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In this week's podcast QCon chair Wesley Reisz talks to Machine learning research scientist John Langford.  Topics include his Machine Learning system Vowpal Wabbit, designed to be very efficient and incorporating some of the latest algorithms in the space.  Vowpal Wabbit is used for news personalisation on MSN.  They also discuss how to get started in the field and it’s shift from academic research to industry use.

Why listen to this podcast:

 - Vowpal Wabbit is a ML system that attempts to incorporate some of the latest machine learning algorithms.
 - How to learn ML: take a class or two, get accustomed with learning theory and practice.
 - ML has moved from the research field into the industry, 4 out of 9 ICML tutorials coming from the industry.
 - It’s hard to predict when you have enough data.
 - AlphaGo is a milestone in artificial intelligence. It uses reinforcement learning, deep learning and existing moves played by Go masters.
 - Deep Learning is currently a disruptive technology in areas such a vision or speech recognition.
 - What’s trendy: Neural Networks, Reinforcement and Contextual Learning.
 - Machine Learning is being commoditized.
 
Notes and links can be found on http://bit.ly/2b4YNqQ

How to Approach Machine Learning

6m:12s To start learning Machine Learning, Langford recommends taking a class or two, mentioning the course by Andrew Ng and another course by Yaser S. Abu-Mostafa.
6m:50s It is recommended to get accustomed with learning theory to avoid some of the rookie's mistakes.

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Show Notes

John Langford on Vowpal Wabbit, Used by MSN, and Machine Learning in Industry

In this week's podcast InfoQ’s editor-in-chief Charles Humble talks to Data Scientist Cathy O’Neil. O'Neil is the author of the blog mathbabe.org. She was the former Director of the Lede Program in Data Practices at Columbia University Graduate School of Journalism, Tow Center and was employed as Data Science Consultant at Johnson Research Labs. O'Neil earned a mathematics Ph.D. from Harvard University. Topics discussed include her book “Weapons of Math Destruction,” predictive policing models, the teacher value added model, approaches to auditing algorithms and whether government regulation of the field is needed.

Why listen to this podcast:

 - There is a class of pernicious big data algorithms that are increasingly controlling society but are not open to scrutiny.
 - Flawed data can result in an algorithm that is, for instance, racist and sexist.  For example, the data used in predictive policing models is racist. But people tend to be overly trusting of algorithms because they are mathematical.
 - Data scientists have to make ethical decisions even if they don’t acknowledge it. Often problems stem from an abdication of responsibility.
 - Auditing for algorithms is still a very young field with ongoing academic research exploring approaches.
 - Government regulation of the industry may well be required.

Notes and links can be found on http://bit.ly/2eYVb9q

Weapons of math destruction

0m:43s - The central thesis of the book is that whilst not all algorithms are bad, there is a class of pernicious big data algorithms that are increasingly controlling society.
1m:32s - The classes of algorithm that O'Neil is concerned about - the weapons of math destruction - have three characteristics: they are widespread and impact on important decisions like whether someone can go to college or get a job, they are somehow secret so that the people who are being targeted don’t know they are being scored or don’t understand how their score is computed; and the third characteristic is they are destructive - they ruin lives.
2m:51s - These characteristics undermine the original intention of the algorithm, which is often trying to solve big society problems with the help of data.

More on this:

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Cathy O'Neil on Pernicious Machine Learning Algorithms and How to Audit Them

Jonas Bonér, CTO of LightBend and creator Akka, discusses using Akka when developing distributed systems. He talks about the Actor Model, and how every Microservice needs to be viewed as a system to be successful. 

