Nom! Designers Make Snacks Their Muse

4thWEB curated content from Snacks Quarterly
By Carey Dunne
(see full article)

In Snacks Quarterly, writers and designers turn snacktime into an unlikely creative exercise.


Brian Rea

Snacks are a mighty and undersung unifier: most people, regardless of creed, class, or dietary restriction, can agree that little morsels of food between meals are great. Snacks Quarterly, an online publication “for the distinguished snack enthusiast,” was launched in early 2014 by artists Brad Simon and Alexander Barrett as an outlet to share their passion for snacking. Since their launch, they’ve published snack-related essays, illustrations, anecdotes, and advice by designers like Erik Marinovich, Josh Cochran, Tim Lahan, Aaron Draplin, Jessica Hische, Brian Rea, Will Bryant, and Dan Christofferson. The quarterly’s third issue has just been released, introduced with a rousing letter from editor-in-chief “Sinclair P. Munch,” which reminds readers that designers can find inspiration in the unlikeliest of places, from White Fudge Oreos to Little Debbie’s Christmas Tree Cakes. The publication makes snacking seem downright virtuous: you’re not just gorging on empty calories, you’re engaging with a powerful muse.

“Snacks are inspiring because they’re always there and they do so much for all of us. Especially designers and writers,” Barrett tells Co.Design in an email “[Snacks are] there when we have to skip lunch to meet a deadline, when we just need a break, and when we need comforting after a bad meeting. But we rarely think about them creatively. It’s nice to take a moment to show them some love.”


Erik Marinovich

The inspiration for Snacks Quarterly struck in late 2013, while Barrett and Simon were in the studio printing a poster about snacks, just for fun. Somewhere on the poster, “Snack Quarterly” showed up in a New Yorker-style font. “It seemed like a real thing,” Barrett says. “Surprisingly, the url was available, which seemed like a sign.” The pair started emailing back and forth, added an “s” to the end of “Snack,” and the first issue was up a few months later.

Here, a few highlights from the most recent issue that show just how creative people can get on a sugar high:


Mike Davis

Mike Davis of Burlesque Design illustrated an epic pyramid of snacks of all shapes and sizes, from watermelons to popcorn to Swiss cheese to whole fish.


David Schwen

David Schwen fixed a Pepperidge Farm cheddar goldfish to a wooden plaque hanging on a wall against goldfish-patterned wallpaper, a punny riff on the familiar visual of a mounted game fish. “For us, great design is smart, obvious, and unexpected,” Barrett says. “Schwen’s piece hit all three marks.”


Typographer extraordinaire Jessica Hische spelled “More Salt”–which many of the best snacks have–in elegant letters made of, yes, salt.

Read the third issue of Snacks Quarterly here.

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

4thWEB curated content from IEEE Spectrum
By Lee Gomes
(see full article)

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report “Frontiers in Massive Data Analysis.” San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014.

Why We Should Stop Using Brain Metaphors When We Talk About Computing

IEEE Spectrum: I infer from your writing that you believe there’s a lot of misinformation out there about deep learning, big data, computer vision, and the like.

Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are. Specifically on the topic of deep learning, it’s largely a rebranding of neural networks, which go back to the 1980s. They actually go back to the 1960s; it seems like every 20 years there is a new wave that involves them. In the current wave, the main success story is the convolutional neural network, but that idea was already present in the previous wave. And one of the problems with both the previous wave, that has unfortunately persisted in the current wave, is that people continue to infer that something involving neuroscience is behind it, and that deep learning is taking advantage of an understanding of how the brain processes information, learns, makes decisions, or copes with large amounts of data. And that is just patently false.

Spectrum: As a member of the media, I take exception to what you just said, because it’s very often the case that academics are desperate for people to write stories about them.

Michael Jordan: Yes, it’s a partnership.

Spectrum: It’s always been my impression that when people in computer science describe how the brain works, they are making horribly reductionist statements that you would never hear from neuroscientists. You called these “cartoon models” of the brain.

