May 20 update: this article has been quoted in multiple technology and science news, garnering >200 facebook likes and >100 tweets in the first few days.
In the age of "big data", technology companies are positioning themselves to allow humans to more easily answer previously "inextricable" problems with interesting techniques. And while there have been some successes -where advanced techniques have been largely embedded into organizations with fanfare- there have also been some recent high-profile failures where companies lumber forward and collapse over their own laces. Examples are from the academically gifted teams behind Google's Flu Trends, facebook's secret experiment, Apple's map software, 23andMe's genetics business, and Kensho's Santa Claus rally call. Maybe we should add Microsoft's HowOldRobot onto this list. Their playful tool was enjoyed all over the world this month, but does it indeed create a better guess as to your age? Here we seek to mathematically answer at what point should we reward only truly unique advancements and not what any probabilist can discern as a highly-profitable private data yield for the sake of a random number generator.
Somewhat aware of this point, their product claims to just know "how old do I look?" from one's face, instead of something more relevant, which is "how old am I?" Clearly they haven't a clue about the latter- and to be fair it is a nearly impossible task that few of us would rationally claim we can do. Assuredly we also assume most people collectively look how old they actually are. Else -for example- if all babies suddenly looked like senior citizens (think of the fabled Benjamin Button), then people would quickly adjust their perception of what a baby looks like (newborns simply would have the current "senior citizen" age look) and that appearance would then become what everyone associates back to a baby's age instead (so we really shouldn't have babies look the age of a "senior citizen".) The idea of not knowing one's age, but instead guessing at how old they look, is filled with relevant follies, as we will soon see with actual output.
To begin, let's see what the application looks like. Contributing a snapshot of myself for science, one can see this application's output in the upper-left of the four images below. This is the best all of the "advanced" machine learning at Microsoft's Bing could throw out there. Their guess is wrong by more than a decade. Or nearly a quarter of reality! It's as bad as if James Franco used the tool to defend his selection of an underage girl for goofing around: "well officer, she looks 21." Guesses like this, given any critical contexts, are simply well off the mark.
We'll return to the other three pictures above, after first discussing some mathematical theory. An important thing to here state is there are ways to link the work done by HowOldRobot to some quantitative understanding of the uncertainty behind these guesses. Microsoft would have been better off providing us with the confidence interval range behind these, but such a crucial data would have been awful transparency for their own marketing! Still, we can use probability theory to understand that at the core of this application they are seeking only a small number of relevant clues about the individual (sure they may claim to analyze dozens of attributes all at once as if this is some sort of fishing expedition, but the large explanations are in only a handful of pre-determined dimensions). Despite all of the information exposed through a face, the science comes down to thinking about a straight image -in many cases- and what might be the gender or race of the individual. It would also glean from the image properties, the random selection of colors and granularity provided to it -and combined with facial changes over time- provide an overall guess on age. Such judgments would process -say- that a celebrated centenarian will be more wrinkled versus a baby.
For the face bone itself, the modification of this over time also interacts and varies as a function of age and gender. Examining the differences in bone shape -and perhaps some of the more subtle connections within it- provides some information then to guess on the gender and age, and in particular where exactly to partition the data between young and old across genders. See these well documented science articles that have been around, for details on these numbers: here, and here.
To try to understand whether the large errors on the self-portraits shown above are either good or bad, we should ask the logical question of what sort of estimate range would a blind monkey, throwing a dart at a conditional age distribution, come up with? It turns out the monkey would be far closer, and so this is the standard by which technology companies should appreciate that we invariably measure the gravity of such a product launch. We can stress now that these machined products may appear as charming toys today, somehow more advanced than traditional calculators they have previously relied on. But these products have the quick potential to later leap into important life-and-death decision engines. For example they could be used to pilot automated, drone-launched missile decisions, or driverless vehicles, or used to peruse a crowded street for a potential criminal. Accuracy and precision have to matter, and so do understanding the egregiousness of errors when they occur.
