Tag Archives: Cloud

Hey Siri, Ready for an Antitrust Lawsuit Against Apple? Guess Who’s Suing.

The AI personal assistant with the “most usage” spanning  connectivity across all smart devices, will be the anchor upon which users will gravitate to control their ‘automated’ lives.  An Amazon commercial just aired which depicted  a dad with his daughter, and the daughter was crying about her boyfriend who happened to be in the front yard yelling for her.  The dad says to Amazon’s Alexa, sprinklers on, and yes, the boyfriend got soaked.

What is so special about top spot for the AI Personal Assistant? Controlling the ‘funnel’ upon which all information is accessed, and actions are taken means the intelligent ability to:

  • Serve up content / information, which could then be mixed in with advertisements, or ‘intelligent suggestions’ based on historical data, i.e. machine learning.
  • Proactive, suggestive actions  may lead to sales of goods and services. e.g. AI Personal Assistant flags potential ‘buys’ from eBay based on user profiles.

Three main sources of AI Personal Assistant value add:

  • A portal to the “outside” world; E.g. If I need information, I wouldn’t “surf the web” I would ask Cortana to go “Research” XYZ;   in the Business Intelligence / data warehousing space, a business analyst may need to run a few queries in order to get the information they wanted.  In the same token, Microsoft Cortana may come back to you several times to ask “for your guidance”
  • An abstraction layer between the user and their apps;  The user need not ‘lift a finger’ to any app outside the Personal Assistant with noted exceptions like playing a game for you.
  • User Profiles derived from the first two points; I.e. data collection on everything from spending habits, or other day to day  rituals.

Proactive and chatty assistants may win the “Assistant of Choice” on all platforms.  Being proactive means collecting data more often then when it’s just you asking questions ADHOC.  Proactive AI Personal Assistants that are Geo Aware may may make “timely appropriate interruptions”(notifications) that may be based on time and location.  E.g. “Don’t forget milk” says Siri,  as your passing the grocery store.  Around the time I leave work Google maps tells me if I have traffic and my ETA.

It’s possible for the [non-native] AI Personal Assistant to become the ‘abstract’ layer on top of ANY mobile OS (iOS, Android), and is the funnel by which all actions / requests are triggered.

Microsoft Corona has an iOS app and widget, which is wrapped around the OS.  Tighter integration may be possible but not allowed by the iOS, the iPhone, and the Apple Co. Note: Google’s Allo does not provide an iOS widget at the time of this writing.

Antitrust violation by mobile smartphone maker Apple:  iOS must allow for the ‘substitution’ of a competitive AI Personal Assistant to be triggered in the same manner as the native Siri,  “press and hold home button” capability that launches the default packaged iOS assistant Siri.
Reminiscent of the Microsoft IE Browser / OS antitrust violations in the past.

Holding the iPhone Home button brings up Siri. There should be an OS setting to swap out which Assistant is to be used with the mobile OS as the default.  Today, the iPhone / iPad iOS only supports “Siri” under the Settings menu.

ANY AI Personal assistant should be allowed to replace the default OS Personal assistant from Amazon’s Alexa, Microsoft’s Cortana to any startup company with expertise and resources needed to build, and deploy a Personal Assistant solution.  Has Apple has taken steps to tightly couple Siri with it’s iOS?

AI Personal Assistant ‘Wish” list:

  • Interactive, Voice Menu Driven Dialog; The AI Personal Assistant should know what installed [mobile] apps exist, as well as their actionable, hierarchical taxonomy of feature / functions.   The Assistant should, for example, ask which application the user wants to use, and if not known by the user, the assistant should verbally / visually list the apps.  After the user selects the app, the Assistant should then provide a list of function choices for that application; e.g. “Press 1 for “Play Song”
    • The interactive voice menu should also provide a level of abstraction when available, e.g. User need not select the app, and just say “Create Reminder”.  There may be several applications on the Smartphone that do the same thing, such as Note Taking and Reminders.  In the OS Settings, under the soon to be NEW menu ‘ AI Personal Assistant’, a list of installed system applications compatible with this “AI Personal Assistant” service layer should be listed, and should be grouped by sets of categories defined by the Mobile OS.
  • Capability to interact with IoT using user defined workflows.  Hardware and software may exist in the Cloud.
  • Ever tighter integration with native as well as 3rd party apps, e.g. Google Allo and Google Keep.

