The Master Switch

The Master Switch: The Rise and Fall of Information Empires was described as "essential reading" by my boss's boss. If you're at all interested in the interplay of technology, economics and politics, I think you'll agree.

Author Tim Wu is the originator of the term "net neutrality" and a law professor at Columbia. He has written a fast-forward history of the information technology industry focusing on the people and corporations that have, over time, controlled the commanding heights of the information economy. The book examines the cartels that held sway over telephone, radio, film, and television leading up to the question of whether the internet will also come to fall under similar domination.

The cycle is the author's term for the progression of any given technology from the wide-open wild-west early days through a process of integration and consolidation to an end state of oligopoly or monopoly. This stasis eventually gets disrupted by newer technology or government intervention, leading to another open phase and a new round of the cycle, empires rising and falling in the process. "The one-time revolutionaries always become the next generation of dictators. That's why we need, in technology, another generation of revolutionaries to upend them."[1]

Open vs. closed systems

The book revolves around the virtues and vices of open and closed systems. Open systems are more adaptable and democratic but have trouble matching the stability, security and efficiency of closed systems. Open systems embrace the advantages of decentralization as espoused in different ways by Friedrich Hayek and Jane Jacobs. But, integrated centralized systems can be reliable and convenient.

Closed systems, of course, appeal to empire builders such as Theodore Vail who created the AT&T Bell System. Wu's knack for sketch biography is put to good use profiling these power-hungry moghuls and the often utopian upstarts that seek to dethrone them. We meet titans, like Vail, and get a glimps into the sometimes contradictory character traits it takes to control an information empire, for example: David Sarnoff, who ruled the Radio Corporation of America (RCA) and NBC; John Reith, founder of the BBC; Adolph Zukor who started Paramount pictures and Ted Turner creator CNN and former head of Time Warner. We also meet hackers like early radio enthusiast Lee De Forest and supressed inventor of FM radio Edwin Armstrong.

The capture of the Internet?

The American system attempts to carefully balance power within the government, but takes a laissez faire approach to private power. If Wu is right and we let things take their natural course, the openness that now characterizes the Internet - the "integrity of the Internet itself as a reliable, independent, and open structure"[2] - may be lost to a period of lockdown. Network effects, the power of integration and economies of scale favor the monopolist. Consumers may decide to favor consistency and convenience over openness and choice only to regret it later. If this is the case, the internet will not remain open automatically but only with concerted effort.

The remedy Wu proposes is a principle of separation akin to the separation of church and state or the separation of powers within the branches of the American government. The common carrier obligation of all infrastructure providers implies net neutrality and opposes verical integration across layers of the network stack. Technology leaders would be expected to self-regulate based on a sense of public duty. The FCC should pursue enforcement with an eye to the special role of information technology in a democratic society. Anti-trust regulation is the back-up, when it's time to bring out the big guns.

Fight on

The Master Switch gives a deeper perspective on the great game playing out in the technology sector. After reading it, you'll recognize the historical themes threading through the open-source movement, the Apple vs Google skirmishes or 2012's battle that defeated the SOPA / PIPA acts. The fight over the future of the Internet is surely not over.

• [1] NPR interview The Past And Future Of Information Empires
• [2] Vanity Fair's reporting on SOPA and PIPA World War 3.0
• Colbert interviews Tim Wu End of Net Neutrality
• The New Yorker's review Tim Wu on Communication, Chaos, and Control
• NYT book review From Hobby to Industry By David Leonhardt
• One on One: Tim Wu, Author of ‘The Master Switch’
• Ars technica disputes the book's premise.
• The Manichean World of Tim Wu
• Why the man who coined the phrase 'net neutrality' feared Apple
• Speaking at the MIT Media Lab
• WGBHForum at the Harvard Book Store
• Speaking at the Berkman Center for Internet & Society at Harvard
• Stanford Center for Internet and Society
• Tim Wu's TED talk
• General Assembly and Tim Wu: Net Neutrality and the Politics of Entrepreneurship
• Review of The Master Switch by "High Tech History"

Who will prosper in the age of smart machines?

What if Siri really worked? ...worked so well that those that mastered co-operation with a digital assistant had a serious competitive advantage over those relying on their own cognitive powers alone?

That question is considered by economist Tyler Cowen in a new book called Average is Over. Previously, Cowen wrote The Great Stagnation and maintains the Marginal Revolution blog and teaches online classes at Marginal Revolution University.

