Three New Perspectives on Whether Artificial Intelligence Threatens or Benefits the World

"Gritty Refraction", Image by Mark Oakley

“Gritty Refraction”, Image by Mark Oakley

As the velocity of the rate of change in today’s technology steadily continues to increase, one of the contributing factors behind this acceleration the rise of artificial intelligence (AI). “Smart” attributes and functionalities are being baked into a multitude of systems that are affecting our lives in many visible and, at other times, transparent ways. Just to name one well-known example of an AI-enabled app is Siri, the voice recognition system in the iPhone. Two recent Subway Fold posts have also examined AI’s applications in law (1) and music (2).

However, notwithstanding all of the technological, social and commercial benefits produced by AI, a widespread reluctance, if not fear, of its capabilities to produce negative effects still persists. Will the future produce consequences resembling those in the Terminator or Matrix movie franchises, the “singularity” predicted by Ray Kurzweil where machine intelligence will eventually surpass human intelligence, or perhaps other more benign and productive outcomes?

During the past two weeks, three articles have appeared where their authors have expressed more upbeat outlooks about AI’s potential. They believe that smarter systems are not going to become the world’s new overlords (3) and, moreover, there is a long way to go before computers will ever achieve human-level intelligence or even consciousness. I highly recommend reading them all in their entirety for their rich content, insights and engaging prose.

I will sum up, annotate and comment upon some of the key points in these pieces, which have quite a bit in common in their optimism, analyses and forecasts.

First is a reassuring column by Dr. Gary Marcus, a university professor and corporate CEO, entitled Artificial Intelligence Isn’t a Threat—Yet, that appeared in the December 12, 2014 edition of The Wall Street Journal. While acknowledging the advances in machine intelligence, he still believes that computers today are nowhere near “anything that looks remotely like human intelligence”. However,  computers do not necessarily need to be “superintelligent” to do significant harm such as wild swings in the equities markets resulting from programming errors.(4)

He is not calling for an end to further research and development in AI. Rather, he urges proceeding with caution with safeguards carefully in place focusing upon on the apps access to other networked systems, in areas such as, but not limited to, medicine and autos. Still, the design, implementation and regulation of such “oversight” has yet to be worked out.

Dr. Marcus believes that we might now be overly concerned about any real threats from AI while still acknowledging potential threats from it. He poses questions about levels of transparency and technologies that assess whether AI programs are functioning as intended. Essentially, a form of “infrastructure” should be  in place to evaluate and “control the results” if needed.

Second, is an article enumerating five key reasons why the AI apocalypse is not nearly at hand right now. It is aptly entitled Will Artificial Intelligence Destroy Humanity? Here are Reasons Not to Worry, by Timothy B. Lee, which was posted on Vox.com on December 19, 2014. The writer asserts that the fears and dangers of AI are far overstated based on his research and interviews with some AI experts. To sum up these factors:

  • Actual “intelligence” is dependent on real world experience such that massive computing power alone will not produce comparable capabilities in machines. The example cited here is studying a foreign language well enough to pass as a native speaker. This involves both book learning and actually speaking with locals in order to include social elements and slang. A computer does not and never will have these experiences nor can they simulate them.
  • Computers, by their very nature, must reply on humans for maintenance, materials, repairs and ultimately, replacement. The current state of robotics development is unable to handle these responsibilities. Quite simply, machines need us and will continue to do so for a long time.
  • Creating a computerized equivalent of a real human’s brain is very tough and remains beyond the reach of today’s circuitry and programming.  Living neurons are indeed quite different in their behaviors and responses than digital devices.  The author cites the modeling of weather simulations as one where progress has been relatively small despite the huge increases in available processing capacity. Moreover, simulating brain activity in the an effort to generate a form of intelligence is relatively far more difficult than modeling weather systems.(5)
  • Relationships, more than intelligence, are needed to acquire power in the real world. Looking at the achievements of recent US presidents, the author states that they gained their achievements by virtue of their networks, personalities and skills at offering rewards and penalties. Thus, machines assist in attaining great technological breakthroughs, but only governments and companies can assemble to capital and resources to implement great projects. Taking this logic further, machines could never take over the world because they utterly lack the capability to work with the large numbers of people needed to even attempt this. (Take that, SkyNet.)
  • Intelligence will become less valuable as its supply increases according to the laws of supply and demand. As the pricing of computing continues to fall, their technological capabilities continues to rise. As the author interprets these market forces, the availability of “super-intelligent computers” will become commoditized and, in turn, produce even more intelligent machines where pricing is competitive. (6)

