Tag Archives: Google

Hostess with the Mostest – Apple Siri, Amazon Alexa, Microsoft Cortana, Google Assistant

Application Integration Opportunities:

  • Microsoft Office, Google G Suite, Apple iWork
    • Advice is integrated within the application, proactive and reactive: When searching in Microsoft Edge, a blinking circle representing Cortana is illuminated.  Cortana says “I’ve collected similar articles on this topic.”  If selected, presents 10 similar results in a right panel to help you find what you need.
  • Personal Data Access and Management
    • The user can vocally access their personal data, and make modifications to that data; E.g. Add entries to their Calendar, and retrieve the current day’s agenda.

Platform Capabilities: Mobile Phone Advantage

Strengthen core telephonic capabilities where competition, Amazon and Microsoft, are relatively week.

  • Ability to record conversations, and push/store content in Cloud, e.g. iCloud.  Cloud Serverless recording mechanism dynamically tags a conversations with “Keywords” creating an Index to the conversation.  Users may search recording, and playback audio clips +/- 10 seconds before and after tagged occurrence.
Calls into the User’s Smartphones May Interact Directly with the Digital Assistant
  • Call Screening – The digital assistant asks for the name of the caller, purpose of the call, and if the matter is “Urgent”
    • A generic “purpose” response, or a list of caller purpose items can be supplied to the caller, e.g. 1) Schedule an Appointment
    • The smartphone’s user would receive the caller’s name, and the purpose as a message back to the UI from the call, currently in a ‘hold’ state,
    • The smartphone user may decide to accept the call, or reject the call and send the caller to voice mail.
  • A  caller may ask to schedule a meeting with the user, and the digital assistant may access the user’s calendar to determine availability.  The digital assistant may schedule a ‘tentative’ appointment within the user’s calendar.
    • If calendar indicates availability, a ‘tentative’ meeting will be entered. The smartphone user would have a list of tasks from the assistant, and one of the tasks is to ‘affirm’ availability of the meetings scheduled.
  • If a caller would like to know the address of the smartphone user’s office, the Digital Assistant may access a database of “generally available” information, and provide it. The Smartphone user may use applications like Google Keep, and any note tagged with a label “Open Access” may be accessible to any caller.
  • Custom business workflows may be triggered through the smartphone, such as “Pay by Phone”.  When a caller is calling a business user’s smartphone, the call goes to “voice mail” or “digital assistant” based on smartphone user’s configuration.  If the user reaches the “Digital Assistant”, there may be a list of options the user may perform, such as “Request for Service” appointment.  The caller would navigate through a voice recognition, one of many defined by the smartphone users’ workflows.

Platform Capabilities: Mobile Multimedia

Either through your mobile Smartphone, or through a portable speaker with voice recognition (VR).

  • Streaming media / music to portable device based on interactions with Digital Assistant.
  • Menu to navigate relevant (to you) news,  and Digital Assistant to read articles through your portable media device (without UI)

Third Party Partnerships: Adding User Base, and Expanding Capabilities

In the form of platform apps (abstraction), or 3rd party APIs which integrate into the Digital Assistant, allowing users to directly execute application commands, e.g. Play Spotify song, My Way by Frank Sinatra.

  • Any “Skill Set” with specialized knowledge: direct Q&A or instructional guidance  – e.g Home Improvement, Cooking
  • eCommerce Personalized Experience – Amazon
  • Home Automation – doors, thermostats
  • Music – Spotify
  • Navigate Set Top Box (STB) – e.g. find a program to watch
  • Video on Demand (VOD) – e.g. set to record entertainment

 

Cloud Serverless Computing: Why? and With Whom?

What is Cloud Serverless Computing?

Based on your application Use Case(s), Cloud Serverless Computing architecture may reduce ongoing costs for application usage, and provide scalability on demand without the Cloud Server Instance management overhead, i.e. costs and effort.
Note: Cloud Serverless Computing is used interchangeability with Functions as a service (FaaS) which makes sense from a developer’s standpoint as they are coding Functions (or Methods), and that’s the level of abstraction.

Microsoft Flow

 

Microsoft Flow Pricing

As listed below, there are three tiers, which includes a free tier for personal use or exploring the platform for your business.  The pay Flow plans seem ridiculously inexpensive based on what business workflow designers receive for the 5 USD or 15 USD per month.  Microsoft Flow has abstracted building workflows so almost anyone can build application workflows or automate business manual workflows leveraging almost any of the popular applications on the market.

