Bring Your Own Glass (BYOG): 90% of smart glasses will be sold to enterprises in 2015


ABI Research expects 2015 to be a big year for smart glasses with unit shipment growth of nearly 150% in 2015, almost of all of which will be in the enterprise and public sector. The market intelligence firm expects over 90% of smart glasses to be sold in to the enterprise or public sector in 2015 e.g., remote assistance, police and military, security, warehouse and barcode scanning, and, in the consumer space for gaming.

“Smart glasses were much hyped in 2014 as a smartphone replacement, largely on the back Google’s Glass product announced in early 2013,” commented ABI Research Senior Practice Director Nick Spencer. “However, 2014 showed the use case for smart glasses is task specific, for example remote assistance, security (facial and number plate recognition), augmented reality, and virtual reality. The Google Glass generalized use case is a primary reason for the changes announced last week.”

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Big Data Analytics and the Internet of Things (Infographic)


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Networked Privacy in the Age of Surveillance, Sousveillance, Coveillance (slides)


At Harvard University’s symposium “Privacy in a Networked World,” Lee Rainie presented the latest survey findings about privacy from the Pew Research Center.

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The State of Big Data and Data Science in Japan



Source: Japan News

Compared to other countries, Japan has been weak in the field of so-called big data.

The Internal Affairs and Communications Ministry plans to launch a free online course on basic data science targeting corporate workers and others, beginning in the middle of March.

Professors from the University of Tokyo and others will teach the course, which will focus on giving students a basic understanding of analysis, including how to view and understand data. A video nearly 10 minutes long will be shown four to seven times a week, and quizzes as well as final exams will be taken using laptops, smartphones or other devices.

The “office for considering the development of data scientists,” launched by 10 companies and organizations, including IT companies NTT Data Corp. and Nihon Unisys Ltd., established a system in June 2014 to clarify the skill and knowledge required for five different levels, such as “beginners” and “instructors.” This system is then applied to training programs provided in each company.

In addition, the Japan DataScientist Society, created by 30 companies and groups including major advertising agency Dentsu Inc. and major online search company Yahoo Japan Corp., publicly announced a similar system in December 2014. However, these activities fail to follow a common path.

According to a survey conducted by the Japan Users Association of Information Systems, only slightly more than 8 percent of companies currently utilize big data in their systems (including those in the process of doing so). A major factor contributing to this is that Japanese students of universities and other educational institutions have few opportunities to learn statistics, especially those in the arts and humanities fields.


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2015 Marketing Technology Landscape (Infographic)–1,876 Companies!



Scott Brinker says:

Even I was surprised that the number of vendors nearly doubled from last year’s edition, which charted an already-staggering 947 companies…

My intention with this graphic is to visually demonstrate four points:

  1. Marketing has unquestionably become a technology-powered discipline.
  2. The quantity of martech ventures is a barometer of how much marketing is evolving.
  3. The marketing technology field is heterogenous, with a very broad range of products.
  4. To thrive in this environment, marketing should steadily develop its technical talent.

When you consider the implications of those four points, the “technology management” piece of this is non-trivial — but it’s definitely not the biggest hurdle most companies face. The real challenge is changing how firms think and behave in this hyper-connected, always-on, customer-controlled digital world. The nature of marketing has exploded from an ancillary communications function to the Grand Central Station of customer experience. And the bar for delivering great customer experiences is rising rapidly.

That’s the real challenge. And most companies are still early in the maturity curve of such transformations. The difficulty of trying to sort out the marketing technology landscape is merely an echo of that revolution.

The good news is that most of the marketing technology innovations on this landscape are designed to help marketers conquer that revolution. They’re by no means miracle transformation pills (“instant relief, just add money!”). But when applied in the service of a well-organized, strategically-sound, executive-led digital transformation effort, these technologies are your friend. They can imbue your organization with superhero powers.

So please, don’t be disturbed by this landscape per se. We can navigate the technology.

But you should probably be daunted by the larger transformation of your organization that this landscape heralds — if you’re not, at least a little, you probably don’t yet appreciate just how massive of a management challenge that really is.

The Myth of Marketing Technology Categories

The most objectionable aspect of this graphic is its categorization. My choice of categories, the labels I used for them, which companies I placed in which category, etc., are all subject to debate. However, this isn’t just a problem with my graphic — this is a challenge across the industry (as any product manager for any of these companies will surely testify).