Apple could already be making the changes as a natural course of their product evolution.  Even if the ‘big boys’ don’t want to stir up a hornet’s nest, all you need is VC and a few good programmers to pick a fight with Apple.

Cloud Storage: Ingestion, Management, and Sharing

Cloud Storage Solutions need differentiation that matters, a tipping point to select one platform over the other.

Common Platforms Used:

Differentiation may come in the form of:

  • Collaborative Content Creation Software, such as DropBox Paper enables individuals or teams to produce content, all the while leveraging the Storage platform for e.g. version control,
  • Embedded integration in a suite of content creation applications, such as Microsoft Office, and OneDrive.
  • Making the storage solution available to developers, such as with AWS S3, and Box.  Developers may create apps powered by the Box Platform or custom integrations with Box
  • iCloud enables users to backup their smartphone, as well tightly integrating with the capture and sharing of content, e.g. Photos.

Cloud Content Lifecycle Categories:

  • Content Creation
    • 3rd Party (e.g. Camera) or Integrated Platform Products
  • Content Ingestion
    • Capture Content and Associated Metadata
  • Content Collaboration
    • Share, Update and Distribution
  • Content Discovery
    • Surface Content; Searching and Drill Down
  • Retention Rules
    • Auto expire pointer to content, or underlying content

Cloud Content Ingestion Services:

Cloud Ingestion Services
Cloud Ingestion Services

Building AI Is Hard—So Facebook Is Building AI That Builds AI

“…companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art [AI] forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.”


This article represents a true picture of where we are today for the average consumer and producer of information, and the companies that repurpose information, e.g. in the form of advertisements.  
The advancement and current progress of Artificial Intelligence, Machine Learning, analogously paints a picture akin to the 1970s with computers that fill rooms, and accept punch cards as input.
Today’s consumers have mobile computing power that is on par to the whole rooms of the 1970s; however, “more compute power” in a tinier package may not be the path to AI sentience.  How AI algorithm models are computed might need to take an alternate approach.  
In a classical computation system, a bit would have to be in one state or the other. However quantum mechanics allows the qubit to be in a superposition of both states at the same time, a property which is fundamental to quantum computing.
The construction, and validation of Artificial Intelligence, Machine Learning, algorithm models should be engineered on a Quantum Computing framework.

G.E. Plans Big Entry into IoT, Providing Analytics and Predictive Rules

G.E. Plans App Store for Gears of Industry

The investment of $500 million annually signals the importance of the so-called Internet of Things to the future of manufacturing.

G.E. expects revenue of $6 billion from software in 2015, a 50 percent increase in one year. Much of this is from a pattern-finding system called Predix.  G.E. calls its new service the Predix Cloud, and hopes it will be used by both customers and competitors, along with independent software developers. “We can take sensor data from anybody, though it’s optimized for our own products,” Mr. Ruh said.

[Competitive solutions from IBM, Microsoft, and Google] raises the stakes for G.E. “It’s a whole new competition for them,” said Yefim Natis, a senior analyst with Gartner. “To run businesses in a modern way you have to be analytic and predictive.”

G.E. is running the Predix Cloud on a combination of G.E. computers, the vast computing resources of Amazon Web Services, and a few [local] providers, like China Telecom.

China, along with countries like Germany, [are] sensitive about moving its data offshore, or even holding information on computers in the United States.  
The practice of “Ring fencing”  data exists in dozens of jurisdictions globally.  Ring fencing of data may be a legal and/or regulatory issue, that may inhibit the global growth of cloud services moving forward.

Source: G.E. Plans App Store for Gears of Industry

Cloud Storage and DAM Solutions: Don’t Reign in the Beast

Are you trying to apply metadata on individual files or en masse, attempting to make the vast  growth of cloud storage usage manageable, meaningful storage?

Best practices leverage a consistent hierarchy, an Information Architecture in which to store and retrieve information, excellent.