It doesn’t seem like 3rd party [data] Connectors and Template creators receive any direct monetary value from the Microsoft Flow platform.  Although workflow designers and business owners may be swayed to purchase 3rd party product licenses for the use of their core technology.

Microsoft Flow Pricing
Microsoft Flow Pricing

Microsoft Azure Functions

Process events with a serverless code architecture.  An event-based serverless compute experience to accelerate development. Scale based on demand and pay only for the resources you consume.

Google Cloud  Serverless

Properly designed microservices have a single responsibility and can independently scale. With traditional applications being broken up into 100s of microservices, traditional platform technologies can lead to significant increase in management and infrastructure costs. Google Cloud Platform’s serverless products mitigates these challenges and help you create cost-effective microservices.

Google Serverless Application Development
Google Serverless Application Development

 

Google Serverless Analytics and Machine Learning
Google Serverless Analytics and Machine Learning

 

Google Serverless Use Cases
Google Serverless Use Cases

 

Amazon AWS  Lambda

AWS provides a set of fully managed services that you can use to build and run serverless applications. You use these services to build serverless applications that don’t require provisioning, maintaining, and administering servers for backend components such as compute, databases, storage, stream processing, message queueing, and more. You also no longer need to worry about ensuring application fault tolerance and availability. Instead, AWS handles all of these capabilities for you, allowing you to focus on product innovation and get faster time-to-market. It’s important to note that Amazon was the first contender in this space with a 2014 product launch.

IBM Bluemix OpenWhisk

Execute code on demand in a highly scalable serverless environment.  Create and run event-driven apps that scale on demand.

  • Focus on essential event-driven logic, not on maintaining servers
  • Integrate with a catalog of services
  • Pay for actual usage rather than projected peaks

The OpenWhisk serverless architecture accelerates development as a set of small, distinct, and independent actions. By abstracting away infrastructure, OpenWhisk frees members of small teams to rapidly work on different pieces of code simultaneously, keeping the overall focus on creating user experiences customers want.

What’s Next?

Serverless Computing is a decision that needs to be made based on the usage profile of your application.  For the right use case, serverless computing is an excellent choice that is ready for prime time and can provide significant cost savings.

There’s an excellent article, recently published July 16th, 2017 by  Moshe Kranc called, “Serverless Computing: Ready for Prime Time” which at a high level can help you determine if your application is a candidate for Serverless Computing.


See Also:
  1. “Serverless computing architecture, microservices boost cloud outlook” by Mike Pfeiffer
  2. “What is serverless computing? A primer from the DevOps point of view” by J Steven Perry

Google Search Enables Users to Upload Images for Searching with Visual Recognition. Yahoo and Bing…Not Yet

The ultimate goal, in my mind, is to have the capability within a Search Engine to be able to upload an image, then the search engine analyzes the image, and finds comparable images within some degree of variation, as dictated in the search properties.  The search engine may also derive metadata from the uploaded image such as attributes specific to the image object(s) types.  For example,  determine if a person [object] is “Joyful” or “Angry”.

As of the writing of this article,  search engines Yahoo and Microsoft Bing do not have the capability to upload an image and perform image/pattern recognition, and return results.   Behold, Google’s search engine has the ability to use some type of pattern matching, and find instances of your image across the world wide web.    From the Google Search “home page”, select “Images”, or after a text search, select the “Images” menu item.  From there, an additional icon appears, a camera with the hint text “Search by Image”.  Select the Camera icon, and you are presented with options on how Google can acquire your image, e.g. upload, or an image URL.

Google Search Upload Images
Google Search Upload Images

Select the “Upload an Image” tab, choose a file, and upload.  I used a fictional character, Max Headroom.   The search results were very good (see below).   I also attempted an uncommon shape, and it did not meet my expectations.   The poor performance of matching this possibly “unique” shape is mostly likely due to how the Google Image Classifier Model was defined, and correlating training data that tested the classifier model.  If the shape is “Unique” the Google Search Image Engine did it’s job.