"Increasingly, machines are providing not only the brawn but the brains, too, and that raises the question of where humans fit into this picture — who will prosper and who won’t in this new kind of machine economy?"

Tyler Cowen's answers

Who will gain: People who can collaborate with smart machines; life long learners; people with marketing skills; motivators.

"Sheer technical skill can be done by the machines, but integrating the tech side with an attention-grabbing innovation is a lot harder."

The psychological aspect is interesting. The traditional techy nerd (ahem... hello, self) has a psychology adapted to machines. But, machines are gaining the capacity to interface on a level adapted human psychology.

Who will lose: People who compete with smart machines; people who are squeamish about being tracked, evaluated and rated; the sick; bohemians; political radicals.

On being quantified

"Computing and software will make it easier to measure performance and productivity. [...] In essence everyone will suffer the fate of professional chess players, who always know when they have lost a game, have an exact numerical rating for their overall performance, and find excuses for failure hard to come by."

On hipsters

"These urban areas [he doesn't mention Portland by name] are full of people who are bright, culturally literate, Internet-savvy and far from committed to the idea of hard work directed toward earning a good middle-class living. We’ll need a new name for the group of people who have the incomes of the lower middle class and the cultural habits of the wealthy or upper middle class. They will spread a libertarian worldview that working for other people full time is an abominable way to get by."

How many will prosper

The current trend of unequal wealth distribution will only continue as technological literacy takes on new dimensions. "Big data" makes it easier to measure and grade our skills and failings. Apex skills are the ability to grab human attention, to motivate, to manage humans and machines in collaboration.

Another, not quite contrasting view comes from Northwestern University economist Robert Gordon. His paper "Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds", suggests that "the rapid progress made over the past 250 years could well turn out to be a unique episode in human history." In particular, he raises the possibility that the internet and digital and mobile technology may contribute less to productivity than previous industrial revolutions.

Technology can create winner-take-all situations, where a few capture enormous gains leaving the also-rans with little. A lot depends on the distribution of technology's benefits.

More

• Who will prosper in the new world? in the New York Times Opinionator section
• If you don't like to read, here's the video version: Smart Machines: The Next Big Thing For Smart Human Beings
• Average is Over, Tyler Cowen's latest book
• Thomas Friedman on Average is Over and Average is over, part 2.
• Of course, I was totally hip years ago to the potential for algorithms to know their users, when I asked: Does Google know if you're honest?
• The prospects of integration between digital tools and human psychology appears in Christopher Steiner's Automate This.
• Hacking education
• The online education tidal wave

Automate This!

The invention of the printing press by German blacksmith Johannes Gutenberg in 1439, the foundational event of the infomation age, is a common touchstone for technology stories, appearing in the opening chapter of both Nate Silver's The Signal and the Noise and Viktor Mayer-Schonberger and Kenneth Cukier's Big Data.

Automate This!, by Christopher Steiner, comes at current technology trends from a more mathy angle, tracing roots back to Leibniz and Gauss. Here, it's algorithms rather than data that take center stage. Data and algorithms are two sides of the same coin, really. But, it's nice to some of the heros of CS nerds everywhere get their due: Al Khwarizmi, Fibonacci, Pascal, the Bernoullis, Euler, George Boole, Ada Lovelace and Claude Shannon.

Automate This! is more anecdotal than Big Data, avoiding sweeping conclusions except the one announced in bold letters as the title of the last chapter: "The future belongs to the algorithms and their creators." The stories, harvested from Steiner's years as a tech journalist at Forbes, cover finance and the start-up scene, but also medicine, music and analysis of personality.

Many of the same players from Nate Silver's book or from Big Data make an appearance here as well: Moneyball baseball manager Billy Beane, game-theorist and political scientist Bruce Bueno de Mesquita, and Facebook data scientist and Cloudera founder Jeff Hammerbacher.

Finance

In the chapter on algorithmic trading we meet hungarian-born electronic-trading pioneer Thomas Peterffy, who built financial models in software in the 80's before it was cool by hacking a NASDAQ terminal.

