Going the consulting path, on your own, is no small feat. Do you have what it takes to persist, survive, and thrive?
Army of One – Not only do you need to perform your CONSULTANCY role, but you also have to be bookkeeper, sales and marketing, looking for new opportunities.
The Gap Between Gigs – To all recruiters and hiring managers – it’s not a bad thing to have gaps in a candidate’s resume. Its the way of life in our gig economy. We are constantly hunting for just the right opportunity in a sea of hundreds or thousands of candidates per role.
Keeping Up With Market Trends – Online learning platforms such as Pluralsight, keep their content fresh, relevant, and in line with your career path.
Networking, Networking, Networking – at every opportunity, build your network of contacts and keep them in the know
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.
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.
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.
Out of the total 7450 tweets collected from all the search criteria, 548 tweets were from the Search Criteria “Learning” (22).
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.
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.
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.
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
Individuals and Interactions over Process and Tools
Stereotypical software developers are introverts, heads down, coding. Articulating where they are in the development lifecycle sometimes heavily relies upon tools for measuring progress such as JIRA, Product Backlog status of User Stories, e.g. “In Progress” with an Effort estimation of 3.
“Blocked” User Stories may require the implementation team to “break out of their shell” and work with their teammates to “unblock” Product Backlog items. It breaks people out of their comfort zone. We need to discuss options and opportunities for removing blockers. “All for One, and One for all”
Working Product over Comprehensive Documentation
Over a decade or so ago, the measure of my merit was the complete test coverage of requirements for software implementation. Back then I was a QA lead, and my focus was to make sure all use cases for the software under development had complete test coverage.
Requirements changes from our business through our business analysts must be vetted with the QA team so use cases/test cases must be updated to ensure coverage. Sometimes a dependency of one requirement had a ripple effect throughout the software, so lots of documentation updates were required. Milestone dates were in many cases fixed, so teams were squeezed to do more with less time.
Flash forward to today, and leveraging Agile principles, I breathe a slight sigh of relief. Iterating product delivery via sprints every 2 weeks is supremely better than attempting to traverse updates to Business Requirements Documents (BRD), and technical specs. User Stories in a Backlog are much more succinct, and in some cases, a bit more abstract leaving functionality open to some level of ambiguity and interpretation.
Sprint Close scrum ceremonies every two weeks with our Product Owner, the central mouthpiece for the definition of the software product helps define the path forward. Did we get it right? Where do we need to make changes? There is no substitute for an evolving product and accompanying dialog with our Product Owner.
Customer Collaboration over Contract Negotiation
Both sides of the aisle seem to agree, building a solution with iterative input from the customer enables the product vision to be realized far better than without frequent touchpoints.
Statements of Work (SoW) to engage 3rd party solutions integrators (SI) may be abstract in some way. Holding vendors accountable for loosely formed requirements is tenuous at best. Quibbling about he said, she said is a waste of time.
Fail fast, engage regularly and often with our [Business] Product Owner enables us to collaborate on a working solution. The focus is on the evolving product vision and not the paper trail.
Responding to Change over Following a Plan
A “last-minute” change request? It could push back our timelines and accompanying milestones. Dates can’t change, and teams need to absorb the changes, i.e. nights and weekends. Responding to incremental changes at a regular cadence is a sustainable life cycle.
A relic of the Waterfall model is the construct of a “gate” process. In order for a project to achieve a milestone, the project/solution would need to achieve certain criteria that would allow it to go to the next phase of the project. For example, going from solidifying requirements in a Business Requirements Doc (BRD) to the software implementation phase.
In Agile, we leverage the Product Owner (PO) and the Product Backlog to determine what gets done and when. A Product Backlog item (PBI) may cover the full lifecycle of a Feature, from requirements to implementation. The Product Owner dictates acceptance of the PBI based on the status/transparency of the Backlog, such as the criticality of the Bugs linked to the PBI. Product quality and implemented functionality are transparent to the PO, who will determine the next steps such as release the software, and/or go through another iteration/sprint. Iterations are a defined cadence agreed to by the implementation team and the Product owner, typically, 2-week sprints.
Agile, Hybrid Environments: Opportunities for Synergy
Epics, Features, Product Backlog Items, and Tasks are object types in a Backlog that enable the PO and the team to link objectsand plan over multiple sprints. Epics or Themes of Sprints are “high level”, potentially strategic initiatives. Features roll up into Epics as a part of several sprints. Either Epics or Features may be high enough level to link to Psydo Project Milestones for a product roadmap of deliverables, and solicitation outside the team.
Aggregation of Product Backlog Items, Effort Estimations, roll up into Features, and then up into Epics, which roughly equate to milestone timelines.
The “Definition of Done” (DoD) for a Product Backlog Item may require 0 outstanding Bugs with the severity of “Critical” linked to this PBI. The DoD criteria could be analogous to a traditional Quality Assurance gate.
Tasks that are production rollout activities, without a project plan, should be planned for in future sprints, akin to estimating when items may be completed in the proper sequence. Some of the Tasks may be placed conservatively in “early” sprints and may require items to be “pushed forward” after each of the iterations.
