Tag Archives: Artificial Intelligence

Microsoft’s Plethora of Portals

As I was looking through Microsoft’s catalog of applications, it occurred to me just how many of their platforms are information-centric and seemed to overlap in functionality. Where should I go when I want to get stuff done, find information or produce it? Since the early days of AOL and AltaVista, we’ve seen the awesome power of a “Jump Page” as the starting point for our information journey.

Microsoft, which one do I choose?

From one software vendor’s perspective, we’ve got many options. What’s the best option for me? Seems like there should be opportunities to gain synergies between available Microsoft platforms.

Bing.com

Searching for information on the internet? News, images, encyclopedias, Wikipedia, whatever you need, and more is on the web. Microsoft Bing helps you find what you need regardless if you’re using text or an image to search for like for like information. It also serves up “relevant” information on the jump page, news mixed with advertisements. There is also a feature enabling you to add carousel “boxes”. for example, containing latest MS Word files used, synergy from Office.com

Office.com

Word, PowerPoint, Excel, Visio, Power BI… If you’ve created content or want to create content using Microsoft applications, Office.com is the one-stop-shop for all your Office apps and the content created using these applications.

SharePoint

Another portal to a universe of information around a centric theme, such as collaboration/interaction with product/project team members, an Intranet, SharePoint site with one or multiple teams. At the most fundamental level is the capability to collaborate/interact with teams, potentially leveraging Microsoft collaboration tools. Just one of many of its capabilities “out of the box” is a document management solution and the use of version control.

SharePoint can also be used for any type of Internet/web platform, i.e., a public-facing portal platform. However, SharePoint, in fact, is a sharing tool in which the authors of the website can share video presentations, shared calendars of public events, and a plethora of customized lists.

Yammer

Engaging your people is more critical than ever. Yammer connects leaders, communicators, and employees to build communities, share knowledge, and engage everyone. I’m thinking synonymous with a bulletin board. The implementation of Yammer looks like Facebook for the Enterprise.

  • Use the Home feed to stay on top of what matters, tap into the knowledge of others, and build on existing work.
  • Search for experts, conversations, and files.
  • Join communities to stay informed, connect with your coworkers, and gather ideas.
  • Join in the conversation, react, reply to, and share posts.
  • @ mention someone to loop them in.
  • Attach a file, gif, photo, or video to enhance your post.
  • Praise someone in your network to celebrate a success, or just to say thanks.
  • Create a virtual event that your community can ask a question and participate live or watch the recording afterwards.
  • Use polls to crowd source feedback and get answers fast.
  • Stay connected outside the office with the Yammer mobile app.
  • Use Yammer in Microsoft Teams, SharePoint, or Outlook.

“Yammer helps you connect and engage across your organization so that you can discuss ideas, share updates, and network with others.”

Microsoft Teams

For any team, there is a wealth of information varying from the group or single Chats, Teams, Calls, Files, and practically integration for almost all Microsoft applications and beyond. The extensibility of MS Teams seems relatively boundless, such as integrations with Wikis, SharePoint document folders, etc. From what I can tell, many organizations just use Teams for the group, or individual Chat channels are barely grazing the surface of MS Teams’ capabilities.

Setup of MS Teams, Teams “landing” page is a great place to start constructing your “living space” within MS Teams. From there, you can carve out space for all things related to the team. For example, in the “Team ABC” Team channel, you can add N number of “tabs” relating to everything from an embedded Wiki to specific SharePoint folders for the team’s product specifications. A team could even create an embedded Azure DevOps [Kanban] Board to show progress and essentially “live in” your MS Team, team channel.

Another porta;l overlap, Microsoft Teams Communities, seems to equate to Yammer.

Delve

What is Delve – Microsoft 365?

Use Delve to manage your Microsoft 365 profile and to discover and organize the information that’s likely to be most interesting to you right now – across Microsoft 365.

Delve never changes any permissions, so you’ll only see documents that you already have access to. Other people will not see your private documents. Learn more about privacy.