Michael Jordan: I wouldn’t want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

Spectrum: In addition to criticizing cartoon models of the brain, you actually go further and criticize the whole idea of “neural realism”—the belief that just because a particular hardware or software system shares some putative characteristic of the brain, it’s going to be more intelligent. What do you think of computer scientists who say, for example, “My system is brainlike because it is massively parallel.”

Michael Jordan: Well, these are metaphors, which can be useful. Flows and pipelines are metaphors that come out of circuits of various kinds. I think in the early 1980s, computer science was dominated by sequential architectures, by the von Neumann paradigm of a stored program that was executed sequentially, and as a consequence, there was a need to try to break out of that. And so people looked for metaphors of the highly parallel brain. And that was a useful thing.

But as the topic evolved, it was not neural realism that led to most of the progress. The algorithm that has proved the most successful for deep learning is based on a technique called back propagation. You have these layers of processing units, and you get an output from the end of the layers, and you propagate a signal backwards through the layers to change all the parameters. It’s pretty clear the brain doesn’t do something like that. This was definitely a step away from neural realism, but it led to significant progress. But people tend to lump that particular success story together with all the other attempts to build brainlike systems that haven’t been nearly as successful.

Spectrum: Another point you’ve made regarding the failure of neural realism is that there is nothing very neural about neural networks.

Michael Jordan: There are no spikes in deep-learning systems. There are no dendrites. And they have bidirectional signals that the brain doesn’t have.

We don’t know how neurons learn. Is it actually just a small change in the synaptic weight that’s responsible for learning? That’s what these artificial neural networks are doing. In the brain, we have precious little idea how learning is actually taking place.

Spectrum: I read all the time about engineers describing their new chip designs in what seems to me to be an incredible abuse of language. They talk about the “neurons” or the “synapses” on their chips. But that can’t possibly be the case; a neuron is a living, breathing cell of unbelievable complexity. Aren’t engineers appropriating the language of biology to describe structures that have nothing remotely close to the complexity of biological systems?

Michael Jordan: Well, I want to be a little careful here. I think it’s important to distinguish two areas where the word neural is currently being used.

One of them is in deep learning. And there, each “neuron” is really a cartoon. It’s a linear-weighted sum that’s passed through a nonlinearity. Anyone in electrical engineering would recognize those kinds of nonlinear systems. Calling that a neuron is clearly, at best, a shorthand. It’s really a cartoon. There is a procedure called logistic regression in statistics that dates from the 1950s, which had nothing to do with neurons but which is exactly the same little piece of architecture.

A second area involves what you were describing and is aiming to get closer to a simulation of an actual brain, or at least to a simplified model of actual neural circuitry, if I understand correctly. But the problem I see is that the research is not coupled with any understanding of what algorithmically this system might do. It’s not coupled with a learning system that takes in data and solves problems, like in vision. It’s really just a piece of architecture with the hope that someday people will discover algorithms that are useful for it. And there’s no clear reason that hope should be borne out. It is based, I believe, on faith, that if you build something like the brain, that it will become clear what it can do.

Spectrum: If you could, would you declare a ban on using the biology of the brain as a model in computation?

Michael Jordan: No. You should get inspiration from wherever you can get it. As I alluded to before, back in the 1980s, it was actually helpful to say, “Let’s move out of the sequential, von Neumann paradigm and think more about highly parallel systems.” But in this current era, where it’s clear that the detailed processing the brain is doing is not informing algorithmic process, I think it’s inappropriate to use the brain to make claims about what we’ve achieved. We don’t know how the brain processes visual information.

Our Foggy Vision About Machine Vision

Spectrum: You’ve used the word hype in talking about vision system research. Lately there seems to be an epidemic of stories about how computers have tackled the vision problem, and that computers have become just as good as people at vision. Do you think that’s even close to being true?

Michael Jordan: Well, humans are able to deal with cluttered scenes. They are able to deal with huge numbers of categories. They can deal with inferences about the scene: “What if I sit down on that?” “What if I put something on top of something?” These are far beyond the capability of today’s machines. Deep learning is good at certain kinds of image classification. “What object is in this scene?”

But the computational vision problem is vast. It’s like saying when that apple fell out of the tree, we understood all of physics. Yeah, we understood something more about forces and acceleration. That was important. In vision, we now have a tool that solves a certain class of problems. But to say it solves all problems is foolish.

