# Labels have been eclipsed

We journey to Lexington, South Carolina to see witness an eclipse.

My wife talks to a couple — two White women from Charlottesville, perhaps in their 60’s — they lament the terror that descended upon their peaceful, thoughtful town. A Jewish family lies on the grass next to us, the father discovering the beauty and mystery of childhood in the waning sun.

Telescopes in the parking lot near the nature center entrance track the sun. My daughter looks in wonder at red and orange orbs, under the gentle guidance of a woman astronomer. A nine-year old African American boy looks at sun spots in awe, peppering the astronomer with questions. She answers him thoughtfully. His mother says that he wants to be a scientist.

In this small pinpoint in the west of South Carolina — a small nature preserve called Saluda Shoals Park — people are speaking in Hindi, Spanish, and Italian, and Mandarin. Grandmothers the color of the 2pm night encourage their grandchildren to look up.

Young Brothers accentuate their designer athletic apparel with solar shades. The other astronomer gives an impromptu hands on talk about meteorites, looking on as the dark remnant of a star passes between white, tan, black, and brown hands. He gives talks to the schools around the state he says, for free, as a service because of his love of the universe.

As sky goes dark, the cicadas awake. My son is giddy with the excitement of being present in a singular communion with the eternal, absorbing the sublime colors of the solar halo and noting the presence of planets.

As the sun waxes, a father with his ‘fro and mother with dark straight hair and skin the color of sand walk past smiling taking their sleeping child home.

In that moment labels — the inadequate one-dimensional badges affixed to us used to define, oppress and limit — become like the shadows in the eclipsing sun. We become to each other human — familiar brothers, sisters — seeing each other in awe, gratitude, and wonder.

# Artificial Intelligence at Historically Black Colleges (HBCUs)

If you would like to add to it, just send me an email.

Why am I doing this? Last month in AI and the Souls of Black Folk, I tried to make a case that people from all walks of life — particularly those from historically oppressed groups — have a part to play in shaping how the technology evolves. I think that HBCUs can be a catalyst for making AI an inclusive and responsive undertaking.

The list is a starting point.

# May the Bot Be With You: How Algorithms are Supporting Happiness at WordPress.com

A summary of recent NLP work to enhance support happiness.

Our excellent support is a big part of what makes WordPress.com a compelling platform for so many. Each month, we respond to 60,000 support requests on topics ranging from plugins to mapping existing domains to WordPress.com. Some questions arise several times daily, while others require novel solutions from our Happiness Engineers who have a deep understanding of our products, a skill for asking the right questions, a commitment to support, inventiveness, and creativity.

Recently, in looking at how machine learning and natural language processing could be useful to Happiness Engineers (HEs) in responding to support questions, we discovered two places where these technologies offer value.

First, our Happiness Engineers often rely on tried and true responses to some of the more straightforward questions. We call these pre-defined responses predefs. Many HEs develop personal favorites, and some predefs eventually become part of our best practices based on their ability to succinctly express…

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# AI and the Souls of Black Folk

The impact of AI on communities of color — particularly through job displacement and policing — is now undeniable. Given that HBCUs have historically been on the forefront of technology education for the Black community, I am proposing to build a list of current activities (courses, research, seminars, clubs, etc.) at the HBCUs relevant to AI and its wider implications. If you’d like to contribute to the list, I’ll eagerly accept your input! To understand some of my motivations, keep reading.

We’ve now reached the point where the impact of Artificial Intelligence (AI) upon everyday life is undeniable. Everyone takes Siri for granted, your local Walmart can hook you up with a drone that does object recognition, and the introduction of self-driving cars now seems inevitable.

The title of my post is inspired both by W.E.B. Du Bois’s classic The Souls of Black Folk — a collection of essays on the state of African Americans at the start of the 20th century — and by Tracy Kidder’s The Soul of a New Machine which chronicles the development of a computer architecture at the end of the 20th century. I think that at the start of the 21st century, a critical look at how African Americans are impacted by immense technological change is needed. The title tries to capture my central question:

What is the impact of AI and related technologies on the lives of Black folk, and how can we organically shape a future for these technologies that enhances opportunity rather than reifies oppression?

To be honest, I am deeply concerned about the potential AI has for disruptive and devastating impact on communities of color. The Obama administration released a sobering assessment of the  economic impact of AI — it forecasts that changes in the transportation sector alone (trucking and delivery) will mean the elimination of occupations which Black and Brown folk have relied upon for entry into the middle class. Those findings are likely to generalize to other occupations. The prevalence of predictive policing and algorithmic sentencing raise serious concerns about equality and self-determination — especially when mass incarceration and other racial disparities in criminal justice are taken into account.