Empirically this sort of probability problem concerning life age, can't be theoretically solved in closed form. The life tables that invariably drive this behind the scenes employ census information and are similar to a summation of actuarial tables. Hence we must use logical information about the representative population of internet users and their likely photographed cohorts, which Microsoft is making age guesses for. See our Tesla warrants article here for information on the difficulty in modeling customized actuary functions, even though it is certainly possible at times and under proper guidance.
To appreciate the conditional life population likelihoods, just see any of these two below, to get a sense of the distribution layout, by gender (male or female), and by children/youth versus mature/older (0 versus 1). For demonstrative ease, the small and censored fraction (<.05% and nearly all are women) of the global population aged >100, have been categorically truncated for this exercise. The topic of censored-data treatment, through statistical modeling, will be dealt with in a later blog article. For now, we can also see and appreciate the dearth of recent births in the recent global financial crisis. The complete chart, with all four mutually-exclusive and completely exhaustive population segments added to one another, is shown on the lower-right.
Here is the distribution below for just a fraction of the typical partitions based on facial bone changes hat Microsoft's application would be making. They are not nicely mound shaped, and they have this typical age (and just as important the conditional standard deviation):
older male - 33%, age 50 (19)
older female - 6%, age 75 (10)
younger male - 16%, age 18 (13)
younger female - 45%, age 38 (21)
Of course we know the subdistributions of the population (e.g., each colored segment above) is unequal-weighted to begin with. This is just to be analytically complete in describing the random process the computer goes through. The computer does not simply, randomly guess and model off of the entire distribution. This is similar to say FICO credit scores, where the computer seeks first to isolate the user into (legally it must be very rudimentary) demographic buckets first, and then more finely guess the parametric characteristics for each group and how they vary versus the overall population. In the end, we hope the entire "model" works, but the proof is only seen in better and more consistent output, and without Microsoft providing an actual confidence, we on our own here collect and demonstrate their flawed out-of-sample results that give users a false sense of accuracy.
The errors caused by this tool -in failing to have an unbiased guess as to one's age- shows faults right away as we seen it applied above to a single person. For appropriate people, this can also be shown by looking at his or her own pictures from say two decades ago, and noticing how Microsoft's guessed age overestimates then towards a sticky value of just less than 30. As if pivoting about one of the final segment ages above (e.g., see the sampled ones above). For even the three other self-portraits further above, we can see how contortions to the picture, altering the face shape, provide a narrow age bias that is also further in the wrong direction.
This of course is not an issue only for the author, but across the entire human population, where even a correct answer could have been a false negative (e.g., a lucky guess). Let's look at other important contortions, on different types of people. We'll look at Hollywood actresses, a group that is selection biased towards a segment whose very career survival depends on "being young". Bing's product, if anything, would always want to err on the side of looking younger when guessing at this cohorts. But there too, it instead spectacularly at times (nor can it account for pervasive cosmetic surgery and other artificial deformities.)
But look at this Andy Warhol impression of his most illustrious muse, Marilyn Monroe. What would be your age guess on this?
HowOldRobot doesn't even recognize a face in the upper left. But then working clock-wise, it concludes these ages for the other quadruplets: 53, 66, 72. Ouch, though also a reflection of the high and tight, upward bias we see above that is possible for women. All of these guesses are horrible, wrong in the same direction, and the worst offender was a guess at 72 (twice the age at which she died.)
We noticed earlier that the conditional gender probability is not equal. Notice the two upper distributions in the four-some further above. Notice the blue colored male distribution and how for a main segment it must often compete in the middle age categories, with the larger female populations (in red and pink). This only means that on the margins, the "advanced" robot can confuse a middle-age man with a female. Despite the current cultural focus on make-up and beauty, we can see from the information supplied above, that female faces ages faster over their lengthier life-spans. And the false-positives exist in both directions.