The third article presents a likewise sanguine view on the future of AI entitled Apocalypse or Golden Age: What Machine Intelligence Will Do to Us, by Patrick Ehlen, was posted on VentureBeat.com on December 23, 2014. He drew his research from a range of leaders, projects and studies to arrive at similar conclusions that the end of the world as we know it is not at hand because of AI. This piece overlaps with the others on a number of key points. It provides the following additional information and ideas:

  • Well regarded university researchers and tech giants such as Google are pursuing extensive and costly AI research and development programs in conjunction with their ongoing work into such areas as robotics, machine learning, and modeling simple connectomes (see fn.5 below).
  • Unintended bad consequence of well-intentioned research are almost always inevitable. Nonetheless, experts believe that the rate of advancement in this field will continue to accelerate and may well have significant impacts upon the world during the next 20 years.
  • On August 6, 2014, the Pew Internet Research Project published a comprehensive report that was directly on point entitled AI, Robotics, and the Future of Jobs by Aaron Smith and Janna Anderson. This was compiled based on surveys of nearly 1,900 AI experts. To greatly oversimplify the results, while there was largely a consensus view on the progress in this field and ever-increasing integration of AI into numerous areas, there was also a significant split of opinion as the economic,  employment and educational effects of AI in conjunction with robotics. (I highly recommend taking some time to read through this very enlightening report because of its wealth of insights and diversity of perspectives.)
  • Today we are experiencing a “perfect storm” where AI’s progress is further being propelled by the forces of computing power and big data. As a result, we can expect “to create new services that will redefine our expectations”. (7)
  • Certain sectors of our economy will realize greater benefits from the surge in AI than others.(8) This, too, will be likely to cause displacements and realignments in employment in these areas.
  • Changes to relevant social and public policies will be needed in order to successfully adapt to AI-driven effects upon the economy. (This is similar to Dr. Marcus’s views, above, that news forms of safeguards and infrastructure will become necessary.)

I believe that authors Marcus, Lee and Ehlen have all made persuasive cases that AI will continue to produce remarkable new goods, services and markets without any world threatening consequences. Yet they all alert their readers about the unintended and unforeseeable economic and social impacts that likely await us further down the road. My own follow up questions are as follows:

  • Who should take the lead in coordinating the monitoring of these pending changes? Whom should they report to and what, if any, regulatory powers should they have?
  • Will any resulting positive or negative changes attributable to AI be global if and when they manifest themselves, or will they be unevenly distributed in among only certain nations, cities, marketplaces, populations and so on?
  • Is a “negative” impact of AI only in the eye of the beholder? That is, what metrics and analytics exist or need to be developed in order to assess the magnitudes of plus or minus effects? Could such standards be truly objective in their determinations?
  • Assuming that AI development and investment continues to race ahead, will this lead to a possible market/investment bubble or, alternatively, some form of AI Industrial Complex?
  • So, is everyone looking forward to the July 2015 release of Terminator Genisys?

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1.  See Possible Futures for Artificial Intelligence in Law Practice posted on September 1, 2014.

2.  See Spotify Enhances Playlist Recommendations Processing with “Deep Learning” Technology posted  on August 14, 2014.

3.  The origin of the popular “I, for one, welcome our new robot overlords” meme originated in the Season 5 episode 15 of The Simpsons entitled Deep Space Homer. (My favorite scene in this ep is where the – – D’oh! – – potato chips are floating all around the spaceship.)

4.   In Flash Boys (W.W. Norton & Company, 2014), renowned author Michael Lewis did an excellent job of reporting on high-speed trading and the ongoing efforts  to reform it. Included is coverage of the “flash crash” in 2010 when errant program trading caused a temporary steep decline in the stock market.

