Google Supports Outraged Scholars

October 2, 2017

Google has taken issue with a recent list from the Campaign for Accountability (CfA), TechCrunch reports in, “Google Responds to Academic Funding Controversy—with a GIF.” Writer Frederic Lardinois reports that the CfA recently released a list of policy experts and academics who, they say, had received Googley dollars last year. The only problem—many who found themselves on the list dispute their inclusion, saying they had not received any funding from Google or, if they had, it was unrelated to the work the CfA specified. Google issued a response, supporting the protesting experts and academics as well as defending its support of researchers in general. The company also struck back; the article explains:

And in a direct attack on CfA, Google also notes that while the group advocates for transparency, its own corporate funders remain in the shadows. The only backer we know of is Oracle, which is obviously competing with Google in many areas. The group has also recently taken on SolarCity/Tesla. In its blog post, Google also argues that ‘AT&T, the MPAA, ICOMP, FairSearch and dozens more’ fund similar campaigns.

Google later created a GIF in response to requests for elaboration. It shares a series of tweets from some of the affected scholars, in which they detail just where the CfA went wrong in each of their cases. Lardinois continues:

It’s not often that a company like Google makes its own GIF in response to a request for comment, but I gather this goes to show that Google wants to move on from this discussion and let the academics speak for themselves. While the CfA’s methods are less than ideal, there are legitimate questions about how even small amounts of funding can influence research.

So far, Lardinois notes, public discussion on how funding can influence research have centered around pharmaceuticals. He projects it will soon grow, however, to include policy research as tech companies ramp up their funding programs

Cynthia Murrell, October 2, 2017

Searching for a Disguised Face? Some Progress

September 9, 2017

I read “Meeting the Disguised Face Challenge via Deep Convolutional Network.” My interest was piqued because I thought I had seen references to a breakthrough in facial recognition when the subjects of interest were wearing disguises.

I noted these comments in the write up:

  • The method: “Deep convolutional networks are software creations organized into interconnected layers, much like the visual cortex, the part of the brain that processes visual information.”
  • The accuracy: “The fewer facial key points it can see, the worse the software is at recognizing a person in a photo. It’s also thrown off by busy backgrounds, so can only identify a person wearing a cap, glasses and scarf 43 per cent of the time if they’re standing in front of a complicated background.”
  • Work around: “Wearing a rigid mask that covers the whole face, for example, would give current facial recognition systems nothing to go on. And other researchers have developed patterned glasses that are specially designed to trick and confuse AI facial recognition systems.”

To sum up: Promising but real time facial recognition of people walking through an airport entrance remains a challenge. In addition to the computational demands, the false positives can quickly consume available resources.

Stephen E Arnold, September 9, 2017

Helpful Search Operators for Google Users

July 31, 2017

We have found a resource that can help readers google like never before: GoogleGuide’s article is titled simply, “Search Operators.” Unsatisfied with the information she found at Google’s website, mathematician and search enthusiast Nancy Blachman started GoogleGuide to enlighten us all on advanced Google Search methods. In “Search Operators,” she and colleague Jerry Peek educate us on one exacting approach. They write:

The following is an alphabetical list of the search operators. This list includes operators that are not officially supported by Google and not listed in Google’s online help. Note: Google may change how undocumented operators work or may eliminate them completely. Each entry typically includes the syntax, the capabilities, and an example.

The article leads with a table listing the search operators next to the relevant Google service: Web search, image search, groups, etc., which can be cross-referenced with the alphabetical list. Operator functions include useful tasks like searching for specific pages by title, discovering who has linked to a certain website and restricting searches by file type. The team even concludes with a set of exercises for practice with the operators. Check it out to make your internet searches even more efficient.

Cynthia Murrell, July 31, 2017

Facebook: Search Images by the Objects They Contain

July 3, 2017

Has Facebook attained the holy grail of image search? Tech Crunch reports, “Facebook’s AI Unlocks the Ability to Search Photos by What’s in Them.” I imagine this will be helpful to law enforcement.

A platform Facebook originally implemented to help the visually impaired, Lumos (built on top of FBLearner Flow), is now being applied to search functionality across the social network. With this tool, one can search using keywords that describe things in the desired image, rather than relying on tags and captions. Writer John Mannes describes how this works:

Facebook trained an ever-fashionable deep neural network on tens of millions of photos. Facebook’s fortunate in this respect because its platform is already host to billions of captioned images. The model essentially matches search descriptors to features pulled from photos with some degree of probability. After matching terms to images, the model ranks its output using information from both the images and the original search. Facebook also added in weights to prioritize diversity in photo results so you don’t end up with 50 pics of the same thing with small changes in zoom and angle. In practice, all of this should produce more satisfying and relevant results.

