Tuesday, March 27, 2018

The rigged imager

   Recently having purchased smartphone X from store.  M__ attempted numerous self portrait shots.  Numerous more than he could say, and all images seemed to provide a distinction relative to the image that were commonly viewed in front of the mirror.  Why is it always very similar?

Downcast and disheveled face, cheeks puffy and slightly discolored, M__ appeared tired and worn, he appeared haggard.  Obviously outdoor lighting seemed to provide greater improvement for his appearance.  Camera's don't lie, do they? 

The truth of the matter for a ccd imager an image is processed and calibrated in some representational way for all the incoming light, the imager is calibrated in terms of human visual perception.  It isn't accurate in some ways as it is calibrated in an average way to visual perception.  Some see colors differently, and some perceive the vibrance of some colors distinctly relative others...and some even see colors in sounds, and smells, as connected to other senses, that is, given a neurological crossing of wires mixing sensory states.

The truth of the matter is that even a compiled image from a smartphone or camera is likely to be processed whether a user likes it or not.  That is reprocessing an image potentially from a RAW digital format into other media formats which approximate original raw binary data through a number of mathematical transformations, and not permitting all the calibrations that supposedly are meant in emulating a perception like human optics.  The image is filtered, and so it is assumed that the image in a given instantaneous moment that is still image is gathered is representational of its subject matter, save the apparent snow that has gathered in the image...could this be the interference, for instance, of something like cosmic rays having perpetuated when not enough visible light should exist for the duration that a shutter had gathered whatever photons that could be had? 

The light were more sickly yellow than he would have recalled, at least, if it were his eyes, neither had he perceived so much white noise (snow) in the image of a room that appeared darker than he recalled, having shown him in unflattering ways. 

A clinical white wash of light shown overhead as M__ gazed into a mirror.  Yes, perhaps, I can see more a blemish here and there.  Signs of aging that he'd convince himself must have existed only having overlooked this or that, the camera hadn't perpetual misheard him, but looked upon the details in some objective way it would seem...don't we have a tendency to think of ourselves in continuity to a greater depth than extends beyond surface?

M__ noticed over the course of months and years that his face changed, as one might expect with years in aging.  Changing as in some mutability of self, that one would expect, irrespective of the continuity of the self "I" that M__ knew.  I being there, M__ thought. 
A slight shift in angle, a distinct head posture, a facial expression that essentially conveys something beyond a transient misappropriation of body languages that coincided for all purposes to bad timing, but just so happened to be the case more often than not.  So much less thought into that resident expression that betrays all other expressions. 

There is no grudge in that discrimination given by algorithms.  It is posited as certainly as facial recognition employs the framing of a face and has a spatial appropriation created so instantaneous that the tag box need be employed perpetually in the frame() method (until instructed otherwise).  That is where the eyes, nose, and mouth exists.  The curvature of that mouth alongside the topological models that would be instantaneously conceived following the smooth curvature of facial muscular structures.  A lifting of the cheeks, lifts the corners and creases  of the mouths...long since understood by those in art and medicine.  Turn that smile into a frown.  Cross one eye with another...a strange asymmetrical gait is more noticeable than one that is not.  Human minds by evolution discern such noticeable patterns right away, and in sensing discriminate one face from another.   Machine language manipulated all such data in a dizzying way.  Much more rapidly than any human artist could conceive, that is, in continuity of one thirtieth of a frame per second or 1/30 seconds a rendered frame with all necessary ingredients to convey what need be conveyed.

M__ wouldn't have better on a given day that he were apparently tired, wouldn't have felt better on a day that seemed haplessly the same as the day before. 

For the endless string of days, he were postured like a puppet in an endless choreography, all such social capital spent away, and where likely social credit should be less common place.  Restricting his travel was all part of the system, as in the litany and narrative, describing its own self continuity, to readers and onlookers so often far away. 

His mind traversed the image that would be his own self memory, self invention that surely was in his own domain as equally as it were removed.  Something that no one would see in a world where few did gaze so much at the faces of others really.

