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** This is a working draft. Contact us at CovidPassportTracking@gmail.com for corrections or to be added a mailing list for updated versions**          

Updated 24 Mar 2021

This document tracks the implementation status and technology use of Covid Passports by governments and organizations.   This page is maintained by Jake Stief and Paul Baier, volunteers at PathCheck. 

Summary

• Country entry

• 11 countries have digital Covid Passports for entry.  All seem to be using a combination of custom coded apps and government issued printed certificates except for Aruba which has adopted CommonPass

• Airlines travel

• A number of airlines have production systems or pilots using IATA Travel Pass

• Air France has pilot with AOKPass App 

• JetBlue has pilot with CommonPass

• Testing or vaccination centers

• Pharmacies

• In US, Walmart announced partnership with CLEAR and The Common Pass (no info on CVS or Walgreens)

• Company/school (employee access)

• Multiple efforts.  PWC has app for employee survey at entry

• Technology

• Most implementations appear to be using custom developed apps, but multiple products are being worked on

• CLEAR with The Common Pass and IATA Travel Pass seem to be most common

View the  Covid Passport List 

My Initial Experience with Peepeth - "A Blockchain app to replace Twitter" -- 3 cents charge for every post

Peepeth is an Ethereum-based, dapp for microblogging that I tried today.  I'm not a Twitter or Blockchain expert, so I'm sure I've made some mistakes and may have some info wrong.   Kudos for the early experimentation by so many startups.

My initial reactions are mixed.  In exchange for avoiding a central company from censoring my posts, I must pay 3-8 cents for every action (create a profile, change profile background, post a comment, repost, etc.).   It is an early version, but the blockchain benefits are at the data layer and I'm currently skeptical that the required payments and slowness at the UX level is enough to get millions of existing Twitter users to switch.

I'm curious what others think.  I'll monitor and hopefully do an update in 3-4 months.

Screenshots below.

The desktop interface looks like Twitter

When I created my Peepeth profile, it cost me 8 cents to update and took 2.5 minutes

Updating the background picture in my profile cost me 3 cents and took 2.1 min

Understandably, this is an early version, but over time it language about "gas", "ethereum" etc. needs to be hidden or simplified if the average Joe is to be comfortable with this

Message after posting and paying for a post:

Here is one post displayed in Peepeth's proprietary web front end

Here is my data on immutably on public blockchain (accessible without going through Peepeth's proprietary front end, but I think one would need to know my UID)

Transaction on Ethereum

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Prez notes about Algorand, BitCoin Replacement from MIT Professor

Summary

• Algorand is new, replacement blockchain proposal to replace BitCoin and improve on the early technical issues
• I attended IEEE sponsored a presentation on Apr 5 by MIT Professor Silvio Micali attended by around 100 mostly technical people at MIT.   The audience was generally impressed and only asked clarifying questions.   There were no questions that were highly skeptical nor did I meet people who were
• Algorand aims to address many of the BitCoin issues of scalability, security, poor governance, 
• Part of the core concept is an assumption that most actors are honest
• Algorand is a startup funded with $4M from Pillar and USV
• Paul’s view: this is worth watching to see feedback from the community and to understand the limits when it starts being built as it seemed too good to be true.
• Multiple presentations are on YouTube about Algorand

Notes

• Keys features of a distributed ledger
• Readable by all
• Writeable by all
• Tamperproof
• Good for
• Notarization and storage
• Ordering of chãos
• Disintermediation
• Transatlantic use manual escrow.  Blockchain with smart contract (conditional statement
• Payments and cryptocurrencies
• Bitcoin vs Algorand: disagreement in on implementation, not core concept of 
• Bitcoin uses eventual consensus via proof of work
• Proof of work: mining
• Bitcoin technical problems
• A massive amount of electricity
• The exogenous concentration of power in miners, and therefore corruptible because of concentration and low margin business
• 3 mining pools control Bitcoin
• 2 mining pools control Ethereum
• Concentration power is antithetical to goals of a blockchain
• Exogenous vulnerability – miners go bankrupt
• Scalability
• Unclear if BitCoin supports 100M users
• Ambiguity because of forks (two miners solving at the same time)
• Long true latency
• SECURITY?
• Not against network attacks
• Algorand
• overview
• No forks
• No proof of works
• Use Byzantine Agreement
• Main idea: message-passing Byzantine agreement
• Main assumption: the honest majority of money
• Advantages
• Trivial computation: no miners
• True decentralization: a single class of users
• Finality of payments (no forks)
• Scalability

Craigslist allows option for accepting for CyptoCurrency when selling something

I noticed this option today when placing Craigslist add to sell a used car.   Not sure when they started offering this option

Notes from Underscore.vc Blockchain Conference Mar 22, 2018

300 people attended this excellent event (agenda here).  Kudos to Underscore.vc on a very good event!  My summary notes below.