 5For an absolutely fascinating deep and wide analysis of current and future projects to map out all of the billions of connections among the neurons in the human brain, I suggest reading Connectome: How the Brain’s Wiring Makes Us Who We Are (Houghton Mifflin, 2012), by Sebastian Seung.  See also a most interesting column about the work of Dr. Seung and others by James Gorman in the November 10, 2014 edition of The New York Times entitled Learning How Little We Know About the Brain. (For the sake of all humanity, let’s hope these scientists don’t decide to use Homer J. Simpson, at fn.3 above, as a test subject for their work.)

6.  This factor is also closely related to the effects of Moore’s Law which states that the number of transistors that can be packed onto a chip doubles almost doubles in two years (later revised to 18 months). This was originally conceived by Gordon E. Moore, a legendary computer scientists and one of the founders of Intel. This principal has held up for nearly fifty years since it was first published.

7.  This technological convergence is fully and enthusiastically  explored in an excellent article by Kevin Kelly entitled The Three Breakthroughs That Have Finally Unleashed AI on the World in the November 2014 issue of WIRED.

8This seems like a perfect opportunity to invoke the often quoted maxim by master sci-fi and speculative fiction author William Gibson that “The future is already here – it’s just not very evenly distributed.”

Updates on Recent Posts Re: Music’s Big Data, Deep Learning, VR Movies, Regular Movies’ Effects on Our Brains, Storytelling and, of Course, Zombies

This week has seen the publication of an exciting series of news stories and commentaries that provide a very timely opportunity to update six recent Subway Fold posts. The common thread running through the original posts and these new pieces is the highly inventive mixing, mutating and monetizing of pop culture and science. Please put on your virtual 3-D glasses let’s see what’s out there.

The December 10, 2014 Subway Fold post entitled Is Big Data Calling and Calculating the Tune in Today’s Global Music Market? explored the apps, companies and trends that have become the key drivers in the current global music business. Adding to the big data strategies and implementations for three more major music companies and their rosters of artists was a very informative report in the December 15, 2014 edition of The Wall Street Journal by Hannah Karp entitled Music Business Plays to Big Data’s Beat. (A subscription for the full text required a subscription to WSJonline.com, but the story also appeared in full on Nasdaq.com clickable here.) As described in detail in this report, Universal Music, Warner Music, and Sony Music have all created sophisticated systems to parse numerous data sources and apply customized analytics for planning and executing marketing campaigns.

Next for an alternative and somewhat retro approach, a veteran music retailer named Sal Nunziato wrote a piece on the Op Ed page of The New York Times on the very same day entitled Elegy for the ‘Suits’. He blamed the Internet more than the music labels for the current state of music where “anyone with a computer, a kazoo and an untuned guitar” can release their music  online regardless of its quality. Thus, the ‘suits’ he nostalgically misses were the music company execs who exerted  more controlled upon the quantity and quality of music available to the public.

Likewise covering the tuning up of another major force in today’s online music streaming industry was an August 14, 2014 Subway Fold post entitled Spotify Enhances Playlist Recommendations Processing with “Deep Learning” Technology. This summarized a report about how deep learning technology was being successfully applied to improve the accuracy and responsiveness of Spotify’s recommendation engine. Presenting an even stronger case that you-ain’t-seen-nothing-yet in this field was an engaging analysis of some still largely unseen developments in deep learning posted on December 15, 2014, on Gigaom.com entitled What We Read About Deep Learning is Just the Tip of the Iceberg by Derrick Harris. These include experimental systems being tested by the likes of Google, Facebook and Microsoft. As well, there were a series of intriguing presentations and demos at the recent Neural Information Processing Systems conference held in Montreal. As detailed here with a wealth of supporting links, many of these advanced systems and methods are expected to gain more press and publicity in 2015.

