Artificial Intelligence for Business: Morning Brew-ish Take

This wonderful summary of class discussions in my TO433 AI for Business course is from my brilliant student Greg Cervenak . Greg was bored in quarantine and decided to make this course reflection look and feel like a Morning Brew article. Introducing, TO 433 Brew, a newsletter that boils down the 4 coolest components of the course interwoven with some current events.

AI4Business BREW: The Highlights

#1: Benefitting from COVID-19 unemployment??

If you are a machine — yes! These past few weeks have been rough, with recent weekly jobless claims exceeding 22 million individuals in the United States due to COVID-19 financial burdens. For companies that are managing to maintain employment in these times, they will be looking to automation in the near future to decrease payroll expenses even slightly. For those who had to let a massive number of employees go, they are likely already resorting to AI and machines to inexpensively replace the jobs that they had to eliminate. Further, as the job market recovers at the end of the current recession, will companies be as willing to immediately return to the job market, or will they attempt to fill “open” roles with computers for a fraction of the long-term cost to hedge their payroll ahead of the next economic downturn. Chances are, even if not widespread, some companies will have this mentality, making it more challenging for low-skilled labor to return to the workforce. It is a great time to be a machine “looking for work” — or a machine looking to be built, for that point.

Earlier in the semester, I strongly advocated for the point that the rise of artificial intelligence would cause upskilling of the workforce rather than replacement. In fact, to quote my first discussion argument:

it’s a virtuous cycle in my opinion: increased prevalence of AI —> more education ability and fewer low skilled jobs needed —> low-skilled workers learn faster and transition from jobs that can be automated to jobs that help drive the future of artificial intelligence —> more AI —> repeat.

In these unprecedented times, I am less convinced, given that companies simply do not have the cash to upskill their workforce, and it makes more sense to decrease payroll by simply replacing workers rather than training them. This boils down to one point that we have explored a lot throughout this course: nobody knows for sure what will happen, and events can trigger major changes in the trajectory for the industry. In just 3.5 short months, my entire outlook on the future of machine learning’s impact on employment drastically changed, and I am confident as more events or technologies develop, that will continue to change.

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Publishing to WordPress from MS-Word

It is a whole new world out there. I am trying to see if I can publish to WordPress from MS-Word.

This is a test post with some text content, some images and some references. Let’s see if this works.

I using a pretty standard approach of building an MS-Word .docx file with the Blog template, connecting MS-Word to WordPress using built in authentication in Word, and just following the default process for publishing.

Update: Couple of things I needed to do in WP Console after the Word publishing process – assigning a featured image and assigning categories and tags. Overall the results are satisfactory especially if you first “publish as draft” and then touch-up the draft in WP Console before publishing.

Missing my students!

Now that the semester is over, I truly miss interacting with my bright students every day in person. My last in-person class session was way back in second week of March. Below is the picture of the last class session I was in – my wonderful students in TO433 Artificial Intelligence for Business class. All of them have now graduated, left campus and will be starting their professional careers with a couple of months. Upwards and onwards my wonderful students.

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We Don’t Have an AI Trust Issue, We have an Industry Reputation Issue

Trust in Artificial Intelligence products is crucial for adoption and use of AI. There has been significant amount of recent research on of trust in AI  (Rossi, 2018; Siau & Wang, 2018) . The topic has been widely discussed in practice as well (Khalegi, 2019). Trust in AI is in integral part of my Artificial Intelligence in Business courses at Ross. As part of coursework I typically ask students to post their reflections on class discussions. My brilliant student Yuko Lopez posted the following:

The more I consider the trust component of AI, the more convinced I am that the market will look through the AI solution directly to the organization and people behind the product. Companies that have reputations anchored in integrity, transparency and philanthropy will, in my view will be advantaged in launching critical AI solutions. Those that have built this social capital will have first mover advantage in certain applications that require impeccable reputations. Those that do not will have to partner / acquire or develop this equity.

