- Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays — we created a hand-held and cost-effective cellphone-based colorimetric microplate reader that implements a routine hospital test used to identify HIV and other conditions. (via RtoZ)
- Amazon Launchpad — a showcase for new hardware startups, who might well be worried about Amazon’s “watch what sells and sell a generic version of it” business model.
- Challenges to Adopting Stronger Consistency at Scale (PDF) — It is not obvious that a system that trades stronger consistency for increased latency or reduced availability would be a net benefit to people using Facebook, especially when compared against a weakly consistent system that resolves many inconsistencies with ad hoc mechanisms.
- The White House’s Alpha Geeks — Megan Smith for President. I realize now there’s two things we techies should do — one is go where there are lots of us, like MIT or Silicon Valley or whatever, because you can move really fast and do extraordinary things. The other is, go where you’re rare. … It’s almost like you’re a frog in boiling water; you don’t really realize how un-diverse it is until you’re in a normal diverse American innovative community like the President’s team. And then you go back and you’re like, wow. You feel, “Man, this industry is so awesome and yet we’re missing all of this talent.”
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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. Read more…
The O'Reilly Radar Podcast: Robert Brunner on IoT pitfalls, Ammunition, and the movement toward automation.
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For this week’s Radar Podcast, I had the opportunity to sit down with Robert Brunner, founder of the Ammunition design studio. Brunner talked about how design can help mitigate IoT pitfalls, what drove him to found Ammunition, and why he’s fascinated with design’s role in the movement toward automation.
Here are a few of the highlights from our chat:
One of the biggest pitfalls I’m seeing in how companies are approaching the Internet of Things, especially in the consumer market, is, literally, not paying attention to people — how people understand products and how they interact with them and what they mean to them.
It was this broader experience and understanding of what [a product] is and what it does in people’s lives, and what it means to them — that’s experienced not just through the thing, but how they learn about it, how they buy it, what happens when they open up the box, what happens when they use the product, what happens when the product breaks; all these things add up to how you feel about it and, ultimately, how you relate to a company. That was the foundation of [Ammunition].
Ultimately, I define design as the purposeful creation of things.
The O’Reilly Design Podcast: Aaron Irizarry on getting and keeping a seat at the table.
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Welcome to the inaugural episode of our newly launched O’Reilly Design Podcast. In this podcast episode, I chat with Aaron Irizarry. Irizarry is the director of UX for product design at Nasdaq, co-author of “Discussing Design” with Adam Connor, and a member of the program committee for O’Reilly’s Design Conference.
Design at Nasdaq: A growing team
I first noted Nasdaq’s commitment to design when talking to Irizarry about his book and the design conference hosted by Nasdaq that Irizarry helps develop:
It’s interesting to see an organization that didn’t have a product design team as of, what — 2011, I believe. To see the need for that, bring someone in, hire them to establish a team (which is my boss, Chris), and then see just the transition and the growth within the company, and how they embraced product design. We had to work a lot, and really educate and pitch in the beginning, explain to them the value of certain aspects of the job we were doing, whether that was research, usability, testing, why we were wanting to do more design of browser and rapid prototyping, and things like that.
We believe we’re helping structure and build, and I think we still have work to do as a design-led organization. We recently did our Pro/Design conference in New York. Our opening speaker was the president of Nasdaq, and to hear her reference the design team’s research, and to be in marketing meetings, and discussing the personas that we created, and to hear the president of Nasdaq speak about these kind of artifacts and items that we feel are crucial to design and the design process, it was a mark for us like, ‘We’re really starting to make a mark here. We’re starting to show the value of what these things are,’ not just because we want design, but we believe that this approach to design is going to be really good for the product, and in the end, good for the business. Read more…
We need to provide people with proper access, interaction, and use of technology so that it serves their needs.
Download a free copy of “The New Design Fundamentals,” a curated collection of chapters from the O’Reilly Design library. Editor’s note: this post is an excerpt from “Tragic Design,” by Jonathan Shariat, which is included in the collection.I love people.
I love technology and I love design, and I love the power they have to help people.
That is why when I learned they had cost a young girl her life, it hurt me deeply and I couldn’t stop thinking about it for weeks.
My wife, a nursing student, was sharing with her teacher how passionate I am about technology in health care. Her teacher rebutted, saying she thought we needed less technology in health care and shared a story that caused her to feel so strongly that way.
This is the story that inspired me to write this book and I would like to share it with you.
Jenny, as we will call her to protect the patient’s identity, was a young girl who was diagnosed with cancer. She was in and out of the hospital for a number of years and was finally discharged. A while later she relapsed and returned to be given a very strong chemo treating medicine. This medicine is so strong and so toxic that it requires pre-hydration and post-hydration for three days with I.V. fluid.
However, after the medicine was administered, the nurses who were attending to the charting software, entering in everything required of them and making the appropriate orders, missed a very critical piece of information: Jenny was supposed to be given three days of I.V. hydration post treatment. The experienced nurses made this critical error because they were too distracted trying to figure out the software they were using.
When the morning nurse came in the next day, they saw that Jenny had died of toxicity and dehydration. All because these very seasoned nurses were preoccupied trying to figure out this interface (figure 1-1). Read more…
Using topology to uncover the shape of your data: An interview with Gurjeet Singh.
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.
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.
- 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…