Break

            Bittersweet chocolate has one use, cooking. Likewise, this bittersweet end to my exploration of Laber Labs can be helpful if, and only if, I apply it to create, to innovate, to learn. It was a day of mastery. I aimed to demonstrate my knowledge of forecasting, finalize my “proficiency” in Python, and provide further, substantial contribution to the image-labeling project. Why must everything good come in three? That is, except for imaging labels, which I can honestly say came in the laborious magnitude of 155, give or take two.

Will I miss the wit and intellect? Well, will Beauty miss the Beast? Will the pig miss the blanket? I suppose this is how I will leave, trapped in bad metaphors yet loaded with memories that shall carry me through a summer and a lifetime. In the words of another, “So long, farewell, auf Wiedersehen, goodbye.”

It Helps to Be Human

Forecast, evaluate, repeat. Forecast, evaluate, repeat. Interestingly, these words somehow represent both the iterative process through which I finalized my FPP presentation as well as its content. I predicted how each slide would turn out, analyzed the final outcome, readjusted as I saw fit. So much of what we do, day to day, involves creating judgmental forecasts. Lately, the push has been to digitize, to create rigorous numerical models that match our qualitative perceptions and raise them a quantitative level of precision.

Throughout the past couple of days, I’ve been thinking about “big data.” Some find fault with the notion that we can simply mathematize decision-making. Perhaps, they fear a loss of control and accountability. These complaints are more than valid; they are imperative and worthy of considerable deliberation as our society sets its course for future decades. Blindly trusting computer simulations and estimations ignores the human side of real-world problems. One-size fits all algorithms are helpful, but they must always maintain outlets for personal alterations, wiggle room if you will. Give us room to wiggle, and we will give you room to forecast, evaluate, and repeat, be it with supercomputers or mere pencil and paper.

Fun fact for any of you League of Legend players (I believe it is a video game of some sort):

NC State students could use you for help on their project. See the above graphic’s details.

Laber Labor

Steadily, I continued to labor at Laber Labs on my reading of Rob Hyndman and George Athanasopoulos’ Forecasting Principles and Practice. These Aussies provide helpful tips on the art and science of prediction, tips I happily digested throughout the morning.

Come afternoon, Hongjian gave me phase one of a primer on the Python programming language. A brief mix-up in time and place failed dramatically in its attempt to curtail our progress. We persevered! The “list” data has surprising, and thankfully simplistic, functionality.

For instance, if ‘a’ equals [9, 8, 6, 5], I can write:

a.append(4)

a.extend([3,2,1])

a.insert(2,7)

a.reverse()

and I wouldn’t even have to add the all-helpful a.sort() because ‘a’ would now equal [1,2,3,4,5,6,7,8,9]. Splendid!

In What Language Does a Pirate Code?*

It’s my turn; how the dynamic has shifted! After a week dabbling in the various projects and research interests of these graduate students, I am now trying to learn about forecasting using time series. Basically, the task is using data to make predictions.

However, this activity requires some computer science knowledge. Thus, I am trying to gain minimal proficiency in R. It’s tough going but rewarding when small success find their way to the console.

What’s nice is how easy it is to graph large datasets. One quick function call, and, whazzam, there it is. Off to explore some more…

 

*Answer: Arrr.

More Acronyms

BOM. Bureau of Mines. However, I’ve come to accept that a more fitting name would be Bureau of Minds. As each day progresses, and I get a glimpse of the figures who populate this building, I am continually awestruck. These doctoral students, given enough time, will truly absolve us of all societal dilemmas. No doubt about it. Their work can only be described as astonishingly diverse and comprehensively sophisticated.

Today, Cole and Cade, a quirky duo with near-literary rapport, showed me the intricacies of supply-chain/flight pattern optimization in the corporate world and computer vision in the sporting realm. Tasked with labeling images, I made real contributions to their project!