In the same chapter, I gained new respect for financial commentator Jim Cramer. In contrast to his buffoonish on-screen persona, his real-time analysis of the May 2010 flash-crash was both "uncharacteristically calm" and uncannily accurate. As blue-chip stocks like JNJ dived to near-zero, he made the savvy assessment, "That's not a real price. Just go and buy it!" and, as prices recovered only minutes later, "You'll never know what happened here." There's little doubt that algorithmic trading was the culprit, but the unanswered question is whether it was a bot run amok or an intentional strategy that worked a little too well. Too bad, if you did buy they probably canceled it.

Music

Less scarily, algorithms can rate a pop song's chances of becoming a hit single. Serious composer and professor of music David Cope uses hand-coded (in LiSP) programs to compose music, pushing boundaries in automating the creative process.

Medicine

Having mastered Jeopardy, IBM's Watson is gearing up to take a crack at medical diagnostics, which is a field Hammerbacher thinks is ripe for hacking.

Psych and soc

Computers are beginning to understand people, which gives them a leg up on me, I'd have to say. Taibi Kahler developed a classification system for personality types based on patterns in language usage. Used by NASA psychiatrist Terry McGuire to compose well-balanced flight crews, the system divides personality into six bins: emotions-driven, thoughts-based, actions-driven, reflection-driven, opinions-driven, and reactions-based. If you know people more than superficially, they probably don't fit neatly into one of those categories, but some do (by which I mean they have me pretty well pegged).

At Cornell, Jon Kleinberg's research provides clues to the natural pecking order that emerges among working or social relationships - Malcolm Gladwell's influencers detected programmatically. One wonders if corporate hierarchies were better aligned with such psychological factors would the result be a harmonious workplace where everyone knows and occupies their right place? Or a brave new world of speciation into some technological caste system?

What next?

Perhaps surprisingly, Steiner cites the Kaufmann Foundation's Financialization and Its Entrepreneurial Consequences on "the damage wrought by wall street" - the brain drain toward finance and away from actual productive activity. The book ends with the hopeful message that the decline of finance will set quantitative minds free to work on creative entrepreneurial projects. For the next generation, there's a plea for urgently needed improvements in quantitative education, especially at the high-school level.

Automate This! is a quick and fun read. Steiner's glasses are bit rose-tinted at times and his book will make you feel like a chump if you haven't made a fortune algorithmically gaming the markets or disrupting some backwards corner of the economy. As my work-mates put it, we're living proof that tech-skills are a necessary but not sufficient condition.

Links

• Breakdown: A Glimpse Inside the 'Flash Crash'
• Losing the race against the machines:
  • More jobs for machines, not people
  • Brynjolfsson and McAfee Race against the machines
  • TED Talk
  • Losing the race against the machines
• CMU game theorist and computer scientist Tuomas Sandholm dabbles in poker-bots
• This Tech Bubble Is Different
• Bruce Bueno de Mesquita on the Colbert Report
• Bruce Bueno de Mesquita TED Talk on Iran

Kahler's personality types

• Emotions-driven: form relationships, get to know people, tense situations -> dramatic, overreative
• Thoughts-based: do away with pleasantries, straight to the facts. Rigid pragmatism, humorless, pedantic, controlling
• Actions-driven: crave action, progress, always, pushing, charming. Pressure -> impulsive, irrational, vengeful
• Reflection-driven: calm and imaginative, think about what could be rather than work with what is, can dig into a new subject for hours, applying knowledge to the real work is a weakness
• Opinions-driven: see one side, stick to their opinions in the face of proof. Persistent workers, but can be judgmental, suspicious and sensitive
• Reactions-based: rebels, spontaneous, creative and playful. React strongly, either, "I love it!" or "That sucks!" Under pressure, can be stubborn, negative, and blameful

Big Data Book

Big Data: A Revolution That Will Transform How We Live, Work, and Think asks what happens when data goes from scarce to abundant. The book expands on The data deluge, a report appearing in the Economist in early 2010, providing a concise overview of a topic that is at the same time over-hyped and also genuinely transformative.

Authors Kenneth Cukier, Data Editor at the Economist, and Viktor Mayer-Schonberger, Oxford professor of public policy, look at big data trends through a business oriented lens.

Their book identifies two important shifts in thinking as key to dealing with big data: comfort with the messy uncertainty of probability and what the book refers to as correlations over causality. This just means that some tasks are better suited to a correlative or pattern matching approach rather than modeling from first principles. No one drives a car by thinking about physics.