In theory, a modern democracy should allow impacted communities to raise concerns about a technology and then foster the deliberative processes necessary to fairly address those concerns. In theory, the open source movement provides a model through which communities can identify and develop technologies that serve their particular needs.

You might respond that “technology is colorblind, science is colorblind, it shouldn’t matter whether there are any Black folk involved at all in the development of and policy making around AI technology“. I think in this case particularly, it matters a great deal. AI, looking back over its history, is itself an endeavor that grapples with the question of what it means to be human — it is an endeavor that demands broad societal input.

Aside from President Obama’s initiative, I see very little presence of the disenfranchised in discussions on the future course of AI. For example, OpenAI is a research institute of sorts formed with the express purpose of “discovering and enacting the path to safe artificial general intelligence“. Despite lofty claims OpenAI seems to have the traditional Silicon Valley underrepresentation.

So all that said, what is the simplest concrete contribution I can make?

I have spent most of my career in AI. I grew up in Atlanta, attended Morehouse College and Georgia Tech through the Atlanta University Center’s Dual Degree Program, and went on later to complete a doctorate in computer science at the University of Chicago focusing on robot planning and learning. Along the way I studied and worked with other Black people doing advanced computing, witnessed Black people found successful technology startups and saw Black women and men lead successful academic careers in these fields. On the one hand, the diversity (exclusion?) figures we see from Facebook and Google seem at odds with  that experience. On the other hand, it jibes with the experience of being “the one and only” in many places I’ve worked or studied at. I wanted to begin to quantify and understand the dimensions and particulars of exclusion — things just don’t seem to add up, so perhaps we are looking in the wrong places and asking the wrong questions when we conclude there are no Black folk doing AI?

The Historically Black Colleges and Universities (HBCUs) provided the fertile intellectual soil in which Du Bois’s ideas sprouted and grew. So I thought my first concrete step would be to take inspiration from Tracy Chou’s Women in Software Engineering and put together a set of Google spreadsheets that document how HBCUs are looking at AI.

I created the spreadsheet AI at HBCUs. Please give it a look. Right now it is an aspirational document in that it tries to gather up any kind of activity at the HBCUs related to AI, Machine Learning, or Data Science. Hopefully it can be the basis of other kinds of summary statistics, update posts,  or active development efforts.

I’ve split the document into a number of sheets:

 Sheet Name Description HBCUS Name and address information for US HBCUs based on information obtained from IPEDS Course Information on AI related courses taught at the institution. Any department. Grants Information on grants received by the HBCU for AI related work Publications Publications on AI related topics. Clubs Student related clubs. For example a robotics club, drone club, a group formed for Kaggle competitions, etc. Workshops and Seminars Has the institution hosted any seminars or workshops? Links to videos would be great. Outreach Any Saturday events for grade schoolers? Teach-ins for community organizers? Graduate Placements Any numbers on the graduates who’ve gone on to careers, graduate school or internships in AI related fields.

Here’s how I think this could work. If you are a  faculty or a student at a HBCU, you can for the time being send an email to me charles.cearl@gmail.com with information on courses, seminars, research, clubs, outreach programs or other related activity at your institution. I’ll manually post your information to the relevant sheet. If there’s enough interest, I can just set this up to allow direct update (through pull requests or direct editing of the relevant sheet). I’m open to suggestions on formatting, information gathering, and overall focus.

Let’s get the discussion started!

# Welcoming New Colleagues — a Data-Based Story

One of the many reasons it’s a joy to work at Automattic

With over 545 employees spread over more than 50 countries, Automattic is one of the largest distributed companies in the world.

Being distributed means that we, as Automatticians, work on our common goal to democratize publishing from wherever we wish. It also means that we heavily rely on online communication.

Besides providing flexibility, working in a distributed company brings challenges when you meet in person. Does X from team I/O appreciate informal humor?What is the hobby of Y, a member of the Happiness Engineering team?Does Z, our team’s HR person, smile a lot or only when taking a gravatar picture? The answers to these questions are trivial to get in a “traditional” company but not in a distributed environment.

However, this information is critical. It helps us relate to one another and form strong relationships that nourish creativity and cooperation. If the challenge doesn’t seem hard enough to…

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# Scheme in 33 Miniatures

This weekend I was trying to wake my old brain up by looking and the clever and mind-bending problems from my graduate school. I have really gotten excited about Madhur Tulsi‘s Mathematical Toolkit class — as a “data scientist”/”machine learning engineer” (I sometimes wonder what these designations mean — heck I just am grateful to have been able to work on A.I. with all its challenges and contradictions for a couple of decades) I find myself treasuring the notes and exercises.