Google Image Search Results – Max Headroom
Max Headroom Google Search Results
Max Headroom Google Search Results

 

Google Image Search Results – Odd Shaped Metal Object
Google Search Results - Odd Shaped Metal Object
Google Search Results – Odd Shaped Metal Object

The Google Search Image Engine was able to “Classify” the image as “metal”, so that’s good.  However I would have liked to see better matches under the “Visually Similar Image” section.  Again, this is probably due to the image classification process, and potentially the diversity of image samples.

A Few Questions for Google

How often is the Classifier Modeling process executed (i.e. training the classifier), and the model tested?  How are new images incorporated into the Classifier model?  Are the user uploaded images now included in the Model (after model training is run again)?    Is Google Search Image incorporating ALL Internet images into Classifier Model(s)?  Is an alternate AI Image Recognition process used beyond Classifier Models?

Behind the Scenes

In addition, Google has provided a Cloud Vision API as part of their Google Cloud Platform.

I’m not sure if the Cloud Vision API uses the same technology as Google’s Search Image Engine, but it’s worth noting.  After reaching the Cloud Vision API starting page, go to the “Try the API” section, and upload your image.  I tried a number of samples, including my odd shaped metal, and I uploaded the image.  I think it performed fairly well on the “labels” (i.e. image attributes)

Odd Shaped Metal Sample Image
Odd Shaped Metal Sample Image

Using the Google Cloud Vision API, to determine if there were any WEB matches with my odd shaped metal object, the search came up with no results.  In contrast, using Google’s Search Image Engine produced some “similar” web results.

Odd Shaped Metal Sample Image Web Results
Odd Shaped Metal Sample Image Web Results

Finally, I tested the Google Cloud Vision API with a self portrait image.  THIS was so cool.

Google Vision API - Face Attributes
Google Vision API – Face Attributes

The API brought back several image attributes specific to “Faces”.  It attempts to identify certain complex facial attributes, things like emotions, e.g. Joy, and Sorrow.

Google Vision API - Labels
Google Vision API – Labels

The API brought back the “Standard” set of Labels which show how the Classifier identified this image as a “Person”, such as Forehead and Chin.

Google Vision API - Web
Google Vision API – Web

Finally, the Google Cloud Vision API brought back the Web references, things like it identified me as a Project Manager, and an obscure reference to Zurg in my Twitter Bio.

The Google Cloud Vision API, and their own baked in Google Search Image Engine are extremely enticing, but yet have a ways to go in terms of accuracy %.  Of course,  I tried using my face in the Google Search Image Engine, and looking at the “Visually Similar Images” didn’t retrieve any images of me, or even a distant cousin (maybe?)

Google Image Search Engine: Ian Face Image
Google Image Search Engine: Ian Face Image

 

Amazon’s Alexa vs. Google’s Assistant: Same Questions, Different Answers

Excellent article by  .

Amazon’s Echo and Google’s Home are the two most compelling products in the new smart-speaker market. It’s a fascinating space to watch, for it is of substantial strategic importance to both companies as well as several more that will enter the fray soon. Why is this? Whatever device you outfit your home with will influence many downstream purchasing decisions, from automation hardware to digital media and even to where you order dog food. Because of this strategic importance, the leading players are investing vast amounts of money to make their product the market leader.

These devices have a broad range of functionality, most of which is not discussed in this article. As such, it is a review not of the devices overall, but rather simply their function as answer engines. You can, on a whim, ask them almost any question and they will try to answer it. I have both devices on my desk, and almost immediately I noticed something very puzzling: They often give different answers to the same questions. Not opinion questions, you understand, but factual questions, the kinds of things you would expect them to be in full agreement on, such as the number of seconds in a year.

How can this be? Assuming they correctly understand the words in the question, how can they give different answers to the same straightforward questions? Upon inspection, it turns out there are ten reasons, each of which reveals an inherent limitation of artificial intelligence as we currently know it…


Addendum to the Article:

As someone who has worked with Artificial Intelligence in some shape or form for the last 20 years, I’d like to throw in my commentary on the article.

