"machine learning" entries

Bridging the divide: Business users and machine learning experts

The O'Reilly Data Show Podcast: Alice Zheng on feature representations, model evaluation, and machine learning models.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

606px-IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881As tools for advanced analytics become more accessible, data scientist’s roles will evolve. Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the reality is that expertise in advanced algorithms is just one aspect of industrial data science.

During the latest episode of the O’Reilly Data Show podcast, I sat down with Alice Zheng, one of Strata + Hadoop World’s most popular speakers. She has a gift for explaining complex topics to a broad audience, through presentations and in writing. We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

Making machine learning accessible

People who work at getting analytics adopted and deployed learn early on the importance of working with domain/business experts. As excited as I am about the growing number of tools that open up analytics to business users, the interplay between data experts (data scientists, data engineers) and domain experts remains important. In fact, human-in-the-loop systems are being used in many critical data pipelines. Zheng recounts her experience working with business analysts:

It’s not enough to tell someone, “This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.” As a builder of these applications, you need to understand what the algorithm is doing in order to make it better. As a user who ultimately consumes the results, it can be really frustrating to not understand how they were produced. When we worked with analysts in Windows or in Bing, we were analyzing computer system logs. That’s very difficult for a human being to understand. We definitely had to work with the experts who understood the semantics of the logs in order to make progress. They had to understand what the machine learning algorithms were doing in order to provide useful feedback. Read more…

Comments: 4
Four short links: 27 August 2015

Four short links: 27 August 2015

Chrome as APT, Nature's Mimicry, Information Extraction, and Better 3D Printing

  1. The Advanced Persistent Threat You Have: Google Chrome (PDF) — argues that if you can’t detect and classify Google Chrome’s self-updating behavior, you’re not in a position to know when you’re hit by malware that also downloads and executes code from the net that updates executables and system files.
  2. Things Mimicking Other Things — nifty visual catalog/graph of camouflage and imitation in nature.
  3. MITIE — permissively-licensed (Boost) tools for named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors.
  4. MultiFab Prints 10 Materials At Once — and uses computer vision to self-calibrate and self-correct, as well as letting users embed objects (e.g., circuit boards) in the print. developed by CSAIL researchers from low-cost, off-the-shelf components that cost a total of $7,000

Data-driven neuroscience

The O'Reilly Radar Podcast: Bradley Voytek on data's role in neuroscience, the brain scanner, and zombie brains in STEM.


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In this week’s Radar Podcast, O’Reilly’s Mac Slocum chats with Bradley Voytek, an assistant professor of cognitive science and neuroscience at UC San Diego. Voytek talks about using data-driven approaches in his neuroscience work, the brain scanner project, and applying cognitive neuroscience to the zombie brain.

Here are a few snippets from their chat:

In the neurosciences, we’ve got something like three million peer reviewed publications to go through. When I was working on my Ph.D., I was very interested, in particular, in two brain regions. I wanted to know how these two brain regions connect, what are the inputs to them and where do they output to. In my naivety as a Ph.D. student, I had assumed there would be some sort of nice 3D visualization, where I could click on a brain region and see all of its inputs and outputs. Such a thing did not exist — still doesn’t, really. So instead, I ended up spending three or four months of my Ph.D. combing through papers written in the 1970s … and I kept thinking to myself, this is ridiculous, and this just stewed in the back of my mind for a really long time.

Sitting at home [with my wife], I said, I think I’ve figured out how to address this problem I’m working on, which is basically very simple text mining. Lets just scrape the text of these three million papers, or at least the titles and abstracts, and see what words co-occur frequently together. It was very rudimentary text mining, with the idea that if words co-occur frequently … this might give us an index of how related things are, and she challenged me to a code-off.

Read more…

Comment: 1

Unsupervised learning, attention, and other mysteries

How to almost necessarily succeed: An interview with Google research scientist Ilya Sutskever.

Get notified when our free report “Future of Machine Intelligence: Perspectives from Leading Practitioners” is available for download. The following interview is one of many that will be included in the report.

633px-Jan_Steen_-_A_School_for_Boys_and_Girls_-_Google_Art_ProjectIlya Sutskever is a research scientist at Google and the author of numerous publications on neural networks and related topics. Sutskever is a co-founder of DNNresearch and was named Canada’s first Google Fellow.

Key Takeaways:

  1. Since humans can solve perception problems very quickly, despite our neurons being relatively slow, moderately deep and large neural networks have enabled machines to succeed in a similar fashion.
  2. Unsupervised learning is still a mystery, but a full understanding of that domain has the potential to fundamentally transform the field of machine learning.
  3. Attention models represent a promising direction for powerful learning algorithms that require ever less data to be successful on harder problems.

David Beyer: Let’s start with your background. What was the evolution of your interest in machine learning, and how did you zero-in on your Ph.D. work?

Ilya Sutskever: I started my Ph.D. just before deep learning became a thing. I was working on a number of different projects, mostly centered around neural networks. My understanding of the field crystallized when collaborating with James Martens on the Hessian-free optimizer. At the time, greedy layer-wise training (training one layer at a time) was extremely popular. Working on the Hessian-free optimizer helped me understand that if you just train a very large and deep neural network on a lot of data, you will almost necessarily succeed. Read more…

Comment: 1

A “bottom-up” approach to data unification

How machine learning plus expert sourcing can unify customer data at scale.


Watch the free webcast Integrating Customer Data at Scale to learn how Toyota Motor Europe was able to unify its customer data at scale.