Friday, March 2, 2018

Recent Study on the Ride Share Gig Economy and Added Thoughts

Here is the study:

http://ceepr.mit.edu/files/papers/2018-005-Brief.pdf

Notable highlight:

"We perform a detailed analysis of Uber and Lyft ride-hailing driver economics by pairing results from a survey of over 1100 drivers with detailed vehicle cost information. Results show that per hour worked, median profit from driving is $3.37/hour before taxes, and 74% of drivers earn less than the minimum wage in their state. 30% of drivers are actually losing money once vehicle expenses are included. On a per-mile basis, median gross driver revenue is $0.59/mile but vehicle operating expenses reduce real driver profit to a median of $0.29/mile. For tax purposes the $0.54/mile standard mileage deduction in 2016 means that nearly half of drivers can declare a loss on their taxes. If drivers are fully able to capitalize on these losses for tax purposes, 73.5% of an estimated U.S. market $4.8B in annual ride-hailing driver profit is untaxed."

And this is the rub for an industry that maintains a substantial market.

Added thoughts:

Uber or Lyft having externalized transportation cost infrastructure to ride share drivers removes incentive in provisioning scarcity to ride share driver supply.  While on the other hand, potentially encouraging market growth for demand, as some consumer have related, the average time to service could be in some cases considerably lower relative to traditional cab services.   Market saturation of supply drivers likely leads more likely to shortened arrival times for supply customers while ensuring greater likelihood of ride share orders being fulfilled as opposed to cancelled.  Also having externalized transportation costs in such way, it has the advantage in cost leveraging the cost per ride to consumer while maintaining profit margins relative to traditional ride services.  This displacement, of course, is passed to drivers of the ride share service.

Here are additional factors that can make pay rates quite low:

  • Cost per ride doesn't pay as much when minimum hourly pay rates are non existent.  
  • The frequency of rides per hour are small alongside small mileage added to lengthier deployment times and mileage in providing a ride in the first place.  That is, unpaid travel distance meeting small paid travel distance.  
  • Too much downtime travel distance.
  • Too many drivers in queue for a given location (e.g., waiting at the airport with 96 others in queue), not enough demand.
Maximizing travel fares:
  • Frequenting locations where clients may be utilizing transportation in specialized ways as opposed to a primary source of transportation.     
  • Choosing locations where clients that use ride share as primary means more frequently may be more likely to use the service, in absence to public transportation offering, for shorter trips and especially in non specialized ways.  
  • Events based ride shares could potentially have better pay outs since these are more likely to require highway miles and lengthier travel times.  
  • Pickups with drop off at the airport especially where travel distance to and from are increased. 
  • Reducing non paid travel time.
  • Finding minimum distance routes to higher frequency ride share demand locations.
  • Use multiple ride shares (if possible). 
  • Reducing total per day travel miles while increasing the number of rides per day.  
  • Offering ride share when the pay makes sense.  What is a base hour rate goal?  Getting paid to do other things when typical base hour rate during such time isn't in keeping.  
  • Doing ride share in conjunction with other types of paid services (e.g., not Uber delivers but other types of specialty delivery services that pay decent).  Thus signing on to Uber to offset downtime loss of income when other delivery and/or transport services are slow as means to supplement primary income as opposed to primary means.   
Because Ride Share conceptualizes the market of drivers as intelligently driven by the supply of drivers and consumers more so, there is likely more managerial stress burden placed upon drivers to make critical economic decisions as to whether or not the market is viable as a means to income.  The gig economy is revolutionizing the ways that time is spent and the valuation for such.  Like the outset of the industrial revolution and all ramifications entailed by emerging technologies and sociological manifestation therein, it as likely that society is being transformed to think in different ways about the utilization of time and resources.  There is the power of potential exploitation and maybe in the future greater empowerment for individuals in maximizing their returns for time spent.  Proliferating diversification of gig related work is a likely reality for our economy.  Increasingly companies, corporations may be looking to pay independent contractors for task related work as opposed to lengthier paid downtime stays.  This inherently puts greater time management stress on individuals in such economy to maintain some paid work load when it is necessary, but also being intelligently cognizant in ways that were less commonplace in the past.  Knowledge and task based services as they become increasingly transient in terms of continued usage will likely mean less are as highly specialized in providing services and more likely having better adaptive management skills and/or utilizing adaptive management services that makes more likely effectiveness of individuals in being able to deliver profitably skills sets, services and products.  One should predict that our future economy will likely put greater premiums (not less) for task service related deliveries if it is ever to be sustainable.  Of course, externalizing lean efficiency is yet another thing... 

Thus a maxim:  Don't be afraid to express your worth for what you are doing and don't settle for less.

Oblivion

 Between the fascination of an upcoming pandemic ridden college football season, Taylor Swift, and Kim Kardashian, wildfires, crazier weathe...