Maybe you’ll meet them during the Project Kickoff. Maybe you’ll first hear from them during a biweekly Steering Committee. Or maybe you will first hear from them three months into the project at a quarterly meeting with the CIO and the rest of his portfolio. Maybe you will never hear from them directly.
The politics of requirements gathering and prioritization is a daunting process. I’m not going to drudge up all the stories and categorize them here because it’s a painful process.
Why are some of your milestones in your project plan:
• the milestone exists within someone’s year end evaluation
• the requirements of a milestone are so bipolar, they are bound to fail. Need a project to bucket the requirements to say “we tried”, and we can pin it to a project.
• backing into established project timelines based on expectations set at the highest levels, e.g. regulatory compliance
Legal and Compliance Stakeholders
Global representation of legal and compliance requirements are a dichotomy of legal precedence between jurisdictions.
Many a project managed using waterfall kept me balancing the needs and wants of Stakeholders from all walks of life, some exuberantly voicing their opinions regardless of their position of power, or lack therein. The Agile Product Owner (PO) is a relief of burden, a single mouthpiece of the business, which dictates backlog priority.
Does Agile make the requirements gathering and prioritization pain go away? Possibly. There are various implementations of Agile, hybrid situations, and there are lots of tools out there to help manage the Product Backlog (requirements). Another exercise, developing User Journeys, working with your Personas / actors to derive their story, that is telling and lots of fun.
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.
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:
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.
…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.
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.
Better Demographic Penetration and Transparency to More Accurately Determine Creative Media Asset Worth
News Media Assets
News Media Assets are created by writers of non-fictional work, coverage of various topics targeted towards the periodical demographic.
Selling Advertising Space
Layered within the news media product, consists of News Media Assets and sold advertisement space. Ad positioning throughout the news media product may have commonality between the product or service being advertised and the news media asset. A goal is the smooth transition between reader of asset and advertisement.
Revenue Models For News Media Assets
Deriving revenue from sponsors of news Media Assets
Subscription Base of News Media Assets, regular frequency of news media product to subscriber base.
Social Media – News Feeds
The news agencies post to public news feeds a “teaser” headline, a sentence or two describing the news media asset, and a teaser image all to lure prospective readers to clink a link to the news media publisher’s platform. At that point, the publisher sets the “ground rules” for the potential subscriber, e.g. 10 free articles a month, then their digital subscription price of NN goes into effect.
Social Media – InMail (I.e. eMail within the platform)
InMail through the social media platform can come from a variety of sources, for example:
Former colleague looking to reconnect
Recruiter looking to pitch a potential role
Sales / Marketing InMail targeting you as a potential customer of their product or service
The Tools to get the Job Done
As a prior client of LinkedIn Advertising for both ad placement and Sponsored InMail, I found the tools provided and the granularity upon which to refine the demographics impressive, and not lacking in any way.
Personable, Targeted Marketing of News Media Assets, sponsored by 3rd party promoting their product or service.
Delivering News Media Assets to your digital door step, with advertising partners speckled into the asset. Because of the granularity of the InMail advertising controls demographics are at a level of precision. Beyond what a magazine or newspaper, digital or print, can offer.
it’s all about the targeted audience and the granularity of the data collected and then leveraged to meet the desired audience. Much more personal than a link back to the publisher’s platform.
Just like there are expenses to do business in print or traditional digital, the price of doing business with a platform like LinkedIn Sponsored InMail, would be absorbed by the news media agency, net advertisement placement for advertisements.
Although the LinkedIN Social platform was used forreference, other platforms may be leveraged, depending upon the product or services being marketed, such as a Facebook People Magazine article relevant to their demographic, partnership / sponsorship.
Fake News – Not a Problem
Since News Media Agencies will now pair with “sponsors” or commonly know as advertisers, both parties, the news agency and the sponsor have “skin in the game”, it is less likely to be a factitious article.
Remember when you could make free mobile calls after 9:30 PM weeknights, and all weekend? For awhile the mobile carriers competed on the time when “off-peak” started, from 10 PM to 8:30 PM. A whole hour and a half! These days we have unlimited domestic calling all the time.
So, now we have varying degrees of data plans, such as AT&T Wireless 3 GB, 9 GB, or unlimited per month, but there are caps where after 22 GB data transfer speeds are slowed down. 22 gigs seem like a lot until you have kids using Snapchat and TikTok.
When you think about it, data peak is when you may not be in a hot spot. At night, you’re at home using your own WiFi, or at an establishment with their complimentary WiFi. Weekends and weekdays are a bit scattered. Your work may have WiFi, but weekdays “on peak” are mostly commuting times, the “rush hour(s)”,
Can wireless carriers bring back on and off-peak for data? The simplest approach: “turn off the meter” during off-peak data periods. Maybe on-peak the consumer can elect 5G, when available, and off-peak at 4G LTE? Our Smartphones can identify low consuming bandwidth opportunities, e.g. when the phone is locked, text messages without graphics and email are semi-passive states. Maybe users are able to prioritize their apps data usage? What about those “chatty” apps that you rarely use? Smartphone settings may show you those apps bandwidth consumption as opportunities to prioritize them lower than your priority apps.
Skeptic, and think there are no Peak or Off-Peak periods with data? Check the business analytics. I’m sure wireless carriers have a depth of understanding for their own business intelligence (BI).