FEATURED STORY

Four short links: 26 May 2015

Four short links: 26 May 2015

Keyboard Programming, Oblique Strategies, Engineering Ethics, and Visualisation Gallery

  1. Introduction to Keyboard Programming — what happens when you press a key. (hint: a lot)
  2. Oblique Strategies: Prompts for ProgrammersDo it both ways. Very often doing it both ways is faster than analyzing which is best. Now you also have experimental data instead of just theoretical. Add a toggle if possible. This will let you choose later. Some mistakes are cheaper to make than to avoid.
  3. The Responsibility We Have as Software EngineersWhere’s our Hippocratic Oath, our “First, Do No Harm?” Remember that moment when Google went from “amazing wonderful thing we didn’t have before, which makes our lives so much better” to “another big scary company and holy shit it knows a lot about us!”? That’s coming for our industry and the software engineering profession in particular.
  4. Gallery of Concept Visualisation — plenty I hadn’t seen before.
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How Shazam predicts pop hits

The O'Reilly Radar Podcast: Cait O'Riordan on Shazam's predictive analytics, and Francine Bennett on using data for evil.

Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

record_player_from_1920s_Marcin_Wichary_FlickrIn this week’s Radar Podcast, I chat with Cait O’Riordan, VP of product, music and platforms at Shazam. She talks about the current state of predictive analytics and how Shazam is able to predict the success of a song, often in the first few hours after its release. We also talk about the Internet of Things and how products like the Apple Watch affect Shazam’s product life cycles as well as the behaviors of their users.

Predicting the next pop hit

Shazam has more than 100 million monthly active users, and its users Shazam more than 20 million times per day. This, of course, generates a ton of data that Shazam uses in myriad ways, not the least of which is to predict the success of a song. O’Riordan explained how they approach their user data and how they’re able to accurately predict pop hits (and misses):

What’s interesting from a data perspective is when someone takes their phone out of their pocket, unlocks it, finds the Shazam app, and hits the big blue button, they’re not just saying, “I want to know the name of this song.” They’re saying, “I like this song sufficiently to do that.” There’s an amount of effort there that implies some level of liking. That’s really interesting, because you combine that really interesting intention on the part of the user plus the massive data set, you can cut that in lots and lots of different ways. We use it for lots of different things.

At the most basic level, we’re looking at what songs are going to be popular. We can predict, with a relative amount of accuracy, what will hit the Top 100 Billboard Chart 33 days out, roughly. We can look at that in lots of different territories as well. We can also look and see, in the first few hours of a track, whether a big track is going to go on to be successful. We can look at which particular part of the track is encouraging people to Shazam and what makes a popular hit. We know that, for example, for a big pop hit, you’ve got about 10 seconds to convince somebody to find the Shazam app and press that button. There are lots of different ways that we can look at that data, going right into the details of a particular song, zooming out worldwide, or looking in different territories just due to that big worldwide and very engaged audience.

Read more…

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Cultivating change

Cultivate is O'Reilly's conference committed to training the people who will lead successful teams, now and in the future.

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Attend Cultivate July 20 and 21, in Portland, Oregon. Cultivate is our conference looking at the challenges facing modern management and aiming to train a new generation of business leaders who understand the relationship between corporate culture and corporate prosperity.

Leadership has changed — and in a big way — since the Web started upending the status quo two decades ago. That’s why we’re launching our new Cultivate event; we realized that businesses need new types of leaders, and that O’Reilly is uniquely positioned to help engineers step up to the job.

At the start of the 21st century, Google was in its infancy; Facebook didn’t exist; and Barnes & Noble, not Amazon, was the dominant force in the book industry. As we’ve watched these companies grow, we’ve realized that every business is a software business, and that the factors that made Google, Facebook, and Amazon successful can be applied outside the Web. Every business, from your dentist’s office to Walmart, is critically dependent on software. As Marc Andreessen put it, software is eating the world.

As companies evolve into software businesses, they become more dependent on engineers for leadership. But an engineer’s training rarely includes leadership and management skills. How do you make the transition from technical problems to management problems, which are rarely technical? How do you become an agent for growth and change within your company? And what sorts of growth and change are necessary?

The slogan “every business is a software business” doesn’t explain much, until we think about how software businesses are different. Software can be updated easily. It took software developers the better part of 50 years to realize that, but they have. That kind of rapid iteration is now moving into other products. Read more…

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What today’s fitness technology means for tomorrow’s office

How the IoT could help organizations create a better employee experience.