Delve is a content curation platform for the person it’s most relevant to…you. It gives the appearance of a user experience similar to carousels of video streaming apps. There are “Popular Documents” carousels and other carousels that are based on the most recent access. Based on how files are saved based on who can access content is how the platform gives you a treasure trove of documents you never knew you had access to or existed. It actually paints a potential compliance nightmare if people select the default document access as “…anyone within my organization…”.

Outlook.com / Best of MSN

Another portal of information focused around you: your email, your calendar, your To-Dos, and your contacts/people. It’s not just your communication with anyone, e.g., your project team members; it’s organizing your life on a smaller scale, e.g., To-Dos. You can also access other shared calendars, such as a team release schedule or a PTO schedule.

The Best of MSN is information, i.e., news around your interests, a digest of information relevant to you, delivered in an email format. Other digests of information from other sources may be curated and sent if subscribed.

Mediums to Traverse Information: AR, VR…

The visual paradigms used to query and access information may drastically influence the user’s capacity to digest the relevant information. For example, in an Augmented Reality (AR) experience, querying, identifying information, and then applying it, serving up the content in a way most conducive to a user’s experience is vital.

Users can’t just “Google It” and serve up the results like magic. The next evolution of querying information and serving up content in a medium to maximize its usability is key and is most evident when using Augmented Reality (AR). If you’re building something, instructions may be overlayed by the physical elements/parts in front of the user. Even the context of the step number would allow the virtual images to overlay the parts.

Automated and Manual Content Curation is a MUST for all Portals

Categories, Tags, Images, and all other associations from object A to everything else, the Meta of Existence, are essential for proper information dissemination and digestion. If you can tag any object with metadata, you can teach an AI/search engine to identify it in a relevant query. Implementing an Induction Engine, a type of Artificial Intelligence that proposes rules based on historic patterns is a must to improve query accuracy over time.

Next level, “Information applications” – Improved Living with Alzheimer’s

Next Ecosystem: Google..?

AR Sudoku Solver Uses Machine Learning To Solve Puzzles Instantly

Very novel concept, applying Augmented Reality and Artificial Intelligence (i.e. Machine Learning) to solving puzzles, such as Sudoko.  Maybe not so novel considering AR uses in manufacturing.

Next, we’ll be using similar technology for human to human negotiations, reading body language, understanding logical arguments, reading human emotion, and to rebut remarks in a debate.

Litigators watch out… Or, co-counsel?   Maybe a hand of Poker?

Source: AR Sudoku Solver Uses Machine Learning To Solve Puzzles Instantly

Follow the Breadcrumbs: Identify and Transform

Trends – High Occurrence, Word Associations

Over the last two decades, I’ve been involved in several solutions that incorporated artificial intelligence and in some cases machine learning. I’ve understood at the architectural level, and in some cases, a deeper dive.

I’ve had the urge to perform a data trending exercise, where not only do we identify existing trends, similar to “out of the box” Twitter capabilities, we can also augment “the message” as trends unfold. Also, probably AI 101. However, I wanted to submerge myself in understanding this Data Science project. My Solution Statement: Given a list of my interests, we can derive sentence fragments from Twitter, traverse the tweet, parsing each word off as a possible “breadcrumb”. Then remove the Stop Words, and voila, words that can identify trends, and can be used to create/modify trends.

Finally, to give the breadcrumbs, and those “words of interest” greater depth, using the Oxford Dictionaries API we can enrich the data with things like their Thesaurus and Synonyms.

Gotta Have a Hobby

It’s been a while now that I’ve been hooked on Microsoft Power Automate, formerly known as Microsoft Flow. It’s relatively inexpensive and has the capabilities to be a tremendous resource for almost ANY project. There is a FREE version, and then the paid version is $15 per month. No brainer to pick the $15 tier with bonus data connectors.

I’ve had the opportunity to explore the platform and create workflows. Some fun examples, initially, using MS Flow, I parsed RSS feeds, and if a criterion was met, I’d get an email. I did the same with a Twitter feed. I then kicked it up a notch and inserted these records of interest into a database. The library of Templates and Connectors is staggering, and I suggest you take a look if you’re in a position where you need to collect and transform data, followed by a Load and a notification process.