Returning to the here and now at end of 2014, the current release of the movie adaptation of the novel Wild by Cheryl Strayed (Knopf, 2011), has been further formatted into 3-minute supplemental virtual reality movie as reported in the December 15, 2014 edition of The New York Times by Michael Cieply in an article entitled Virtual Reality ‘Wild’ Trek. This fits right in with the developments covered in the December 10, 2014 Subway Fold post entitled A Full Slate of Virtual Reality Movies and Experiences Scheduled at the 2015 Sundance Film Festival as this short film is also scheduled to be presented at the 2015 Sundance festival. Using Oculus and Samsung VR technology, this is an immersive meeting with the lead character, played by actress Reese Witherspoon, while she is hiking in the wilderness. She is quoted as being very pleased with the final results of this VR production.

The next set of analyses and enhancements to our cinematic experience, continuing right along with the September 3, 2014 Subway Fold post entitled Applying MRI Technology to Determine the Effects of Movies and Music on Our Brains, concerns a newly published book that explains the science of how movies affect our brains entitled Flicker: Your Brain on Movies (Oxford University Press, 2014), by Dr. Jeffrey Zacks. The author was interviewed during a fascinating segment of the December 18, 2014 broadcast of The Brian Lehrer Show on WYNC radio. Among other things, he spoke about why audiences cry during movies (even when the films are not very good), sometimes root for the villain, and move to duck out of the way when an object on the screen seems to be coming right at them such as the giant bolder rolling after Indiana Jones at the start of Raiders of the Lost Ark. Much of this is intentionally done by the filmmakers to manipulate audiences into heightened emotional responses to key events as they unfold on the big screen.

Of course, all movie making involves the art and science of storytelling skills as discussed in the November 4, 2014 Subway Fold post entitled Say, Did You Hear the Story About the Science and Benefits of Being an Effective Storyteller?. In a very practical and insightful article in the December 12, 2014 edition of The New York Times by Alina Tugend entitled Storytelling Your Way to a Better Job or a Stronger Start-Up there are some helpful applications for today’s marketplace. As concisely stated in this piece “You need to have a good story.” It describes in detail how there are now consultants, charging meaningful fees, with new approaches and techniques who assist people in improving their skills in order to become more persuasive storytellers. Among others interviewed for this story was Dr. Paul J. Zak, who wrote the recent article on The Harvard Business Review Blog which was the basis for the November 4th Subway Fold post. It concludes with five helpful pointers to spin a compelling yarn for your listeners.

Finally, the best story told on TV during the 2014 season was – – in a fictional world where brains take on an entirely different significance – –  The Walking Dead on AMC in terms of the extraordinary number of tweets about ongoing adventures Sheriff Rick and the Grimes Gang. This was covered on Nielsen.com on December 15, 2014 in a post entitled Tops of 2014: Social TV.  TWD averaged twice as many tweets as its next competitor in the ongoing series category. This follows up directly with the July 31, 2014 Subway Fold post entitled New Analytical Twitter Traffic Report on US TV Shows During the 2013 – 2014 Season.  As I read scores of TWD tweets on the mid-season finale myself, everyone will miss you, Beth.

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As a major fan of TWD, I would like to take the opportunity add my own brief review about the tragic events in Episode 5.8:

I think that in the end, Beth was a form of avatar for the entire show. She traveled many miles from lying on her bed in Season 2 completely unable to function and progressing to Season 5 as a realist concerning herself and the group’s survival. Rather than resigning herself to be held a captive ward in the hospital, she was determined to escape no matter what and was so proud of helping Jonah to escape.

She awakened and arose to be a survivor and a committed member of the Grimes Gang, just as everyone else has done during the past five years. That is, Beth’s journey reflects the entire group’s journey. She, and the Grimes Gang, up to this point have survived all of the threats they faced and endured all of the horrors they have seen. They will all survive but this death with have more serious repercussions than perhaps any other death up until this point. Maggie, Daryl, Rick, Carol and Carl, the core of the GG, will not soon recover from this.

What I still do not understand is why, given that she was finally free in the hospital’s hallway, did she jeopardize her life by going after the lead officer with a scissors. It seemed to be somewhat at odds with Beth’s character as someone who had survived until now on her own determination and close bond with the group. She had nothing to gain by such a reckless act in the middle of a very volatile situation. Was it a sacrifice to save Jonah? Did she realize that the cop was holding a gun at that point? Was she just overtaken by the motivation that desperate times sometimes call for desperate measures?