Beyond that, capabilities computer science has documented and used time and time again, checksum algorithms. Used frequently after a file transfer to verify the file you requested is the file you received.  Most / All Enterprise DAM solutions use some type of technology to ‘allow’ the enforcement of unique assets [upon upload].  In cloud storage and photo solutions targeted toward the individual, consumer side, the feature does not appear to be up ‘close and personal’ to the user experience, thus building a huge expanse of duplicate data (documents, photos, music, etc.).  Another feature, a database [primary] key has been used for decades to identify that a record of data is unique.

Our family sharing alone has thousands and thousands of photos and music. The names of the files could be different for many of the same digital assets.  Sometimes file names are the same, but the metadata between the same files is not unique, but provides value. Tools for ‘merging’ metadata, DAM tools have value to help manage digital assets.

Cloud storage usage is growing exponentially, and metadata alone won’t help rope in the beast. Maybe ADHOC or periodic indexing of files [e.g. by #checksum algorithm] could take on the task of identifying duplicate assets?  Duplicate  assets could be viewed by the user in an exception report?  Less boring, upon upload, ‘on the fly’ let the user know the asset is already in storage, and show a two column diff. of the metadata.

It’s a pain for me, and quite possibly many cloud storage users.  As more people jump on cloud storage, this feature should be front and center to help users grow into their new virtual warehouse.

The industry of cloud storage most likely believes for the common consumer, storage is ‘cheap’, just provide more.  At some stage, the cloud providers may look to DAM tools as the cost of managing a users’ storage rises.  Tools like:

  • duplicate digital assets, files. Use exception reporting to identify the duplicates, and enable [bulk] corrective action, and/or upon upload, duplicate ‘error/warning’ message.
  • Dynamic metadata tagging upon [bulk] upload using object recognition.  Correlating and cataloging one or more [type] objects in a picture using defined Information Architecture.  In addition, leveraging facial recognition for updates to metadata tagging.
    • e.g. “beach” objects: sand, ocean; [Ian Roseman] surfing;
  • Brief questionnaires may enable the user to ‘smartly’ ingest the digital assets; e.g. ‘themes’ of current upload; e.g. a family, or relationship tree to  extend facial recognition correlations.
    • e.g. themes – summer; party; New Year’s Eve
    • e.g. relationship tree – office / work
  • Pan Information Architecture (IA) spanning multiple cloud storage [silos]. e.g. for Photos, spanning [shared] ‘albums’
  • Publically published / shared components of an IA;  e.g. Legal documents;  standards and reuse

Google Introduces their Cloud, Digital Asset Management (DAM) solution

Although this is a saturated space, with many products, some highly recommended, I thought this idea might interest those involved in the Digital Asset Management space.  Based on the maturity of existing products, and cost, it’s up to you, build or buy.  The following may provide an opportunity for augmenting existing Google products, and overlaying a custom solution.

Google products can be integrated across their suite of solutions and may produce a cloud based, secure, Digital Asset Management, DAM solution.   In this use case, the digital assets are Media (e.g. videos, still images)

A Google DAM may be created by leveraging existing features of Google Plus, Google Drive, YouTube, and other Google products, as well as building / extending additional functionality, e.g. Google Plus API, to create a DAM solution.   An over arching custom framework weaves these products together to act as the DAM.

Google Digital Asset Management (New)

  1. A dashboard for Digital Asset Management should be created, which articulates, at a glance, where project media assets are in their life cycle, e.g. ingestion, transcoding, editing media, adding meta data, inclusion / editing of closed captions, workflow approvals, etc.
  2. Creation and maintenance of project asset folder structure within storage such as Google Drive for active projects as well as Google Cloud Storage for archived content.  Ingested content to arrive in the project folders.
  3. Ability to use [Google YouTube] default encoding / transcoding functionality, or optionally leverage alternate cloud accessible transcoding solutions.
  4. A basic DAM UI may provide user interaction with the project and asset meta data.
  5. Components of the DAM should allow plug in integration with other components on the  market today, such as an ingestion solution.

Google Drive and Google Cloud Storage.  Cloud storage offers large quantities of storage e.g. for Media (video, audio), economically.

