View
428
Download
5
Category
Preview:
Citation preview
SingularityU Executive Program
Day 1 - Technology foundations
Networks, Computing & sensor
Brad Templeton
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
OSI model
Transports
Networks
Data link
Physical
–Marc Andreessen
“Software is eating the world”
Software & hardware
Software lets you visualise the physical; takes you above hardware & infrastructure
This flexibility allows you to delay decisions (!)
Bandwidth abundance
Google Loon
Facebook Drones
Expect more connectivity
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
Ambient intelligence
Cheaper, low power, smaller
Sensors
Computing
Networks
NetworkingProblems 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!
Sensors applications
Supply chain management (huge!)
Pipes, factories, trucks & etc
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
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
Quantum Computing
Specific problems to solve:
factoring big numbers —> huge threat to cryptography (public key)
optimisation problems
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
Bitcoin & open hackable culture
Bitcoin lets anybody innovate in Money, Title, Transactions
Ethereum platform
Research: smart contracts
Summary
Artificial Intelligence
Neil Jacobstein
AgendaWhat is AI?
What is already accomplished
What is under the hood
Applications
Implications
Action items & recommendations
What is AI?
Pattern recognition
to
Solve practical problems
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
3 mythsYou 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
AI comes with tradeoffs
One side: job disruption, human identity, risk amplification
Other side: faster, cheaper, better problem solving
4 key areas
Verification - meets the spec
Validation - spec meets the problem/situation
Security
Control
Nice movies about AI
Ex_Machina
Imitation game
Chappi
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
Where we areVicarious —> 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
More on where we are
VIV - spin-off from Siri
Intelligence become a utility
IBM watson
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
AI value added
Augment human skill
Expands range of possibilities
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
Books
Race against the machine
Kasparov - How life imitates chess
AI changes balance of power —> Radical abundance by Drexler
Abundance by Diamandis
RecommendationsCrowdsource
Utilize
Use publicly available tools
AI platforms (Google, Watson, Amazon, Microsoft)
Rethinking business processes
Notes from Q&A
What excites most? —> AI drives us to evidence based decision making
AI needs data!
Summary
ForecastingPaul Saffo
Intro
Frequency of earthquakes —> same exponential behaviour
Inflection point vs critical point
Uncertainty is intrinsic
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
Components
Strategic - easy
Critical indicator - hard
Receptive conviction - hardest (act decisively on incomplete information)
Case: Rochfort & Nimitz (Midway base)
Driving forces
Constants - Moore’s Law, other exponential technologies & processes
Cycles - Kondratev’s cycles
Novelties - breakthroughs
Summary
Digital BiologyRay McCauley
3 components
Reading - DNA sequencing
Writing - Genetical Engineering
Hack - Democratisation of tool
Reading
ReadingDNA 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
Software forclinical genetics
Personalise
Invite
Cancer genetics
Foundation medicine
StationX
More companies
9PCR
MinION - hand sets for sequencing
Second Genome
Microbiome
Writing
ReadingApE - plasmid editor (software development)
DNA 2.0 -> print, build, test
CRISP/Cas9
Drag’n’Drop Engineering
Genome editing with CRISP —> toolkit for genes
Companies
Bluebird Bio
Editas Medicine
Gunsight
Lysogen
Spark
Biohacking
BioCurious
hackerspace for biotech
Sunnyvale, California
membership & classes
2nd popular FAQhow 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
Summary
brought to you byBayram Annakov, App in the Air
http://medium.com/@bayramannakov
Recommended