Understanding neural function and virtual reality

[A version of this article appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Poppy Crum explains that what matters is efficiency in identifying and emphasizing relevant data.

Like many data scientists, I’m excited about advances in large-scale machine learning, particularly recent success stories in computer vision and speech recognition. But I’m also cognizant of the fact that press coverage tends to inflate what current systems can do, and their similarities to how the brain works.

During the latest episode of the O’Reilly Data Show Podcast, I had a chance to speak with Poppy Crum, a neuroscientist who gave a well-received keynote at Strata + Hadoop World in San Jose. She leads a research group at Dolby Labs and teaches a popular course at Stanford on Neuroplasticity in Musical Gaming. I wanted to get her take on AI and virtual reality systems, and hear about her experience building a team of researchers from diverse disciplines.

Understanding neural function

While it can sometimes be nice to mimic nature, in the case of the brain, machine learning researchers recognize that understanding and identifying the essential neural processes is much more critical. A related example cited by machine learning researchers is flight: wing flapping and feathers aren’t critical, but an understanding of physics and aerodynamics is essential.

Crum and other neuroscience researchers express the same sentiment. She points out that a more meaningful goal should be to “extract and integrate relevant neural processing strategies when applicable, but also identify where there may be opportunities to be more efficient.”

The goal in technology shouldn’t be to build algorithms that mimic neural function. Rather, it’s to understand neural function. … The brain is basically, in many cases, a Rube Goldberg machine. We’ve got this limited set of evolutionary building blocks that we are able to use to get to a sort of very complex end state. We need to be able to extract when that’s relevant and integrate relevant neural processing strategies when it’s applicable. We also want to be able to identify that there are opportunities to be more efficient and more relevant. I think of it as table manners. You have to know all the rules before you can break them. That’s the big difference between being really cool or being a complete heathen. The same thing kind of exists in this area. How we get to the end state, we may be able to compromise, but we absolutely need to be thinking about what matters in neural function for perception. From my world, where we can’t compromise is on the output. I really feel like we need a lot more work in this area. Continue reading

6 reasons why I like KeystoneML

[A version of this article appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Ben Recht on optimization, compressed sensing, and large-scale machine learning pipelines.

As we put the finishing touches on what promises to be another outstanding Hardcore Data Science Day at Strata + Hadoop World in New York, I sat down with my co-organizer Ben Recht for the the latest episode of the O’Reilly Data Show Podcast. Recht is a UC Berkeley faculty member and member of AMPLab, and his research spans many areas of interest to data scientists including optimization, compressed sensing, statistics, and machine learning.

At the 2014 Strata + Hadoop World in NYC, Recht gave an overview of a nascent AMPLab research initiative into machine learning pipelines. The research team behind the project recently released an alpha version of a new software framework called KeystoneML, which gives developers a chance to test out some of the ideas that Recht outlined in his talk last year. We devoted a portion of this Data Show episode to machine learning pipelines in general, and a discussion of KeystoneML in particular.

Since its release in May, I’ve had a chance to play around with KeystoneML and while it’s quite new, there are several things I already like about it:

KeystoneML opens up new data types

Most data scientists don’t normally play around with images or audio files. KeystoneML ships with easy to use sample pipelines for computer vision and speech. As more data loaders get created, KeystoneML will enable data scientists to leverage many more new data types and tackle new problems. Continue reading

Apache Spark in the Enterprise and in China

Enterprise Adoption

IBM’s announcements at the recent Spark Summit in SF bodes well for enterprise adoption of Spark. Ben Horowitz jokingly referred to IBM’s endorsement as akin to a Rabbi blessing Spark as kosher for use in an enterprise.

I recently sat down with a set of luminaries at the Spark Summit and asked them about how Spark is perceived in enterprises. Below is a selection of responses:

  • Mike Olson, Chairman and co-founder of Cloudera
  • Martin Van Rysk, SVP of Engineering at Datastax
  • Beth Smith, GM of the analytics Platform at IBM

China

As I noted in a recent Radar article, Spark is very popular in China:

Unbeknownst to many, the largest Spark deployments are in China. Tencent publicly discussed their 8,000 node Spark cluster at Strata + Hadoop World in San Jose this past February, and other large Chinese companies such as Alibaba and Baidu have shared lessons from running large Spark deployments.

I recently spoke with a variety of people who’ve been following the rise of Apache Spark in China. Here are some highlights:

  • Michael A. Greene, VP at Intel
  • Reynold Xin, co-founder of Databricks
  • Bing Xiao, Head of Big Data at Huawei

Why data preparation frameworks rely on human-in-the-loop systems

[A version of this article appears on the O’Reilly Radar.]

As I’ve written in previous posts, data preparation and data enrichment are exciting areas for entrepreneurs, investors, and researchers. Startups like Trifacta, Tamr, Paxata, Alteryx, and CrowdFlower continue to innovate and attract enterprise customers. I’ve also noticed that companies — that don’t specialize in these areas — are increasingly eager to highlight data preparation capabilities in their products and services.

During a recent episode of the O’Reilly Data Show Podcast, I spoke with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr. We discussed how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.

Academic database research in data preparation

Given the importance of data integrity, it’s no surprise that the database research community has long been interested in data preparation and data wrangling. Ilyas explained how his work in probabilistic databases led to research projects in data cleaning:

In the database theory community, these problems of handling, dealing with data inconsistency, and consistent query answering have been a celebrated area of research. However, it has been also difficult to communicate these results to industry. And database practitioners, if you like, they were more into the well-structured data and assuming a lot of good properties around this data, [and they were also] more interested in indexing this data, storing it, moving it from one place to another. And now, dealing with this large amount of diverse heterogeneous data with tons of errors, sidled across all business units in the same enterprise became a necessity. You cannot really avoid that anymore. And that triggered a new line of research for pragmatic ways of doing data cleaning and integration. … The acquisition layer in that stack has to deal with large sets of formats and sources. And you will hear about things like adapters and source adapters. And it became a market on its own, how to get access and tap into these sources, because these are kind of the long tail of data.

…The way I came into this subject was also funny because we were talking about the subject called probabilistic databases and how to deal with data uncertainty. And that morphed into trying to find data sets that have uncertainty. And then we were shocked by how dirty the data is and how data cleaning is a task that’s worth looking at.

Continue reading