Category Archives: Media

Data Management, Entertainment, Media, Movies, Technology

Apple iOS Opportunity for Streaming Applications

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It’s Like “Fast Cash” from an ATM

What seems to be low-hanging fruit for video streaming applications has not widely been adapted. If a user of the iPhone presses and holds down the App icon a “Shortcut Menu” for the app will appear. Adding a shortcut menu item called “My List” of movies and TVs would allow the application user to jump to their items of interest.

It’s a great way to skip the application menu navigation from within the app and jump to any point in the application that may be of frequent interest. Many of the stream services do not have this capability.

First Prize – Best in Breed – Amazon Prime

2nd Prize Goes to Paramount Plus

Tied for Last Place – Really??

Major streaming services, including Apple TV do not take advantage of this usability feature,

Entertainment, Fun, Media, Movies, Technology

Netflix Is Testing A Way To Limit Password Sharing

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r Netflix is testing a way it can limit password sharing, in what could signal a notable shift of the streaming giant’s posture toward users.“Is this your account?” an on-screen notification asks some of those trying to log on with credentials from someone outside their household, according to users’ screenshots. “If you don’t live with the owner of this account, you need your own account to keep watching.”Users can then enter their own information and create an account, which comes with a 30-day free trial in certain territories.“This test is designed to help ensure that people using Netflix accounts are authorized to do so,” a company spokesperson said in a statement.

Source: Netflix Is Testing A Way To Limit Password Sharing – Deadline

Two Factor Authentication verse Location-Based 

This measure is an ineffective approach at best, and a hindrance, worst-case scenario to those valid Netflix users who travel often and take their streaming service on the road.  Many other Internet Services, beyond content streaming,  are now implementing a 2-Factor Authentication (2-FA) approach.  With 2-FA, a user will log into the Netflix app, and then is sent an email or text message with an authentication code.  The code is then used to complete the login of the Software as a Service (SaaS).  This approach could be extended to VOD  streaming services, and for each account “Profile”,  there is a defined mobile number and email address where the access code can be sent.   Only the default account profile can unlock the security details for profiles, allowing the assignment of mobile numbers and email addresses.

How Will Consumers React?

The initial pilot solution seems like a half measure at the moment. I’m not familiar with how they will implement the location-based, “Outside Your Household” solution because of a legitimate use case where some people who have subscriptions actively travel, for example. Surely, these people who travel will appear to be in various locations, according to network topology. On the other side, if you apply a multifactor authentication approach, that’s bound to be more successful in inhibiting the “password sharing” issue. Netflix defines/reevaluates a maximum number of user-profiles per account. Will this help generate more revenue for the “fledgling” streaming service, or anger their audience who may take flight to one of the many other services offered. It’s not the cheapest streaming service in town. Let’s see.

AI, Artificial Intelligence, Digital Assistant, Economy, Entertainment, Machine Learning, Media, Technology

Help Wanted: Civil War Reenactment Soldiers to Improve AI Models

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I just read an article on Digital PC Magazine, “Human Help Wanted: Why AI Is Terrible at Content Moderation” which started to get my neurons firing.

Problem Statement

Every day, Facebook’s artificial intelligence algorithms tackle the enormous task of finding and removing millions of posts containing spam, hate speech, nudity, violence, and terrorist propaganda. And though the company has access to some of the world’s most coveted talent and technology, it’s struggling to find and remove toxic content fast enough.

Ben Dickson
July 10, 2019 1:36PM EST

I’ve worked at several software companies which leveraged Artifical Intelligence, Machine Learning to recognize patterns, correlations. The larger the data sets, in general, the higher the accuracy of the predictions. The outliers in the data, the noise, “falls out” of the data set. Without quality, large training data, Artificial Intelligence makes more mistakes.

In terms of speech recognition, image classification, and natural language processing (NLP), in general, programs like chatbots, digital assistants, are becoming more accurate because of their sample size, training data sets are large, and there is no shortage of these data types. For example, there are many ways I can ask my digital assistant for something, like “Get the movie times”. Training a digital assistant, at a high level, would be to catalog how many ways can I ask for “something”, achieve my goal. I can go and create that list. I could write a few dozen questions, but still, my sample data set would be too small. Amazon has a crowdsourcing platform, Amazon Mechanical Turk, which I can request they build me the data sets, thousands of questions, and correlated goals.