Throughout the entire week, having the opportunity to work with so many different types of experts, I was fascinated by how often the idea of ethics in statistics came up. I have no background or demonstrated interest in the arcane mathematics of their research, but I genuinely appreciated the discussions that took a zoomed-out perspective on the impact of their labors. Some snapshots:

  • Is it ethical to create clinical trials where some individuals are purposefully given “inferior” treatment?
  • Is it ethical to use democratic opposition to certain types of speech in order to censor social media posts?
  • Is it ethical to treat medical study participants differently based on the ratio of the whole population with the ailment to that in the sample?
  • Is it ethical to abandon statistical standards in fields of business for the sake of manipulating models and predictions?

Big questions, these are, but it is wonderful to see that the most quantitatively-gifted among us are open to partnering with ethicists and humanists to secure a better future for all.

Acronyms

DTR. Daring tree rodents? Death, taxes, religion? Don’t teach reservedly? No, says Kyle, it stands for dynamic treatment regimes. Ah, yes, dynamic treatment regimes.

This morning, I entered the Labs knowing precious little about DTR, but come noontime, my outlook had brightened. Dynamic treatment regimes? Yes, please. More, more, the children cried. Such methods are the future, cleverly-defined sets of policies that produce optimal patient results. If you pattern-recognizing observers have failed to notice any trends in my “work” thus far, I shall make them abundantly clear in one word: optimization. All of these mathematical armaments and statistical frameworks seek to identify the best answer to a designated problem. It’s fittingly Trumpian—the BEST, give me the BEST!

Kyle, down to earth and unbelievably intelligent, gave me a brief rundown of his work as well as a needed refresher on reinforced learning. If needed, I’m fairly confident I could explain these concepts at a two-year-old level. In fact, I’d welcome the opportunity…

More learning awaits, but will it be reinforced…?

Learning

Visual Accompaniment (must click)

These cats can code! Khuzaima quickly showed me that he is more proficient in command line than I am in English. I was simultaneously impressed and taken aback. Then explaining his work, he really professed his expertise.

Khuzaima currently focuses on self-driving technology through imitation and reinforcement learning. He spent much of the afternoon explaining these terms to me. Afterwards, I knew significantly more but still, pathetically little in the grand scheme of things, though due in no part whatsoever to his teaching. Khuzaima’s patience is equally admirable.

In the afternoon, he let me take a test drive of the scaled down cart. After some playing around, he demonstrated the “Autopilot” feature, acknowledging that it has some kinks (see the above video) to work out. Nonetheless, I was fascinated.

On top of the automatization research, he gave me a taste of his other big project, and I was hooked. His algorithm uses crowd-sourced consensus to identify Twitter posts deemed as propaganda. Such a program has many applications, and represents how data can be used to improve online applications. So be warned, you Tweeters, Khuzaima’s coming for you!

It Begins

 

 

 

“It’s near the Free Expression Tunnel.” These words guided me through the expansive NC State campus and muggy ninety-degree heat to room 110 of the Bureau of Mines. Alas, I made it to Laber Labs, and walking through that tunnel represented everything that this program aims to teach me. Laber Labs thrives on the cutting edge intersection of big data and decision making. But its work thrives on the idea of free expression, the notion that individuals deserve to have specialized treatment and outcomes, whatever that looks like, in whatever field.

Of course, language like “treatment” or “outcomes” evokes the clinical setting. And make no mistake. Today, I worked with Eric Rose, a PhD student who specializes in designing optimized patient plans through a series of algorithm-guided choices. Essentially, he seeks to create the ideal system, where given x, and if y, prescribe z. When he succeeds, on average, people will receive the most favorable outcome.

Eric’s work is beautiful and far over my head, replete with eye-scrunching pages of mathematical symbols. He also, though, incorporates these sophisticated methods into computer games that showcase the valuable advances he has made. My biggest achievement of the session was adding a small chunk of code to one such application (visible through clicking on the above link and clicking down on “Games” to the “Flying Squirrel” option):

            if (heightKnot1 > heightKnot2){

               valley = bisect;

            }

            else {

               valley = bisect + 1;

            };




            tolerance = 0.1;

            if ((noSnakes == 0) & (Math.abs(this.terrain.snakes[nearestSnake].position.x - this.terrain.knotsX[valley]) < tolerance)){

               snakeInVal = 1;

            }

            else {

               snakeInVal = 0;

            };

            features.push(snakeInVal);

 

Not much, but it’s a start!

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