  1. Google Drive ingestion of assets may occur through an automated process, such as a drop folder within an FTP site.  The folder may be polled every N seconds by the Google DAM orchestration, or other 3rd party orchestration product, and ingested into Google Drive.  The ingested files are placed into a project folder designated by the accompanying XML meta file.
  2. The version control of assets, implemented by Google Drive and the DAM to facilitate collaboration and approval.
  3. Distribution and publishing media to designated people and locations, such as to social media channels, may be automatically triggered by DAM orchestration polling Google Drive custom meta data changes.   On demand publishing is also achievable through the DAM.
  4. Archiving project assets to custom locations, such as Google Cloud solution, may be triggered by a project meta data status modification, or on demand through the DAM.
  5. Assets may be spawned into other assets, such as clips.  Derived child assets are correlated with the master, or parent asset within the DAM asset meta data to trace back to origin.  Eliminates redundancy of asset, enabling users to easily find related files and reuse all or a portion of the asset.

Google Docs

  1. Documents required to accompany each media project, such as production guidelines, may go through several iterations before they are complete.  Many of the components of a document may be static.  Google Docs may incorporate ‘Document Assembly’ technology for automation of document construction.

Google’s YouTube

  1. Editing media either using default YouTube functionality, or using third party software, e.g. Adobe suite
  2. Enable caption creation and editing  may use YouTube or third party software.
  3. The addition & modification of meta data according to the corporate taxonomy may be added or modified through [custom] YouTube fields, or directly through the Google DAM Db where the project data resides.

Google’s Google Plus +

  1. G+ project page may be used for project and asset collaboration
  2. Project team members may subscribe to the project page to receive notifications on changes, such as new sub clips
  3. Asset workflow notifications,  human and automated:
    1. Asset modification approvals (i.e. G+ API <- -> DAM Db) through custom fields in G + page
    2. Changes to assets (i.e. collaboration) notifications,
    3. [Automated] e.g. ingestion in progress, or completed updates.
    4. [Automated] Process notifications: e.g. ‘distribution to XYZ’ and ‘transcoding N workflow’.  G + may include links to assets.
  4. Google Plus for in-house, and outside org. team(s) collaboration
  5. G + UI may trigger actions, such as ingestion e.g.  by specifying a specific Google Drive link, and a configured workflow.

Google Custom Search

  1. Allows for the search of assets within a project, within all projects within a silo of business, and across entire organization of assets.
  2. Ability to find and share DAM motion pictures, still images, and text assets with individuals, groups, project teams in or outside the organization.  Google Plus to facilitate sharing.
  3. Asset meta data will e.g. describe how the assets may be used for distribution, digital distribution rights.   Users and groups are implemented within G+, control of asset distribution may be implemented in Google Plus, and/or custom Google Search.

Here are a list of DAM vendors.

WORM Storage in the Cloud?

WORM Storage, or Write Once Read Many, is ideal for archiving data, such as electronic communications.  I was just wondering out of the big, commodity cloud storage vendors, such as Amazon (S3), Windows, or Google, for example, what their offerings would be included but not limited to physical media retrieval upon request, such legal requests, or the client requests a backup of the storage.

Parse, Index, and Playing Music in the Cloud

In other articles I’ve mentioned audio indexing of most frequently used words in podcasts and videos, and now I am saying this sexy growing hashtag applies to music tracks now playing, as an example, someone plays a song in their cloud player, the lyrics are already parse and indexed for the song, so the Amazon cloud player already can provide the lyrics that are most used, and produce a cloud of tags, more used are bigger.  Then overlapping that they are able to give the index across all songs now playing, so now we have a dynamic tag cloud with words from lyrics getting bigger, and smaller as they are played in their cloud player.

The user is also able to click a word and drill down to get a list of all the songs currently playing with the lyric word.  One caveat, some songs group words together,  so it might not make sense out of context.  Amazon could apply some rules in the AI engine that specify if certain lyrics are grouped together, then they constitute one tag.

This feature would be another great way to diacover new songs for the music aficionado.

