What is Ripple for? (Part 4 - xCurrent)

Ripple is known for XRP, yet one of their products xCurrent works independently of a token, so what does it do exactly?

Part 1 - Introduction
Part 2 - Cash Management

Part 3 - Processing transfer requests in a bank

Experiencing Consensus 2018 in New York

The last time I was in New York was in 1994. I had barely heard of the Internet at the time and it would be another year before I browse my first web page. It’s fun to be back more than 20 years later when another long term technological change is underway. New York has changed too: more Starbucks and less police sirens.

So how is it like to attend a big blockchain event? Is it full of people talking about elliptic curve mathematics and Merkle tree parsing algorithms? Not quite. A few years ago I went to Microsoft TechEd in Orlando -now called Microsoft Ignite- which is a Mecca for software developers.
Consensus draws a very different crowd than Microsoft conferences. Attendees include company founders, CEOs, CIOs, CTOs, evangelists, crypto investors, fund managers, financial analysts, students, journalists, lawyers, compliance people, tax people, consultants... Some wear suits and ties, others coloured hair and tattoos. If they have long hair they’re cryptographers.

Consensus is an event organised every year by Coindesk. This year 8500 people registered, more than twice the number Coindesk was anticipating a few months ago.
The Hilton Midtown near Broadway was packed: often it was hard to cut through the crowd in-between sessions. Next year Coindesk might have to pick a bigger place -Microsoft is known for booking Ignite in conference centers large enough to comfortably park a few A380s.

Consensus has many tracks going on in parallel so you have to keep your FOMO under control. Refrain from doing sessions back to back or it can be draining. The main presentations were happening in the Grand Ballroom: big stage, two giant screens and a sketching artist illustrating the talks live.

I met a few people who didn’t go to any of the sessions and just walked around the floors, talking to anyone and networking. Breakfast was served in the ballrooms, another opportunity for networking around bagels and muffins. In the evening the ballrooms turned into bars with free flow alcohol.

Some highlights and takeaways:

Day1

• Don Tapscott (Blockchain Research Institute) described a taxonomy of blockchain tokens. He classifies them in seven types, which is an interesting way of making sense of the 1800+ tokens that have been created to date.
  
  1. cryptocurrencies (BTC, Dash, )
  2. platforms (ETH, EOS, …)
  3. utility tokens (can be redeemed for a service, such as cloud storage for instance)
  4. security tokens (used to raise money)
  5. natural asset tokens (carbon credit, …)
  6. crypto collectibles (crypto kitties)
  7. crypto-fiat currencies, stable coins.
  
  Don Tapscott co-wrote a book titled The Blockchain Revolution.

Live sketches by Heather Klar

• John Bullard, a researcher from the Federal Reserve argued that cryptocurrencies would make economies more complicated. He suggested we might come back to a situation similar to the 1830’s when most of the notes in the US were privately printed notes (notes valid only for a particular bank).
• Some engaging presentations on Layer 2 and scalability: Amy Yin from Coinbase (@yin_ami) explained the benefits of extended keys, which allows a business to generate as many public keys as needed when getting paid by customers while maintaining a single seed. Muneeb Ali (@muneeb) highlighted the risk of forgetting about scalability issues when coding.
• What programming languages are used when writing production blockchain code? During a conversation involving Charlie Lee (founder of Litecoin) and David Schwartz (chief cryptographer at Ripple), Jutta Steiner (Parity, ex Ethereum) said “For robust implementation we went for Rust and web assembly”. 

Day 2

Hyperledger Projects

• Bridget Van Kralingen described how IBM is actively working on micro-finance with Stellar. IBM has its hands in a bunch of Hyperledger projects: food safety, traceability of diamonds, tracking of donations and carbon trading. After her talk I went to the Hyperledger booth to check out some demos: I saw one about tracing the origin of a bag of coffee you buy from the shelf all the way back to the harvest. With your iPhone you scan a QR code stuck on the package and that brings up the full history Fedex-style:
  
  
  
  
  
  
  

Security for institutional investors

• Ledger -the French guys behind the Nano S- were sponsoring a roundtable on the theme of crypto funds. Nicolas Bacca and Eric Larcheveque talked about a new service called Ledger Vault. The idea is to outsource the complexity of managing private keys: instead of using a bunch of Nanos you store private keys on a SHM (Secure Hardware Module) locked in bunkers in various locations. It’s easy to see how this can be useful to people who manage portfolios of cryptos. For instance the Japanese bank Nomura will use Ledger Vault to offer crypto custody services to its customers. This is a big thing. Soon more and more banks will do this.
• You now have people specialised in selling or advising on crypto funds. So who is selling crypto funds? One example is Grayscale Investments who offers 8 products. Another example is Fundstrat Global Advisors whose chief analyst Thomas Lee (@funstrat) described a model developed by his company to value cryptocurrencies. He mentioned his model was currently valuating BTC at USD36000.