In domains such as natural language and biology we'd like to know the underlying theory, but don't. At least, not yet. Where theory is insufficiently developed to directly answer pressing questions, a correlative, probabilistic approach is the best we can do. The merits of this concession to incomplete information can be debated, and was by Norvig and Chomsky. But, empirical disciplines like medicine and engineering rely on it - let alone the really messy worlds of economics and politics. It's often necessary to act in absence of mechanistic understanding.

As data goes from a scarcity to abundance, judgment and interpretation remain valuable. What changes is that you don't necessarily have to formulate a question and only then collect data that may help answer it. Another strategy becomes viable - collect a trove of data first and then interrogate it. That may bother hypothesis-driven traditionalists, but once you have a high-throughput data source - a DNA sequencer, a sensor networks or an e-commerce site - it makes sense to ask yourself what questions you can ask of that data.

Datafication

One industry or discipline after another has undergone the transition from information scarcity to super-abundance. One of the last hold-outs is medicine, where the potential impact is huge. Internet companies are accumulating vast data hordes streaming in through social networks and smartphones. The process has something of the character of a land-rush. Given the economies of scale, each category has its dominant player.

The dark side

As for obligatory dark-side of big data, the book worries about "the prospect of being penalized by for things we haven't even done yet, based on big data's ability to predict our future behavior."

While this won't keep me up at night, technology is not necessarily the liberating force that we may hope it is, nor is it necessarily a tool of Orwellian control. Certain technologies lend themselves more readily one way or the other.

A more pedestrian "threat to human agency" comes from marketers and political consultants equipped with data feeds, powerful machines and brainy statisticians, and able to manipulate people into consuming useless junk and acting against their own interests. Given detailed individual profiles and constant feedback on the effectiveness of targeted marketing, the puppet masters are likely to keep getting better at pulling our strings.

Data governance

To safeguard privacy and ethical uses of data, the book advocates a self-policing professional association of data scientists similar to those existing for accountants, lawyers and brokers. Personally, I'm a skeptical that these organizations have much in the way of teeth. An informed public debate might be a healthier option.

Bolder thinking in this area comes from Harvard's cyber-law group. Their VRM (for vendor relationship management) project proposes a protocol by which individuals and vendors can specify the terms under which they are willing to share data. Software can automate the negotiation and present options where terms are compatible. Precedents for these ideas can be found in finance where bonds and options are standardized contracts and can thus be fluidly traded.

The new oil?

Land, labor and capital are the classic factors of production - the primary inputs to the economy - or so they teach in Econ 101. Technology certainly belongs on that list and data does as well. The original robber barons sat in castles overlooking the Rhine or the Danube taxing the flow of goods and travelers around Europe. The term was later applied to giant industrial firms built on other flows: oil, rail and steel. Companies like Google, Facebook and Amazon extract wealth from a new flow: that of data. It's time to think of data as another factor of economic production, a raw material out of which something can be made.

Links, links, links

• Comparing Big Data with the Signal and the Noise
• The Rise of Big DataHow It's Changing the Way We Think About the World By Kenneth Neil Cukier and Viktor Mayer-Schoenberger published in Foreign Affairs May/June 2013
• How Big Data Changes Our Relationship With Information by Irving Wladawsky-Berger in the WSJ, July 5, 2013
• The Age of Big Data by Steve Lohr published in the NY Times in February of 2012
• Jer Thorpe says, Big Data Is Not the New Oil
• Niccolo Tempini finds that rather than showing how the impact of data-driven innovations will advance the march of humankind, the authors merely present a thin collection of happy-ending business stories.
• Norvig vs. Chomsky and the Fight for the Future of AI
• Doc Searls on the Intention Economy
• Patients Know Best
• Some colleagues are working on data governance for bio-medical research: US-EU Scientific Research Collaborations: Sage Bionetworks’ Experience Navigating the Complex Regulatory Landscape and Consent to Research
• Is ‘Google Flu Trends’ Prescient Or Wrong?

Hacking education

On-line education is blossoming in a virtuous cycle of innovation, threatening disruption to expensive traditional universities and opening access to higher learning for anyone with an internet connection and a curious mind.

For developers, the on-line education boom means rich opportunities to learn, to create learning environments, and to analyze the data collected in the process of running massive open online courses - hacking education itself.

Coursera, an early leader, signed up 17 more universities on top of the 12 that joined in July and are now offering 198 classes from 33 schools.

