The feature can already be seen in ComodIT’s own application store and on the website of lifestreaming platform Storytlr, but is now available for anyone to use. In effect, it makes the installation of server-based apps a lot more like that of mobile apps – an almost one-click experience that even allows users to “share” the apps in question on Facebook and Twitter.

“You can embed the application – just copy and paste [a few lines of Javascript] and put it on your website. You add the direct install blue button and you allow anyone to install that application directly from your website,” ComodIT CEO Daniel Bartz told me.

Bartz suggested this approach would overcome the traditional open-source server software installation experience, which sometimes involves multi-page tutorials. The test-drive aspect is pretty neat too: when that option is chosen, ComodIT basically installs and runs the app for a free 100 minutes in an Amazon EC2 micro instance.

It makes marketing a bit easier for developers and of course it steers people towards ComodIT’s own distribution platform and wider services. As Bartz explained:

“When you click ‘direct install’, in fact you connect to ComodIT and you create a server on which you will install an OS and all the things that have to be done for installing applications. We’re automating the manual procedure – we do this like we do for any other pieces of ComodIT following the devops approach.

“Behind the scenes, we’re activating recipes for deployments. Within your ComodIT account you have access to all the recipes and descriptions that you usually have. The next step is deploying the application not only for testing but also for production, with all the ComodIT features like autoscaling and autobackup.

“We bring the user onto the platform and, as we have a business model based on the number of servers you’re managing with ComodIT, if you have more users installing the application through the direct install button, we’re a happy provider.”

It’s a smart idea and one that could give ComodIT a boost as it competes with the likes of Opscode and Puppet Labs (see disclosure) for devops’ attention.

ComodIT was a finalist in our Structure:Europe LaunchPad competition last year. This year’s Structure:Europe will take place in London from 18-19 September and, if you can’t wait until then for a high-level get-together around cloud automation and other such topics, don’t forget that our San Francisco Structure event is coming up on 19-20 June, too.

Here’s a video explaining how ComodIT’s direct install button works:

Disclosure:Puppet Labs is backed by True Ventures, a venture capital firm that is an investor in the parent company of this blog, Giga Omni Media. Om Malik, founder of Giga Omni Media, is also a venture partner at True.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• A near-term outlook for big data
• Connected world: the consumer technology revolution
• Ups and downs for cleantech in Q1

Last week, New York Times reporter Steve Lohr wrote about the possibility of a big data bubble forming because people rely too much on data at the expense of experience and intuition. It got me thinking about all the technologies and algorithms I’ve covered, about all the discussions I’ve had about why a data scientist is more than just a statistician who can write MapReduce jobs. Nearly everywhere, it seems to me (save for, as Lohr cites, unique uses such as algorithmic trading), big data really is less about replacing human intuition than it is about augmenting the human experience by making it easier, faster and more efficient.

Like the purpose-built robots that have revolutionized manufacturing, today’s methods for processing and analyzing data are fast, scalable and precise, but they don’t yet (in most cases) make our decisions. Big data can make life and business a lot more efficient, but for the time being, human judgment and willpower are still very much in control.

Offloading grunt work to the machines

We’ve recently covered some obvious examples of this. Take, for example, recent university research demonstrating how media researchers could use machine learning and natural-language processing to save themselves the work of manually reading and coding every piece of text they wish to analyze as part of a study. Algorithms — like robots in manufacturing — are doing the mindless, repetitive tasks of discerning subject matter, keywords and sentiment, but researchers are still the ones poring over those results and telling us what it all means.

A couple months ago, I spoke with Recommind CEO Bob Tennant about how attorneys are using software to pore through terabytes worth of electronic documents during the discovery process. Predictive coding, as it’s called, frees them up to focus more on case strategy than on the tedium of analyzing every single PDF and email message to figure out if it’s relevant to a case. However, he noted, although the software typically does a better job than a person alone would do, most law firms still use a hybrid man-machine approach to leverage the strengths of both and ensure nothing gets missed. And the software certainly doesn’t assess a document’s relative legal relevance in light of a case’s facts and craft an argument around it.

A screenshot of the Analyst Overview

Even software products such as BeyondCore, which aim to minimize human involvement in the data analysis process as much as possible, are actually just about making business people more efficient. In this case, people are only integral to the first and final steps — selecting the metric with which they’re concerned and then interpreting the statistical correlations, respectively. The messy middle step of asking the right questions is (in theory) eliminated by software that analyzes all the possible correlations and scores and presents them accordingly.

