Special secret stuff with s3cmd

According to my coworker, "Amazon's S3 is the best thing since sliced bread." For working with S3, s3cmd is a handy little tool. It's documentation is a bit on the sparse side, but, what do you expect for free?

One gotcha with S3 is that buckets and the files in them have entirely distinct ACLs. This can lead to scenarios where the owner of a bucket can't work with the files in it. An easy way for this to come about is to log into the S3 console and create a bucket with one set of credentials, then upload files with a tool like s3cmd under another set of credentials.

cd /path/to/files/
s3cmd -v sync . s3://damnbucket.bucketowners.org

You can give permissions to the bucket owner like so:

s3cmd setacl --acl-grant=full_control:i_own_the_damn_bucket@bucketowners.org --recursive s3://damnbucket.bucketowners.org

You might also want to make the files public, so they can be served as a static website.

s3cmd setacl --acl-public --recursive s3://damnbucket.bucketowners.org

AWS Command Line Interface

I've been using s3cmd for a while, out of habit, but maybe it's time to try to Amazon's AWS Command Line Interface, which just had their version 1.0 release.

From a brief look, AWS CLI looks nice. You can do the same sync operation as above and make files public in one command:

aws s3 sync . s3://damnbucket.bucketowners.org --acl public-read

Amazon is very mysterious about how to specify the target of a grant of permissions, aka the grantee. I tried to give permission to the owner of a bucket, but kept getting an error. Some more examples in the docs would help! I also get "Invalid Id" for no apparent reason in the permissions section of the web UI for S3, so maybe I'm just clueless.

aws s3api put-object-acl --bucket damnbucket.bucketowners.org --grant-full-control i_own_the_damn_bucket@bucketowners.org --key genindex.html
#> A client error (InvalidArgument) occurred: Argument format not recognized.

As far as I could tell, the AWS CLI tool seems to be missing the --recursive option that we used with s3cmd. That seems like a fairly essential thing.

Also, I couldn't get the profile feature to work:

aws s3 ls s3://damnbucket.bucketowners.org --profile docgenerator
#>The config profile (docgenerator) could not be found

NOTE: Many thanks to Mitch Garnaat, I now know how the --profile switch works. Contrary to the documentation, you need a heading in your config file like this: [profile docgenerator] rather than like this: [docgenerator].

I'm glad Amazon is taking the lead in developing this tool, and I'm sure they'll keep making it better. And, there's a Github repo, so I'm guessing that means they take pull requests.

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

Generate UUIDs in R

Here a snippet of R to generate a Version 4 UUID. Dunno why there wouldn't be an official function for that in the standard libraries, but if there is, I couldn't find it.

## Version 4 UUIDs have the form:
##    xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx
##    where x is any hexadecimal digit and
##    y is one of 8, 9, A, or B
##    f47ac10b-58cc-4372-a567-0e02b2c3d479
uuid <- function(uppercase=FALSE) {

  hex_digits <- c(as.character(0:9), letters[1:6])
  hex_digits <- if (uppercase) toupper(hex_digits) else hex_digits

  y_digits <- hex_digits[9:12]

  paste(
    paste0(
      sample(hex_digits, 8, replace=TRUE),
      collapse=''),
    paste0(
      sample(hex_digits, 4, replace=TRUE),
      collapse=''),
    paste0(
      '4',
      paste0(sample(hex_digits, 3, replace=TRUE),
             collapse=''),
      collapse=''),
    paste0(
      sample(y_digits,1),
      paste0(sample(hex_digits, 3, replace=TRUE),
             collapse=''),
      collapse=''),
    paste0(
      sample(hex_digits, 12, replace=TRUE),
      collapse=''),
    sep='-')
}
}

View as a gist: https://gist.github.com/cbare/5979354

Note: Thanks to Carl Witthoft for pointing out that my first version was totally broken. Turns out calling sample with __replace=TRUE__ greatly expands the possible UUIDs you might generate!

Carl also says, "In general, as I understand it, the value of UUID codes is directly dependent on the quality of the pseudo-random number generator behind them, so I’d recommend reading some R-related literature to make sure “sample” will be good enough for your purposes."