First, new categories keep emerging, often as rebellious offshoots from previous categories. For example, I charted “Influencer Marketing” separate from “Social Media Marketing” this year, because of the momentum of so many companies using that label. But it’s arguably a subset rather than a separate category, with enormous overlap between them. As product managers vie to differentiate their products, alternate labels and variations flood the digital airwaves. Most vendors wrestle with wanting to dominate a category of their own invention (“blue ocean”) yet compete in larger and more popular fields (“red ocean”). It makes for a frothy brew of ever-shifting category labels.

Second, many categories contain radically different kinds of software. Look no further than the “Content Marketing” category. Based on popular usage of the term, most people would agree there is a category of content marketing software. But when you examine all the myriad of products offered under that umbrella — production, workflow, curation, distribution, resource markets, analytics, etc. — you quickly realize they aren’t apples-to-apples: there are oranges, bananas, pears, and a whole exotic fruit basket in there.

Many of the products in such a category are complementary, not competitive. They’re not mutually exclusive. And they’re also not interchangeable.

Third, and a major source of confusion, categories of technologies and categories of “jobs marketers want to do” are often conflated (tip o’ the hat to Clay Christensen). But they’re not the same. For instance, almost all of the categories on this graphic have products that could be applied in a content marketing program. (In fact, if there is an overarching theme to the landscape, it’s that the “content marketing movement,” writ large, is the engine of the marketing technology landscape, and vice versa.) Marketers should strive for clarity about their “jobs to be done” and consider tools that help them achieve that, without getting too hung up on the category labels of the tools themselves.

And fourth, many innovative products have an architecture or a value proposition that inherently spans multiple categories. As Neeraj Agrawal of Battery Ventures said, “I think we’re still in the early innings, maybe the fourth inning” of this grand transformation of marketing. Indeed, many of the popular category labels that we use today — “marketing automation” — have baggage from earlier mental models of digital marketing. I believe it’s a really good thing that many entrepreneurs in this sector are inventing solutions that are untethered by those labels. But it does make them hard to categorize.


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The Simple Pictures Artificial Intelligence Still Can’t Recognize



Earlier this month, Clune discussed these findings with fellow researchers at the Neural Information Processing Systems conference in Montreal. The event brought together some of the brightest thinkers working in artificial intelligence. The reactions sorted into two rough groups. One group—generally older, with more experience in the field—saw how the study made sense. They might’ve predicated a different outcome, but at the same time, they found the results perfectly understandable.

The second group, comprised of people who perhaps hadn’t spent as much time thinking about what makes today’s computer brains tick, were struck by the findings. At least initially, they were surprised these powerful algorithms could be so plainly wrong. Mind you, these were still people publishing papers on neural networks and hanging out at one of the year’s brainiest AI gatherings.

To Clune, the bifurcated response was telling: It suggested a sort of generational shift in the field. A handful of years ago, the people working with AI were building AI. These days, the networks are good enough that researchers are simply taking what’s out there and putting it to work. “In many cases you can take these algorithms off the shelf and have them help you with your problem,” Clune says. “There is an absolute gold rush of people coming in and using them.”

That’s not necessarily a bad thing. But as more stuff is built on top of AI, it will only become more vital to probe it for shortcomings like these. If it really just takes a string of pixels to make an algorithm certain that a photo shows an innocuous furry animal, think how easy it could be to slip pornography undetected through safe search filters. In the short term, Clune hopes the study will spur other researchers to work on algorithms that take images’ global structure into account. In other words, algorithms that make computer vision more like human vision.

But what does “recognize” mean? The two groups of AI researchers described above don’t include AI researchers (e.g., Oren Etzioni) that argues that for a computer to be “intelligent,” it needs to understand what it “sees,” not just identify or classify it. “Recognize” means understanding concepts, not just pattern matching.

Here’s a video clip of Richard Feynman (HT Farnam Street) about why recognizing the difference between knowing the name of something and understanding it is so important for humans.

See that bird? It’s a brown-throated thrush, but in Germany it’s called a halzenfugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird. You only know something about people; what they call the bird.


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How People Ignored Each Other Before Smartphones



Source: @SBartner


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