MTurk enables companies to harness the collective intelligence, skills, and insights from a global workforce to streamline business processes, augment data collection and analysis, and accelerate machine learning development.

Amazon Mechanical Turk: Access a global, on-demand, 24×7 workforce

Video “Scene” Recognition – Annotated Data Sets for a Wide Variety of Scene Themes

In silent films, the plot was conveyed by the use of title cards, written indications of the plot and key dialogue lines. Unfortunately, silent films are not making a comeback. In order to achieve a high rate of successful identification of activities within a given video clip, video libraries of metadata need to be created, that capture:

  • Media / Video Asset, Unique Identifier
  • Scene Clip IN and OUT timecodes
  • Scene Theme(s), similar to Natural language processing (NLP), Goals = Utterances / Sentences
    • E.g. Man drinking water; Woman playing Tennis
  • Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in images. Image recognition is used to perform a large number of machine-based visual tasks, such as labeling the content of images with meta-tags

Not Enough Data

Here is an example of how Social Media, such as Facebook, attempts to deal with video deemed inappropriate for their platform:

In March, a shooter in New Zealand live-streamed the brutal killing of 51 people in two mosques on Facebook. But the social-media giant’s algorithms failed to detect the gruesome video. It took Facebook an hour to take the video down, and even then, the company was hard-pressed to deal with users who reposted the video.

Ben Dickson
July 10, 2019 1:36PM EST

…in many cases, such as violent content, there aren’t enough examples to train a reliable AI model. “Thankfully, we don’t have a lot of examples of real people shooting other people,” Yann LeCun, Facebook’s chief artificial-intelligence scientist, told Bloomberg.

Ben Dickson
July 10, 2019 1:36PM EST

Opportunities for Actors and Curators of Video Content: Dramatizations

All those thousands of people who perform, creating videos of content that range the gamut from playing video games to “unboxing” collectible items. The actors who perform dramatizations could add tags to their videos indicating as per above, documenting themes for a given skit. If actors post their videos on YouTube or proprietary crowdsourcing platforms, they would be entitled to some revenue for the use of their licensed video.

Disclosure Regarding Flag Controversy

I now realize there are politics around Nike “tipping their hat” toward the Betsy Ross flag. However, when I referenced the flag in this blog post, I was thinking of the American Revolution, and the 13 colonies flag. I didn’t think the title would resonate with readers, “Help Wanted: Amerian Revolutionary war Reenactment Soldiers to Improve AI Models.”, so I took some creative liberty.

Business, Economy, Entertainment, Finance, Media, Technology

Social Media: News Feed verse App InMail

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Better Demographic Penetration and Transparency to More Accurately Determine Creative Media Asset Worth

News Media Assets

News Media Assets are created by writers of non-fictional work, coverage of various topics targeted towards the periodical demographic.

Selling Advertising Space

Layered within the news media product, consists of News Media Assets and sold advertisement space. Ad positioning throughout the news media product may have commonality between the product or service being advertised and the news media asset. A goal is the smooth transition between reader of asset and advertisement.

Revenue Models For News Media Assets

  • Deriving revenue from sponsors of news Media Assets
  • Subscription Base of News Media Assets, regular frequency of news media product to subscriber base.

Social Media – News Feeds

The news agencies post to public news feeds a “teaser” headline, a sentence or two describing the news media asset, and a teaser image all to lure prospective readers to clink a link to the news media publisher’s platform. At that point, the publisher sets the “ground rules” for the potential subscriber, e.g. 10 free articles a month, then their digital subscription price of NN goes into effect.

Social Media – InMail (I.e. eMail within the platform)

InMail through the social media platform can come from a variety of sources, for example:

  • Former colleague looking to reconnect
  • Recruiter looking to pitch a potential role
  • Sales / Marketing InMail targeting you as a potential customer of their product or service
The Tools to get the Job Done

As a prior client of LinkedIn Advertising for both ad placement and Sponsored InMail, I found the tools provided and the granularity upon which to refine the demographics impressive, and not lacking in any way.

Personable, Targeted Marketing of News Media Assets, sponsored by 3rd party promoting their product or service.

Delivering News Media Assets to your digital door step, with advertising partners speckled into the asset. Because of the granularity of the InMail advertising controls demographics are at a level of precision. Beyond what a magazine or newspaper, digital or print, can offer.

it’s all about the targeted audience and the granularity of the data collected and then leveraged to meet the desired audience. Much more personal than a link back to the publisher’s platform.