Facebook expects to extrapolate this technology to the wealth of videos it continues to amass. This could be helpful to a user searching for personal videos, of course, but just consider the marketing potential. The article continues:

Pulling content from photos and videos provides an original vector to improve targeting. Eventually it would be nice to see a fully integrated system where one could pull information, say searching a dress you really liked in a video, and relate it back to something on Marketplace or even connect you directly with an ad-partner to improve customer experiences while keeping revenue growth afloat.

Mannes reminds us Facebook is operating amidst fierce competition in this area. Pinterest, for example, enables users to search images by the objects they contain. Google may be the furthest along, though; that inventive company has developed its own image captioning model that boasts an accuracy rate of over 90% when either identifying objects or classifying actions within images.

Cynthia Murrell, July 3, 2017

 

Giffying All the Way to Profits

June 7, 2017

Giphy, the GIF search engine has secured $150 funding at $600 million valuations. What started as a web crawler is on its way to profitability.

Business Insider in an article titled Inside the GIF Factory: How Giphy Plans to Build a Real Business by Animating the Internet says:

Giphy isn’t profitable yet. In fact, the company doesn’t even have a reliable means of generating revenue at this point. But now that GIFs are an ingrained aspect of online behavior, the company is hard at work drafting a blueprint to turn its popular service into a money-making business.

Though there are multiple ways to monetize GIFs, a mainstay of personal messages and online forums and social media networks, Alex Chung, the founder is yet to find a way to monetize it. Giphy can be integrated into various communication tools for inserting reaction GIFs into comments. Internet users also flock to the website to get entertained. The website claims to have 150 million users daily. With that kind of user base, it would not be difficult for the company to turn profitable.

Vishal Ingole, June 7, 2017

Snapchat Introduces Search Feature

May 29, 2017

Photo-sharing app Snapchat is late to the search game, but it has now arrived. The Daily Mail reports, “Snapchat Introduces a ‘Universal Search’ Feature: Tool Lets You Create Groups and Find New People to Follow.” Writer Abigail Beall explains:

Snapchat’s universal search bar hopes to address an issue some users had with the photograph-sharing app – the difficulty in finding new people to follow and gaining a large following. Previously, the only way people could gain a following was by sharing their username, or Snapcode, outside of the app. The new search bar, that will always be present at the top of the app, will allow people to find users easily through searching, discovering and groups. …

 

The new feature also lets users create groups, to combine snaps. Previously, boxes for finding specific conversations, accounts to follow and Stories or Discover channels were all in different places.

The tool was implemented for some Android users in mid-January, with availability to all Android and iOS users to follow “soon.” Beall notes the development was predicted by some last August after Snapchat acquired Vurb, a mobile search startup founded in 2011 and based in San Francisco.

Snap Inc., Snapchat’s parent company, bills itself as a camera company that is reinventing the camera. The company has acquired nine other enterprises since its founding in 2011. Snap is now selling (through their special vending machines!)  Spectacles, sunglasses with a camera on each temple that, of course, link right in with Snapchat.

Cynthia Murrell, May 29, 2017

Search Pinterest Pictures Without Pinterest

April 25, 2017

Pinterest is the beloved social media network, where users can post pictures, make comments, get decorating ideas, and recipes.  However, Recode tells us about a new implausible Google Chrome extension: “Pinterest Will Now Let You Search For Products Using Any Image You Find Online-Without Visiting Pinterest.”  Pinterest just launched a new Google Chrome extension that allows users to save images seen online as they browse.  The extension will work like this:

The new tool lets you select an item in any photograph online, and ask Pinterest to surface similar items using its image recognition software.  For example: If you see an image of sunglasses you like on Nordstrom.com, you could use the extension to browse similar glasses from Pinterest without ever leaving Nordstrom’s website.  If you click on one of the search results, you’ll then be taken to Pinterest.

Pinterest wants to leverage itself as an image search engine for all images, in real life and on the Internet.  Evan Sharp, Pinterest co-founder, said that users, should not “..have to put their thoughts into words to find great ideas.”  Visual search technology already exists, but only on Pinterest’s Web site.

