Advanced Analytics

Experiments in Generative Art with RStats

I am experimenting with generative art – essentially creating computer generated art. Here is my first creation – I am calling it StarCircle. I love how it has a 3-D aspect to it – almost spherical.

This is created in R using the ggplot package. Code for creating this is simple enough. Caution – it does take a while to run and render even in my decently powerful home desktop.

suppressMessages(library(tidyverse))
seq(-10, 10, by = 0.01) %>%
expand.grid(x=., y=.) %>%
ggplot(aes(x=(x+9.5*sin(y)), y=(y+9.5*cos(x)))) +
geom_point(alpha=.01,
shape=20, #Shape 20 is essentially a filled circle
color="blue",
size=0)+theme_void()+
coord_polar()

I cannot claim any credits for originality here though. I am following the lead of  Antonio Sánchez Chinchón. 

The process for creating StarCircle was quite iterative – a lot of hit-and-miss, trial-and-error. The output is very sensitive to even small changes in code parameters. For example, let us try changing the ggplot command to the following:

seq(-10, 10, by = 0.05) %>%
expand.grid(x=., y=.) %>%
ggplot(aes(x=(x+3.1*sin(y)), y=(y+19.1*cos(x)))) +
geom_point(alpha=.01,
color="darkgreen",
size=0)+theme_void()+
coord_polar()

The code above leads to this beautiful delicate Green Butterfly:I am teaching two courses in Data Visualization in Fall. This is part of my ongoing efforts to include more creative elements in those courses. Especially for the course TO404: Big Data Manipulation and Visualization as the course is based on R and this content will fit right in.

Here is a red version of StarCircle:

More generative art below the fold.

Continue reading →

Artificial Intelligence

Chief AI Officer: Origin Story

Age of AI is coming – and with that – the time for CAIO: Chief Artificial Intelligence Officer is coming too. CAIO is supposed to be the point person in the organization with the responsibility of effective deployment of AI. I motivate students in my Artificial Intelligence for Business classes with the pitch that the class will prepare them for a career path leading to the role of a CAIO.

Opinions are mixed on whether you need a CAIO, whether it is a good idea to have one. Andrew Ng in HBR says Yes:

The benefit of a chief AI officer is having someone who can make sure AI gets applied across silos… A dedicated AI team has a higher chance of attracting AI talent and maintaining standards… Hiring the right AI leader can dramatically increases your odds of success.

Tarun Gangwani at CIO.com agrees: The CAIO would most likely be a seasoned businessperson with direct experience working on data analytics, cloud computing and machine learning technologies. On the other hand, Kristian Hammond at HBR is a strong No:

The very nature of the role aims at bringing the hammer of AI to the nails of whatever problems are lying around. This well-educated, well-paid, and highly motivated individual will comb your organization looking for places to apply AI technologies, effectively making the goal to use AI rather than to solve real problems.

Well – whichever way your loyalties lie (for the record – I am a Yes, at least in this initial phase when dedicated resources are needed to ramp up, deploy and extract value from AI investments), you would agree that would be great to see how this new buzzword: Chief Artificial Intelligence Officer came about. Thanks to a quick LexisNexis search, cleaning/exploration of resulting data and some judicious editorial decisions by me; here is the origin story of CAIO.

Continue reading →

Artificial Intelligence/Student Shoutout

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.

Continue reading →

Artificial Intelligence/Machine Learning/Student Shoutout

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.