Some may know that this is similar to a popular idea to what was created by a 20th-century Harvard statistician's, self-named Chernoff's faces. Unlike changing markers or lines on a graph, the complex face can capture and communicate cues that serve as feature variables. A human might see Chernoff faces in any physician's office, where only a single dimension is used to express happy through sad/pain. By simply altering the value of the mouth and eyes, along the illustrations, and the patient pointing to the facial expression they most relate to. We can see below that an adjustment can also be made in a small number of other ways, say to the length and width of the face, as another measure to communicate. Note that touting the transformation of a high-dimension, color imagery that exists today is overboard when the Microsoft age-guessing errors are this bad.
Returning now to the above scientific computer capture of the author, in blue, similar to a screen the government may capture behind the scenes in a public venue. The properties here could have the potential of a binary indicator value at each point along the image, reducing all of the easy to understand face information to quick security print in the magnitude of ~2(40*50) combinations. Unfortunately this is still too much information and too costly to store and retrieve. So instead the science migrates to a lower magnitude, excluding a portion of the eyeglass portion of the picture above, of roughly ~2100. And the compacted number of eigen principals behind this author's face (or any other) is even lower still, which is why contortions to the self-portrait above don't change the age guess much as so little weight is being placed on most of the dozen or so variables that are truly minor in the end. One of the weaknesses of the machine learning approach is that it assumes that every data set has the capability to be addressed meaningfully through it's process. Sometimes the products are still far off from being an important tool, one that the underlying science and math hasn't been well recognized for, and won't be unless further technological and mathematical advancements can be made.
Imagine the whimsical and brute force nature of mixing together Play-Doh, which comes with the individual colors (standing in for ages) segregated into different containers. Anyone can mathematically present a color model at he time of purchase, where each container equals separates and explains a single color. Put a few colors side-by-side, and a traditional linear model makes sense still (similar to a magician using parallel and linear blades to saw through a volunteer laying in a box). Now we will think about things more complicated representative of real world data. Say the elegance of the Chinese philosophical symbol 太極圖 (known as yin and yang in modern culture). Intermediate mathematicians can still come up with a more sophisticated mathematical expression to bisect the two opposing colors there.
It took modern artist Jeff Koons two decades to put this together. Twenty years is greater than the standard error of some of the subdistribution life estimates above! What linear traditional mathematical model could pierce through the above stunning, multi-dimensional Play-Doh expression? As an advanced probabilist, it is easy to conclude there is none. And it is improbable that we can use sophisticated machine learning and big data to finely explain the location of each of the like colors, as if they were from a single continuous, non-porous unit. These are life limitations that scientists and business people can run into trouble if they chase too hard against a bad problem.
Big data algorithms don't care as much about mathematical accuracy as they do with their core strength of display and summary. At times you might assume every museum exhibit, concerning something similar with Play-Doh, would look similar. It takes a fine problem to have a mathematical logic and meaning to it. Put differently, their interpretation of the artistic and multi-dimensional model above is that every color is in a certain place for a reason, similar to the fundamental, ex ante logic we had for the colors in the population distribution manufactured earlier in this article.
Even as Microsoft's model aims (and successfully does to a partial though insufficient degree) to reduce the variability in age guessing, from a universal set of the population, we showed here that the conditional volatility allows for heterogeneous errors in large, pre-defined segments of the population at any point in time. This makes to any reasonable quantitative person, the Microsoft product fail versus how it is advertised. It also completely fails in different ways as could have been generally been predicted, and presents a definite, permanent setback in moving to fast forward in innovation roll-outs. The product failed unaccountably (by Microsoft anyway) with this author. And with Andy Warhol and Marilyn Monroe. And with James Franco. Social media is populated with other cases of breakdown.
In the final analysis, it will always be wiser counsel for companies esteemed as Google and Apple and Microsoft, to operate and promote within their confines of what's possible, given the technical and resource gaps that still exist. Given the large-stakes risks, gaffes and secret data solicitation should be avoidable, in order to more secure the public's trust. There are many accomplishments already and human improvements to not give false product launches.