Spectrum: How big of a class of problems in vision are we able to solve now, compared with the totality of what humans can do?

Michael Jordan: With face recognition, it’s been clear for a while now that it can be solved. Beyond faces, you can also talk about other categories of objects: “There’s a cup in the scene.” “There’s a dog in the scene.” But it’s still a hard problem to talk about many kinds of different objects in the same scene and how they relate to each other, or how a person or a robot would interact with that scene. There are many, many hard problems that are far from solved.

Spectrum: Even in facial recognition, my impression is that it still only works if you’ve got pretty clean images to begin with.

Michael Jordan: Again, it’s an engineering problem to make it better. As you will see over time, it will get better. But this business about “revolutionary” is overwrought.

Why Big Data Could Be a Big Fail

Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.

Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.

Spectrum: How so?

Michael Jordan: In a classical database, you have maybe a few thousand people in them. You can think of those as the rows of the database. And the columns would be the features of those people: their age, height, weight, income, et cetera.

Now, the number of combinations of these columns grows exponentially with the number of columns. So if you have many, many columns—and we do in modern databases—you’ll get up into millions and millions of attributes for each person.

Now, if I start allowing myself to look at all of the combinations of these features—if you live in Beijing, and you ride bike to work, and you work in a certain job, and are a certain age—what’s the probability you will have a certain disease or you will like my advertisement? Now I’m getting combinations of millions of attributes, and the number of such combinations is exponential; it gets to be the size of the number of atoms in the universe.

Those are the hypotheses that I’m willing to consider. And for any particular database, I will find some combination of columns that will predict perfectly any outcome, just by chance alone. If I just look at all the people who have a heart attack and compare them to all the people that don’t have a heart attack, and I’m looking for combinations of the columns that predict heart attacks, I will find all kinds of spurious combinations of columns, because there are huge numbers of them.

So it’s like having billions of monkeys typing. One of them will write Shakespeare.

Spectrum:Do you think this aspect of big data is currently underappreciated?

Michael Jordan: Definitely.

Spectrum: What are some of the things that people are promising for big data that you don’t think they will be able to deliver?

Michael Jordan: I think data analysis can deliver inferences at certain levels of quality. But we have to be clear about what levels of quality. We have to have error bars around all our predictions. That is something that’s missing in much of the current machine learning literature.

Spectrum: What will happen if people working with data don’t heed your advice?

Michael Jordan: I like to use the analogy of building bridges. If I have no principles, and I build thousands of bridges without any actual science, lots of them will fall down, and great disasters will occur.

Similarly here, if people use data and inferences they can make with the data without any concern about error bars, about heterogeneity, about noisy data, about the sampling pattern, about all the kinds of things that you have to be serious about if you’re an engineer and a statistician—then you will make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori. You will just produce these outputs and hope for the best.

And so that’s where we are currently. A lot of people are building things hoping that they work, and sometimes they will. And in some sense, there’s nothing wrong with that; it’s exploratory. But society as a whole can’t tolerate that; we can’t just hope that these things work. Eventually, we have to give real guarantees. Civil engineers eventually learned to build bridges that were guaranteed to stand up. So with big data, it will take decades, I suspect, to get a real engineering approach, so that you can say with some assurance that you are giving out reasonable answers and are quantifying the likelihood of errors.

Spectrum: Do we currently have the tools to provide those error bars?

Michael Jordan: We are just getting this engineering science assembled. We have many ideas that come from hundreds of years of statistics and computer science. And we’re working on putting them together, making them scalable. A lot of the ideas for controlling what are called familywise errors, where I have many hypotheses and want to know my error rate, have emerged over the last 30 years. But many of them haven’t been studied computationally. It’s hard mathematics and engineering to work all this out, and it will take time.

It’s not a year or two. It will take decades to get right. We are still learning how to do big data well.

Spectrum: When you read about big data and health care, every third story seems to be about all the amazing clinical insights we’ll get almost automatically, merely by collecting data from everyone, especially in the cloud.