Just like there are expenses to do business in print or traditional digital, the price of doing business with a platform like LinkedIn Sponsored InMail, would be absorbed by the news media agency, net advertisement placement for advertisements.

Although the LinkedIN Social platform was used for reference, other platforms may be leveraged, depending upon the product or services being marketed, such as a Facebook People Magazine article relevant to their demographic, partnership / sponsorship.

Fake News – Not a Problem

Since News Media Agencies will now pair with “sponsors” or commonly know as advertisers, both parties, the news agency and the sponsor have “skin in the game”, it is less likely to be a factitious article.

Business, Economy, Media, Mobility, Technology

Free Nights and Weekends Makes a Comeback

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Remember when you could make free mobile calls after 9:30 PM weeknights, and all weekend? For awhile the mobile carriers competed on the time when “off-peak” started, from 10 PM to 8:30 PM. A whole hour and a half! These days we have unlimited domestic calling all the time.

So, now we have varying degrees of data plans, such as AT&T Wireless 3 GB, 9 GB, or unlimited per month, but there are caps where after 22 GB data transfer speeds are slowed down.  22 gigs seem like a lot until you have kids using Snapchat and TikTok.

When you think about it, data peak is when you may not be in a hot spot. At night, you’re at home using your own WiFi, or at an establishment with their complimentary WiFi. Weekends and weekdays are a bit scattered. Your work may have WiFi, but weekdays “on peak” are mostly commuting times, the “rush hour(s)”,

Can wireless carriers bring back on and off-peak for data?  The simplest approach:  “turn off the meter” during off-peak data periods.  Maybe on-peak the consumer can elect 5G, when available, and off-peak at 4G LTE? Our Smartphones can identify low consuming bandwidth opportunities, e.g. when the phone is locked, text messages without graphics and email are semi-passive states. Maybe users are able to prioritize their apps data usage? What about those “chatty” apps that you rarely use? Smartphone settings may show you those apps bandwidth consumption as opportunities to prioritize them lower than your priority apps.

Skeptic, and think there are no Peak or Off-Peak periods with data?  Check the business analytics.  I’m sure wireless carriers have a depth of understanding for their own business intelligence (BI).

AI, Artificial Intelligence, Economy, Law, Media, Science, Technology

IBM didn’t inform people when it used their Flickr photos for facial recognition training – The Verge

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The problem is more widespread then highlighted in the article.  It’s not just these high profile companies using “public domain” images to annotate with facial recognition notes and training machine learning (ML) models.  Anyone can scan the Internet for images of people, and build a vast library of faces.  These faces can then be used to train ML models.  In fact, using public domain images from “the Internet” will cut across multiple data sources, not just Flickr, which increases the sample size, and may improve the model.

The rules around the uses of “Public Domain” image licensing may need to be updated, and possibly a simple solution, add a watermark to any images that do not have permission to be used for facial recognition model training.  All image processors may be required to include a preprocessor to detect the watermark in the image, and if found, skip the image from being included in the training of models.

Source: IBM didn’t inform people when it used their Flickr photos for facial recognition training – The Verge

Advertising, Business, Data Warehouse, Digital Assistant, Economy, Finance, Information Architecture (IA), Media, Personal, Technology

People Turn Toward “Data Banks” to Commoditize on their Purchase and User Behavior Profiles

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Anyone who is anti “Big Brother”, this may not be the article for you, in fact, skip it. 🙂

 

The Pendulum Swings Away from GDPR

In the not so distant future, “Data Bank” companies consisting of Subject Matter Experts (SME) across all verticals,  may process your data feeds collected from your purchase and user behavior profiles.  Consumers will be encouraged to submit their data profiles into a Data Bank who will offer incentives such as a reduction of insurance premiums to cash back rewards.

 

Everything from activity trackers, home automation, to vehicular automation data may be captured and aggregated.    The data collected can then be sliced and diced to provide macro and micro views of the information.    On the abstract, macro level the information may allow for demographic, statistical correlations, which may contribute to corporate strategy. On a granular view, the data will provide “data banks” the opportunity to sift through data to perform analysis and correlations that lead to actionable information.