In this sense, one of the better descriptions I’ve heard about actually using data in the corporate world came from ClickFox CEO Marco Pacelli, who compared it to figuring out which few of dozens of cockroaches to kill when the light comes on. Big data, like the flick of the light switch, can show people what’s really going on under the surface. But a smart executive still must figure out how to best solve the problem, capitalize on the opportunity or just put the situation into perspective.

Algorithms can only be so human

Of course, those examples are easy and largely ignore the world of really big data that exists on the web and presents its own its own challenges. Lohr, for example, citing Eli Pariser’s “The Filter Bubble: What the Internet Is Hiding From You,” noted a particular fear “that the algorithms that are shaping my digital world are too simple-minded, rather than too smart.” That’s an astute observation in a world of hyper-personalization, where one could easily find himself snowblind by the content, products, etc., he’s supposedly interested in, making it all the more difficult to gain visibility into the broader world.

But perhaps we’re just expecting the web to be smarter than it is and, really, smarter than any service built on the idea of scale probably should be. For example, web and mobile apps, ranging from Amazon Web Services to Instagram, are only able to automate processes for potentially billions of users because they offer fairly generic services (subscription req’d). Broadly applicable features and non-negotiable terms of service (however problematic) mean companies can focus on building great products rather than wasting time negotiating features and terms with every user.

You want data security or site reliability? Figure it out yourself or wait for your service provider to do it on its own time.

A sample interest graph from Gravity.

Why should personalization algorithms be any different? They can do a heck of a job automating the discovery of stuff we’re interested in, but creating a model intelligent enough to know when any given individual wants to — or needs to — view content outside their their typical interests could prove incredibly challenging for services that deliver personalization in part by identifying broad patterns in user behavior. It’s just not what they’re designed to do.

The web is an expansive place: If we as web users really don’t want to be slaves to algorithms and our usernames, maybe it’s up to us to log out, clear our caches and go do some anonymous digging.

Melding man and machine

That being said, the people tasked with creating the algorithms that power so many web services do seem to understand the need for human input in the model-building process, at least. Even machine learning — a term that conjures up images artificial intelligence and self-aware computer networks — is often just a tool to make data scientists’ lives easier through automation.

Smart data scientists knows know they can’t trust the machines alone, which is why companies doing everything from predicting the content you’ll like to predicting your credit risk have figured out how to make machines work for humans instead of replacing them. Yes, machine learning algorithms and big data technologies analyze a volume of data points that humans could never do, uncovering complex relationships the naked eye could never spot. But once the heavy lifting is done, humans come in and use their subject-matter expertise and logic to prune off bad connections, add context and maybe even inject a little serendipity into the final algorithms.

Whether it’s corporate business intelligence or the consumer web, though, all of this is about automation. Data-minded people have always used data to aid in decision-making without ignoring their instincts. Big data just lets them learn a lot more, a lot faster.

We’ll be talking a lot more about these issues and more at Structure: Data, from March 20-21 in New York, so feel free to mark your calendars. In the meantime, here’s a clip from last year’s event with lots of discussion about machine learning, including how humans will continue to play a role.

Feature image courtesy of Shutterstock user Nataliya Hora.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Connected world: the consumer technology revolution
• NewNet Q4: Platform mania and social commerce shakeout
• NewNet Q4: Platform mania and social commerce shakeout

Updated: A Chicago-based media startup called Journatic, which we profiled earlier this year, has sparked a firestorm of controversy over the outsourcing of hyper-local journalism by newspapers such as the Chicago Tribune, after a staffer revealed that the company added fake bylines to its material — which in some cases is compiled by freelancers in other countries. A number of the startup’s clients have dropped the service as a result, while others are trying to essentially recreate it within their newsrooms. But despite the furor over what some see as Journatic’s unethical methods, the harsh reality is that the economic conditions that led the Tribune and others to make use of the service are not going away any time soon.

As we described in our post — which was based on an interview with Journatic founder and former journalist Brian Timpone — the service uses freelancers and staff to compile the kind of local news that typically appears in weekly community newspapers or the local section of a daily like the Tribune: that is, announcements about local sporting events, residents who have won awards, council meetings and so on. In many cases, the content is produced by a local staffer who pulls information from a database or website (or in some cases calls a local business) along with freelancers who work in the Philippines and elsewhere, and are paid either an hourly rate or on a per-piece basis.

Accelerating the death of newspapers or adapting to it?

