"Innovation" entries

Four short links: 24 June 2015

Four short links: 24 June 2015

Big Data Architecture, Leaving the UK, GPU-powered Queries, and Gongkai in the West

  1. 100 Big Data Architecture Papers (Anil Madan) — you’ll either find them fascinating essential reading … or a stellar cure for insomnia.
  2. Software Companies Leaving UK Because of Government’s Surveillance Plans (Ars Technica) — to Amsterdam, to NYC, and to TBD.
  3. MapD: Massive Throughput Database Queries with LLVM and GPUs (nvidia) — The most powerful GPU currently available is the NVIDIA Tesla K80 Accelerator, with up to 8.74 teraflops of compute performance and nearly 500 GB/sec of memory bandwidth. By supporting up to eight of these cards per server, we see orders-of-magnitude better performance on standard data analytics tasks, enabling a user to visually filter and aggregate billions of rows in tens of milliseconds, all without indexing.
  4. Why It’s Often Easier to Innovate in China than the US (Bunnie Huang) — We did some research into the legal frameworks and challenges around absorbing gongkai IP into the Western ecosystem, and we believe we’ve found a path to repatriate some of the IP from gongkai into proper open source.
Comment

Signals from Strata + Hadoop World 2015 in London

Key insights from Strata + Hadoop World 2015 in London.

People from across the data world came together this week for Strata + Hadoop World 2015 in London. Below we’ve assembled notable keynotes, interviews, and insights from the event.

Shazam already knows the next big hit

“With relative accuracy, we can predict 33 days out what song will go to No. 1 on the Billboard charts in the U.S.,” says Cait O’Riordan, VP of product for music and platforms at Shazam. O’Riordan walks through the data points and trendlines — including the “shape of a pop song” — that give Shazam hints about hits.

Read more…

Comment: 1

Unleash innovation in the enterprise

Winning organizations continually experiment.

I constantly hear how enterprises are poor at innovation, bad at product development and unresponsive to business change. So it begs the question, why do so many organizations get it wrong? And what are the key factors to consider when trying to innovate in large organizations?

Typically the factors constraining innovation are conflicting business goals, competing priorities, localized performance measures and success criteria. While these have traditionally been the tools of management — to control workforce behavior and output — in highly competitive and quickly evolving business environments they also have had the adverse effects of killing creativity, responsiveness and ingenuity.

So what are the components needed to unleash innovation in enterprise?

Read more…

Comment

Avoid disruption through exploration

Support experimentation and continuously evaluate to stay ahead.

Atomic_Laboratory_Experiment_on_Atomic_Materials_-_GPN-2000-000663_crop

Businesses have always come and gone, but these days it seems that companies can fall from market dominance to bankruptcy in the blink of an eye. Kodak, Blockbuster and HMV are just a few recent victims of the rapid market disruption that defines the current era.

Where did these once iconic companies go wrong? To my mind, they forgot to keep challenging their assumptions about what business they were actually in.

Businesses have two options when they plan for the road ahead: they can put all their eggs into one basket, and risk losing everything if that basket has a hole in the bottom, or they can make a number of small bets, accepting that some will fail while others succeed.

Taking the latter approach, and making many small bets on innovation, transforms the boardroom into a roulette table. Unlike a punter in a casino, however, businesses cannot afford to stop making bets.

Business models are transient and prone to disruption by changes in markets and the external competitive environment, advances in design and technology, and wider social and economic change. Organizations that misjudge their purpose, or cannot sense and then adapt to these changes, will perish.

The sad truth is that too many established organizations focus most of their time and resources on executing and optimizing their existing business models in order to maximize profits. They forget to experiment and explore new ideas for customer needs of tomorrow.

Read more…

Comment

Innovation requires a new mind-set: The O’Reilly Radar Podcast

Tim O'Reilly and Carl Bass discuss the future of making things, and Astro Teller on Google X's approach to solving big problems.

Editor’s note: you can subscribe to the O’Reilly Radar Podcast through iTunes, SoundCloud, or directly through our podcast’s RSS feed.

I recently lamented the lag in innovation in relation to the speed of technological advancements — do we really need a connected toaster that will sell itself if neglected? Subsequently, I had a conversation with Josh Clark that made me rethink that position; Clark pointed out that play is an important aspect of innovation, and that such whimsical creations as drum pants could ultimately lead to more profound innovations.

In the first segment of this podcast episode, Tim O’Reilly and Autodesk CEO Carl Bass have a wide-ranging discussion about the future of making things. Bass notes that innovation tends to start by “looking at the rear window”:

“The first naïve response is to take a new technology and do the old thing with it. It takes a while until you can start reimagining things…the first thing that you need is this new tool set in software, hardware, and materials, but the more important thing — and the more difficult thing, obviously — is a new mind-set. How are you going to think about this problem differently? How are you going to reimagine what you can do? That’s the exciting part.”