 

Is it secure?  Do you care if a hacker steals your weight loss information? May not be an issue if collected Purchase and Use Behavior Profiles aggregate into a Blockchain general ledger.  Data Curators and Aggregators work with SMEs to correlate the data into:

  • Canned, ‘intelligent’ reports targeted for a specific subject matter, or across silos of data types
  • ‘Universes’ (i.e.  Business Objects) of data that may be ‘mined’ by consumer approved, ‘trusted’ third party companies, e.g. your insurance companies.
  • Actionable information based on AI subject matter rules engines and consumer rule transparency may be provided.

 

 “Data Banks” may be required to report to their customers who agreed to sell their data examples of specific rows of the data, which was sold on a “Data Market”.

Consumers may have the option of sharing their personal data with specific companies by proxy, through a ‘data bank’ granular to the data point collected.  Sharing of Purchase and User Behavior Profiles:

  1. may lower [or raise] your insurance premiums
  2. provide discounts on preventive health care products and services, e.g. vitamins to yoga classes
  3. Targeted, affordable,  medicine that may redirect the choice of the doctor to an alternate.  The MD would be contacted to validate the alternate.

 

The curriated data collected may be harnessed by thousands of affinity groups to offer very discrete products and services.  Purchase and User Behavior Profiles,  correlated information stretches beyond any consumer relationship experienced today.

 

At some point, health insurance companies may require you to wear a tracker to increase or slash premiums.  Auto Insurance companies may offer discounts for access to car smart data to make sure suggested maintenance guidelines for service are met.

 

You may approve your “data bank” to give access to specific soliciting government agencies or private firms looking to analyze data for their studies. You may qualify based on the demographic, abstracted data points collected for incentives provided may be tax credits, or paying studies.

Purchase and User Behavior Profiles:  Adoption and Affordability

If ‘Data Banks’ are allowed to collect Internet of Things (IoT) device profile and the devices themselves are cost prohibitive.  here are a few ways to increase their adoption:

  1.  [US] tax coupons to enable the buyer, at the time of purchase, to save money.  For example, a 100 USD discount applied at the time of purchase of an Activity Tracker, with the stipulation that you may agree,  at some point, to participate in a study.
  2. Government subsidies: the cost of aggregating and archiving Purchase and Behavioral profiles through annual tax deductions.  Today, tax incentives may allow you to purchase an IoT device if the cost is an itemized medical tax deduction, such as an Activity Tracker that monitors your heart rate, if your medical condition requires it.
  3. Auto, Life, Homeowners, and Health policyholders may qualify for additional insurance deductions
  4. Affinity branded IoT devices, such as American Lung Association may sell a logo branded Activity Tracker.  People may sponsor the owner of the tracking pedometer to raise funds for the cause.

The World Bank has a repository of data, World DataBank, which seems to store a large depth of information:

World Bank Open Data: free and open access to data about development in countries around the globe.”

Here is the article that inspired me to write this article:

http://www.marketwatch.com/story/you-might-be-wearing-a-health-tracker-at-work-one-day-2015-03-11

 

Privacy and Data Protection Creates Data Markets

Initiatives such as General Data Protection Regulation (GDPR) and other privacy initiatives which seek to constrict access to your data to you as the “owner”, as a byproduct, create opportunities for you to sell your data.  

 

Blockchain: Purchase, and User Behavior Profiles

As your “vault”, “Data Banks” will collect and maintain your two primary datasets:

  1. As a consumer of goods and services, a Purchase Profile is established and evolves over time.  Online purchases are automatically collected, curated, appended with metadata, and stored in a data vault [Blockchain].  “Offline” purchases at some point, may become a hybrid [on/off] line purchase, with advances in traditional monetary exchanges, and would follow the online transaction model.
  2. User Behavior (UB)  profiles, both on and offline will be collected and stored for analytical purposes.  A user behavior “session” is a use case of activity where YOU are the prime actor.  Each session would create a single UB transaction and are also stored in a “Data Vault”.   UB use cases may not lead to any purchases.

Not all Purchase and User Behavior profiles are created equal.  Eg. One person’s profile may show a monthly spend higher than another.  The consumer who purchases more may be entitled to more benefits.

These datasets wholly owned by the consumer, are safely stored, propagated, and immutable with a solution such as with a Blockchain general ledger.