Consider, too, that she was Herschel’s daughter and her character reflected what she had learned from him: 1. Both learned to see things differently and adapted when the circumstances changed. 2. Both faced sacrifices and danger with great dignity. (Recall Herschel’s acknowledging grin towards Rick right before the Governor murdered the elder of the survivors, and then Beth’s defiant grin when she saw that Jonah had escaped.) 3. Both were resilient insofar as Herschel adapting to the loss of his leg and Beth recovering from her father’s murder. 4. Both sought to comfort others as Herschel stayed with the flu patients and Beth finally drew Daryl out about his terrible family life. Recall also, the three very effective times during her history on the show when Beth’s singing gave great comfort to the others. Indeed, she was a saintly figure but as this story arc wore on, her demise seemed to be foretold.

TWD remains, for me, an absolutely brilliant show in terms of its characters, narrative and presentation.

 

 

 

 

Is Big Data Calling and Calculating the Tune in Today’s Global Music Market?

music-159870_1280-1The extraordinary degree to which big data apps and analytical services are now affecting the marketing, economics, talent development and the popularity of new tunes, has just been thoroughly and expertly explored in an article in the November 2014 issue of The Atlantic entitled The Shazam Effect, by Derek Thompson. This report covers this phenomenon across a multitude of musical genres and commercial venues. I highly recommend checking out this piece in its entirety for a true sense of this ongoing revolution in terms of the leading participants and the fascinating issues concerning business and creativity.

The following is my own summary, annotations and commentary upon just some of the key – – forgive me – – players, market data and open issues worth, well, noting.

I.  Today’s Key Music Business Data Players:

  • Shazam on its surface is an app that helps users to identify a particular song or melody. To date, it have been downloaded half a billion times and is searched 20 million times each day. It can identify emerging songs with breakout potential months in advance. While users enjoy its ability to readily identify a song, the music industry engage it as and early radar array. As well, it assists in identify unknown performers for talent scouts and agents.
  • Pandora and Spotify data sets are used by concert promoters and performers to shape touring venues and set lists. (X-ref to this August 14, 2014 Subway Fold post entitled Spotify Enhances Playlist Recommendations Processing with “Deep Learning” Technology.) One of Pandor’s executives, Eric Bieschke,  is quoted that his online service is not driven by a singular algorithm, but rather “a meta-algorithm that directs all of the other algorithms” to enable users to select songs and artists from the vast  troves of music across the Web.
  • Next Big Sound is a dedicated music analytics firm that gathers, blends and assesses relevant data streams from Spotify, Instagram and other online sources. In turn it sifts through this to identify 100 possible music stars to emerge during the next year. They are currently achieving a success rate of twenty percent. The company also offers a subscription based customizable search tool called “Find”  that will gather and assess selected data flows from Twitter, Facebook and other social platforms. They have found performers’ Wikipedia pages to be valuable predictors.
  • iHeartMedia (previously known as Clear Channel¹) uses Shazam to gauge the virality of new songs and Nielsen Audio deployment of tech called Portable People Meters to track individuals’ radio listening, (X-ref to this July 31, 2014 Subway Fold post about Nielsen’s data and analytics work entitled New Analytical Twitter Traffic Report on US TV Shows During the 2013 – 2014 Season.) HitPredictor, a subsidiary of iHeartRadio, accurately forecasts hits prior to their release by playing them for a large online test audience in order to solicit their feedback.
  • Billboard Top 100 (BT100) combines point-of-sales sales data, download music numbers and Nielsen’s listening metrics. One result is that songs remain on the BT100 longer. As a result of this “the relative value of a hot has exploded”. Thus, the top 1% of recording artists earn 77% of all recorded music sales, while the top 10 selling songs have increased their capture of the market by 82% during the last decade. This is indeed a market where the revenue rich continue to get richer revenues.