  1. Human Utterances and their Correlation to Goal / Intent Recognition.  There are innumerable ways to ask for something you want.  The ‘ask’ is a ‘human utterance’ which should trigger the ‘goal / intent’ of what knowledge the person is requesting.  AI Chat Bots, digital agents, have a table of these utterances which all roll up to a single goal.  Hundreds of utterances may be supplied per goal.  In fact, Amazon has a service, Mechanical Turk, the Artificial Artificial Intelligence, which you may “Ask workers to complete HITs – Human Intelligence Tasks – and get results using Mechanical Turk”.   They boast access to a global, on-demand, 24 x 7 workforce to get thousands of HITs completed in minutes.  There are also ways in which the AI Digital Agent may ‘rephrase’ what the AI considers utterances that are closely related.  Companies like IBM look toward human recognition, accuracy of comprehension as 95% of the words in a given conversation.  On March 7, IBM announced it had become the first to hone in on that benchmark, having achieved a 5.5% error rate.
  2. Algorithmic ‘weighted’ Selection verses Curated Content.   It makes sense based on how these two companies ‘grew up’, that Amazon relies on their curated content acquisitions such as Evi,  a technology company which specialises in knowledge base and semantic search engine software. Its first product was an answer engine that aimed to directly answer questions on any subject posed in plain English text, which is accomplished using a database of discrete facts.   “Google, on the other hand, pulls many of its answers straight from the web. In fact, you know how sometimes you do a search in Google and the answer comes up in snippet form at the top of the results? Well, often Google Assistant simply reads those answers.”  Truncated answers equate to incorrect answers.
  3. Instead of a direct Q&A style approach, where a human utterance, question, triggers an intent/goal , a process by which ‘clarifying questions‘ maybe asked by the AI digital agent.  A dialog workflow may disambiguate the goal by narrowing down what the user is looking for.  This disambiguation process is a part of common technique in human interaction, and is represented in a workflow diagram with logic decision paths. It seems this technique may require human guidance, and prone to bias, error and additional overhead for content curation.
  4. Who are the content curators for knowledge, providing ‘factual’ answers, and/or opinions?  Are curators ‘self proclaimed’ Subject Matter Experts (SMEs), people entitled with degrees in History?  or IT / business analysts making the content decisions?
  5. Questions requesting opinionated information may vary greatly between AI platform, and between questions within the same AI knowledge base.  Opinions may offend, be intentionally biased, sour the AI / human experience.

Evaluating fobi.io Chatbot Powered By Google Forms: AI Digital Agent?

Interesting approach to an AI Chatbot implementation.  The business process owner creates one or more Google Forms containing questions and answers, and converts/deploys to a chatbot using fobi.io.  All the questions for [potential] customers/users are captured in a multitude of forms.  Without any code, and within minutes, an interactive chatbot can be produced and deployed for client use.

The trade off for rapid deployment and without coding is a rigid approach of triggering user desired “Goal/Intents”.  It seems a single goal/intent is mapped to a single Google Form.  As opposed to a digital agent, which leverages utterances to trigger the user’s intended goal/intent.  Before starting the chat, the user must select the appropriate Google Form, with the guidance of the content curator.

Another trade off is, it seems, no integration on the backend to execute a business process, essential to many chatbot workflows. For example, given an Invoice ID, the chatbot may search in a transactional database, then retrieve and display the full invoice.  Actually, I may be incorrect. On the Google Forms side, there is a Script Editor. Seems powerful and scary all at the same time.

Another trade off that seems to exist, more on the Google Forms side, is building not just a Form with a list of Questions, but a Consumer Process Workflow, that allows the business to provide an interactive dialog based on answers users provide.  For example, a Yes/No or multichoice answer may lead to alternate sets of questions [and actions].  It doesn’t appear there is any workflow tool provided to structure the Google Forms / fobi.io chatbot Q&A.

However, there are still many business cases for the product, especially for small to mid size organizations.

* Business Estimates – although there is no logic workflow to guide the Q&A sessions with [prospective] customers, the business still may derive the initial information they require to make an initial assessment.  It seems a Web form, and this fobi.io / Google Forms solution seems very comparable in capability, its just a change in the median in which the user interacts to collect the information.

One additional note, Google Forms is not a free product.  Looks like it’s a part of the G Suite. Free two week trial, then the basic plan is $5 per month, which comes with other products as well.  Click here for pricing details.