Michael Jordan: You can’t be completely a skeptic or completely an optimist about this. It is somewhere in the middle. But if you list all the hypotheses that come out of some analysis of data, some fraction of them will be useful. You just won’t know which fraction. So if you just grab a few of them—say, if you eat oat bran you won’t have stomach cancer or something, because the data seem to suggest that—there’s some chance you will get lucky. The data will provide some support.

But unless you’re actually doing the full-scale engineering statistical analysis to provide some error bars and quantify the errors, it’s gambling. It’s better than just gambling without data. That’s pure roulette. This is kind of partial roulette.

Spectrum: What adverse consequences might await the big-data field if we remain on the trajectory you’re describing?

Michael Jordan: The main one will be a “big-data winter.” After a bubble, when people invested and a lot of companies overpromised without providing serious analysis, it will bust. And soon, in a two- to five-year span, people will say, “The whole big-data thing came and went. It died. It was wrong.” I am predicting that. It’s what happens in these cycles when there is too much hype, i.e., assertions not based on an understanding of what the real problems are or on an understanding that solving the problems will take decades, that we will make steady progress but that we haven’t had a major leap in technical progress. And then there will be a period during which it will be very hard to get resources to do data analysis. The field will continue to go forward, because it’s real, and it’s needed. But the backlash will hurt a large number of important projects.

What He’d Do With $1 Billion

Spectrum: Considering the amount of money that is spent on it, the science behind serving up ads still seems incredibly primitive. I have a hobby of searching for information about silly Kickstarter projects, mostly to see how preposterous they are, and I end up getting served ads from the same companies for many months.

Michael Jordan: Well, again, it’s a spectrum. It depends on how a system has been engineered and what domain we’re talking about. In certain narrow domains, it can be very good, and in very broad domains, where the semantics are much murkier, it can be very poor. I personally find Amazon’s recommendation system for books and music to be very, very good. That’s because they have large amounts of data, and the domain is rather circumscribed. With domains like shirts or shoes, it’s murkier semantically, and they have less data, and so it’s much poorer.

There are still many problems, but the people who build these systems are hard at work on them. What we’re getting into at this point is semantics and human preferences. If I buy a refrigerator, that doesn’t show that I am interested in refrigerators in general. I’ve already bought my refrigerator, and I’m probably not likely to still be interested in them. Whereas if I buy a song by Taylor Swift, I’m more likely to buy more songs by her. That has to do with the specific semantics of singers and products and items. To get that right across the wide spectrum of human interests requires a large amount of data and a large amount of engineering.

Spectrum: You’ve said that if you had an unrestricted $1 billion grant, you would work on natural language processing. What would you do that Google isn’t doing with Google Translate?

Michael Jordan: I am sure that Google is doing everything I would do. But I don’t think Google Translate, which involves machine translation, is the only language problem. Another example of a good language problem is question answering, like “What’s the second-biggest city in California that is not near a river?” If I typed that sentence into Google currently, I’m not likely to get a useful response.

Spectrum:So are you saying that for a billion dollars, you could, at least as far as natural language is concerned, solve the problem of generalized knowledge and end up with the big enchilada of AI: machines that think like people?

Michael Jordan: So you’d want to carve off a smaller problem that is not about everything, but which nonetheless allows you to make progress. That’s what we do in research. I might take a specific domain. In fact, we worked on question-answering in geography. That would allow me to focus on certain kinds of relationships and certain kinds of data, but not everything in the world.

Spectrum: So to make advances in question answering, will you need to constrain them to a specific domain?

Michael Jordan: It’s an empirical question about how much progress you could make. It has to do with how much data is available in these domains. How much you could pay people to actually start to write down some of those things they knew about these domains. How many labels you have.

Spectrum: It seems disappointing that even with a billion dollars, we still might end up with a system that isn’t generalized, but that only works in just one domain.

Michael Jordan: That’s typically how each of these technologies has evolved. We talked about vision earlier. The earliest vision systems were face-recognition systems. That’s domain bound. But that’s where we started to see some early progress and had a sense that things might work. Similarly with speech, the earliest progress was on single detached words. And then slowly, it started to get to be where you could do whole sentences. It’s always that kind of progression, from something circumscribed to something less and less so.