Founders Daphne Koller & Andrew Ng published an article in Forbes, Log On and Learn: The Promise of Access in Online Education. Koller, whose "Probabilistic Graphical Models" strained my few remaining brain cells, spoke at TED on What we’re learning from online education

An Old Scholar - Salomon Koninck

A Seattle Times piece on the recent wave of educational startups featured enthusiastic comments from Ed Lazowska at UW and Vitalina Komashko at Sage Bionetworks.

Why some of the best universities are giving away their courses Each has answers. But basically it comes down to these: To serve the greater good. To win a public-relations race. And, most especially, to enhance reputations.

On-line education is a perfect complement for hotness that is data science. Not only is it a means for transferring trendy skills, but the data collected in the process should have amazing things to teach us about learning.

Not all the action is in cyber-space, either. If you have a hungry mind that needs feeding, you can:

• study for coursera classes at meetups in 928 cities around the world
• study improvisation with Jazz vibraphonist Gary Burton (Your neighbors will love it!)
• go to Hacker School, Startup School or hacker dojo
• attend Biohacker Boot Camp at genspace
• learn about the Design of Computer Programs from the CTO of Google

The only limit is your own bandwidth. That, and the tolerance of your spouse.

The flashy technology is new but, the ideal of open access to knowledge has been around for a long time. The Seattle Times quotes Dave Cillay, executive director of WSU Online, "We've had MOOCs and open learning resources for centuries. They're called libraries."

Echoing Carnegie and his libraries, the Gates Foundation announced in June $9 million in grants for on-line secondary education, including a million to the MIT/Harvard venture edX.

I remember poking my head into a cinder-block schoolhouse in a tiny village in Laos, back in my traveling days. There were 2 books; a book that appeared to be equal parts farming manual and government propaganda and another of Buddhist scripture. The potential to mitigate that kind of information-poverty in the remote corners of the world is one of the most exciting aspects of on-line education.

Building on previous innovation is key to progress, especially in science and technology. Hacking education will help information flow faster, getting people to the frontier where they can start pushing the envelope and maybe make the world a slightly better place. That's why these are exciting times for those that love learning.

More

Still don't believe me that there's a lot going on? Here's more Ed-tech news:

• Scaling higher education
• Economists Tyler Cowen and Alex Tabarrok introduced Marginal Revolution University. The first course offered at MRUniversity is Development Economics.
• An NPR story on Coursera Online Education Grows Up, And For Now, It's Free
• College 2.0: Inside the Coursera Contract: How an Upstart Company Might Profit From Free Courses
• The School of Data is a collaboration between the Open Knowledge Foundation and P2PU (Peer to Peer University)
• University of the People: A Free Online University Tests the Waters
• CodeNow teaches coding to kids in DC. "Coding is the new literacy. It gives individuals the power to innovate and create."
• Three Tips For a New Wave of Ed-Tech Entrepreneurs from the founder of ShowMe

Microarrays

Microarrays are one of the workhorses of modern biology. Measuring transcript levels enables studies of differential expression - asking what the difference is, at the gene expression level, for example, between cancer tumor cells and normal cells.

Bruz Marzolf, who up 'til recently ran my local microarray facility, spoke recently, tracing the journey of microarrays through the full technology life-cycle, starting in 1995 with the publication of Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray in Science. Bruz put microarrays in the category of a utility technology, but not quite to the point of commoditization as there remain major differences between manufacturers.

• Affymetrix, first to commercialize microarray technologies, is the 800 pound gorilla. Their photolithography process borrows from computer chip manufacturing and their standardized probe sets are well supported by tools such as Bioconductor. The technology is robust but producing the masks is quite expensive, thus custom arrays are not economical.
• Agilent, which spun out of HP, uses ink-jet technology. Custom arrays can be designed using Agilent's eArray software. Agilent arrays come in a variety of resolutions including 8x60k, 1x244k and 1x1m with 60mer probes.
• Illumina builds arrays out of beads coated in oligo probes. Beads are laid out randomly on the slides, necessitating a layout discovery step. These chips have extra redundancy to account for randomness in bead-probe count.
• Nimblegen's maskless photolithography process is more flexible for custom arrays. Nimblegen provides arrays in 385K, 4x72K, and 12x135K resolutions using 60mer probes. They emphasize high array-to-array data reproducibility.

