Data has a shape

Using topology to uncover the shape of your data: An interview with Gurjeet Singh.


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As part of our ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Gurjeet Singh. Singh is CEO and co-founder of Ayasdi, a company that leverages machine intelligence software to automate and accelerate discovery of data insights. Author of numerous patents and publications in top mathematics and computer science journals, Singh has developed key mathematical and machine learning algorithms for topological data analysis.

Key Takeaways

  • The field of topology studies the mapping of one space into another through continuous deformations.
  • Machine learning algorithms produce functional mappings from an input space to an output space and lend themselves to be understood using the formalisms of topology.
  • A topological approach allows you to study data sets without assuming a shape beforehand and to combine various machine learning techniques while maintaining guarantees about the underlying shape of the data.

David Beyer: Let’s get started by talking about your background and how you got to where you are today.

Gurjeet Singh: I am a mathematician and a computer scientist, originally from India. I got my start in the field at Texas Instruments, building integrated software and performing digital design. While at TI, I got to work on a project using clusters of specialized chips called Digital Signal Processors (DSPs) to solve computationally hard math problems.

As an engineer by training, I had a visceral fear of advanced math. I didn’t want to be found out as a fake, so I enrolled in the Computational Math program at Stanford. There, I was able to apply some of my DSP work to solving partial differential equations and demonstrate that a fluid dynamics researcher need not buy a supercomputer anymore; they could just employ a cluster of DSPs to run the system. I then spent some time in mechanical engineering building similar GPU-based partial differential equation solvers for mechanical systems. Finally, I worked in Andrew Ng’s lab at Stanford, building a quadruped robot and programming it to learn to walk by itself. Read more…


Announcing BioCoder issue 8

BioCoder 8: neuroscience, robotics, gene editing, microbiome sequence analysis, and more.

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We are thrilled to announce the eighth issue of BioCoder. This marks two years of diverse, educational, and cutting-edge content, and this issue is no exception. Highlighted in this issue are technologies and tools that span neuroscience, diagnostics, robotics, gene editing, microbiome sequence analysis, and more.

Glen Martin interviewed Tim Marzullo, co-founder of Backyard Brains, to learn more about how their easy-to-use kits, like the RoboRoach, demonstrate how nervous systems work.

Marc DeJohn from Biomeme discusses their smartphone diagnostics technology for on-site gene detection of disease, biothreat targets, and much more.

Daniel Modulevsky, Charles Cuerrier, and Andrew Pelling from Pelling Lab at the University of Ottawa discuss different types of open source biomaterials for regenerative medicine and their use of de-cellularized apple tissue to generate 3D scaffolds for cells. If you follow their tutorial, you can do it, too!

aBioBot, highlighted by co-founder Raghu Machiraju, is a device that uses visual sensing and feedback to perform encodable laboratory tasks. Machiraju argues that “progress in biotechnology will come from the use of open user interfaces and open-specification middleware to drive and operate flexible robotic platforms.” Read more…


Moving toward a zero UI to orchestrate the IoT

The O'Reilly Radar Podcast: Andy Goodman on intangible interfaces, and Cory Doctorow on the DMCA.


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In this week’s Radar Podcast episode, O’Reilly’s Mac Slocum chats with Andy Goodman, group director of Fjord’s Design Strategy. Goodman talks about the shift away from screen-based interfaces to intangible interfaces, what he calls “zero UI.” He also addresses the evolutionary path of embeddables, noting that “we already have machines inside us.”

Here are a few of the highlights:

Sensing technologies are allowing us to distribute our computers around our bodies and around our environments, moving away from monolithic experiences, a single device, to an orchestration of devices all working together with us at the center.

Our visual sense is the most important to us, so taking that away [with zero UI] actually leaves us, in some ways, a bit more vulnerable to things going wrong — we can’t see what is an error state in a haptic experience…it’s possible that we’re setting ourselves a lot of design challenges that we don’t know we have to solve yet.

Read more…


6 reasons why I like KeystoneML

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. Read more…


The race to build ‘‘big data machines’’ in financial investing

Robot wealth managers and approaches will grow and offer alternative ways of investing.


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Editor’s note: This post originally published in Big Data at Mary Ann Liebert, Inc., Publishers, in Volume 3, Issue 2, on June 18, 2015, under the title “Should You Trust Your Money to a Robot?” It is republished here with permission.

Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions. The analysis reveals areas in the investment landscape where machines are already very active and those where machines are likely to make significant inroads in the next few years.

Computers are making more and more decisions for us, and increasingly so in areas that require human judgment. Driverless cars, which seemed like science fiction until recently, are expected to become common in the next 10 years. There is a palpable increase in machine intelligence across the touchpoints of our lives, driven by the proliferation of data feeding into intelligent algorithms capable of learning useful patterns and acting on them. We are living through one of the greatest revolutions in our lifestyles, in which computers are increasingly engaged in our lives and decision-making, to a degree that it has become second nature. Recommendations on Amazon or auto-suggestions on Google are now so routine, we find it strange to encounter interfaces that don’t anticipate what we want. The intelligence revolution is well under way, with or without our conscious approval or consent. We are entering the era of intelligence as a service, with access to building blocks for building powerful new applications. Read more…


Augmenting the human experience: AR, wearable tech, and the IoT

As augmented reality technologies emerge, we must place the focus on serving human needs.

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Otto Lilienthal on August 16, 1894, with his “kleiner Schlagflügelapparat.”

Augmented reality (AR), wearable technology, and the Internet of Things (IoT) are all really about human augmentation. They are coming together to create a new reality that will forever change the way we experience the world. As these technologies emerge, we must place the focus on serving human needs.

The Internet of Things and Humans

Tim O’Reilly suggested the word “Humans” be appended to the term IoT. “This is a powerful way to think about the Internet of Things because it focuses the mind on the human experience of it, not just the things themselves,” wrote O’Reilly. “My point is that when you think about the Internet of Things, you should be thinking about the complex system of interaction between humans and things, and asking yourself how sensors, cloud intelligence, and actuators (which may be other humans for now) make it possible to do things differently.”

I share O’Reilly’s vision for the IoTH and propose we extend this perspective and apply it to the new AR that is emerging: let’s take the focus away from the technology and instead emphasize the human experience.

The definition of AR we have come to understand is a digital layer of information (including images, text, video, and 3D animations) viewed on top of the physical world through a smartphone, tablet, or eyewear. This definition of AR is expanding to include things like wearable technology, sensors, and artificial intelligence (AI) to interpret your surroundings and deliver a contextual experience that is meaningful and unique to you. It’s about a new sensory awareness, deeper intelligence, and heightened interaction with our world and each other. Read more…