Spectrum: Why do we even need better question-answering? Doesn’t Google work well enough as it is?

Michael Jordan: Google has a very strong natural language group working on exactly this, because they recognize that they are very poor at certain kinds of queries. For example, using the word not. Humans want to use the word not. For example, “Give me a city that is not near a river.” In the current Google search engine, that’s not treated very well.

How Not to Talk About the Singularity

Spectrum: Turning now to some other topics, if you were talking to someone in Silicon Valley, and they said to you, “You know, Professor Jordan, I’m a really big believer in the singularity,” would your opinion of them go up or down?

Michael Jordan: I luckily never run into such people.

Spectrum: Oh, come on.

Michael Jordan: I really don’t. I live in an intellectual shell of engineers and mathematicians.

Spectrum: But if you did encounter someone like that, what would you do?

Michael Jordan: I would take off my academic hat, and I would just act like a human being thinking about what’s going to happen in a few decades, and I would be entertained just like when I read science fiction. It doesn’t inform anything I do academically.

Spectrum: Okay, but knowing what you do academically, what do you think about it?

Michael Jordan: My understanding is that it’s not an academic discipline. Rather, it’s partly philosophy about how society changes, how individuals change, and it’s partly literature, like science fiction, thinking through the consequences of a technology change. But they don’t produce algorithmic ideas as far as I can tell, because I don’t ever see them, that inform us about how to make technological progress.

What He Cares About More Than Whether P = NP

Spectrum: Do you have a guess about whether P = NP? Do you care?

Michael Jordan: I tend to be not so worried about the difference between polynomial and exponential. I’m more interested in low-degree polynomial—linear time, linear space. P versus NP has to do with categorization of algorithms as being polynomial, which means they are tractable and exponential, which means they’re not.

I think most people would agree that probably P is not equal to NP. As a piece of mathematics, it’s very interesting to know. But it’s not a hard and sharp distinction. There are many exponential time algorithms that, partly because of the growth of modern computers, are still viable in certain circumscribed domains. And moreover, for the largest problems, polynomial is not enough. Polynomial just means that it grows at a certain superlinear rate, like quadric or cubic. But it really needs to grow linearly. So if you get five more data points, you need five more amounts of processing. Or even sublinearly, like logarithmic. As I get 100 new data points, it grows by two; if I get 1,000, it grows by three.

That’s the ideal. Those are the kinds of algorithms we have to focus on. And that is very far away from the P versus NP issue. It’s a very important and interesting intellectual question, but it doesn’t inform that much about what we work on.

Spectrum: Same question about quantum computing.

Michael Jordan: I am curious about all these things academically. It’s real. It’s interesting. It doesn’t really have an impact on my area of research.

What the Turing Test Really Means

Spectrum: Will a machine pass the Turing test in your lifetime?

Michael Jordan: I think you will get a slow accumulation of capabilities, including in domains like speech and vision and natural language. There will probably not ever be a single moment in which we would want to say, “There is now a new intelligent entity in the universe.” I think that systems like Google already provide a certain level of artificial intelligence.

Spectrum: They are definitely useful, but they would never be confused with being a human being.

Michael Jordan: No, they wouldn’t be. I don’t think most of us think the Turing test is a very clear demarcation. Rather, we all know intelligence when we see it, and it emerges slowly in all the devices around us. It doesn’t have to be embodied in a single entity. I can just notice that the infrastructure around me got more intelligent. All of us are noticing that all of the time.

Spectrum: When you say “intelligent,” are you just using it as a synonym for “useful”?

Michael Jordan: Yes. What our generation finds surprising—that a computer recognizes our needs and wants and desires, in some ways—our children find less surprising, and our children’s children will find even less surprising. It will just be assumed that the environment around us is adaptive; it’s predictive; it’s robust. That will include the ability to interact with your environment in natural language. At some point, you’ll be surprised by being able to have a natural conversation with your environment. Right now we can sort of do that, within very limited domains. We can access our bank accounts, for example. They are very, very primitive. But as time goes on, we will see those things get more subtle, more robust, more broad. As some point, we’ll say, “Wow, that’s very different when I was a kid.” The Turing test has helped get the field started, but in the end, it will be sort of like Groundhog Day—a media event, but something that’s not really important.