As an aside, our group uses custom spotted arrays and Agilent arrays. We tried Nimblegen and found that inter-array consistency was excellent, but inter-probe consistency was not. Below we see the ribosomal RNAs and adjacent genes with total RNA measured by a custom Agilent array (in blue) plotted next to a custom Nimblegen array (in green). To be fair there might be other explanations for what we saw, but it certainly looks like there is significant variability between probes that we would expect to have identical readings.

In RNA-Seq: a revolutionary tool for transcriptomics (Nature Reviews Genetics, 2009), Zhong Wang, Mark Gerstein & Michael Snyder show this comparison between microarrays and RNA-Seq.

While RNA-seq, no doubt, has a higher dynamic range, does it really have less noise? Some folks say so. With tens or hundreds of thousands of probes, fairly dense coverage of whole microbial genomes is possible. If you know what you're looking for, microarrays are still cheaper. Discovery oriented work is going increasingly toward sequencing.

Links

• The technology life-cycle curve slide comes from a good talk by Simon Wardley given at OSCON in 2010.
• MicroArray Quality Control (MAQC) project, Shi et al, Nature Biotechnology, 2006
• RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
• RNA-Seq: a revolutionary tool for transcriptomics
• Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays

The future of money

Back during the first dot-com bubble, PayPal got started with revolutionary intentions. One of the founders, Peter Thiel, recently herd urging college students to log in, drop out, and start up, was even more radical back then.

In his book The PayPal Wars, early PayPal marketing guru Eric M. Jackson recounts a stirring speech Thiel gave to the company's early staff.
"PayPal will give citizens worldwide more direct control over their currencies than they ever had before," Thiel said. "It will be nearly impossible for corrupt governments to steal wealth from their people through their old means because if they try the people will switch to dollars or pounds or yen, in effect dumping the worthless local currency for something more secure."
Unfortunately, that vision never panned out. PayPal thrived when it came to innovating and adapting to stay a step ahead of its early competitors. But the company proved less adept at slaying its more formidable antagonists: Lawyers and politicians.

Jack Dorsey's Square, a web 2.0 and mobile compliant version of PayPal, talks disruption but has already accepted money from VISA. It's a shrewd business model: build anything reasonably credible in the payments space and the odds of being bought out by one of the incumbents are about 1000 percent. Dwolla is a digital and mobile payments startup from Iowa, who's early funding came from a credit union.

Startups aren't the only ones who want a piece of VISA's 37% profit margin on 8.6 billion in revenue. Big tech companies, like Apple and Google are getting into the payments game, too. Google just rolled out Google wallet, stirring privacy concerns and getting sued by PayPal.
These days, the wild-eyed radicals look past these tame corporate offerings to Bitcoin, a peer to peer digital currency created in 2009 by the mysterious Satoshi Nakamoto. Bitcoin relies on public key cryptography and digital signatures to guarantee payment and receipt. Satoshi's key insight was the means of validating transactions. Rather than clearing through a central authority, the system validates transactions through a distributed proof-of-work system, relying on the majority of honest nodes on the peer to peer network to solve cryptographic puzzles faster than any attacker.

The idea of an unregulated decentralized currency appeals to some, but don't expect the government to like it. The potential for black markets and money laundering has already drawn scrutiny and calls for a crack down. They're undoubtedly wondering how to tax it.
Possibly a bigger threat, Bitcoin has also attracted the attention of thieves. Last week, Mt. Gox, the currency's largest exchange, was hacked. The system relies critically on the security of end-user machines, a shaky proposition. One Bitcoin user, aptly named, allinvain, reported $500,000 worth stolen. A Bitcoin harvesting trojan has already been spotted in the wild.
Whether Bitcoin can overcome these problems or not, it's sure to be a wild ride. Bitcoin's technical underpinnings are fascinating and an impressive ecosystem has quickly sprung up it. There are dealers, exchanges, an escrow service, charities and a place to keep your treasure horde online.
I'm curious to see how much of the existing financial system gets ported to Bitcoin. Is fractional reserve banking in Bitcoin possible? Or how about securities denominated in Bitcoin? If you're a sceptic, can you sell short? One thing I love about Bitcoin is the mixture of engineering and economics, and even more, the engineering of economics. Of course, this comes with all the caveats and warnings of version 0.1.
The future of money is here. Are we read for it?