This sounds wise, but I'm not sure if I'm smart enough to follow up on it. It could be that the randomness of these UUIDs is less than ideal.

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

RESTful APIs

At Clojure-West, this past March, former ThoughtWorker Siva Jagadeesan spoke on on how to build good web APIs using Resource Oriented Architecture (ROA) and Clojure.

The talk isn't really specific to Clojure, but is really more a primer on REST APIs.

Rants about true REST versus REST-ish, REST influenced, or partially RESTful are not especially interesting. That's not what this is. It's a nicely pragmatic guide to the architectural patterns and why you might use them.

1. The swamp of POX (plain old XML, or more likely JSON, these days): At first glance, many, myself included, take REST to mean RPC over HTTP with XML/JSON.
2. URI: Start thinking in terms of resources.
3. HTTP: The HTTP verbs together with URIs define a uniform interface. CRUD operations are always handled the same way. So are error conditions.
4. Hypermedia (HATEOAS): Possible state transitions are given in the message body, allowing the logic of the application to reside (mostly? completely?) on the server side. The key here is "discoverability of actions on a resource."

RESTful Clojure by Siva Jagadeesan

More

• Richardson Maturity Model: steps toward the glory of REST
• Roy Feilding's REST paper
• Clojure tools for REST APIs: Bishop and liberator
• Christopher Miles on Bishop: Makes Your Web Service Shiny and Why Is My Web Service API Crappy?
• REST and designing APIs
• Clojure: Web Socket Introduction
• RESTful principles
• Exploring the implementation aspects of the REST architectural style
• BlueEyes
• What Makes a Great Open API?, a talk from OSCON 2012 by John Musser of ProgrammableWeb
• Atlassian REST API Design Guidelines
• The REST API Design Handbook
• A 2006 article, Tim Bray on REST
• Versioning: how to express this in a RESTful way? domain.org/myresource/version/1 implies that version 2 is a different resource. Would it be better to express versioning as an orthogonal dimension? A URL defines a resource, we set a header to ask for a version?

The Dream 8 Challenges

The 8th iteration of the DREAM Challenges are underway.

DREAM is something like the Kaggle of computational biology with an open science bent. Participating teams apply machine learning and statistical modeling methods to biological problems, competing to achieve the best predictive accuracy.

This year's three challenges focus on reverse engineering cancer, toxicology and the kinetics of the cell.

• HPN-DREAM Breast Cancer Network Inference Challenge
  Infer the signaling networks in breast cancer cell lines
• NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge
  Predict individual response to environmental and pharmaceutical chemicals
• The Whole-Cell Parameter Estimation DREAM Challenge
  Infer the kinetic parameters underlying biological processes in whole cell models

Sage Bionetworks (my employer) has teamed up with DREAM to offer our Synapse platform as an integral part of the challenges. Synapse is targeted at providing a platform for hosting data analysis projects, much like GitHub is a platform for software development projects.

My own part in Synapse is on the Python client and a bit on the R client. I expect to get totally pummeled by bug reports once participation in the challenges really gets going.

Open data, collaborative innovation and reproducible science

The goal of Synapse is to enable scientists to combine data, source code, provenance, prose and figures to tell a story with data. The emphasis is on open data and collaboration, but full access control and governance for restricted access is built in.

In contrast to Kaggle, the DREAM Challenges are run in the spirit of open science. Winning models become part of the scientific record rather than the intellectual property of the organizers. Sharing code and building on other contestant's models is encouraged in with hopes of forming networks of collaborative innovation.

Aside from lively competition, these challenges are a great way to compare the virtues of various methods on a standardized problem. Synapse is aiming to become an environment for hosting standard open data sets and documenting reproducible methods for deriving models from them.

Winning methods will be presented at the RECOMB/ISCB Conference in Toronto this fall.

So, if you want to sharpen your data science chops on some important open biological problems, check out the DREAM8 challenges.

More on DREAM, Sage Bionetworks, and Challenges

• May the Best Model Win
• Wisdom of crowds for robust gene network inference
• Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling.
• Metcalfe's law and the biology information commons
• Synapse – a Kaggle for molecular medicine?

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?