In a recent interview with the public-radio show This American Life, a staffer who worked for Journatic described how the company would sometimes use fake bylines on its content — allegedly to disguise the fact that they were compiled by non-residents — and also how reporters working for the service in other locations would try to cover up the fact that they were not in the community they were writing about. Ryan Smith told the Poynter Institute’s Anna Tarkov that he came to believe that this behavior was wrong, in part because it was doing a disservice to local journalism:

I felt like the company I was working for was accelerating the death of the newspaper, luring many members of the industry into their own demise with the promise of short-term savings.

This week, many of the newspapers that were working with Journatic said they have severed that relationship, including the Chicago Sun-Times and a number of papers owned by the GateHouse chain. A spokesman for GateHouse told Poynter that the newspaper company is working on a project that will more or less duplicate what Journatic was doing, by centralizing the production of local community news for the entire chain — and also said that the service didn’t really achieve what GateHouse hoped it would, which was to free up staff to take on more in-depth journalistic pursuits.

Journatic founder Timpone, meanwhile, told media blogger Jim Romenesko that the idea of using fake bylines was implemented for a sister company called Blockshopper, which automates and aggregates real-estate listings. He said that fake names were used because some freelancers had been the target of harassment — and also because the items were often produced by a combination of algorithms, U.S-based editors and freelancers rather than a single person. That practice has stopped, said Timpone, and items that are part of the Tribune’s TribLocal sites now say they come from the “Neighborhood News Service.”

Much of the reaction to the Journatic story has focused on how the fake bylines — and the way reporters described who they were in phone interviews — were designed to simulate hyper-local content, and how this is an unethical or at least unappealing thing for newspapers to do (although some have pointed out that newspapers have always used content that appears with “fake” bylines, including advice columns such as Ann Landers). Mandy Jenkins, who works at Digital First Media and writes a blog called Zombie Journalism, said that readers should demand locally-produced content from their newspapers as part of a “Buy Local” campaign.

Outsourcing and automation are part of the future

The uncomfortable reality, however, is that the Tribune and other newspapers started using Journatic because it was a lot cheaper than generating that kind of content with staff reporters, and newspapers have been scrambling to cut costs as their print-advertising revenue continues to free fall. The Tribune, for example, laid off 22 employees when it outsourced its hyper-local content to Journatic — and while GateHouse says it plans to create similar content in-house, it is still centralizing the production of that content somewhere else (although it may be closer than the Philippines).

Even AOL, which has tried hard to recreate some of the community-newspaper model with its Patch network of local bloggers and reporters, has found that the costs of doing this are almost prohibitive (it has spent over $150 million so far) and the advertising revenue that it derives from those operations is barely worth the trouble. Local bloggers — who have a much more personal connection to their audience — can fill some of the gap, but that is likely to be more of a labor of love than a commercial enterprise.

Is faking hyper-local content the answer? Probably not. But it’s also true that most newspapers can’t afford to continue producing a lot of the kind of content that Journatic generated. The fake-byline issue is a bit of a red herring in that sense: while it would be nice to think that a “Buy Local” campaign would convince newspapers to devote more resources to it, the fact is that most people don’t buy that kind of content at all. And the newspapers that outsourced it wanted to do so in part so they could (theoretically at least) concentrate on more important journalism, although whether they actually do so or not remains to be seen.

At the end of the day, centralized and partly-automated production of that sort of generic content is likely a reality for newspapers — or even fully-automated production, from services like Narrative Science, which generates sports stories, business stories and an increasing range of other content using algorithms instead of human reporters and editors. It may not be the kind of future that all journalists or news consumers would like to see, but it is probably the future nevertheless.

Update: In a phone and email conversation after this post was published, Brian Timpone said that the Journatic contracts with both GateHouse and the Chicago Sun-Times had already expired before the fake-byline report came out, and that only one client has dropped the service because of the report. He also said that fabricated names were only ever used for BlockShopper real-estate listings, and that most of these were created in 2009 and was given to newspapers as a placeholder until more local content could be created. The Journatic founder said the decision not to remove these fake bylines was “absolutely a mistake” and that the company is changing its process so that doesn’t happen again — and also to prevent writers from using pseudonyms, which happened with some Journatic stories from one writer that ran in the San Francisco Chronicle.

Post and thumbnail images courtesy of Flickr user David Boyle and Jim Romenesko

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Content Farms: The Players, The Benefits, The Risks
• How Media Companies Can Compete Online
• Frenemy mine: The pros and cons of social partnerships for online media companies

Some examples from the Bing blog, where on{X} was announced:

• Wouldn’t you like to program your phone to automatically send a reply SMS with your current location as you’re driving when your wife texts you a “where?” message?
• How about programming your phone to automatically show you today’s agenda as you step into the office?
• Or show you the weather when you wake up in the morning?