Contributing Author: Claire Niech

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Attend O’Reilly’s Solid Conference, June 23–25, in San Francisco. Solid is our conference exploring how the collision of software and hardware is fueling the creation of a software-enhanced, networked physical world.

At 5:37 a.m., Nina’s alarm softly begins to buzz and glow. It has calculated her recovery time based on her previous day’s workout and monitored her sleep tracker to identify the best point in her REM cycle to wake her up. After rising, she grabs a healthy breakfast and her PrepPad or Drop connected kitchen scale records the fat, protein, calories, and carbohydrates in her meal.

For athletes like Nina, this kind of technology-enabled tracking is now standard. When Nina hits the gym for her daily routine, she warms up on a treadmill equipped with sensors to help gauge when she is striking at her optimal force. As she practices technique and form, a ‘smart’ surface records the location and duration of each move. Her training regimen is personalized based on this data; ‘instead of working off a generalized idea of what an athlete needs to be successful, [data analysis] has identified the specific abilities that a player requires to excel in a given sport.’ (From Faster, Higher, Stronger, by Mark McClusky)

Professional athletes today increasingly rely on Internet-connected devices and sensors to boost performance. Yet, the potential of such devices — commonly called the “Internet of Things” — extends beyond sports and fitness; as “weekend warriors” begin to bring these technologies mainstream, it is not hard to imagine that similar devices may soon also help us better understand other complex personal pursuits, such as creativity and productivity at work. As Laszlo Bock, who runs Google’s People Operations, notes: “We all have our opinions and case studies, but there is precious little scientific certainty around how to build great work environments, cultivate high-performing teams, maximize productivity, or enhance happiness.”

Today, many organizations tackle these questions with an industrial-organizational approach, diagnosing the issues most relevant to their workforce using tools such as annual surveys and benchmarking. But today’s approach seldom offers insight on “what works” — ways to track, teach, or reinforce new behaviors, or to see if specific initiatives are achieving the desired effect. Solutions to complex challenges like productivity or satisfaction often vary by organization, and demand better, real-time measurement and testing to enable experimentation.

By weaving together our physical and digital environments, sensors could help organizations analyze how factors like mood, focus, social engagement, or movement contribute to the employee experience — and even help replicate or enhance this experience. Consider how this new technology could impact how companies do work, assess outcomes, and enable employees to thrive. Read more…

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Data science makes an impact on Wall Street

The O'Reilly Data Show Podcast: Gary Kazantsev on how big data and data science are making a difference in finance.

Charging_Bull_Sam_valadi_FlickrHaving started my career in industry, working on problems in finance, I’ve always appreciated how challenging it is to build consistently profitable systems in this extremely competitive domain. When I served as quant at a hedge fund in the late 1990s and early 2000s, I worked primarily with price data (time-series). I quickly found that it was difficult to find and sustain profitable trading strategies that leveraged data sources that everyone else in the industry examined exhaustively. In the early-to-mid 2000s the hedge fund industry began incorporating many more data sources, and today you’re likely to find many finance industry professionals at big data and data science events like Strata + Hadoop World.

During the latest episode of the O’Reilly Data Show Podcast, I had a great conversation with one of the leading data scientists in finance: Gary Kazantsev runs the R&D Machine Learning group at Bloomberg LP. As a former quant, I wanted to know the types of problems Kazantsev and his group work on, and the tools and techniques they’ve found useful. We also talked about data science, data engineering, and recruiting data professionals for Wall Street. Read more…

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Tying software and hardware together through art

The O'Reilly Solid Podcast: Andy Cavatorta and Jamie Zigelbaum on art that combines physical and digital.

One of the theses behind our Solid Conference is that the stacks — the ranges of knowledge that technologists need to understand — are expanding so that the formerly separate disciplines of hardware and software are merging. Specific expertise is still critical, but the future lies in systems that integrate physical and virtual, and developing those effectively requires the ability to understand both sides at some basic level.

Installation art is a great place to look for those seamless integrations, and we’re excited to feature a couple of interesting installations at Solid. Our latest episode of the Solid Podcast takes us to Flatbush Avenue in Brooklyn, home to a collective of designers and engineers called Dark Matter Manufacturing, where David Cranor and I spoke with Andy Cavatorta and Jamie Zigelbaum. Cavatorta and Zigelbaum both create installations; Cavatorta works with sound and robotics, and Zigelbaum’s projects explore communication and interaction.

Cavatorta’s Dervishes installation will appear at O’Reilly Solid, June 23-25. He will also speak on “Music, machines, and meaning: What art teaches us about robotics and networks.” Read more…

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