What Problem are we Trying to Solve?

How are trends formed, how are they influenced, and what factors influence them? The most influential people providing input to a trend? Influential based on location? Does language play a factor on how trends are developed? End Goal: driving trends, and not just observing them.

Witches Brew – Experiment Ingredients:

Obtaining and Scrubbing Data

Articles I’ve read regarding Data Science projects revolved around 5 steps:

  1. Obtain Data
  2. Scrub Data
  3. Explore Data
  4. Model Data
  5. Interpreting Data

The rest of this post will mostly revolve around steps 1 and 2. Here is a great article that goes through each of the steps in more detail: 5 Steps of a Data Science Project Lifecycle

Capturing and Preparing the Data

The data set is arguably the most important aspect of Machine Learning. Not having a set of data that conforms to the bell curve and consists of all outliers will produce an inaccurate reflection of the present, and poor prediction of the future.

First, I created a table of search criteria based on topics that interest me.

Search Criteria List

Then I created a Microsoft Flow for each of the search criteria to capture tweets with the search text, and insert the results into a database table.

MS Flow - Twitter : Ingestion of Learning Tweets
MS Flow – Twitter: Ingestion of Learning Tweets

Out of the total 7450 tweets collected from all the search criteria, 548 tweets were from the Search Criteria “Learning” (22).

Data Ingestion - Twitter
Data Ingestion – Twitter

After you’ve obtained the data, you will need to parse the Tweet text into “breadcrumbs”, which “lead a path” to the Search Criteria.

Machine Learning and Structured Query Language (SQL)

This entire predictive trend analysis could be much easier with a more restrictive syntax language like SQL instead of English Tweets. Parsing SQL statements would be easier to make correlations. For example, the SQL structure can be represented such as: SELECT Col1, Col2 FROM TableA where Col2 = ‘ABC’. Based on the data set size, we may be able to extrapolate and correlate rows returned to provide valuable insights, e.g. projected impact performance of the query to the data warehouse.

R language and R Studio

Preparing Data Sets Using Tools Designed to Perform Data Science.

R language and R Studio seems to be very powerful when dealing with large data sets, and syntax makes it easy to “clean” the data set. However, I still prefer SQL Server and a decent query tool. Maybe my opinion will change over time. The most helpful thing I’ve seen from R studio is to create new data frames and the ability to rollback to a point in time, i.e. the previous version of the data set.

Changing column data type on the fly in R studio is also immensely valuable. For example, the data in the column are integers but the data table/column definition is a string or varchar. The user would have to drop the table in SQL DB, recreate the table with the new data type, and then reload the data. Not so with R.

Email Composer: Persona Point of View (POV) Reviews

First, there was Spell Check, next Thesaurus, Synonyms, contextual grammar suggestions, and now Persona, Point of View Reviews. Between the immensely accurate and omnipresent #Grammarly and #Google’s #Gmail Predictive Text, I starting thinking about the next step in the AI and Human partnership on crafting communications.

Google Gmail Predictive Text

Google gMail predictive text had me thinking about AI possibilities within an email, and it occurred to me, I understand what I’m trying to communicate to my email recipients but do I really know how my message is being interpreted?

Google gMail has this eerily accurate auto suggestive capability, as you type out your email sentence gMail suggests the next word or words that you plan on typing. As you type auto suggestive sentence fragments appear to the right of the cursor. It’s like reading your mind. The most common word or words that are predicted to come next in the composer’s eMail.

Personas

In the software development world, it’s a categorization or grouping of people that may play a similar role, behave in a consistent fashion. For example, we may have a lifecycle of parking meters, where the primary goal is the collection of parking fees. In this case, personas may include “meter attendant”, and “the consumer”. These two personas have different goals, and how they behave can be categorized. There are many such roles within and outside a business context.

In many software development tools that enable people to collect and track user stories or requirements, the tools also allow you to define and correlate personas with user stories.