I like the idea here, although it’s targeted at tech-types and not mainstream consumers. However, with the simple recipe approach, anyone can get started with it. I’m not a programmer but I was able to easily set up a test rule that opens my browser to Techmeme’s site whenever I unlock my phone.

Three aspects of on{X} are intriguing. First, it provides user access to the sensors on a smartphone or tablet to help make things happen. That means access to location, core apps, the camera, microphone and more.

Second, the back-end of the service is in the cloud. Users can program or create recipes in a browser and they’re pushed down to the on{X} app on an Android device. Lastly, it’s interesting that Microsoft debuted the app on Android and not on its own Windows Phone devices. I suspect that’s because the Android crowd has a history of tinkering with phones, custom ROMs and apps.

Regardless, the concept is solid: Our smartphones are smart, but could be smarter by taking advantage of sensors that can see, hear or sense the environment around them. I’ll keep on{X} on my device for a few weeks and see what kinds of interesting ideas I can cook up with it.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Analyzing the wearable computing market
• Is Android broken and if so, will Google fix it?
• A look back at mobile in Q1

AT&T is starting out with seven applications: security cameras; window/door sensors; smoke, carbon monoxide, motion and glass break detectors; remote door lock controls; remote thermostat access; moisture detection sensors; and remote home appliance controls. The devices all connect back to a home control hub through Wi-Fi or Z-Wave radios. The home broadband connection supplies the link back to AT&T’s security monitoring center and customers can access the connected home service through a Web portal or smartphone app.

Though AT&T is initially trialing the service in two of its U-Verse markets, Ma Bell said it would be ISP agnostic. AT&T also won’t require customers to sign up for it’s mobile service to use the smartphone and tablet apps (it didn’t say which smartphone platforms would be supported).

AT&T certainly isn’t the only company looking to tap into the home automation trend, and not even the first operator. Verizon started a smart home pilot with Z-Wave last year, launching the service commercially in June. Time Warner Cable and Comcast have their own home-energy management services as well. AT&T’s smart home plans seem particular ambitious, though. Instead of reselling M2M devices and providing raw connectivity, AT&T is trying to tie all of these disparate sensors and devices together to create a more intelligent connected home platform.

At Mobile World Congress, AT&T first demoed Digital Life’s capabilities, showing off the ability to create home profiles that could group automation functions together. For instance, when the front door is unlocked the thermostat could immediately turn on and the blinds automatically open. Or if an oven warning sensor goes off, the platform not only ships an SMS warning message to the homeowner’s smartphone but a link to a live video feed from the kitchen camera.

AT&T also made the odd move of licensing its smart home technology to other operators internationally before it started offering it to its own customers back home. AT&T apparently isn’t just content to just sell smart home service. It wants to compete with iControl and other platform providers in connected home management as well.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Who and what to watch in the new era of the living room
• CES 2012: a recap and analysis
• Carrier IQ and the continued erosion of operator trust

Many who look at the automation of cloud operations, and the introduction of techniques like machine learning to both applications and operations automation, immediately bring up the image of a computing network that organizes into an intelligent being that is much smarter than us, and ultimately sees humanity as adversarial in some way.

People look at the giant data centers being assembled by the likes of Google, Amazon and Facebook, and they hear about changing scale of computing and the accompanying automation that enables it, and they see science fiction mirrored in life. Those that believe in the “singularity,” a point in time where we humans invent an artificial intelligence smarter than ourselves, see the cloud as an enabling infrastructure.

If we cloud computing fans keep at it, we are going to create our own demise. So we should cut it out already.

Systems, like humans, are diverse

But I don’t share that fear. No, I look at human society and politics, the complex adaptive-systems nature of cloud computing, and I fear something much more depressing: the total leisure paradise of the movie Wall-E.

Understanding why means understanding how the evolution of cloud computing is different from the story of Skynet. According to one fan site, Skynet was the result of a decision to build a centralized computer to coordinate a network of devices, vehicles, robots and weapons, aimed at creating the ultimate defense of the United States.

The cloud, however, is going to form the infrastructure for a loosely assembled network of applications, each of which is designed and operated by a different human social structure, such as a business, a computer science study group, a hacker hobbiest, etc. And these applications will be designed and deployed in various technical, political, cultural and economical goals, all of which will create a level of diversity that will make centralized control all but impossible.

Think of our own global political infrastructure. We can’t even agree on the laws we codify within a single town, much less a county, a state—or at any other scale, for that matter. How is it we expect millions, or billions, or perhaps even more, independent software systems to self-organize into a single intelligence?