AI, Business, Media, Technology

Microsoft Productivity Suite – Content Creation, Ingestion, Curation, Search, and Repurpose

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Auto Curation: AI Rules Engine Processing

There are, of course, 3rd party platforms that perform very well, are feature rich, and agnostic to all file types.  For example, within a very short period of time, low cost, and possibly a few plugins, a WordPress site can be configured and deployed to suit your needs of Digital Asset Managment (DAM).  The long-term goal is to incorporate techniques such as Auto Curation to any/all files, leveraging an ever-growing intelligent taxonomy, a taxonomy built on user-defined labels/tags, as well an AI rules engine with ML techniques.   OneDrive, as a cloud storage platform, may bridge the gap between JUST cloud storage and a DAM.

Ingestion and Curation Workflow

Content Creation Apps and Auto Curation

  • The ability for Content Creation applications, such as Microsoft Word, to capture not only the user-defined tags but also the context of the tags relating to the content.
    • When ingesting a Microsoft PowerPoint presentation, after consuming the file, and Auto Curation process can extract “reusable components” of the file, such as slide header/name, and the correlated content such as a table, chart, or graphics.
    • Ingesting Microsoft Excel and Auto Curation of Workbooks may yield “reusable components” stored as metadata tags, and their correlated content, such as chart and table names.
    • Ingesting and Auto Curation of Microsoft Word documents may build a classic Index for all the most frequently occurring words, and augment the manually user-defined tags in the file.
    • Ingestion of Photos [and Videos] into and Intelligent Cloud Storage Platform, during the Auto Curation process, may identify commonly identifiable objects, such as trees or people.  These objects would be automatically tagged through the Auto Curation process after Ingestion.
  • Ability to extract the content file metadata, objects and text tags, to be stored in a standard format to be extracted by DAMs, or Intelligent Cloud Storage Platforms with file and metadata search capabilities.  Could OneDrive be that intelligent platform?
  • A user can search for a file title or throughout the Manual and Auto Curated, defined metadata associated with the file.  The DAM or Intelligent Cloud Storage Platform provides both search results.   “Reusable components” of files are also searchable. 
    • For “Reusable Components” to be parsed out of the files to be separate entities, a process needs to occur after Ingestion Auto Curration.
  • Content Creation application, user-entry tag/text fields should have “drop-down” access to the search index populated with auto/manual created tags.

Auto Curation and Intelligent Cloud Storage

  • The intelligence of Auto Curation should be built into the Cloud Storage Platform, e.g. potentially OneDrive.
  • At a minimum, auto curation should update the cloud storage platform indexing engine to correlate files and metadata.
  • Auto Curation is the ‘secret sauce’ that “digests” the content to build the search engine index, which contains identified objects (e.g. tag and text or coordinates)  automatically
    • Auto Curation may leverage a rules engine (AI) and apply user configurable rules such as “keyword density” thresholds
    • Artificial Intelligence, Machine Learning rules may be applied to the content to derive additional labels/tags.
  • If leveraging version control of the intelligent cloud storage platform, each iteration should “re-index” the content, and update the Auto Curation metadata tags.  User-created tags are untouched.
  • If no user-defined labels/tags exist, upon ingestion, the user may be prompted for tags

Auto Curation and “3rd Party” Sources

In the context of sources such as a Twitter feed, there exists no incorporation of feeds into an Intelligent Cloud Storage.  OneDrive, Cloud Intelligent Storage may import feeds from 3rd party sources, and each Tweet would be defined as an object which is searchable along with its metadata (e.g. likes; tags).

Operating System, Intelligent Cloud Storage/DAM

The Intelligent Cloud Storage and DAM solutions should have integrated search capabilities, so on the OS (mobile or desktop) level, the discovery of content through the OS search of tagged metadata is possible.

Current State

  1. OneDrive has no ability to search Microsoft Word tags
  2. The UI for all Productivity Tools must have a comprehensive and simple design for leveraging an existing taxonomy for manual tagging, and the ability to add hints for auto curation
    1. Currently, Microsoft Word has two fields to collect metadata about the file.  It’s obscurely found at the “Save As” dialog.
      1. The “Save As” dialogue box allows a user to add tags and authors but only when using the MS Word desktop version.  The Online (Cloud) version of Word has no such option when saving to Microsoft OneDrive Cloud Storage
  3. Auto Curation (Artificial Intelligence, AI) must inspect the MS Productivity suite tools, and extract tags automatically which does not exist today.
  4. No manual taging or Auto Curation/Facial Recognition exists.