AI & ML

It can take dozens of back-and-forth emails just to set up a quick meeting. Now, an artificial intelligence personal assistant can do it for you. Dennis Mortensen, CEO of x.ai, explains why you shouldn’t be afraid.

Goodbye to Personal Assistants

In science-fiction, you’ll encounter a lot of computers that can feel... HAL 9000, Deckard, WALL-E. But what about computers that can really sense your emotions? MIT Professor Rosalind Picard takes a look at this artificial-feeling future.

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Computers that Know You're Mad

What is A.I. or artificial intelligence but the 'space of possible minds', argues Murray Shanahan, scientific advisor on the movie Ex Machina and Professor of Cognitive Robotics at Imperial College London.

In this special episode of the a16z Podcast brought to you on the ground from London, Shanahan -- along with journalist-turned-entrepreneur Azeem Azhar (who also curates The Exponential View newsletter on AI and more) and The Economist Deputy Editor Tom Standage (the author of several tech history books) -- we discuss the past, present, and future of A.I. ... as well as how it fits (or doesn't fit) with machine learning and deep learning.

But where are we now in the A.I. evolution? What players do we think will lead, if not win, the current race? And how should we think about issues such as ethics and automation of jobs without descending into obvious extremes? All this and more, including a surprise easter egg in Ex Machina shared by Shanahan, whose work influenced the movie.

a16z Podcast: Artificial Intelligence and the 'Space of Possible Minds'

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

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

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

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