One gem that I found is the text Thirty-three Miniatures: Mathematical and Algorithmic Applications of Linear Algebra by Jiřì Matoušek. This is full of memorable tricks and insights that leverage linear algebra. While the text is mostly about proofs, I used some of the algorithms presented discussions do some Lisp programming this weekend.

I started off with the first two miniatures on Fibonacci numbers — the first presenting a fast method to get the nth Fibonacci number and the second which is probably not so fast is yet elegant in that it presents a function to compute Fibonacci numbers.

So first, I wanted to re-awaken my Lisp programming brain. Racket is definitely one of the more innovative Lisp languages to have emerged over the last 10 years. You can think of Racket as a package that provides a collection of Lisp-languages, among them standard Scheme — a paired down Lisp dialect.

The first implementation achieves $O(logn)$ running time by using exponentiation by squaring. This is achieved by first realizing that the Fibonacci recurrence can be written in terms of a matrix polynomial $\begin{pmatrix} F_(n+1)\\ F_(n)\end{pmatrix} = M^n \begin{pmatrix} 1\\ 0\end{pmatrix}$ where $M = \begin{pmatrix} 1 & 1\\ 1 & 0\end{pmatrix}$. I hadn’t known this but $n$ is a power of 2 we can compute the $M^n$ by repeatedly squaring $M$ and then the case where $n$ is odd is taken care of by a multiply. I’m guessing that the core exponentiation routine would use this (or a faster method) anyway, but still interesting to consider.

So first to see if a quick exponentiation by squaring does against the method provided in the racket math/matrix package. Here’s my quick recursive method, based on the tail recursive method in the wikipedia article

;;identity
(define one-matrix (matrix [[1 0] [0 1]]))
;;recursive exponentiation by squaring
(define (exp-by-squaring x n)
(letrec
(
[exp-by-squaring-aux
(lambda (y x n)
(cond
[(= n 0) y]
[(= n 1) (matrix* y x)]
[(= (modulo n 2) 0)
(exp-by-squaring-aux y (matrix* x x) (/ n 2))]
[else
(exp-by-squaring-aux
(matrix* y x) (matrix* x x) (/ (- n 1) 2))]))])
(exp-by-squaring-aux one-matrix x n)))

And here is a quick running time analysis

> (time (dotimes (x 1000) (exp-by-squaring (matrix [[1 1 ] [1 0]]) 100)))
cpu time: 793 real time: 802 gc time: 175
> (time (dotimes (x 1000) (matrix-expt (matrix [[1 1 ] [1 0]]) 100)))
cpu time: 143 real time: 144 gc time: 36
>

Looks like I’m better off with the library — although curious to know what the library implementation is.

So sticking with the matrix-expt library function:

;;;-- Miniature 1: Fibonacci Numbers, Quickly
(define one-zero-matrix (matrix [[1 0]]))

(define fib-matrix (matrix [[1 1 ] [1 0]]))

(define (fast-fib n)
;;; Fast method of computing nth fibonnaci number
(let* (
[m-expt (matrix-expt fib-matrix n)]
[idx0 0]
[idx1 1]
[prod (matrix* one-zero-matrix m-expt )]
)
(matrix-ref prod idx0 idx1 )))

Now here’s an old-school tail recursive implementation

;;; Old school fib
(define (os-fib n)
(letrec ([os-fib-helper
(lambda (n a b)
(cond
[(= n 0) b]
[else
(os-fib-helper (- n 1) b (+ a b) )]))])
(cond
[(= n 0) 0]
[(= n 1) 1]
[else
(os-fib-helper (- n 2) 1 1)])))

Now let’s look at how they compare.

> (time (dotimes (x 10000) (fast-fib 100)))
cpu time: 2554 real time: 2612 gc time: 617
> (time (dotimes (x 10000) (os-fib 100)))
cpu time: 41 real time: 41 gc time: 11

Wow. Maybe we’ll have to go to type checked racket to see any gains there!

Typed racket claims to get more of a boost in performance by using type hints. Here is the typed version of the fast Fibonacci

;;; -- Miniature 1: Fibonacci Numbers, Quickly (with type hints)
(: one-zero-matrix (Matrix Integer))
(define one-zero-matrix (matrix [[1 0]]))

(: fib-matrix (Matrix Integer))
(define fib-matrix (matrix [[1 1 ] [1 0]]))

(: fast-fib (-> Integer Integer))
(define (fast-fib n)
;;; Fast method of computing nth fibonnaci number
(let*
(
[m-expt (matrix-expt fib-matrix n)]
[idx0 : Integer 0]
[idx1 : Integer 1]
[prod : (Matrix Any) (matrix* one-zero-matrix m-expt )]
)
(cast (matrix-ref prod idx0 idx1 ) Integer)))