II. Current Market Influences and Trends:

  • Wisdom of the Crowds:  Before the advent of big data, music company execs largely relied on their own instincts in choosing artists and products to promote. Now with the advent of these sophisticated apps and services, they are relying on upon a group f principles known as the wisdom of the crowds². Very simply stated, large and diverse groups of people, such as the web-wide millions using these services, is more likely to make more accurate decisions and forecasts than smaller groups and/or experts in the relevant field(s).
  • The Long Tail Effect:  As noted above, there is an intense and very small concentration among artists for whom big data and analytics is producing economic rewards.³
  • Social Media:  Some, but not all to the same degree, of these platforms are now the major drivers in marketing new artists and their new music. They might even be more influential than the tradition of drawing audiences to live concerts.
  • Radio Airplay:  This mainstream media, while maintaining its ongoing relevance in the music business, likewise replies just as heavily on all of the social media and data analytics in order to “connect all these dots”. The Wisdom of the Crowds also plays an integral part of radio programming.*
  • Overproduction of Repetitive and Bland Music:  Music industry people whom Thompson approached for this article expressed concern that the data-driven nature of today’s market is producing a “clustering” of music in different genres and, in turn, noticeable levels of sameness and copycat acts. Nonetheless, he further writes that research shows that listeners very often seek out familiar music they have heard many times before.
  • Effects Upon Musical Artists: Notwithstanding the prior point, musicians and composers are aware of this phenomenon but generally have limited its effects upon their creative output. As well, some will add variations and imperfections to their live performances in order to keep them sounding fresh. (X-ref to this August 11, 2014 Subway Fold Post entitled The Spirit of Rock and Roll Lives on Little Steven’s Underground Garage about how, among other things, this is one of the basic tenets of  Garage Rock.)

III. Ongoing Issues:

  • At the very heart of all of this activity is, as precisely framed by Thompson “What do people want to hear next?”
  • While the music business is significantly benefiting from the accuracy of all of this data and calculation, is it likewise producing “better”, more diverse and imaginative music for audiences to consume?

My own additional questions include:

  • Despite the Long Tail effect, are artists is the much longer end of the curve still accruing some demonstrable benefits from big data insofar are being heard by larger audiences online and in concert?
  • Based upon the monumental amounts of past, present and future data about music and the music industry, could deep learning and other artificial intelligence (AI) methods be used to produce genuine hit songs in multiple genres, without any human intervention? Alternatively, is the human touch always needed in the musical arts? If the answer ever turns out to be “not always”, what are the implications?
  • Could analytics and AI produce a new genre of music that is not necessarily a hybrid? That is, are there sounds, rhythms, arrangements, styles, tablatures and so on that have not yet emerged and can be entirely machine synthesized?
  • The article mentions that Led Zeppelin’s iconic Stairway to Heaven was never played much after its initial release and that it never landed on the BT100. As an experiment to test the validity and accuracy of today’s music data apps and services, what would happen if many such great hit were retroactively tested? Would any be proven to be hits that never should have occurred according to today’s tech and, conversely, are there obscure songs from years ago that would produce results indicating they should have been hits? Could or even should, such results be used to further fine tune, if not develop new musical data methods and metrics?
  • What other new opportunities will arise, based on this merger or art and science, for entrepreneurs, artists, talent scouts and agents, established music companies, and concert halls?

December 19, 2014 Update: 

Adding to the big data strategies and implementations for three more major music companies and their rosters of artists was a very informative report in the December 15, 2014 edition of The Wall Street Journal by Hannah Karp entitled Music Business Plays to Big Data’s Beat. (A subscription for the full text required a subscription to WSJonline.com, but the story also appeared in full on Nasdaq.com clickable here.) As described in detail in this report, Universal Music, Warner Music, and Sony Music have all created sophisticated systems to parse numerous data sources and apply customized analytics for planning and executing marketing campaigns.

Next for an alternative and somewhat retro approach, a veteran music retailer named Sal Nunziato wrote a piece on the Op Ed page of The New York Times on the very same day entitled Elegy for the ‘Suits’. He blamed the Internet more than the music labels for the current state of music where “anyone with a computer, a kazoo and an untuned guitar” can release their music  online regardless of its quality. Thus, the ‘suits’ he nostalgically misses were the music company execs who exerted  more controlled upon the quantity and quality of music available to the public.