Although this “chatbot” tries to quickly provide a mechanism to turn a form to a chatbot, it seems it’s still just a form at the end of the day.  I’m interested to see more products from Zoi.ai soon

Beyond Google Search of Personal Data – Proactive, AI Digital Assistant 

As per previous Post, Google Searches Your Personal Data (Calendar, Gmail, Photos), and Produces Consolidated Results, why can’t the Google Assistant take advantage of the same data sources?

Google may attempt to leapfrog their Digital Assistant competition by taking advantage of their ability to search against all Google products.  The more personal data a Digital Assistant may access, the greater the potential for increased value per conversation.

As a first step,  Google’s “Personal”  Search tab in their Search UI has access to Google Calendar, Photos, and your Gmail data.  No doubt other Google products are coming soon.

Big benefits are not just for the consumer to  search through their Personal Goggle data, but provide that consolidated view to the AI Assistant.  Does the Google [Digital] Assistant already have access to Google Keep data, for example.  Is providing Google’s “Personal” search results a dependency to broadening the Digital Assistant’s access and usage?  If so, these…

interactions are most likely based on a reactive model, rather than proactive dialogs, i.e. the Assistant initiating the conversation with the human.

Note: The “Google App” for mobile platforms does:

“What you need, before you ask. Stay a step ahead with Now cards about traffic for your commute, news, birthdays, scores and more.”

I’m not sure how proactive the Google AI is built to provide, but most likely, it’s barely scratching the service of what’s possible.

Modeling Personal, AI + Human Interactions

Starting from N number of accessible data sources, searching for actionable data points, correlating these data points to others, and then escalating to the human as a dynamic or predefined Assistant Consumer Workflow (ACW).  Proactive, AI Digital Assistant initiates human contact to engage in commerce without otherwise being triggered by the consumer.

Actionable data point correlations can trigger multiple goals in parallel.  However, the execution of goal based rules would need to be managed.  The consumer doesn’t want to be bombarded with AI Assistant suggestions, but at the same time, “choice” opportunities may be appropriate, as the Google [mobile] App has implemented ‘Cards’ of bite size data, consumable from the UI, at the user’s discretion.

As an ongoing ‘background’ AI / ML process, Digital Assistant ‘server side’ agent may derive correlations between one or more data source records to get a deeper perspective of the person’s life, and potentially be proactive about providing input to the consumer decision making process.

Bass Fishing Trip
Bass Fishing Trip

For example,

  • The proactive Google Assistant may suggest to book your annual fishing trip soon.  Elevated Interaction to Consumer / User.
  • The Assistant may search Gmail records referring to an annual fishing trip ‘last year’ in August. AI background server side parameter / profile search.   Predefined Assistant Consumer Workflow (ACW) – “Annual Events” Category.  Building workflows that are ‘predefined’ for a core set of goals/rules.
  • AI Assistant may search user’s photo archive on the server side.   Any photo metadata could be garnished from search, including date time stamps, abstracted to include ‘Season’ of Year, and other synonym tags.
  • Photos from around ‘August’ may be earmarked for Assistant use
  • Photos may be geo tagged,  e.g. Lake Champlain, which is known for its fishing.
  •  All objects in the image may be stored as image metadata. Using image object recognition against all photos in the consumer’s repository,  goal / rule execution may occur against pictures from last August, the Assistant may identify the “fishing buddies” posing with a huge “Bass fish”.
  • In addition to the Assistant making the suggestion re: booking the trip, Google’s Assistant may bring up ‘highlighted’ photos from last fishing trip to ‘encourage’ the person to take the trip.

This type of interaction, the Assistant has the ability to proactively ‘coerce’ and influence the human decision making process.  Building these interactive models of communication, and the ‘management’ process to govern the AI Assistant is within reach.

Predefined Assistant Consumer / User Workflows (ACW) may be created by third parties, such as Travel Agencies, or by industry groups, such as foods, “low hanging fruit” easy to implement the “time to get more milk” .  Or, food may not be the best place to start, i.e. Amazon Dash

 

Using Google to Search Personal Data: Calendar, Gmail, Photos, and …

On June 16th, 2017,  post reviewed for relevant updates.

Reported by the Verge,  Google adds new Personal tab to search results to show Gmail and Photos content on May 26th.

Google seems to be rolling out a new feature in search results that adds a “Personal” tab to show content from [personal] private sources, like your Gmail account and Google Photos library. The addition of the tab was first reported by Search Engine Roundtable, which spotted the change earlier today.