Tips to Research Tokens 

• Alex Tapscott brought up the example of the 1800’s red flag act to illustrate the risk of over-regulation. To describe the position of the crypto market today: “we have reached the second half of the chess board”.

• Alex Sunnarborg from Tretras Capital gave some tips on how to research a cryptocurrency. Here is what to look at:
• transaction amount and size
• fees
• distribution of mining effort
• supply: what is circulating, what is locked in? Percentage of money held by top 100 holders?
• GitHub commits
• regulatory risks

• He added: "Short term price movements are driven by noise, not fundamentals" Yep.

Day 3

This day was more calm, everyone coming in late because of the parties from the night before.

I had conversations with people from Ripple, Dash, Ledger, Shapeshift, Interbit, Volt... then crashed into bed by 4pm.

In the end...
In the end Consensus 2018 was an exciting event that stood out by the number and diversity of its attendees.

This weird idea thrown into the public by some anonymous dude almost 10 years ago with a 9-page whitepaper and open-source code  has now infected the minds of hundreds of thousands of people from all sectors (tech, law, investment, charity, environment, tax...). They are building development platforms, interoperability protocols, secure storage systems, investment vehicles, education material... building blocks and bits and pieces that will be used as infrastructure over the next 20 years or more.

What is Ripple for? (Part3 - Processing transfer requests in a bank)

Ripple addresses the problem of international cash transfers between banks: sounds like something that should be electronic and instant, doesn't it? Well let's dig dipper and have a look at what actually happens when a client requests a transfer...

Part 1 - Introduction
Part 2 - Cash Management

What is Ripple for? (Part2 - Cash Management)

Ripple addresses the problem of international cash transfers between banks. This is part of a larger area called Cash Management. So what is cash management and why are international cash transfers so slow?

Check out Part 1 here

Image processing, NLP and blockchain visualisations with Google APIs

On Tuesday this week Data Science SG organised a meetup at Google's premises in Singapore.

Background
Google offers APIs for Machine Learning (ML APIs), which are pre-trained AI algorithms. Out of the box, ML APIs allow you for instance to categorise a sentence from a news article or distinguish between a picture of a cat and that of a turtle.

On the other end of the spectrum Google provides TensorFlow which allows you to build your neural network model from scratch: this involves tinkering with layers, biases, functions and providing your own training data.

Half-way in between is AutoML where you can train the pre-trained ML APIs using something called transfer learning. With transfer learning the first layers of the neural network model are kept while the last layers are modified and re-trained with your own data.

The three speakers were Google developer advocates. What is a "developer advocate"? Well apparently it is someone who toys around with Google APIs mostly for fun and gives demos about it, which sounds like a pretty cool job to me.

Image recognition
Markku Lepisto (@markkulepisto) showed how he trained AutoML at identifying stamps on  letters received by banks. Because he didn't have the original scans of actual customer data, he wrote a small Python program to automatically generate training data by combining images of stamps with random pages from scanned books downloaded from Project Gutenberg.

Natural Language Processing
Sara Robinson (@srobtweets) briefly described the Google Natural Language API, which is pre-trained at doing categorisation -for instance working out that a sentence from a news article talks about baseball, as well as very simple sentiment extraction -extracting entities and positive/negative sentiment from of a customer review for instance. The online tool also allows you to visualise the syntax of the sentence (grammatical breakdown).

Then Sara asked: how would you go about building a model using your own custom categories?

She showed how to do it using TensorFlow which comes with Python and C++ interfaces. Some of the APIs are low-level (construct the model layer by layer), others are wrappers such as Keras.
For training data, Google BigQuery has large datasets ready to be used such as a list of StackOverflow questions along with their tags (C#, SQL, Javascript, ...).

Bitcoin and Ethereum visualisations with BigQuery
The last part of the evening was left to Allen day (@allenday) in front of a hungry audience.
Allen decided to use BigQuery to analyse transaction data from the Bitcoin and Ethereum blockchains.
BigQuery synchronises with the Bitcoin ledger every 10min, which is good enough to have a near real-time view of the Bitcoin blockchain.

Allen used Gephi to develop interesting visualisations showing the movement of funds between wallets... More details here.

This a Google Easter Bunny

What is Ripple used for? (Part 1)

This is the first of a series of videos about Ripple and its applications.

1 - The main Ripple products
2 - What is Cash Management?
3 - What happen behind the scenes?
4 - Use of XCurrent
5 - Use of XRapid