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