Notes

Blockchain Scalability

• Obviously a massive problem.  Ethereum 10tx/sec vs Visa 80K/tx
• Notable comment
• the current state is horrific.  As volume increases, things get worse (more latency, fees go up). "Scalability is moving in the opposite direction" 
• Each marginal customer in Visa cost LESS, but with blockchain each marginal customers cost MORE 
• Overall, technical attendees believe the scalability problem would be solved (likely with feature tradeoffs) but no agreement on how quickly.  

ICO / Token offerings

• 80% of ICO last year had negative returns
• Earliest ones (Bitcoin, ETH) had best returns with declining returns for newer ones
• Less mega $ ICO are expected, more around $20-30M
• Conflict arising between ICO and VC fiduciary responsibilities
• US companies will domicile in Cayman and other countries to get around emerging US regulatory tightening

WilmerHale partner (and ex-SEC staffer) on ICO clampdown

• SEC is trying to tap down ICO very aggressively.  SEC was caught off guard last year by the explosion of ICO and working to get it cleaned up.  Getting this cleaned up and tapped down is the SEC’s #1 priority
• There clearly be a flurry of SEC enforcement actions

Token classification: security vs utility

• Lots of discussion of Security vs Utility token classification.  The general feeling is that utility token must have immediate use and demonstration of value when ICO raised. A “Chucky Cheese” token that can be used immediately after issuance (few startups are doing this.  hard for some applications) and for which it is hard to see how the problem could be solved without using a utility token

Smart Contract (SC) development

• Lots of challenges. "Do we really need SC for these proposed applications?"
• SC are immutable. How do you handle changes or amendments?
• All the data is public.  How do you handle private data?
• “It is impossible to code on Ethereum” "Why do we need to learn another language for SC?
• SC is painful today but less painful than 3 months ago and trend line is improving but too slow
• Severe lack of tools for SC; open source and big sw companies need to improve
• Ethereum is the leading environment for SC but other platforms have better developer experience

Use cases and non-crypto apps with network usage

• CryptoKitties using 15% of Ethereum's network remains stunning.
• A few promising companies presented (FirstBlood – gaming (1000 sessions per day), possibly Bloom for credit score)
• [Paul view: But many teams and ideas seem focused on ICO fundraising and technical challenges, and not details of customer willingness to use/pay and 10x uniqueness versus non-blockchain apps]

2 min summary: Notes from HBS AI/ML Investor Panel - Unique Access to Clean Data Essential

Last Saturday, an HBS student club (CODE Club) hosted a Machine Learning Workshop.   Over 100 attendees heard speakers from MIT, large companies (Google, etc.) and several startups.  Below are my notes from the AI/ML investor panel.  The emphasis on unique access to clean data was refreshing to hear and is consistent with my experience.

Notes from the AI/ML investor Panel

Rick Grinnell (Glasswing Ventures)

• His investment focus is on vertical opportunities.   Verticals are more attractive because of vertical-specific data and a clearer line to customers' budget
• He encourages startup teams to have a ML knowledgeable person from the beginning.  This is not a market where several business orientated MBA students can start a company with a MVP using offshore development.  

Habbib Haddad (MIT Media Lab E14 Fund)

• He focuses on investment opportunities with unique and large clean data sets (often vertical plays in agriculture, fintech, etc.) 
• CLEAN DATA are critical
• Product managers today must know the new language to talk with data scientist

Mackey Craven (Open View)

• Attractive investments must have clear path to clean data "moat."   Moats can be created via unique access and contractual terms 
• Computing power and algorithms are now a commodity and thus are not the basis of competitive advantage.    Defensibility centers on business knowledge and access to clean data that grows uniquely over time and gets to market first, thus flywheel to clean data

Sunil Nagaraj (Ubiquity Ventures) - moderator

10 Takeaways from AI World Conference in Boston in Dec

Last month I attended the 3 day AI World Conference in Boston with 2,300 others.  The event was cross-industry (digital advertising, health care, autonomous cars, security, etc.) with speakers from leading firms such as MIT, Goldman Sachs, Forrester, Phillips Health Care, Toyota, iRobot, etc.   Below are my key takeaways from my 15 pages of notes and conversations with dozens of people.

