Tag Archives: onedrive

Platform Independent AI Model for Images: AI Builder, Easily Utilized by 3rd Party Apps

With all the discourse on OpenAI’s ChatGPT and Natural language processing (NLP), I’d like to steer the conversation toward images/video and object recognition. This is another area in artificial intelligence primed for growth with many use cases. Arguably, it’s not as shocking, bending our society at its core, creating college papers with limited input, but Object Recognition can seem “magical.” AI object recognition may turn art into science, as easy as AI reading your palm to tell your future. AI object recognition will bring consumers more data points from which Augmented Reality (AR) overlays digital images within an analog world of tangible objects.

Microsoft’s AI Builder – Platform Independent

Microsoft’s Power Automate AI [model] Builder has the functionality to get us started on the journey of utilizing images, tagging them with objects we recognize, and then training the AI model to recognize objects in our “production” images. Microsoft provides tools to build AI [image] models (library of images with human, tagged objects) quickly and easily. How you leverage these AI models is the foundation of “future” applications. Some applications are already here, but not mass production. The necessary ingredient: taking away the proprietary building of AI models, such as in social media applications.

In many social media applications, users can tag faces in their images for various reasons, mostly who to share their content/images with. In most cases, images can also be tagged with a specific location. Each AI image/object model is proprietary and not shared between social media applications. If there was a standards body, an AI model could be created/maintained outside of the social media applications. Portable AI object recognition models with a wide array of applications that support it’s use, such as social media applications. Later on, we’ll discuss Microsoft’s AI Model builder, externalized from any one application, and because it’s Microsoft, it’s intuitive. ūüôā

An industry standards body could collaborate and define what AI models look like their features, and most importantly, the portability formats. Then the industry, such as social media apps, can elect to adopt features that are and are not supported by their applications.

Use Cases for Detecting Objects in Images

Why doesn’t everyone have an AI model containing tagged objects within images and videos of the user’s design? Why indeed.

1 – Brands / Product Placement from Content Creators

Just about everyone today is a content creator, producing images and videos for their own personal and business social media feeds, Twitter, Instagram, Snap, Meta, YouTube, and TikTok, to name a few. AI models should be portable enough to integrate with social media applications where tags could be used to identify branded apparel, jewelry, appliances, etc. Tags could also contain metadata, allowing content consumers to follow tagged objects to a specified URL. Clicks and the promotion of products and services.

2 – Object Recognition for Face Detection

Has it all been done? Facebook/Meta, OneDrive, iCloud, and other services have already tried or are implementing some form of object detection in the photos you post. Each of these existing services implements object detection at some level:

  • Identify the faces in your photos, but need you to tag those faces and some “metadata” will be associated with these photos
  • Dynamically grouping/tagging all “Portrait” pictures of a specific individual or events from a specific day and location, like a family vacation.
  • Some image types, JPEGs, PNG, GIF, etc., allow you to add metadata to the files on your own, e.g. so you can search for pictures on the OS level of implementation.
3 – Operational Assistance through object recognition using AR
  • Constructing “complex” components in an assembly line where Augmented Reality (AR) can overlay the next step in assembly with the existing object to help transition the object to the next step in assembly.
  • Assistance putting together IKEA furniture, like the assembly line use case, but for home use.
  • Gaming, everything from Mario Kart Live to Light Saber duels against the infamous Darth Vader.
4 – Palm Reading and other Visual Analytics
  • Predictive weather patterns
5 – Visual Search through Search Engines and Proprietary Applications with Specific Knowledge Base Alignment
  • CoinSnap iPhone App scans both sides of the coin and then goes on to identify the coin, building a user’s collection.
  • Microsoft Bing’s Visual Search and Integration with MSFT Edge
  • Medical Applications, Leveraging AI, e.g., Image Models – Radiology
Radiology – Reading the Tea Leaves

Radiology builds a model of possible issues throughout the body. Creating images with specific types of fractures can empower the autodetection of any issues with the use of AI. If it was a non-proprietary model, radiologists worldwide could contribute to that AI model. The displacement of radiology jobs may inhibit the open non-proprietary nature of the use case, and the AI model may need to be built independently of open input from all radiologists.

Microsoft’s AI Builder – Detect Objects in Images

Microsoft’s AI model builder can help the user build models in minutes. Object Detection, Custom Model, Detect custom objects in images is the “template” you want to use to build a model to detect objects, e.g. people, cars, anything, rather quickly, and can enable users to add images (i.e. train model) to become a better model over time.