Read more…

Comments: 3
Four short links: 4 August 2014

Four short links: 4 August 2014

Web Spreadsheet, Correlated Novelty, A/B Ethics, and Replicated Data Structures

  1. EtherCalcopen source web-based spreadsheet.
  2. Dynamics of Correlated Novelties (Nature) — paper on “the adjacent possible”. Here we propose a simple mathematical model that mimics the process of exploring a physical, biological, or conceptual space that enlarges whenever a novelty occurs. The model, a generalization of Polya’s urn, predicts statistical laws for the rate at which novelties happen (Heaps’ law) and for the probability distribution on the space explored (Zipf’s law), as well as signatures of the process by which one novelty sets the stage for another. (via Steven Strogatz)
  3. On The Media Interview with OKCupid CEO — relevant to the debate on ethics of A/B tests. I preferred this to Tim Carmody’s rant.
  4. CRDTs as Alternative to APIswhen using CRDTs to tie your system together, you don’t need to resort to using impoverished representations that simply never come anywhere near the representational power of the data structures you use in your programs at runtime. See also this paper on Convergent and Commutative Replicated Data Types.
Comment
Four short links: 23 April 2014

Four short links: 23 April 2014

Mobile UX, Ideation Tools, Causal Consistency, and Intellectual Ventures Patent Fail

  1. Samsung UX (Scribd) — little shop of self-catalogued UX horrors, courtesy discovery in a lawsuit. Dated (Android G1 as competition) but rewarding to see there are signs of self-awareness in the companies that inflict unusability on the world.
  2. Tools for Ideation and Problem Solving (Dan Lockton) — comprehensive and analytical take on different systems for ideas and solutions.
  3. Don’t Settle for Eventual Consistency (ACM) — proposes “causal consistency”, prototyped in COPS and Eiger from Princeton.
  4. Intellectual Ventures Loses Patent Case (Ars Technica) — The Capital One case ended last Wednesday, when a Virginia federal judge threw out the two IV patents that remained in the case. It’s the first IV patent case seen through to a judgment, and it ended in a total loss for the patent-holding giant: both patents were invalidated, one on multiple grounds.
Comment
Four short links: 17 April 2014

Four short links: 17 April 2014

Foresight and Innovation, Artificial Intelligence, Consumer IoT, and Gender Disparity


  1. Playbook for Strategic Foresight & Innovation — MANY pages of framework and exercises. Good for what it is, but also as a model for how to disseminate your ideas and frame for the world to consume.
  2. Why I’m a Crabby Patty About AI and Cognitive Science (Fredrik Deboer) — huzzah! the current lack of progress in artificial intelligence is not a problem of insufficient processing power. Talking about progress in artificial intelligence by talking about increasing processor power is simply a non sequitur. If we knew the problems to be solved by more powerful processors, we’d already have solved some of the central questions!
  3. Four Types of Consumer Internet of Things Things (BERG London) — nice frame for the different needs of the different types of products and services.
  4. We Can Do Bettera visualisation of the gender disparity in engineering teams in the tech industry.
Comment

Disposable architecture?

Technology is now outpacing innovation, fostering a culture of disposability.

I’ve noticed a number of faint signals recently pointing to a general shift in the speed of technology and the repercussions it’s having on the products we’re seeing come to market. This recent Tweet from Tom Scott got me really thinking about it:

Scott’s comment brought me back to a recent conversation I had with Princeton architecture student Alastair Stokes. I’d asked Stokes whether the technology challenges of designing a building to last 100+ years are more difficult today than they were in, say, 1900 — or if it’s as difficult, just different. He said the challenges might be more difficult today, but regardless, maybe technology is changing the solution: we shouldn’t try to design buildings today to last 100 years, but design them so they’ll last for, say, 20 years and then be replaced. Read more…

Comments: 3
Four short links: 10 March 2014

Four short links: 10 March 2014

Wolfram Language, Historic Innovation, SF Culture Wars, and Privacy's Death

  1. Wolfram Language — a broad attempt to integrate types, operations, and databases along with deployment, parallelism, and real-time I/O. The demo video is impressive, not just in execution but in ambition. Healthy skepticism still necessary.
  2. Maury, Innovation, and Change (Cory Ondrejka) — amazing historical story of open data, analysis, visualisation, and change. In the mid-1800’s, over the course of 15 years, a disabled Lieutenant changed the US Navy and the world. He did it by finding space to maneuver (as a trouble maker exiled to the Navy Depot), demonstrating value with his early publications, and creating a massive network effect by establishing the Naval Observatory as the clearing house for Navigational data. 150 years before Web 2.0, he built a valuable service around common APIs and aggregated data by distributing it freely to the people who needed it.
  3. Commuter Shuttle and 21-Hayes EB Bus Stop Observations (Vimeo) — timelapse of 6:15AM to 9:15AM at an SF bus stop Worth watching if you’re outside SF and wondering what they’re talking about when the locals rage against SF becoming a bedroom community for Valley workers.
  4. A Day of Speaking Truth to Power (Quinn Norton) — It was a room that had written off privacy as an archaic structure. I tried to push back, not only by pointing out this was the opening days of networked life, and so custom hadn’t caught up yet, but also by recommending danah boyd’s new book It’s Complicated repeatedly. To claim “people trade privacy for free email therefore privacy is dead” is like 1800s sweatshop owners claiming “people trade long hours in unpleasant conditions for miserable pay therefore human rights are dead”. Report of privacy’s death are greatly exaggerated.
Comment