Whitney Grace, April 25, 2017

Chipping Away at Social Content with Pictures

February 27, 2017

Analytics are catching up to content. In a recent ZDNet article, Digimind Partners with Ditto to Add Image Recognition to Social Media Monitoring, we are reminded images reign supreme on social media. Between Pinterest, Snapchat and Instagram, messages are often conveyed through images as opposed to text. Capitalizing on this, an intelligence software company Digimind has announced a partnership with Ditto Labs to introduce image-recognition technology into their social media monitoring software called Digimind Social. We learned,

 “The Ditto integration lets brands identify the use of their logos across Twitter no matter the item or context. The detected images are then collected and processed on Digimind Social in the same way textual references, articles, or social media postings are analysed. Logos that are small, obscured, upside down, or in cluttered image montages are recognised. Object and scene recognition means that brands can position their products exactly where there customers are using them. Sentiment is measured by the amount of people in the image and counts how many of them are smiling. It even identifies objects such as bags, cars, car logos, or shoes.”

 It was only a matter of time before these types of features emerged in social media monitoring. For years now, images have been shown to increase engagement even on platforms that began focused more on text. Will we see more watermarked logos on images? More creative ways to visually identify brands? Both are likely and we will be watching to see what transpires.

 Megan Feil, February 27, 2017

 

Pinterest Offers the Impulse Shopper a Slice of Wonderfulness

February 20, 2017

How about point-and-click impulse buying? Sound good? Pinterest has merged looking at pictures with spending money for stuff.

Navigate to “Pinterest’s New ‘Lens’ IDs Objects and Helps You Buy Them.” I know that I spend hours looking at pictures on Pinterest. When I see wedding snapshots and notice a pair of shoes to die for, I can buy them with a click… almost. My hunch is that some children may find Pinterest buying as easy as Alexa Echo and Dot buying.

I learned:

[Pinterest] announced a new feature called Lens, which will enable people to snap a picture of an item inside the Pinterest app. The app will then suggest objects it thinks are related. Think Shazam but for objects, not music. Surfacing the products will make it easier for people to take action, according to Pinterest. That could include everything from making a purchase to cooking a meal.

One of Pinterest’s wizards (Evan Sharp) allegedly said:

“Sometimes you spot something out in the world that looks interesting, but when you try to search for it online later, words fail you.” The new technology, Sharp said, “is capable of seeing the world the way you do.”

Isn’t the consumerization of no word search a life saver? Now I need a new gown to complement my size 11 triple E high heels. There’s a bourbon tasting in Harrod’s Creek next week, and I have to be a trend setter before we go squirrel hunting.

Stephen E Arnold, February 20, 2017

Blippar: Your Phone May Recognize You, Not Just a Product

January 4, 2017

I read “Blippar AI Visual Search Engine Recognizes Faces in Real Time.” The main point of the write up is that you can point your phone at something, and the phone will recognize that thing or person. The flip side is that if your phone has a camera which can see you, your phone makes it easy for “someone” to recognize you. Isn’t that special? Blippar info is at this link.

I learned:

Blippar expanded its augmented reality visual search browser on Tuesday to recognize faces in real time with a simple smartphone camera and return information about that person.

The write up talks about how consumers will drool over this feature. My thought was, “Gee, wouldn’t that function be useful for surveillance purposes?”

The write up included this statement:

The feature allows users to point the camera phone at any real person or their image in a picture on television and the Blippar app returns information about the person from the company’s database filled with more than three billion facts. Real-time facial recognition is the latest tool, amidst expansion in artificial intelligence and deep-learning capabilities.

Yep. Just another “tool.”

Blippar includes a feature for humans who want to be recognized:

For public figures, their faces will be automatically discovered with information drawn from Blipparsphere, the company’s visual knowledge Graph that pulls information from publicly accessible sources, which was released earlier this year. Public figures can also set up their own AR Face Profile. The tool enables them to engage with their fans and to communicate information that is important to them by leveraging their most personal brand — their face.  Users also can create fact profiles — Augmented Reality profiles on someone’s face, which users create so they can express who they are visually.Users can view each other’s profiles that have been uploaded and published and can add pictures or YouTube videos, as well as AR moods and much more to express themselves in the moment.

Why not convert existing images to tokens or hashes and then just match faces? Maybe not. Who would want to do this to sell toothpaste?

Stephen E Arnold, January 4, 2017

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