Does Company/Industry Reputation Affect Trust in AI?

This is an interesting way to look at trust in AI. Much of the work on trust in AI has focused on characteristics of AI – explainability, bias mitigation, usability, reliability and so on. However, the truth is that consumers may have very little understanding of AI and how it works. Consumers are likely to form opinions about the AI based on their opinion of the technology company behind the AI rather than the AI itself.

I tried to explore this idea. Data in this field is hard to come by. However, the good folks at Pew release their American Trends Panel data in public domain. One of their data collection waves (Wave 35 to be precise) looked at public perception of AI and technology companies. 

I have done some preliminary exploration of this data below with some quick interpretation of the analysis. Please note that this is very quick and dirty work – so adjust your expectations accordingly.

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Learn to Code and “Get Above the API”

I teach very technical classes that often require students to code – in a business school. I am often asked whether business school students (e.g MBA and BBA) should bother learning how to code. I have always responded in the affirmative. I have even been categorical that no college student should graduate without at least knowing how to read a piece of code.

I typically motivate the argument with the slide below: Coding is the new Excel!

My message to my students has been that even if you yourself, as a business student, may not be writing code for a living; you will surely work with, manage and lead people who will write code. Understanding code is going to be essential for you to be a good manager, an effective leader.

Thanks to Derek Gan, one of my bright students in my TO433: Artificial Intelligence for Business class, I now have an alternative motivation. You want to learn coding because you want to “get above the API“. Its a very interesting way of looking at how to get yourself ready for the job market of tomorrow where AI threatens to automate many of the middle management jobs that are the bread and butter of business school student recruiting.

As middle management jobs get replaced by an AI, it creates a divide in the job market. As Peter Reinhardt puts it so eloquently (in reference to companies like Uber):

The software layer between the company and their armies of contractors eliminates a huge amount of middle management, and creates a worrisome disconnect between jobs that will be automated, and jobs of increasing leverage and value.

The software layer, or the API, or the AI, depending upon your preferred nomenclature becomes the dividing line between jobs that are just another cog in the machine with the AI determining every aspect of your job – how many hours you work, how much you get paid, which rides do you pick etc – the Below the API; and jobs where you actually get to design, develop, operate the machine, the AI – the Above the API.

Depending upon how much below the API a job is, it will straight be up automated. So a clear priority for students should be to acquire skills that will keep them Above the API. Coding, Data Analytics, Artificial Intelligence are all part of the picture.

So, my students, you should know how to at least read a piece of code because you want to “Stay Above the API”.

Featured image is borrowed from this Forbes article: Google Cabs And Uber Bots Will Challenge Jobs ‘Below The API’

Update: Included link to the LinkedIn page for Derek.

Updating R Packages for New Version

R Version Check

We are getting a new version of R. Yay! According to R-Project, the Version 4.0.0 was released yesterday. I guess I will need to update my R. Let’s check what I am running right now.

paste(version$major, version$minor,sep = ".")
## [1] "3.6.1"

Yup – I am at 3.6.1 so I definitely need to update. Time to launch my default update script:

Updating R Version

# R Update Script for Windows
# eval = FALSE, I am not ready to update yet.
install.packages("installr")
library(installr)
updateR()

However, before I can update, I need to make sure that all my packages will get moved to the new version. So time for a quick bit of code to ensure that.

Saving All Packages

# First save a list of all installed packages
packagelist <- installed.packages()

# Now save the list to a local file
saveRDS(packagelist, "packagelist.rds")

Now we are good to update R version using the code in previous section. Note that if you are using RStudio, it is typically better to call updateR function from raw R session and not from an RStudio session.

Loading Packages in Updated R Version

So you have updated your R version. All went smooth. Now you would want all your packages back. Here you go:

# First read the list of R packages
listofpackages <- readRDS("packagelist.rds")
# Now install all the packages in the list to the new version
install.packages(listofpackages[,1])

That’s it. All your packages would not have moved to your updated R version.