PS

I'm ready! Support this blog. Tips accepted here: 15Y9pepdBG9GJxyCc6HgsQS39BvsBUqi1W

HTC Incredible internal memory

My phone, an HTC Droid Incredible, may be hopelessly antiquated by the standards of true mobile hipsters. Still, it came with a generous 8GB internal storage. Too bad the SD card is a chintzy 2GB. These days, you get more than 2 gigs on an abacus. It seems like Android wants to use internal storage for the OS and apps, reserving the SD card for media, which makes that 8GB/2GB split a little awkward. I filled that 2GB right up with choice sides of Miles and 'Trane in no time. And, what do I need with 8 gigs worth of apps? What am I running, Bloatpad 2.0? So, anyway, I decided I wanted to use the empty 6 plus gigs on the internal storage for some Thelonious. So, can I do that?

Well, the Help/How to thing at HTC says, "...Music only plays audio files saved on the storage card...". Well, I use another media player anyway - Meridian. Then there's an article called Programmitically accessing internal storage (not SD card) on Verizon HTC Droid Incredible (Android), which says, "To be quite honest, the internal storage is a joke. Just think of it as a flash drive..."

But, it turns out, you can access music and other media on the internal storage. You just have to know that the internal storage is mounted as /emmc. Maybe they shoulda called it /WTF.

Art house video games

Where are the art house video games? I remember reading that the novel was once considered a time waster for idlers well beneath the level of serious art. What is art, anyway? TV spent decades in the shallows before growing artistic pretensions. These days, you can take a university class about The Wire. Maybe soon we'll be able to take a class in Halo studies, or the semiotics of Grand Theft Auto?

Silly, maybe, but games show a lot more potential than, say, Twitter. Unless someone starts tweeting profound insights in haiku. Have you ever seen a Facebook page you'd describe as raw, edgy, or deep?

Games are, at least, amenable to a Lord of the Rings style quest where the main point is to explore a rich fantasy world. What games are lacking, so far as I know, is the ability to be transformative. How does the writer develop characters when the protagonist, or protagonists, are real people with a will of their own? To induce a change - growth, learning - in a character outside of the writer's control... that would be the real trick.

But the potential of games is there as well. The visuals and audio are already well developed. Interactivity with the game world and the shared experience of multiplayer games is where the untapped potential lies. The medium may be the message, but to succeed on an artistic level games need a message. They need more to say than, "Let's blow shit up!"

Know any games that rise to the level of real art? Put your nominations in comments...

Tech Industry Gossip

Welcome to the new decade: Java is a restricted platform, Google is evil, Apple is a monopoly and Microsoft are the underdogs

I mostly try to ignore tech industry gossip. But, there's a lot of it, lately. And underlying the shenanigans are big changes in the computing landscape.

First, there's the flurry of lawsuits. This is illustrated nicely by the Economist in The great patent battle. There are several similar graphs of the patent thicket floating around. IP law is increasingly being used as a tool to lock customers in and competitors out. We can expect the (sarcastically named) "Citizens United" ruling on campaign finance to result in more of this particular kind of antisocial behavior.

Google and Facebook are in a pitched battle over your personal data and engineering talent. Google engineers, apparently, are trying to jump over to Facebook prior to what promises to be a huge IPO.

Apple caused quite a kerfuffle by deprecating Java on Mac OS X. After remaining ominously silent for weeks, Oracle seems to have lined up both Apple and IBM behind OpenJDK. Apache Harmony looks to be a casualty of this maneuvering. Harmony, probably not coincidentally, is the basis for parts of Google's Android and Oracle is suing Google over Android's use of Java technology.

Microsoft seems to be waning in importance along with the desktop in general. Ray Ozzie, Chief Architect since 2005, announced that he was leaving, following Robbie Bach of the XBox division and Stephen Elop, now running Nokia. I spoke with one MS marketing guy who said of Ozzie, "Lost him? I'd say we got rid of him!" A lesser noticed departure, that of Jython and Iron Python creator Jim Hugunin may also be telling. Profitable stagnation seems to be the game plan there.

Adobe's been struggling to the point where the NYTimes asked where does Adobe go from here? They took a beating over flash performance and rumors circulated briefly of a buyout by Microsoft.

The cloud is where a lot of the action in software development is moving. Mobile has been growing in importance by leaps and bounds ever since the launch of the iPhone. Cloud computing and consumer devices like smart phones, tablets, and even Kindles are complementary to a certain extent. The economics of cloud computing are hard to argue with. (See James Hamilton's slides and video on data centers.)