;;Hacky runner to allow my bootleg benchmark
(: run-fast-fib-100 (-> Integer Integer))
(define (run-fast-fib-100 n)
(dotimes (x n)
(fast-fib 100))
n)
> (time (run-fast-fib-100 10000))
cpu time: 689 real time: 728 gc time: 251
- : Integer
10000
> (time (run-fast-fib-100 10000))
cpu time: 672 real time: 733 gc time: 244
- : Integer
10000
> (time (run-fast-fib-100 10000))
cpu time: 704 real time: 750 gc time: 267
- : Integer
10000
> (time (dotimes (x 10000) (os-fib 100)))
cpu time: 28 real time: 28 gc time: 0
> (time (dotimes (x 10000) (os-fib 100)))
cpu time: 31 real time: 30 gc time: 0
> (time (dotimes (x 10000) (os-fib 100)))
cpu time: 35 real time: 35 gc time: 0
>

About an order of magnitude better, but still order of magnitude worse than the iterative version though. Better off sticking with the simple iteration.

Now in the second miniature, Matoušek derives a numeric expression for the $nth$ Fibonacci number by solving a system of linear equations that follow from the recurrence. This is $F_n = 1/\sqrt{5} \left[ \right( (1 + \sqrt{5})/2 \left) ^n - \right( (1 - \sqrt{5})/2 \left) ^n \right]$

;;; -- Miniature 2: Fibonacci Numbers, the Formula(: root-five Real)
(define root-five (sqrt 5.0))

(define inv-root-five (/ 1.0 root-five))

(define first-tau-root (/ (+ 1.0 root-five) 2.0))

(define second-tau-root (/ (- 1.0 root-five) 2.0))

(define (fib-root n)
;;; Not so fast but fun ?
(exact-round
(* inv-root-five
(- (expt first-tau-root n) (expt second-tau-root n))))

How does compare against an “old school” iterative version?

> (time (dotimes (x 10000) (fib-root 100)))
cpu time: 3 real time: 4 gc time: 0
> (time (dotimes (x 10000) (os-fib 100)))
cpu time: 27 real time: 28 gc time: 0

Well, that seems to best it. At least until we get further out in the sequence, where overflow gets to be an issue

> (time (dotimes (x 10000) (fib-root 1200)))
cpu time: 4 real time: 4 gc time: 0
> (time (dotimes (x 10000) (os-fib 1200)))
cpu time: 1271 real time: 1419 gc time: 293
> > (time (dotimes (x 10000) (fib-root 2000)))
. . exact-round: contract violation
expected: rational?
given: +inf.0
> (time (dotimes (x 10000) (os-fib 2000)))
cpu time: 2619 real time: 2893 gc time: 725

So we’ve got constant time with the analytic solution, we might loose the better performance once we get better precision. Interesting. I’ll hold it there for now and contemplate. It’s been a nice Scheme appetizer — a feel my inner Scheme brain re-awakening.

# Reverence for the righteous

Several months ago I read Timothy Snyder’s award winning Black Earth, an important but difficult book on the horrific destruction of millions of lives in the “bloodlands” of Eastern Europe during the Second World War.

Despite the gravity of the book, there was a deep and eternal hope that I found in its stories of the ordinary and extraordinary Jews, Poles, Ukrainians — in other words people — coping with the unimaginable. The grandmother who hid and sheltered strangers, only to see them found out and killed; the farmer who kept three desperate sisters alive despite the risk of brutal death. The faith of saving your neighbor, standing with your neighbor, for years, sun up and sun down, despite the risk. Righteousness.

I read the book because I felt that I needed to keep awareness of the struggles that are now fading into history in mind — the people I knew who had faced that ghastly horror, breathed its stench and survived as testament have now left this realm. As I write this a mosque in Quebec has been attacked, Muslim brothers and sisters, children from Iran, survivors from Syria — people, human beings — are again being caught up in an echo of those dark times. I am, we are, all being called to account — again.

Snyder’s words are poignant:

In the darkest of times and places, a few people rescued Jews for what seems like no earthly reason. These tended to be people who in normal times might seem to take ethical and social norms a bit too literally, and whose fidelity to their expressed principles survived the end of the institutions that supported and defended them.

If these rescuers had anything in common beyond that, it was self-knowledge. When you know yourself, there is little to say.

The events of the last few months, of the last two days have been challenging. The thought of how to respond — whether to run, to shout, to cry, to detach, to pray — we are all gripped by a range of conflicting emotion and inclination.

I just try to hold the truth of those righteous souls in each moment and look for the self-knowledge to respond as a brother, a father, a friend, a fellow person.