watson Ecosystem - 100M venture capital, SU may connect with them
Neil’s workshop at Stanford: using AI in decision making

32. Books
Race against the machine
Kasparov - How life imitates chess
AI changes balance of power —>
Radical abundance by Drexler
Abundance by Diamandis

33. Recommendations
Crowdsource
Utilize
Use publicly available tools
AI platforms (Google, Watson, Amazon,
Microsoft)
Rethinking business processes

34. Notes from Q&A
What excites most? —> AI drives us
to evidence based decision making
AI needs data!

35. Summary

36. Forecasting
Paul Saffo

37. Intro
Frequency of earthquakes —> same
exponential behaviour
Inflection point vs critical point
Uncertainty is intrinsic

38. Intro
Identify your biases.. and not all surprises are bad news
Lousy quality is not a bug, a feature (Kodak and
digital cams)
Sacred cows make the best burgers
Forecasting isn't prediction
Actions in the present affect events in future
Quickly come to conclusions - the whole essence of
forecasting

39. Components
Strategic - easy
Critical indicator - hard
Receptive conviction - hardest (act
decisively on incomplete information)
Case: Rochfort & Nimitz (Midway
base)

40. Driving forces
Constants - Moore’s Law, other
exponential technologies &
processes
Cycles - Kondratev’s cycles
Novelties - breakthroughs

41. Summary

42. Digital Biology
Ray McCauley

43. 3 components
Reading - DNA sequencing
Writing - Genetical Engineering
Hack - Democratisation of tool

44. Reading

45. Reading
DNA Microrays
23 and me
Cost per genome (biology now falls under Moore’s Law):
2001: 3B
2007: 1M
2013: used car
2015: 1K
2016: pizza
2020: flush of a toilet
Actually DNA sequencing tech is even faster than computation: 5-10x vs 1,5-2x

46. Software for
clinical genetics
Personalise
Invite
Cancer genetics
Foundation medicine
StationX

47. More companies
9PCR
MinION - hand sets for sequencing
Second Genome
Microbiome

48. Writing

49. Reading
ApE - plasmid editor (software
development)
DNA 2.0 -> print, build, test
CRISP/Cas9
Drag’n’Drop Engineering
Genome editing with CRISP —> toolkit for
genes

50. Companies
Bluebird Bio
Editas Medicine
Gunsight
Lysogen
Spark

51. Biohacking

52. BioCurious
hackerspace for biotech
Sunnyvale, California
membership & classes

53. 2nd popular FAQ
how did they allow? —> 7 different
levels of government
FBI/BioCurious join conference in 2012
Cow-free Milk & Cheese
Rise of Biotech Garage Inventor
diybiology.org

54. Summary

55. brought to you by
Bayram Annakov, App in the Air
http://medium.com/@bayramannakov