Enterprises that are capable of gaining a unified view of their customer data can achieve added business enhancements and user opportunities. Capturing customer data, however, can be a difficult task, as most systems rely on traditional “top-down” approaches to standardizing data. In a recent O’Reilly webcast, Integrating Customer Data at Scale, Tamr field engineer Alan Wagner hosts a Q&A session with Matt Stevens, the general manager at Toyota Motor Europe, to demonstrate how a leading enterprise uses a third-generation system like Tamr to simplify the process of unifying customer data.

In the webcast, Stevens explains how Toyota Motor Europe has gained a 360-degree view of their customers through the Tamr Data Unification Platform, which takes a machine learning and expert-sourcing “human guided workflow” approach to data unification. Wagner provides a demo of the Tamr platform, applied within a Salesforce application, to demonstrate the ability to capture and unify customer data. Read more…

Four short links: 18 August 2015

Four short links: 18 August 2015

Chris Grainger Ships, Disorderly Data-Centric Languages, PCA for Fun and Fashion, and Know Thy History

  1. Eve, Version 0 (Chris Grainger) — Version 0 contains a database, compiler, query runtime, data editor, and query editor. Basically, it’s a database with an IDE. You can add data both manually or through importing a CSV and then you can create queries over that data using our visual query editor.
  2. BOOM: Berkeley Orders Of Magnitudean effort to explore implementing Cloud software using disorderly, data-centric languages.
  3. Eigenstyle — clever analysis and reconstruction of images through principal component analysis. And here are “prettiest ugly dresses,” those that I classified as dislikes, that the program predicted I would really like.
  4. Turing Digital Archivemany of Turing’s letters, talks, photographs, and unpublished papers, as well as memoirs and obituaries written about him. It contains images of the original documents that are held in the Turing collection at King’s College, Cambridge. (Timely as Jason Scott works to save a manual archive: [1], [2], [3])

We make the software, you make the robots

An interview with Andreas Mueller, on scikit-learn and usable machine learning software.

Get notified when our free report “Evaluating Machine Learning Models: A beginner’s guide to key concepts and pitfalls,” by Alice Zheng, is available for download.


Superpixels example from Andreas Mueller’s thesis paper (PDF), used with permission.

A few weeks ago, I had the pleasure of sitting down (virtually, over Skype) with Andreas Mueller, core developer and maintainer of the popular scikit-learn machine learning library. We had previously bonded over our shared goals of making useful machine learning software, so I jumped at the chance to interview him.

Mueller wears many hats at work. He is one of the key maintainers of the popular Python machine learning library scikit-learn. Holding a doctorate in computer vision from the University of Bonn in Germany, he currently works on open science at New York University’s Center for Data Science. He speaks at conferences around the world and has a fanbase of 5,000+ followers on Twitter and about as many reputation points on Stack Overflow. In other words, this man has got mad street cred. He started out doing pure math in academia, and has now achieved software developer cult idol status. Read more…

Four short links: 4 August 2015

Four short links: 4 August 2015

Data-Flow Graphing, Realtime Predictions, Robot Hotel, and Open-Source RE

  1. Data-flow Graphing in Python (Matt Keeter) — not shared because data-flow graphing is sexy new hot topic that’s gonna set the world on fire (though, I bet that’d make Matt’s day), but because there are entire categories of engineering and operations migraines that are caused by not knowing where your data came from or goes to, when, how, and why. Remember Wirth’s “algorithms + data structures = programs”? Data flows seem like a different slice of “programs.” Perhaps “data flow + typos = programs”?
  2. Machine Learning for Sports and Real-time Predictions (Robohub) — podcast interview for your commute. Real time is gold.
  3. Japan’s Robot Hotel is Serious Business (Engadget) — hotel was architected to suit robots: For the porter robots, we designed the hotel to include wide paths.” Two paths slope around the hotel lobby: one inches up to the second floor, while another follows a gentle decline to guide first-floor guests (slowly, but with their baggage) all the way to their room. Makes sense: at Solid, I spoke to a chap working on robots for existing hotels, and there’s an entire engineering challenge in navigating an elevator that you wouldn’t believe.
  4. bokken — GUI to help open source reverse engineering for code.
Four short links: 31 July 2015

Four short links: 31 July 2015

Robot Swarms, Google Datacenters, VR Ecosystem, and DeepDream Visualised

  1. Buzz: An Extensible Programming Language for Self-Organizing Heterogeneous Robot Swarms (arXiv) — Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as Robot Operating System.
  2. Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network (PDF) — Our datacenter networks run at dozens of sites across the planet, scaling in capacity by 100x over 10 years to more than 1Pbps of bisection bandwidth. Wow, their Wi-Fi must be AMAZING!
  3. Nokia’s VR Ambitions Could Restore Its Tech Lustre (Bloomberg) — the VR ecosystem map is super-interesting.
  4. Visualising GoogleNet Classes — fascinating to see squirrel monkeys and basset hounds emerge from nothing. It’s so tempting to say, “this is what the machine sees in its mind when it thinks of basset hounds,” even though Boring Brain says, “that’s bollocks and you know it!”
Four short links: 30 July 2015

Four short links: 30 July 2015

Catalogue Data, Git for Data, Computer-Generated Handwriting, and Consumer Robots

  1. A Sort of Joy — MOMA’s catalogue was released under CC license, and has even been used to create new art. The performance is probably NSFW at your work without headphones on, but is hilarious. Which I never thought I’d say about a derivative work of a museum catalogue. (via Courtney Johnston)
  2. dat goes beta — the “git for data” goes beta. (via Nelson Minar)
  3. Computer Generated Handwriting — play with it here. (via Evil Mad Scientist Labs)
  4. Japanese Telcos vie for Consumer Robot-as-a-Service Business (Robohub) — NTT says Sota will be deployed in seniors’ homes as early as next March, and can be connected to medical devices to help monitor health conditions. This plays well with Japanese policy to develop and promote technological solutions to its aging population crisis.