I’ve been very vocal about a Google Federated Search, specifically across the user’s data sources, such as Gmail, Calendar, and Keep. Although, it doesn’t seem that Google has implemented Federated Search across all user, Google data sources yet, they’ve picked a few data sources, and started up the mountain.

It seems Google is rolling out this capability iteratively,  and as with Agile/Scrum, it’s to get user feedback, and take slices of deliverables.

Search Roundtable online news didn’t seem to indicate Google has publicly announced this effort, and is perhaps waiting for more sustenance, and more stick time.

As initially reported by Search Engine Roundtable,  the output of Gmail results appear in a single column text output with links to the content, in this case email.

Google Personal Results
Google Personal Search Results –  Gmail

It appears the sequence of the “Personal Search” output:

  • Agenda (Calendar)
  • Photos
  • Gmail

Each of the three app data sources displayed on the “Personal” search enables the user to drill down into the records displayed, e.g.specific email displayed.

Google Personal Search Calendar
Google Personal Search Results –  Calendar

 Group Permissions – Searching

Providing users the ability to search across varied Google repositories (shared calendars, photos, etc.) will enable both business teams, and families ( e.g. Apple’s family iCloud share) to collaborate and share more seamlessly.  At present Cloud Search part of G Suite by Google Cloud offers search across team/org digital assets:

Use the power of Google to search across your company’s content in G Suite. From Gmail and Drive to Docs, Sheets, Slides, Calendar, and more, Google Cloud Search answers your questions and delivers relevant suggestions to help you throughout the day.

 

Learn More? Google Help

Click here  to learn more on, “Search results from your Google products”  At this time, according to this Google post:

You can search for information from other Google products like Gmail, Google Calendar, and Google+.


Dear Google [Search]  Product Owner,

I request Google Docs and Google Keep be in the next data sources to be enabled for the Personal search tab.

Best Regards,

Ian

 

Microsoft to Release AI Digital Agent SDK Integration with Visio and Deploy to Bing Search

Build and deploy a business AI Digital Assistant with the ease of building visio diagrams, or ‘Business Process Workflows’.  In addition, advanced Visio workflows offer external integration, enabling the workflow to retrieve information from external data sources; e.g. SAP CRM; Salesforce.

As a business, Digital Agent subscriber,  Microsoft Bing  search results will contain the business’ AI Digital Assistant created using Visio.  The ‘Chat’ link will invoke the business’ custom Digital Agent.  The Agent has the ability to answer business questions, or lead the user through “complex”, workflows.  For example, the user may ask if a particular store has an item in stock, and then place the order from the search results, with a ‘small’ transaction fee to the business. The Digital Assistant may be hosted with MSFT / Bing or an external server.  Applying the Digital Assistant to search results pushes the transaction to the surface of the stack.

Bing Chat
Bing Digital Chat Agent

Leveraging their existing technologies, Microsoft will leap into the custom AI digital assistant business using Visio to design business process workflows, and Bing for promotion placement, and visibility.  Microsoft can charge the business for the Digital Agent implementation and/or usage licensing.

  • The SDK for Visio that empowers the business user to build business process workflows with ease may have a low to no cost monthly licensing as a part of MSFT’s cloud pricing model.
  • Microsoft may charge the business a “per chat interaction”  fee model, either per chat, or bundles with discounts based on volume.
  • In addition, any revenue generated from the AI Digital Assistant, may be subject to transactional fees by Microsoft.

Why not use Microsoft’s Cortana, or Google’s AI Assistant?  Using a ‘white label’ version of an AI Assistant enables the user to interact with an agent of the search listed business, and that agent has business specific knowledge.  The ‘white label’ AI digital agent is also empowered to perform any automation processes integrated into the user defined, business workflows. Examples include:

  • basic knowledge such as store hours of operation
  • more complex assistance, such as walking a [perspective] client through a process such as “How to Sweat Copper Pipes”.  Many “how to” articles and videos do exist on the Internet already through blogs or youtube.    The AI digital assistant “curator of knowledge”  may ‘recommended’ existing content, or provide their own content.
  • Proprietary information can be disclosed in a narrative using the AI digital agent, e.g.  My order number is 123456B.  What is the status of my order?
  • Actions, such as employee referrals, e.g. I spoke with Kate Smith in the store, and she was a huge help finding what I needed.  I would like to recommend her.  E.g.2. I would like to re-order my ‘favorite’ shampoo with my details on file.  Frequent patrons may reorder a ‘named’ shopping cart.