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1Right Off the Dial  (Faber & Faber, 2009), by Alec Foege, chronicled the causes and effects of the company’s rapid rise in the commercial radio industry. I found this to be an eye-opening and very informative account of rapid consolidation within a specific sector of the mainstream media. I recommend it to anyone interested in this company and topic.

2.  For a well reviewed and highly readable treatise on this, I also very highly recommend The Wisdom of
the Crowds by James Surowiecki (Doubleday, 2004). Also here is a Wikipedia page summarizing some of the main points of the book.

3.  The definitive and superlative book on one of the most interesting outgrowths of online commerce is The Long Tail by Chris Anderson (Hyperion, 2006). Furthermore, I also highly recommend his incredibly diverse and entertaining show on NPR called Studio 360.

*  I still that think Bruce Springsteen’s take on the state of music radio in 2007, Radio Nowhere, deserves a click and listening here. Besides, it’s an exhilarating rocker.

 

Book Review of “More Awesome Than Money”

The rapid rise and ubiquity of Facebook during the last ten years has been a remarkable phenomenon. The figure currently used to express the company’s breadth is that they have more than 1.3 billion user accounts. They have successfully monetized their social platform using a variety of means including, among others, advertizing, networking, communications, and harvesting vast amounts of user data, on their site and elsewhere online, to make the users’ experience more “personal”.

Nonetheless, while most users have become highly dependent on their regular use of Facebook, there are many others who still feel somewhat uncomfortable with its privacy policies and intensive data gathering and analytics.

In 2010, four NYU students heard a presentation by Eben Moglen, a law professor at Columbia University, about the lack of online privacy and overall invasiveness of all of the data relentlessly vacuumed up across the web and used for a multitude of largely invisible purposes. This was the inspiration point for them to join together and try to create a privacy aware and fully decentralized social networked called Diaspora. Most importantly, users would own their individual data and be able to take it with them if they chose to leave. They established it as a non-profit entity that operated on an open source basis for its dedicated global corps of  developers.

The compelling story of the founders and Diaspora has been now been deeply and dramatically told by author Jim Dwyer (the About New York columnist for The New York Times and the author five other books), in his latest book entitled More Awesome Than Money (Viking, October 2014). With their full access and cooperation, he followed these four young men during every phase of Diaspora’s founding, funding and construction and implementation. They were driven by their desire to make a difference to like-minded social network users who wanted true ownership of their own data, rather than many of today’s other typical startups who are looking to strike it rich.

Their noble quest, with its many high and low points, has been very poignantly captured and told here. This not just another geeked out tome about a tech startup that struggles and then hits the jackpot. Rather, this text operates on multiple levels to very skillfully present and weave together, with much pathos and insight, the lives and motivations of the founding four, their rapid relocation and education in the startup culture of Silicon Valley*, and the complexity of achieving their objectives.

Despite their goal to assemble a true technological and philosophical alternative to Facebook and the support they received in their Kickstarter funding campaign, open source coding support, and the goodwill of many potential users seeking something utterly new like Diaspora, there were many obstacles along the way. These included differences that emerged among the core four, overly ambitious release dates and correspondingly high user expectations, funding challenges, and a tragic personal issue of one founder.

Dwyer recounts, with great internal consistency and engaging prose throughout the text, the complex trajectory of Diaspora. Readers will very quickly be drawn into the narrative and the multiple challenges encountered by the young company. As well, for anyone currently involved in a startup or considering taking the leap to launch one, More Awesome Than Money should be considered required reading. Its cover price alone, consider it a form of nominal seed capital if you will, is certain to yield valuable insights into the unique world of the startup.

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*  For another very high quality piece of journalism about a completely different startup in Silicon Valley, see  One Startup’s Struggle to Survive the Silicon Valley Gold Rush, by Gideon Lewis-Kraus in the April 2014 issue of WIRED.