Abbreviations
AI: artificial intelligence
ML: machine learning
IoT: internet of things

Key Takeaways

• The category labels (AI/ML/Big Data/IoT) continue to blur and merge, but “AI” is now commonly used as the larger termer umbrella term
• There are different definitions of AI and there is no trend to consolidate 
• Two speakers said, “Just substitute AI for ML in all places on my presentation”
• Many “AI” case studies (health care, etc.) did not involve mechanical or robotic hardware
• Speakers quoted stats about a large number of “AI” startups but these startups would have been traditionally classified software firms that use ML
• Everything is called AI now: “AI for the Enterprise”  
• “As long as the startup has one machine learning model, we call it a ‘AI’ startup“
• The pace of technology change is relentless and increasing 
• Many felt the pace of technology change continues to accelerate.  People within the AI/ML community stated that  “it is hard to keep up with AI/ML” and most feel this acceleration will continue.   
• Companies and consumers will never be able to keep up which provides more opportunities for vendors to package all these capabilities.
• This increase in technology remains explosive and drives automation
• Goldman Sachs example
• 2000: 600 cash equity traders, no software engineers
• 2017: 2 cash equity traders and 200 software engineers
• Health insurance open enrollment example  
• call center=> web => AI
• While AI and ML are hyped right now, it remains very, very early.  Internet firms (Google, Facebook, Twitter, Amazon) dominate current hiring and case studies
• 80% [my estimate] of activity, advancement, and energy around AI/ML is from internet firms (Google, Facebook, Twitter, Amazon) which have massive data sets and massive teams and have hired the vast majority of ML talent at high wages
• The rest of the companies are WAY behind these
• The successful early applications and ones with the most promise in the next 3 years are those with NARROW feature set that supplement an existing process
• Examples include
• Uber: human driver 95% and uber app 5%.  More automation: multiple pickups (human couldn’t do this easily)
• Social media: scan millions of images for NSFW but have human QA involved for exceptions
• Internet advertising:  use ML to suggest banner ads but add “guardrail” rules for edge cases (e.g. don’t show ads for coffins when you believe someone has a death in the family)
• Healthcare: analyze hundreds of MRI scans as second opinion for doctors
• There were multiple names for this task augmentation with software and ML
• “Augmented intelligence”
• “Computer enabled service” 
• “Co-bots"
• Fully autonomous AI is decades or more away
• Yes the computer can beat the human in a chess game, but the computer couldn’t find the table to sit down ;)
• None of the speakers at the conference believe fully unassisted AI will be rolled out in the next 3 years (autonomous car panel thought it would be 3-10 years before autonomous cars appear in the market beyond current pilots)
• The media hype is way ahead of realistic adoption
• Data (and lots of it) are KING. Models are critical but of zero value without data
• This was a recurring theme, especially from people who have been through any kind of real AI/ML project
• The data collection challenge and cost is material in many industries.  For example, Electronic Health Records (EHR) is burdened by abbreviations, missing data, inconsistent usage, IT data silo’s and other challenges  
•  Models can be imprecise and fooled (several examples)
• Tape on stop sign fools autonomous car



• Poodle or Ostrich? -  Neural network fooled by removal of several pixels (but human eye can still tell the difference)
  
• Is a chihuahua or a muffin?  A labradoodle or fried chicken?

• The data modeling processes are immature and rudimentary when compared to software engineering and the integration of these two teams is non-trivial
• No methodology to develop, test, deploy, and document models exist
• Better tools and processes are the second priority at MIT CSAIL, where they are advocating ModelDB
• Many firms mentioned difficulty in integrating software engineers and data scientists
• “Even at MIT, software engineering department is still too stovepiped from data scientist”
• Neural networks are powerful but many shy away because there is no explainability
• Neural networks remain very problematic because lack of reasons for prediction
• Medical diagnostics (why does model say you have cancer?)  why buy this stock?
• Find way to better explain output from neural networks is top research agenda at MIT per Sam Madden, co-chair of MIT CSAIL

Overall, speakers and attendees are very bullish on the value and competitive differentiation AI/ML bring to the market but the AI/ML market is still in its infancy and the press hype is ahead of realism.  Successful firms that automate more processes in the next 5 years will incorporate AL/ML as the cost of data acquisition and tools continue to decline.