Why Microsoft CEO Satya Nadella Loves What Steve Ballmer Once Despised

4thWEB Curated Content from Wired
By Cade Metz
(see full article)

“I don’t want to fight old battles,” says Microsoft CEO Satya Nadella. “I want to fight new ones.”

It’s Sunday evening, and Nadella is sitting in a glass-enclosed room at the back of a Japanese restaurant in San Francisco’s North Beach neighborhood, eating sushi with a few reporters. As the meal began, one reporter promptly asked “how many women report to him and how many have asked for a raise,” cutting straight to the CEO’s recent public gaffe where he advised women in the tech biz to trust “karma” if they don’t get the pay they feel they deserve, and though Nadella and his PR handlers have kept a tight lid on any discussion of this incident—and his subsequent apology—he’s somewhat freer in addressing another hot-button Microsoft issue: the Linux open source operating system.

Or at least, it used to be a hot-button issue. As Nadella says, he’s not interested in fighting old battles like the one with Linux, the rival to Microsoft’s Windows operating system that former CEO Steve Ballmer once called a “malignant cancer.” In fact, he makes it clear that Microsoft must embrace Linux—and change in other big ways—in order to compete in the modern tech world. Linux, you see, is now an enormous part of the way businesses build their websites and other online software, and Microsoft wants these businesses using its big bet on the future of information technology: the cloud computing service known as Microsoft Azure. “If you don’t jump on the new,” Nadella says, “you don’t survive.”

Part of an enormous shift towards cloud computing—a shift driven by several tech giants, including Amazon and Google—Azure is a way of renting computing power over the internet, without setting up your own hardware. About two years ago, Microsoft revamped the service so that anyone could use this computing power to run Linux as well as Windows, and though the move was hailed by pundits, many still questioned whether it would work—whether people would actually choose to run the open source OS atop a Microsoft service. But, sitting in this high-end Japanese eatery, Nadella reveals that Linux now accounts for 20 percent of all activity on Azure.

The next morning, he’ll say the same thing in public, as he lays out the broader strategy for Microsoft’s cloud computing service at an event in downtown San Francisco. “Microsoft loves Linux,” he’ll proclaim with a smile.

For those who watched Microsoft’s long battle with Linux, it’s still a surprising thing to hear, and it’s indicative of a much larger transformation at Microsoft. The company is not just running Linux atop Azure. It’s offering a version of its Microsoft Office suite for Apple mobile devices. It’s providing a free version of Windows, echoing Google’s Android operating system. It’s open sourcing many of its own software tools. And as Nadella points out, it’s partnering with rivals such as Workday and Salesforce atop its cloud services.

None of this seemed possible during the reign of Steve Ballmer, and so much of it happened on Nadella’s watch, in the months since he took over the CEO post this past February and during his time running the company’s cloud group. Nadella doesn’t take full credit for this shift. But he certainly believes it’s the best way forward for Microsoft—and it is.

What Nadella realizes is that in today’s world, businesses and engineers and consumers use such a vast array of hardware devices, online services, and software tools—and that these arrive from so many different sources: Apple, Google, Amazon, Microsoft, open source projects— and the list goes on. That may not have been the case 20 years ago, during Microsoft’s heyday, but it is now.

As Nadella says, the tech future will not be “winner take all.” And in order to win at least part of this future, Microsoft must concede as much as it takes.

Content Is Not King

4thWEB curated content from Six Pixels of Separation
By Mitch Joel
(see full article)

It’s a pretty bold statement, isn’t it?

Are we making the wrong assumptions about content? What works? What doesn’t work? What makes something go viral? Do you think that BuzzFeed knows the answers to these questions, better than anyone else? Last month, I had the honor of leading a very in-depth conversation with BuzzFeed founder, Jonah Peretti, at an exclusive retreat for senior marketers. He was fascinating (and, I do regret not being able to record that session and turn it into a podcast). While we were hanging out and waiting for the session to begin, I remembered having a very interesting conversation at this past year’s TED event with Ze Frank. Many know Ze as somewhat of an Internet legend. He was making videos go viral long before this thing called YouTube was popular and, more recently, he became the President of BuzzFeed Motion Pictures. Based in LA, his division of BuzzFeed works on all aspects of video creation… and distribution.

