Have cattle dogs will walk (in bone-chilling early winter mornings)

Early morning winter walks with the dogs are both beautiful (bare dark trees against a washed out sky) and chillingly cold. So cold that I wonder if my shoulders will become permanently embedded into my neck once I return to the warmth of the house. Tex, the younger of the two, still requires longer ambles. JJ, the older one, responds to entreaties for walks by snuggling deeper into bed.  Tex, on the other hand, impatiently waits  for me by the door as I don every piece of wool and fleece.  

We take a right out of our cul-de-sac and brace for the cold breeze whipping down the street. Out on the street he sniffles at the requisite number of scent-stops for a good whiff and a bit of Tex splash and then picks up speed, as if tasks completed he can relax and enjoy himself.  I can tell by his eager sniffing and the way he has rolled his shoulders forward that he is preparing to lunge at that morsel of squirrel tail he found yesterday. It’s a tell that game.is.on and he is pushing his body weight forward in expectation of my counter pulls. Fortunately, some other fur found the tasty tidbit last night. Tex relaxes his shoulders and trots on, tongue out, and happy. 

We round the corner near Christensen Park. The ice and snow on the climbing frames and swings twinkles and glistens in the morning light. A few commuters swoop by, all but concealed in layers of Gor-Tex. It’s so cold, thin contrails follow the cyclists. I pull my scarf further up and over my face and check to see if Tex is ok. He’s ok but I’m not sure I am. The Valmont Bike Park feels farther away in the morning chill. 

But through the neighborhood we continue, holiday lights twinkling and setting off a joyful scene against the somberness of the early morning light. Finally, working our way across a state-law protected crosswalk, which appears to be optional for most drivers, we get into the park.

And a feeling of stillness and peace settles into my body. Until Tex decides that munching grass is critical at a particular edge of the park so takes off in the opposite direction in which we are walking. That’s ok because there is a hawk (possibly?) watching us imperviously from one of the tall cotton wood trees, keeping a keener eye on the rabbits that scurry and hop quickly into the underbrush. We pass the dog park, where you can hear the yelps and barks of all sorts of dogs. Tex’s ears perk up, he sniffs the air, stiffens his back as we walk by. 

We round the corner and gaze upon the foothills snow-dusted and iron cold. The long walk back home under the creeping winter sun casting watery shadows on the trail.  

Algorithms and AI — who owns the knowledge

A few months ago, I was doing some research for the team and came across an article about the top algorithms teams use. What was interesting beyond the use of algorithms was the history of some of them and how old they are. For example, Naïve Bayesian Classifier is based on Baye's Theorem (https://en.wikipedia.org/wiki/Bayes%27_theorem) who was alive from 1701–1761. Consider that a theorem that is over 200 years old is now being used in predictive analysis on data produced by technology that did not exist then. I think it’s pretty amazing. Of course, there are many examples of solutions and innovations from the past providing answers to today’s challenges. But it’s easy to get caught up in the pace of today’s innovation: new technologies, new ways of doing things and forget that innovation didn’t start with Uber or Twitter.

This also means that the development and use of new algorithms continues.  Of course, companies like Google and Facebook continue to develop and refine their algorithms. But there is a market for actually selling algorithms, which seems somewhat in defiance the spirit of the whole idea in some ways. But then Google and Netflix have made huge amounts of money based off their algorithms so maybe it’s the right way to be thinking about them.

Another niche that took me by surprise and I don’t know why is the degree to which AI modeling depends upon humans tagging/categorizing things. Sometimes AI or machine learning is portrayed as this sort of this all-powerful autonomous aggregator, able to identify and categorize huge amounts of data with a single input. When in actual fact there have been 100s or 1000s of people tagging photographs, reviewing legal documents or medical results, and getting paid for their efforts. All of this work they’re is doing is feeding these vast AI models with the goal of identifying what…I guess everything. Totally unnerving. I really don’t think I want my mammograms made anonymous, then reviewed, tagged, given a result and then dropped into a system to allow matching of clear scans.

But then maybe I do, if I’m compensated.

I think we, the public, need to start taking ownership of the data we create and disseminate if it’s too be reused and leveraged to create something that businesses in turn sell back to us. If we benefited from the public availability of 200 year old algorithm, it seems we should also not be made to pay for the data we in fact created.