Escalation to a human agent is also a feature.  When the business process workflow dictates, the user may escalate to a human in ‘real-time’, e.g. to a person’s smartphone.

Note: As of yet, Microsoft representatives have made no comment relating to this article.

AI Email Workflows Eliminate Need for Manual Email Responses

When i read the article “How to use Gmail templates to answer emails faster.”  I thought wow, what an 1990s throwback!

Microsoft Outlook has had an AI Email Rules Engine for years and years. From using a simple Wizard to an advanced construction rules user interface. Oh the things you can do. Based on a wide away of ‘out of the box’ identifiers to highly customizable conditions, MS Outlook may take action on the client side of the email transaction or on the server side. What types of actions? All kinds of transactions ranging from ‘out of the box’ to a high degree of customization. And yes, Outlook (in conjunction with MS Exchange) may be identified as a digital asset management (DAM) tool.

Email comes into an inbox, based on “from”, “subject”, contents of email, and a long list of attributes, MS Outlook [optionally with MS Exchange], for example, may push the Email and any attached content, to a server folder, perhaps to Amazon AWS S3, or as simple as an MS Exchange folder.

Then, optionally a ‘backend’ workflow may be triggered, for example, with the use of Microsoft Flow. Where you go from there has almost infinite potential.

Analogously, Google Gmail’s new Inbox UI uses categorization based on ‘some set’ of rules is not something new to the industry, but now Google has the ability. For example, “Group By” through Google’s new Inbox, could be a huge timesaver. Enabling the user to perform actions across predefined email categories, such as delete all “promotional” emails, could be extremely successful. However, I’ve not yet seen the AI rules that identify particular emails as “promotional” verses “financial”. Google is implying these ‘out of the box’ email categories, and the way users interact, take action, are extremely similar per category.

Google may continue to follow in the footsteps of Microsoft, possibly adding the initiation of workflows based on predetermined criteria. Maybe Google will expose its AI (Email) Rules Engine for users to customize their workflows, just as Microsoft did so many years ago.

Although Microsoft’s Outlook (and Exchange) may have been seen as a Digital Asset Management (DAM) tool in the past, the user’s email Inbox folder size could have been identified as one of the few sole inhibitors.  Workaround, of course, using service accounts with vastly higher folder quota / size.

My opinions do not reflect that of my employer.

AI Digital Assistant verse Search Engines

Aren’t AI Digital Assistants just like Search Engines? They both try to recognize your question or human utterance as best as possible to serve up your requested content. E.g.classic FAQ. The difference in the FAQ use case is the proprietary information from the company hosting the digital assistant may not be available on the internet.

Another difference between the Digital Assistant and a Search Engine is the ability of the Digital Assistant to ‘guide’ a person through a series of questions, enabling elaboration, to provide the user a more precise answer.

The Digital Assistant may use an interactive dialog to guide the user through a process, and not just supply the ‘most correct’ responses. Many people have flocked to YouTube for instructional type of interactive medium. When multiple workflow paths can be followed, the Digital Assistant has the upper hand.

The Digital Assistant has the capability of interfacing with 3rd parties (E.g. data stores with API access). For example, there may be a Digital Assistant hosted by Medical Insurance Co that has the ability to not only check the status of a claim, but also send correspondence to a medical practitioner on your behalf. A huge pain to call the insurance company, then the Dr office, then the insurance company again. Even the HIPPA release could be authenticated in real time, in line during the chat.  A digital assistant may be able to create a chat session with multiple participants.

Digital Assistants overruling capabilities over Search Engines are the ability to ‘escalate’ at any time during the Digital Assistant interaction. People are then queued for the next available human agent.

There have been attempts in the past, such as Ask.com (originally known as Ask Jeeves) is a question answering-focused e-business.  Google Questions and Answers (Google Otvety, Google Ответы) was a free knowledge market offered by Google that allowed users to collaboratively find good answers, through the web, to their questions (also referred as Google Knowledge Search).

My opinions are my own, and do not reflect my employer’s viewpoint.