What does Ze Frank and BuzzFeed know about making your content work?

It turns out, that Ze knows a lot about what works. His finding will – without question – surprise you. Ze visited the Paley Media Council in Los Angeles last week for a live event moderated by Re/code senior editor, Dawn Chmielewski. Ze speaks about what brands must do to succeed in a world where the consumer has so many content choices. Personally, I started watching this conversation and wondered if I would hear anything new. After about five minutes, I found myself hitting the pause button and scrambling for my Moleskine to take notes. Ze is an amazing presenter and storyteller. He’s also very calculated and date-driven, when it comes to figuring out what works (and what doesn’t).

You have to watch this: BuzzFeed’s Ze Frank talks to Re/code’s Dawn Chmielewski.

Vine is Blowing Up for Brands – Why?

Vine Blowing Up for BrandsVine is the latest social video marketing platform and it’s taking off like wildfire. CNET reports Vine has 40 million users. And this is even after Instagram decided to copy Vine’s short video concept and use it on its social photo site. Twitter hit the nail perfectly with its six-second, looped videos. And brands are climbing on board to take advantage of their own efforts. And courting prominent Vine users to create videos featuring their brands.

Vine – Six Seconds of the Right Stuff

WebProNews reports Twitter users are just as likely to watch Vine videos as they are YouTube. The short videos match perfectly with the short message platform that Twitter provides. And brands need to learn how to show a concise, visually compelling method to catch attention. There are a variety of industries taking advantage of Vine. Although those that can be condensed into a short visual medium are doing the best. Examples include Urban Outfitter’s clothing selection. As well as Next’s furniture and the “Wolverine” movie. Stop motion is also a commonly used technique to fit more into a single clip.

Going Viral with Vine

You have only a few seconds to catch your customer’s attention. Vine’s six-second limit makes you work within a strict framework. You literally only get those few seconds to figure out how to attract the customer. And keep them watching and get your brand message across.

Humor is always one of your best bets for getting your Vine video to go viral. For example, Dove created a Vine video that shows people bowling with their soap and bath gel products. That’s a cute and clever way to show off their products. If your marketing message isn’t conductive to humor, try using visually distinctive techniques. And don’t forget to take advantage of the endless looping in Vine.

Also, don’t forget the appropriate hashtags. Since you do want to reach as much of your target audience as possible. Lowes has a particularly good Vine account. According to Adverblog, they utilize hashtags very effectively. The home improvement store uses brand specific and general tags, such as #Lowesfixinsix and #Howto. Vine videos are simple to share on other social networks. So don’t skimp on spreading them around and leveraging your existing audience.

Bring in the Vine Experts

Vine has only been out for seven months. But that’s more than enough time for some users to truly figure out what makes Vine tick. Some of these users are brands you can draw inspiration from. And some are budding Vine celebrities who are enjoying their Internet fame. The top Vine users include Jethro Ames. He produces household and how-to videos. Also, Khoa makes his videos out of construction paper. Also, Meagan Cignoli plays up the celebrity angle by posting behind the scenes fashion model Vines. Some of them are also more than happy to take money in exchange for creating custom Vine videos for your business. And promoting it to their entire social network. If you don’t want to go through trial-and-error to figure out exactly what you’re doing on Vine, this is one of the best ways to spend your marketing dollars.

Vine – Great for Slow Internet

YouTube has certainly generated plenty of brand awareness and money for businesses. But there’s one area it has a hard time penetrating. Rural areas of the United States suffer from limited Internet access. Those areas aren’t able to easily watch YouTube videos and other high-bandwidth content without the help of satellite Internet services. The Vine format is fast and quick-loading, whether you have a satellite Internet service such as Hughesnet.com or not. Essentially, Vine provides a similar marketing experience to YouTube without cutting off this massive user demographic.