As in the case of email composition, once the email has been written, the composer may choose to select a category of people they would like to “view from their perspective”. Can the email application define categories of recipients, and then preview these emails from their perspective viewpoints?

What will the selected persona derive from the words arranged in a particular order? What meaning will they attribute to the email?

Use Personas in the formulation of user stories/requirements; understand how Personas will react to “the system”, and changes to the system.

Finally the use of the [email composer] solution based on “actors” or “personas”. What personas’ are “out of the box”? What personas will need to be derived by the email composer’s setup of these categories of people? Wizard-based Persona definitions?

There are already software development tools like Azure DevOps (ADO), which empower teams to manage product backlogs and correlate “User Stories”, or “Product Backlog Items” with Personas. These are static personas, that are completely user-defined, and no intelligence to correlate “user stories” with personas”. Users of ADO must create these links.

Now, technology can assist us to consider the intended audience, a systematic, biased perspective using Artificial Intelligence to inspect your email based on selected “point of view” (a Persons) of the intended email. Maybe your email will be misconstrued as abrasive, and not the intended response.

Deep Learning vs Machine Learning – Overview & Differences – Morioh

Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be..
— Read on morioh.com/p/78e1357f65b0

Help Wanted: Civil War Reenactment Soldiers to Improve AI Models

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.

Riddle of the Sphinx: Improving Machine Learning

Data Correlations Require Perspective

As I was going to St. Ives,

I met a man with seven wives,

Each wife had seven sacks,

Each sack had seven cats,

Each cat had seven kits:

Kits, cats, sacks, and wives,

How many were there going to St. Ives?

One.

This short example may confound man and machine. How does a rules engine work, how does it make correlations to derive an answer to this and other riddles?  If AI, a rules engine is wrong trying to solve this riddle, how does it use machine learning to adjust, and tune its “model” to draw an alternate conclusion to this riddle?

Training rules engines using machine learning and complex riddles may require AI to define relationships not previously considered, analogously to how a boy or man consider solving riddles.  Man has more experiences than a boy, widening their model to increase the possible answer sets. But how to conclude the best answer?  Question sentence fragments may differ over a lifetime, hence the man may have more context as to the number of ways the question sentence fragment may be interpreted.

Adding Context: Historical and Pop Culture

There are some riddles thousands of years old.  They may have spawned from another culture in another time and survived and evolved to take on a whole new meaning.  Understanding the context of the riddle may be the clue to solving it.

Layers of historical culture provide context to the riddle, and the significance of a word or phrase in one period of history may wildly differ.  When you think of “periods of history”, you might think of the pinnacle of the Roman empire, or you may compare the 1960s, the 70s, 80s, etc.

Asking a question of an AI, rules engine, such as a chatbot may need contextual elements, such as geographic location, and “period in history”, additional dimensions to a data model.

Many chatbots have no need for additional context, a referential subtext, they simply are “Expert Systems in a box”.  Now digital assistants may face the need for additional dimensions of context, as a general knowledge digital agent spanning expertise without bounds.

 Sophocles: The Sphinx’s riddle

Written in the fifth century B.C., Oedipus the King is one of the most famous pieces of literature of all time, so it makes sense that it gave us one of the most famous riddles of all time.

What goes on four legs in the morning, on two legs at noon, and on three legs in the evening?

A human.

Humans crawl on hands and knees (“four legs”) as a baby, walk on two legs in mid-life (representing “noon”) and use a walking stick or can (“three legs”) in old age.

A modern interpretation of the riddle may not allow for the correlation and solving the riddle.  As such “three legs”, i.e. a cane, may be elusive, as we think of the elderly on four wheels on a wheelchair.

In all sincerity, this article is not about an AI rules engine “firing rules” using a time dimension, such as:

  • Not letting a person gain entry to a building after a certain period of time, or…
  • Providing a time dimension to “Parental Controls” on a Firewall / Router, the Internet is “cut off” after 11 PM.

Adding a date/time dimension to the question may produce an alternate question. The context of the time changes the “nature” of the question, and therefore the answer as well.

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

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