An extension of this is to get the music sound foot print, sound waves from tracks, and overlay with songs now playing, so if you like the beat or rhythm of a song, you can listen to other songs with similar rhythm or beats now playing, another way to find new songs. The matching rhythm can be an automatic shuffle filtered by genre, if specified.

Microsoft OS & Google Cloud Platform: Owning the Shelf Space

Google at this phase in their business and technology life cycle reminds me of Microsoft, as the trailblazers, when Microsoft was building all their products, and continually trying to own the shelf space of product sets in their desktop platform.  Now that the tides have turned, it seems their cloud platform is growing, and Google’s growth is dominant. Not only are they building out the architecture platform, but they are filling out their shelf space, building out their platform with their products, the mantra, building out the products that fit in their platform, with a preference to build verses buy, acquiring when necessary. The parallelism with Microsoft, and the desktop in the 80s and 90s scary in it’s cyclical nature.

Although Microsoft ‘virtually’ owned, and arguably continues to dominate the desktop, thick client, although loosing ground to a diversity of platforms ever since Red Hat brought Unix popularity, and Macintosh continued to grow in it’s popularity.  Look what happened at Microsoft, lots of stock options, lots of cashing in, and eventually becoming unpopular associated with a passion for their oligopoly, or as the antitrust put it, monopoly in the market of the desktop, owning the desktop platform.  Could that now happen with Google, and will we see the stock split, and other competitive offerings occur, forced by an anti-trust case by the government?  Ouch.  Well, there is no doubt, Google’s cloud platform and product set is growing.  Good for them, and good for us as consumers.  The difference, APIs, and expandability with the Google platform.  Has Google learned the harsh lessons of Microsoft, allowing the extensibility.  Will they run into barriers with partners, upgrades to the APIs, greed, and a movement to own the shelf space.

We will see.  Google, keep your cloud APIs extendable, expose as many APIs as possible, allowing third parties to easily compete and dominate the products within your architecture, even create open source code to your own products within the cloud platform, and promote as many third party products as possible leveraging all of the APIs.

The one thing I have seen so far, which is not a great sign, is trying to incorporate 3rd party products into your cloud where you have competitive offering.  I’d like to see Google step up, for example, and create widgets to WordPress to compete with their blogging platform.  Actively look to plug in third party products into your cloud architecture, avoiding the animosity third parties might have, and there won’t be a need for anti-trust down the road.  Europe is already jumping on that train with anti-trust.  I’d devise a group within Google that looks to integrate, and partner with small to mid size companies, and proactively include them into your platform.  Don’t give anyone a reason to target Google as a monopoly.

See also the article, THE GOOGLE INVESTOR: Google’s FTC Interrogation Not Analogous To Microsoft’s Antitrust History

Google Apps Competes with Nvidia in the Game as a Service Market

I first saw Nvidia’s new GaaS offering at CES 2013.  It has tremendous for game developers as well as players alike.  I then had a looking at the Google Apps Marketplace, and there seemed to be a hole in their Product offerings, no gaming, which is a huge market.  At the moment, it seems geared toward business and education   Many of these applications can integrate into the Google Plus environment, such as Google Plus hangouts, amazing multiple user, technology platforms.

The integration of games seems like a logical step.  If the top installs list has the first product containing ~600 reviews, we know it is a relatively new platform. Also, from the trade papers, I understand Google designs it’s own servers with a lot of mystery around the proprietary technology of their data center server technologies.  One difference, between Nvidia and Google, although, if the technology output, resolution and speed of the games for the players, and the simplicity of the API, or programmer access to the high performance hardware is transparent, then both offerings may be competitive.  Time will tell.

Nvidia web site definition of GaaS:

NVIDIA GRID is the foundation for the ideal on-demand gaming as a service (GaaS), providing tremendous advantages over traditional console gaming systems.

  • Any-device gaming: High-quality, low-latency, multi device gaming on any PC, Mac, tablet, smartphone or TV.

  • Click-to-play simplicity: Anytime access to a library of gaming titles and saved games in the cloud. Play or continue games from any device, anywhere.

  • Less hassle: No new hardware. No complicated setup. No game discs. No digital downloads. No game installations. No game patches.