We noticed as well, both here and generally in life, that faces are extraordinarily beautiful and complex. As an artist they are highly difficult to even draw and explain. Clearly probability and statistics have a place in cracking the riddle behind how they work. One day we might wrest control -to a robot- of quick and life-threatening decisions anywhere in the world. These errors will no longer be a source of pleasure, but rather imply real lives were continuously sacrificed. Right now you wouldn't want a monkey as a TSA agent, on guard to check airline passengers ID and highlight suspicion. We could all appreciate the nuisance and aggravation caused by repeated errors and loop-holes in code. Statistical false-positives (people routinely being inconvenienced) and false-negatives (threats that always go undetected) are both frequent, costly, and will lead to hazardous vulnerabilities. Unfortunately, we operate in a commercial world where Watson and Deep Blue are forced only through brute force (not through something advanced and clever) to stay ahead of humans, but then are advertised as proof that technology companies today can easily solve everything important. Such as strangely (and quickly) stating illogical things that no human alive could: falsely concluding with all of Microsoft's technical muscle, that James Franco is actually an older woman.
In the age of "big data", technology companies are positioning themselves to allow humans to more easily answer previously "inextricable" problems with interesting techniques. And while there have been some successes -where advanced techniques have been largely embedded into organizations with fanfare- there have also been some recent high-profile failures where companies lumber forward and collapse over their own laces. Examples are from the academically gifted teams behind Google's Flu Trends, facebook's secret experiment, Apple's map software, 23andMe's genetics business, and Kensho's Santa Claus rally call. Maybe we should add Microsoft's HowOldRobot onto this list. Their playful tool was enjoyed all over the world this month, but does it indeed create a better guess as to your age? Here we seek to mathematically answer at what point should we reward only truly unique advancements and not what any probabilist can discern as a highly-profitable private data yield for the sake of a random number generator.
Somewhat aware of this point, their product claims to just know "how old do I look?" from one's face, instead of something more relevant, which is "how old am I?" Clearly they haven't a clue about the latter- and to be fair it is a nearly impossible task that few of us would rationally claim we can do. Assuredly we also assume most people collectively look how old they actually are. Else -for example- if all babies suddenly looked like senior citizens (think of the fabled Benjamin Button), then people would quickly adjust their perception of what a baby looks like (newborns simply would have the current "senior citizen" age look) and that appearance would then become what everyone associates back to a baby's age instead (so we really shouldn't have babies look the age of a "senior citizen".) The idea of not knowing one's age, but instead guessing at how old they look, is filled with relevant follies, as we will soon see with actual output.
To begin, let's see what the application looks like. Contributing a snapshot of myself for science, one can see this application's output in the upper-left of the four images below. This is the best all of the "advanced" machine learning at Microsoft's Bing could throw out there. Their guess is wrong by more than a decade. Or nearly a quarter of reality! It's as bad as if James Franco used the tool to defend his selection of an underage girl for goofing around: "well officer, she looks 21." Guesses like this, given any critical contexts, are simply well off the mark.
We'll return to the other three pictures above, after first discussing some mathematical theory. An important thing to here state is there are ways to link the work done by HowOldRobot to some quantitative understanding of the uncertainty behind these guesses. Microsoft would have been better off providing us with the confidence interval range behind these, but such a crucial data would have been awful transparency for their own marketing! Still, we can use probability theory to understand that at the core of this application they are seeking only a small number of relevant clues about the individual (sure they may claim to analyze dozens of attributes all at once as if this is some sort of fishing expedition, but the large explanations are in only a handful of pre-determined dimensions). Despite all of the information exposed through a face, the science comes down to thinking about a straight image -in many cases- and what might be the gender or race of the individual. It would also glean from the image properties, the random selection of colors and granularity provided to it -and combined with facial changes over time- provide an overall guess on age. Such judgments would process -say- that a celebrated centenarian will be more wrinkled versus a baby.
For the face bone itself, the modification of this over time also interacts and varies as a function of age and gender. Examining the differences in bone shape -and perhaps some of the more subtle connections within it- provides some information then to guess on the gender and age, and in particular where exactly to partition the data between young and old across genders. See these well documented science articles that have been around, for details on these numbers: here, and here.