No, I think its much more likely that our automated software starts to see many of the same problems: infighting over resources, skirmishes over rights to establish rules, even perhaps outright war.

Not annihilation, but service to a fault

The story in Wall-E is different. Instead of seeing humanity as something to destroy, the computers in Wall-E see humanity as something to serve—to a fault. They are built by a mega-corporation that promises amazing leisure cruises in space aboard entirely automated spacecraft. When the same corporation (run by humans, mind you) completely destroys the environment on the Earth’s surface, they decide to pack up much of humanity in these space ships, and kick off a multi-hundred year cruise while the planet heals itself (with the help of a robot or two).

For me, the part of Wall-E that strikes home is the level to which the automation and technology on the ships remove humanity from reality, eliminating any need to work, or interact “off-line,” or even experience the real-world versions of what the ship ultimately simulates for its passengers. Humanity becomes a complacent, bloated, uninspired version of itself, willingly.

Because technology promises to help us at least as much as it promises to hurt us, I believe the evolution of the cloud will be pushed toward the former, not the latter. That’s not to say that military, criminal or even just self-centered elements won’t make true utopia unlikely, but we will likely be served by robots, have our cars driven for us, and even take our next job assignment from a social network, perhaps organized by an AI system. In fact, much of that will probably happen relatively soon.

I can imagine a day when we, as humans, go generations without realizing what is changing in us: our giving up of creativity and experimentation, our lack of physical engagement with the world around us, our education of only what we’ve allowed our technology to tell us about the world around us.

Then again, human society is a complex adaptive system made up of complex adaptive systems made up of complex adaptive systems. Somehow, I think someone, somewhere, will find a way to keep us, well, human. Even in the face of the cloud.

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• How to use big data to make better business decisions
• Takeaways from the second quarter in cloud and data
• What converged infrastructure means for the future of the data center staff

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Will Storage Go the Way of The Server?
• How fourth-quarter 2012 will affect IT spending in 2013
• IT spending update, third quarter 2012

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Who and what to watch in the new era of the living room
• Connected world: the consumer technology revolution
• The future of mobile: a segment analysis by GigaOM Pro

But not everyone is fretting about a future of work dominated by machines. Business leaders, technologists and economists are peering into the future of work, but futurists are too, and at least one of them sees reason for optimism. A little competition from machines might improve humans, writes James Lee on the World Future Society blog:

David Autor is an economist at MIT who . . . writes that labor markets worldwide are rapidly becoming polarized and he sees a clustering of job opportunities at opposite ends of the skills spectrum.

At one end of the spectrum are low-paying service-oriented jobs that require personal interaction and the manipulation of machinery in unpredictable environments. Examples might include driving a vehicle in traffic, cooking food in a busy kitchen, or taking care of cranky pre-schoolers. Unless people decide to freight their toddlers to India for cheaper childcare, these tasks will still need to be performed locally . . . To the extent that many service jobs involve human interaction, they also require skills such as empathy and interpersonal communication.

At the other end of the spectrum are jobs that require creativity, ambiguity, and high levels of personal training and judgment. These jobs tend to pay well, because they require skill sets that are more difficult to replicate.

The job opportunities of the future require either high cognitive skills, or well-developed personal skills and common sense. In a nutshell, people will need to be either “smart” or “nice” to be successful . . . Luddites should take notice — computers just might push us to do work that is meaningful and enables us to become better people.

There are several possible objections to this argument, the first being that a reasonable level of shared economic prosperity also underlies the better angels of our nature (i.e., innovation, creativity and kindness). Few symphonies get written or breakthrough products designed by people struggling to buy food, and we tend to be meaner when we feel threatened or shortchanged. If anything close to some of the more apocalyptic economic scenarios came to pass and advancing technology created an unemployable underclass, then the negatives to humanity would almost certainly outweigh the benefits.

Plus, there’s the gut instinct reaction that a certain percentage of people will always be jerks and intelligence will always fall on a bell curve, no matter what sort of economic system we develop. Being smart and nice have always worked well for some. So has being a dictatorial narcissist, and, sadly, maybe it always will. Or maybe adjustments will be made and more machines employed, but our economic system will remain much the same as it has through previous shifts in technology.

Do you think more automation and technology will force humans to up their game to compete?

Image courtesy of Flickr user RedCraig

Related research and analysis from GigaOM Pro:
Subscriber content. Sign up for a free trial.

• Millennials in the enterprise, part 1: strategies for supporting the new digital workforce
• Social first-quarter 2013: analysis and outlook
• 11 steps for scaling a startup