The Future Is Still Not Now

Speaking of progress, the great technological revolution that was supposed to have us all riding in self-driving cars still has not arrived, and by some measures might even be worse now than it used to be. All AI has really seemed to accomplish is the invention of increasingly more parasitic ways to deliver advertising to your various LCD screens.

What a waste of great technology. We carry in our pockets computers that are manyfold more powerful than those that landed human beings on the moon. It would be nice if we could use these machines for something more than taking photos, checking email, staring at advertisements, and surreptitiously looking at porn.

Hardware manufacturers, at least, have done their part. At least, in my opinion, they have. There is no mechanical barrier between us and the future we might rather be living in. The binding constraint seems rather to be that nobody's making the kind of software -- what a tragically ineloquent word "apps" is -- that might genuinely improve our existence.

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Using an extensive spreadsheet full of all kinds of personal data, I was able to develop an explanatory model for my blood glucose readings. The model is partially auto-regressive, since one of the dependent variables for current blood glucose is the previous blood glucose reading and the time since that last reading. The other variables are: total calories eaten at the most recent meal, most recent insulin bolus, and a dummy variable for cardiovascular exercise in the past twenty-four hours.

Each of these variables were significant at 95% confidence, and the coefficients on each variable made logical sense with respect to how we might predict they relate to blood glucose. That is, insulin and exercise reduced blood glucose while calories increased blood glucose. And just as any good endocrinologist will tell you, the higher your previous blood glucose reading, the higher your current one is likely to be.

I had attempted to include a sixth variable, accounting for any period of exercise between the previous and the current blood glucose reading. I included this variable based on the fact that glucose tests have consistently demonstrated that any period of exercise decreases my blood sugar by about 1.1 mg/dL per minute of exercise. (Some forms of exercise do more, and some do less, but on average it's about 1.1 mg/dL.) Unfortunately, this variable was not significant, and the model improved slightly when I omitted it, while the significance and magnitude of the other variables were unaffected.

This five-factor model produced an R-squared of just 0.19. That's a low value, but it tracks the data very closely, even if it doesn't track the variance closely. Thus, the model isn't highly predictive, but it offers good explanatory power. It's possible that I've left out some important factors. I certainly did not include all the data I had. Rather, I came up with an a priori model of what I think should explain my blood sugar, based on my many years of living with my condition, and tested that a priori model against the observations I had.

One way of interpreting these results is banal: I think I have a good idea about what explains my blood sugar, but that doesn't translate into perfect or even "tight" blood glucose control. A more optimistic take on the data is that I have correctly identified a few highly significant explanatory factors and I have empirically validated my thinking; now all I need to do is slowly work toward incorporating more and more of my data into my model. There is certainly no shortage of unused variables in my spreadsheet. Over time, I should be able to improve both my model and my blood sugar control by approaching the problem empirically.

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And so, my dissatisfaction continues. No one's connecting the various data collection points we have and fusing them into something actually useful.

Here's a simple idea that would make Amazon.com a lot of money: Wouldn't it be great if Amazon kept track of the items purchased by me but shipped to various addresses and names in my contacts list? For example, wouldn't it be great if I could just click on my sister's name and find out every Christmas present I sent her via Amazon.com for the past x number of years? Wouldn't it be great if Amazon produced recommendations based on that product history? This is a simple, basic idea that utilizes everything that Amazon is already doing, but grouped on one additional variable. Amazon already knows that I sometimes buy skin cream, and they recommend products accordingly. Why can't they figure out that I never send that skin cream to my home address?

My smart phone can take a photograph of anything I point to, and provide me with a location and price of a similar item on sale somewhere close. What it doesn't do is any level of price-comparison. This is madness. I don't merely want to know one instance of a place that might sell something similar; I obviously want to know: (a) where is the nearest location of such a thing, (b) what is the price of that thing at the nearest location, (c) what is the lowest price for that item at any location, and (d) how soon can such an item arrive if I have it shipped, rather than going to the nearest location? Four simple data points based on information available through simple web querying and using technology that is already sitting on my phone.

Health and fitness apps, ubiquitous on every smart phone, give a person ample ability to track health data, but what good comes from "tracking?" Many of these apps don't even make it obvious how the customer is to download his or her own data for more refined empirical processing. I certainly shouldn't have to consult a "FAQ" just to download a csv containing the stuff I took the time to upload in the first place! A few of these programs do me the additional service of summarizing my data for me on a weekly basis. That, at least, is a step beyond merely using me as a platform to harvest data that I myself cannot use.

But so far, the closest any of these apps has come to offering me any predictive value -- any value at all, beyond merely capturing my health data -- is that Garmin's "Connect" service and Strava's paid platform both offer their own estimates of how much rest I should take before my next workout. Near as I can tell, it's not a personalized service, it's simply the output of a mathematical formula that uses heart rate, watts, and calorie data as its primary inputs. It's a cut above the rest, but I still can't help thinking that, with all the data to which these companies have access -- health and fitness logs from athletes and non-athletes from all over the world, of all age groups -- their algorithms ought to be able to produce more. Only Garmin's service estimates VO2-max, and that's just simple arithmetic based on age, BMI, and the user's current fastest mile time!

Amazon Echo and Google Home devices don't even play music in stereo. Think about it: monaural home stereo systems in the year 2019, when people were recording quadraphonic albums in the 1970s!

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"Silicon Valley" has gone nutzo. After all these years of micro-dosing LSD, studying cryogenics, and living 16 to a townhome, they're no closer to producing anything close to a real technological revolution.

It's always been a bit of a canard that software engineers have no people skills and no sense of design; that they can write the code that will make any idea happen, but that they have no common sense about what the end user actually wants. In my many years working in that industry, I always held out hope that it was just a canard, that real-world people would never be so dumb as to produce millions of lines of computer code that offered no greater insight into my own life than I could produce with a single spreadsheet. Alas, I have to admit that my spreadsheets are many times better than anything Silicon Valley is producing today.

And so, the future waits, locked in a box that can only be opened by someone who wants to solve an actual problem, by someone who is in it for more than just upvotes at StackOverflow.com. So much elegant code in the world, and so few elegant solutions to real-world problems .So many predictive algorithms, and so few ways to predict the outcomes of our most pressing concerns.

It was inevitable that, once the computer and the telephone line had been invented, we would eventually connect the computer to the telephone line. So it is probably inevitable that many of these problems will be solved eventually. In the meantime, think how many resources we waste tracking the arrival of our next package rather than predicting the day on which we'll need to re-order.

Inventory software applied to the common household pantry; so obvious, and yet so far away from fruition.

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