Startup is Visualizing and Interpreting Massive Quantities of Daily Online News Content

"News", Image by Mars Hill Church Seattle

“News”, Image by Mars Hill Church Seattle

Just scratching the surface, some recent Subway Fold posts have focused upon sophisticated efforts, scaling from startups to established companies, that analyze and then try to capitalize upon big data trends in finance¹, sports² , cities³, health care* and law**. Now comes a new report on the work of another interesting startup in this sector called Quid. As reported in a most engaging story posted on VentureBeat.com on November 27, 2014 entitled Quid’s Article-analyzing App Can Tell You Many Things — Like Why You Lost the Senate Race in Iowa by Jordan Novet, they are gathering up, indexing and generating interpretive and insightful visualizations for their clients using data drawn from more than a million online articles each day from more than 50,000 sources.

I highly recommend a full read of this story for all of the fascinating details and accompanying screen captures from these apps. As well, I suggest visiting and exploring Quid’s site for a fuller sense of their products, capabilities and clients. I will briefly recap some of the key points from this story. Furthermore, this article provides a timely opportunity to more closely tie together seven related Subway Fold posts.

The first example provided in the story concerns the firm’s production of a rather striking graphic charting the turn in polling numbers for a Democratic candidate running for an open Senate seat in Iowa following a campaign visit from Hillary Clinton. When Senator Clinton spoke in the state in support of the Democratic candidate, she address women’s issue. Based upon Quid’s analysis of the media coverage of this, the visit seemed to have helped the Republican candidate, a woman, more then the Democratic candidate, a man.

Politics aside, Quid’s main objective is to become a leading software firm in supporting corporate strategy for its clients in markets sectors including, among others, technology, finance and government.

Quid’s systems works by scooping up its source materials online and then distilling out specific “people, places, industries and keywords”. In turn, all of the articles are compared and processed against a specific query. In turn, the software creates visualizations where “clusters” and “anomalies” can be further examined. The analytics also assess relative word counts, magnitudes of links shared on social media, and mentions in blog posts and tweets. (X-ref again to this November 22, 2014 post entitled Minting New Big Data Types and Analytics for Investors that covers, among other startups, one called Dataminr that also extensively analyzes trends in Twitter usage and content data.)

The sample screens here demonstrate the app’s deep levels of detail in analytics and visualization. As part of the company’s demo for the author, he provided a query about companies involved in “deep learning”.  (X-ref to this August 14, 2014 Subway Fold post entitled Spotify Enhances Playlist Recommendations Processing with “Deep Learning” Technology concerning how deep learning is being used to, well, tune up Spotify’s music recommendation system.) As this is an area the writer is familiar with, while he did not find any unexpected names in companies provided in the result, he still found this reassuring insofar as this confirmed that he was aware of all of the key participants in this field.

My follow up questions include:

  • Would it be to Quid’s and/or Dataminr’s advantage(s) to cross-license some of their technology and provide supporting expertise for applying and deploying it and, if so, how?
  • Would Quid’s visualizations benefit if they were ported to 3-D virtual environments such as Hyve-3d where users might be able to “walk” through the data? (X-ref to this August 28,2014 Subway Fold post entitled Hyve-3D: A New 3D Immersive and Collaborative Design System.) That is, does elevating these visualizations from 2-D to 3-D add to the accessibility, accuracy and analytics of the results being sought? Would this be a function or multiple functions of the industry, corporate strategy and granularity of the data sets?
  • What other marketplaces, sciences, professions and products might benefit from Quid’s, Dataminr’s and Hyve-3D’s approaches that have not even been considered yet?

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1.  See this November 22, 2014 post entitled Minting New Big Data Types and Analytics for Investors.

2.  See this October 31, 2014 post entitled New Data Analytics and Video Tools Affecting Defensive Strategies in the NFL and NBA.

3.  See this October 24, 2014 post entitled “I Quant NY” Blog Analyzes Public Data Sets Released by New York City.

*  See this October 3, 2014 post entitled New Startups, Hacks and Conferences Focused Upon Health Data and Analytics .

**  See this August 8, 2014 post entitled New Visualization Service for US Patent and Trademark Data .