Another part of what's changing is a swing of the pendulum away from openness and back towards the walled gardens that most of us thought were left behind in the ashes of Compuserve and AOL. Ironically enough, Apple has become the poster child of walled gardens, with iTunes and the app store. ...the mobile carriers even more so. And the cloud infrastructures of both Microsoft's Azure and (to a lesser degree) Google's App Engine are proprietary. Out of the big 3, Amazon's EC2 is, by far, the most open. Mark Zuckerberg says, "I’m trying to make the world a more open place." But, to Tim Berners-Lee, Facebook and Apple threaten the internet.

There's plenty of money in serving the bulk population. That's why Walmart is so huge. My fear is that in a rush to provide "sugar water" to consumers, the computing industry will neglect the creative people that made the industry so vibrant. But, not to worry. Ray Ozzie's essay Dawn of a new Day does a nice job of putting into perspective the embarrassment of riches that technology has yielded. We're just at the beginning of figuring out what to do with it all.

Probability processor

MIT's Technology Review reports on A New Kind of Microchip, a probability-based processor designed to speed up statistical computations.

The chip works with electrical signals that represent probabilities, instead of 1s and 0s using building blocks known as Bayesian NAND gates. “Whereas a conventional NAND gate outputs a "1" if neither of its inputs match, the output of a Bayesian NAND gate represents the odds that the two input probabilities match. This makes it possible to perform calculations that use probabilities as their input and output.”

“This is not digital computing in the traditional sense,” says Ben Vigoda, founder of Lyric Semiconductor. “We are looking at processing where the values can be between a zero and a one.” (from Wired article Probabilistic Chip Promises Better Flash Memory, Spam Filtering) Vigoda's Analog Logic: Continuous-Time Analog Circuits for Statistical Signal Processing probably spells it all out, if you've got the fortitude to read it. For us light-weights, there's a video Lyric Semiconductor explains its probability chip. It's super-cool that he mentions genomics as a potential application.

Computing has been steadily moving towards more specialized coprocessors, for example the vector capabilities of graphics chips (GPU's). Wouldn't it be neat to have a stats coprocessor alongside your general purpose CPU? (Or inside it like an FPU?) How about a cell processor configuration where you'd get an assortment of CPU cores, graphic/vector GPU cores and probability processor cores?

Upgrade MacBook Pro Memory?

I was thinking of upgrading my 2007 MacBook Pro with more RAM. It came with 2GB, and the specs say it can take up to 3GB, although some online sources say they can successfully install 4GB. Apparently, these older MacBooks map "system functions", I guess meaning IO mapping and ROM into the region between 3GB and 4GB.

... at least 3 GB of RAM should be fully accessible, while when 4 GB of RAM installed, ~700 MB of of the RAM is overlapping critical system functions, making it non-addressable by the system.

OK, so no 4GB for me, but what about replacing one of the 1GB sticks with a 2GB stick for a total of 3GB? It turns out that if I did that, I'd take a small performance hit.

All Intel Core Macs support dual channel memory access if matching modules are installed. The customary estimate is that this gives a 6% - 8% real world performance benefit. The modules do not have to be the same brand. That means it is quite possible but not 100% guaranteed, that adding a 3rd party SODIMM to an Apple supplied SODIMM of the same size will make a matched pair.

Verdict: don't bother...

General purpose programming on the GPU

The video-card manufacturers are at an interesting crossroads. nVidia is really pushing general purpose computing on graphics hardware.

Cool idea, but it can't really take off unless programs are written to detect the presence of capable hardware and load code specially compiled for that particular chipset. In other words, GPGPU code written for nVidia cards won't run on ATI cards. Bummer. Clearly nVidia understands the advantage to be had from a large body of code compiled for their instruction set. They want to be in the position of owning the GPU equivalent of the x86 instruction set. Nice move on their part.

But wouldn't it be much cooler for the rest of us if things were a little more open? Is a common instruction set for graphics cards at all plausable? Like ARM is for embedded devices? Or maybe a common intermediate layer and JIT for all types of GPU code? That would be cool. And something like that would induce a lot more developers to take the plunge into compiling to the GPU, which has got to be a fairly drastically different piece of code relative to a straightforward implementation.