Many other AI Model types exist, such as Text Recognition within images. I suggest exploring the Azure AI Models list to fit your needs.

Current, Available Data Sources for Image Input

  • Current Device
  • SharePoint
  • Azure BLOB

Wish List for Data Sources w/Trigger Notifications

When a new image is uploaded into one of these data sources, a “trigger” can be activated to process the image with the AI Model and apply tags to the images.

  • ADT – video cam
  • DropBox
  • Google Drive
  • Instagram
  • Kodak (yeah, still around)
  • Meta/Facebook
  • OneDrive
  • Ring -video cam
  • Shutterfly
  • Twitter

Get Started: Power Automate, Premium Account

Login to Power Automate with your premium account, and select “AI Builder” menu, then the “Models” menu item. The top left part of the screen, select “New AI Model,” From the list of model types, select “Custom Model, Object Detection”Detect Custom Objects in Images.”

AI Builder - Custom Model
AI Builder – Custom Model

It’s a “Premium” feature of Power Automate, so you must have the Premium license. Select “Get Started”,. The first step is to “Select your model’s domain”, there are three choices, so I selected “Common Objects” to give me the broadest opportunity. Then select “Next”.

AI Builder - Custom Model - Domain
AI Builder – Custom Model – Domain

Next, you need to select all of the objects you want to identify in your images. For demonstration purposes, I added my family’s first names as my objects to train my model to identify in images.

AI Builder - Custom Model - Objects for Model
AI Builder – Custom Model – Objects for Model

Next, you need to “Add example images for your objects.” Microsoft’s guidance is “You need to add at least 15 images for each object you want to detect.” Current data sources include:

Add Images
AI Model – Add Images

I added the minimum recommended images, 15 per object, two objects, 30 images of my family, and random pics over the last year.

Once uploaded, you need to go through each image, draw a box around the image’s objects you want to tag, and then select the object tag.

Part 2 – Completing the Model and its App usage.

Tools of the Trade

2nd Edition – July 2021

Project Managers, Scrum Masters and Agents of Change

If you’re working on any type of project as a Project Manager, Scrum Master, or are part of any change management process, these tools should be in your technology toolkit. Over the years I’ve adopted the tools listed here. Some of these products were already part of the corporate environment, so I was required to use them, sometimes to my chagrin. In other corporate environments, I had the freedom to identify, select, and adopt one or more of these tools for teams I led. I hope this article introduces you to the next tool in your toolkit.

Project and Product Management Tools

Regardless of project implementation methodologies, as an agent of change, tracking requests for change, and approved changes for implementation should be quantified for effort and costs associated with the changes. Categorizing, classifying, prioritizing changes are all possible if changes are captured, tracked and opportunities compared.

Project and Product Management Tools
Project and Product Management Tools

Automation / Workflow

Project management automation? You bet!

Automation/Workflow
Automation/Workflow

Collaboration

Anyone not interested in a collaborative environment for dynamic projects doesn’t know the statement “Share the Blame, Pass the Credit.”

Collaboration
Collaboration

Communication

“There are no words to express…” so say it in a beautiful, graphical presentation that will get your message across.

Communication
Communication

Documentation

Meeting Minutes, Standard Operating Procedures (SOP), Functional Specifications, random notes, images of error messages, etc.

Documentation
Documentation

Financials / Project Reporting

I once had to track a project “THIS BIG“, and it came with a few accountants in tow.

Financials / Project Reporting
Financials / Project Reporting

Download a PDF of the Project Manager / Scrum Master Toolkit

Products in Use Today, and Additional Tools

This list is to highlight the most recent tools I’ve used “in the field”.  Just because I’ve omitted a product or service, it doesn’t mean I don’t advocate their use.  Please see the archive file below on additional tools I’ve used prior to my most recent engagements.

Last Published PM/SM Toolkit from 2017 ( Archive for Reference)

Want to Have Your Product Evaluated?

If you’re interested in a product review of your software targeting Project Managers or Scrum Masters, please contact me with your product information, and I will follow up.

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

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.

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

Time Lock Access: Seal Files in Cloud Storage

Is there value in providing users the ability to apply “Time Lock Access” to files in cloud storage? ¬†Files are securely uploaded by their Owner. ¬†After upload no one, including the Owner, may access / open the file(s). ¬† Only after the date and time provided for the time lock passes, files will be available for access, and action may be taken, e.g. ¬†Automatically email a link to the files. ¬†More complex actions may be attached to the time lock release such as script execution using a simple set of rules as defined by the file Owner.

Solution already exists?  Please send me a link to the cloud integration product / plug in.

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