Why listen to this podcast:

 - Akka is JVM-based framework design for developing distributed systems leveraging the Actor Model - an approach for writing concurrent systems that treat actors as universal primitives and the most successful model with abstraction has been streaming
 - Circuit breakers in Akka are a backup and retry policy; they protect you by capturing failure data and allow you to roll back 
 - Every Microservice needs to be viewed as a system, it needs to have multiple parts that run on different machines in order to function and be fully resilient - thus is a Microsystem
 - Two different trends have emerged when it comes to hardware and environments: one is the trend toward Multi-core, the is a movement toward virtualized environments and the cloud
 - Saga pattern of managing long running transactions in a distributed system fits very well with messaging style architectures

Notes and links can be found on: http://bit.ly/2kwB2eB

Akka
The Actor Model
When Akka and the Actor Model is the perfect choice
Circuit breakers patterns in distributed systems
Two trends toward Multi-core
Reactive Manifesto
Event Driven vs. Message Driven
Reactive Programming and Streams
Microliths to Microsystems
What do you have to get right before you start trying to deploy a distributed systems?
Working with ML / AI at Lightbend to understand tracing through distributed system
Saga Pattern

More on this:
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Jonas Bonér on the Actor Model, Akka, Reactive Programming, Microservices and Distributed Systems

Eric Horesnyi, CEO @streamdata.io, talks to Charles Humble about how hedge funds are applying deep learning as an alternative to the raw speed favoured by HFT to try and curve the market.

Why listen to this podcast:

 - Streamdata.io was originally built for banks and brokers, but more recently hedge funds have begun using the service.
 - Whilst Hedge Funds like Renaissance Technologies have been using mathematical approaches for some time deep learning is now being applied to markets. Common techniques such as gradient descent and back propagation apply equally well to market analysis.
- The data sources used are very broad.  As well as market data the network might be using, sentiment analysis from social networks, social trading data, as well as more unusual data such as retail data, and IoT sensors from farms and factories.
 - By way of contrast High Frequency Trading  focusses on latency. From an infrastructure stand-point  you can play with propagation time, Serilization (the thickness of the pipe), and Processing time for any active component in chain.
 - One current battleground in HFT is around using FPGA to build circuits dedicated to feed handlers.  Companies such as Novasparks are specialists in this area.

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Eric Horesnyi on High Frequency Trading and how Hedge Funds are Applying Deep Learning to Markets

Werner Schuster talks to Martin Hadley, data scientist at University of Oxford. They discuss the state of the R language, the rich R ecosystem that covers development (RStudio), notebooks for publication (R Notebooks, RPubs), writing web apps (Shiny), and the pros/cons of the different data frames implementations.

Why listen to this podcast:

 - R is the tool for working with rectangular data
 - Modern data frame implementations are Tibble and data.table (for large amounts of data)
 - RMarkdown and R Notebooks allow to explore data and then publish it the results and (interactive) visualization
 - Use Shinyapps to publish server side R applications
 - Tidyverse is the place to look for modern R packages

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Martin Hadley on R and the modern R ecosystem

Shubha Nabar is a senior director of data science for Salesforce Einstein. Prior to working for Salesforce, she was a data scientist at LinkedIn and Microsoft.  In the podcast she discusses Salesforce Einstein and the problem space that they are trying to solve, explores the differences between enterprise and consumer for machine learning, and then talks about the Optimus Prime Scala library that they use in Salesforce.

Why listen to this podcast:
* The volume of data, and hardware advances have made it possible to do machine learning to do them a lot faster.
* AI is a science of building intelligent software, encompassing many aspects of intelligence that we tend to think of as human.
* If you can’t measure something, you can’t fix it.
* You have to think about what you can automate, rather than having a human to try and engineer out all those features.
* Get feedback on design.

Nora Jones, a senior software engineer on Netflix’ Chaos Team, talks with Wesley Reisz about what Chaos Engineering means today. She covers what it takes to build a practice, how to establish a strategy, defines cost of impact, and covers key technical considerations when leveraging chaos engineering.

Why listen to this podcast:

- Chaos engineering is a discipline where you formulate hypotheses, perform experiments, and evaluate the results afterwards.
- Injecting a bit of failure over time is going to make your system more resilient in the end.
- Start with Tier 2 or non-critical services first, and build up success stories to grow chaos further.
- As systems become more and more distributed, there becomes a higher need for chaos engineering.
- If you’re running your first experiment, get your service owners in a war room and get them to monitor the results of the test as it is running.

More on this: Quick scan our curated show notes on InfoQ http://bit.ly/2vJoimw

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Shubha Nabar Discusses Einstein, the Machine Learning System in Salesforce

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Eric Horesnyi on High Frequency Trading and how Hedge Funds are Applying Deep Learning to Markets

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