To try to understand whether the large errors on the self-portraits shown above are either good or bad, we should ask the logical question of what sort of estimate range would a blind monkey, throwing a dart at a conditional age distribution, come up with? It turns out the monkey would be far closer, and so this is the standard by which technology companies should appreciate that we invariably measure the gravity of such a product launch. We can stress now that these machined products may appear as charming toys today, somehow more advanced than traditional calculators they have previously relied on. But these products have the quick potential to later leap into important life-and-death decision engines. For example they could be used to pilot automated, drone-launched missile decisions, or driverless vehicles, or used to peruse a crowded street for a potential criminal. Accuracy and precision have to matter, and so do understanding the egregiousness of errors when they occur.
Empirically this sort of probability problem concerning life age, can't be theoretically solved in closed form. The life tables that invariably drive this behind the scenes employ census information and are similar to a summation of actuarial tables. Hence we must use logical information about the representative population of internet users and their likely photographed cohorts, which Microsoft is making age guesses for. See our Tesla warrants article here for information on the difficulty in modeling customized actuary functions, even though it is certainly possible at times and under proper guidance.
To appreciate the conditional life population likelihoods, just see any of these two below, to get a sense of the distribution layout, by gender (male or female), and by children/youth versus mature/older (0 versus 1). For demonstrative ease, the small and censored fraction (<.05% and nearly all are women) of the global population aged >100, have been categorically truncated for this exercise. The topic of censored-data treatment, through statistical modeling, will be dealt with in a later blog article. For now, we can also see and appreciate the dearth of recent births in the recent global financial crisis. The complete chart, with all four mutually-exclusive and completely exhaustive population segments added to one another, is shown on the lower-right.
Here is the distribution below for just a fraction of the typical partitions based on facial bone changes hat Microsoft's application would be making. They are not nicely mound shaped, and they have this typical age (and just as important the conditional standard deviation):
older male - 33%, age 50 (19)
older female - 6%, age 75 (10)
younger male - 16%, age 18 (13)
younger female - 45%, age 38 (21)
Clearly the computer is doing something and not blindly making bogus guesses with each face it recognizes, from the entire distribution. But this is a weaker form of adding value, similar to how central bankers purport to "add value" to their otherwise impossible ability to ever guess the critical turns in the economy, and how the use of machine learning attempts to carve out basic partitions in the data. There are a couple other data distribution portions that we described earlier, and beyond the evolutionary face bone changes we all experience. For example, the computer application would can logically attempt to understand other major face characteristics, and for a younger person they might look at amount of hair, size of nose. For older people they might focus more on the suppleness of eyes, and skin wrinkles. And as we'll see, all of this the computer is easily prone to be deceived about their meaning. Incorporating these greater number of parameters, we can expect the final standard errors in Microsoft's product to come in about 1/3 of what is shown above. Yes there is speed, but we also see there is also high error- it's like having a supercomputer that's twice as fast as your current handheld calculator though it suddenly creates a stream of noisy errors that can't ever be corrected. Also note that this shows that for younger females, the typical errors could be larger than for typical men. Nonetheless, a guess that is off by a decade would be greater than a typical random error (e.g., from a monkey throwing darts) in every selected segment.
Technically speaking, a dart-throwing monkey or anyone would not simply guess anywhere along the entire population age distribution, but rather focus on the component, or zone, sharing the same basic statistical characteristics of the "matching" face. One might ask if, say a trained monkey, throwing a dart wouldn't just aim for the center of such a large and unwieldy distribution shape (nothing similar to a smoothed normal distribution). That would be an incorrect interpretation of our probability analogy. For those readers who think that, instead picture the entire (sub)distribution partitioned into 20 or 100 equal sized spokes. And now each one of those is used to complete the dartboard design, of same number of spokes. The monkey would then blindly aim for the dartboard (the board could be spun along its center if that's a concern).
Technically speaking, a dart-throwing monkey or anyone would not simply guess anywhere along the entire population age distribution, but rather focus on the component, or zone, sharing the same basic statistical characteristics of the "matching" face. One might ask if, say a trained monkey, throwing a dart wouldn't just aim for the center of such a large and unwieldy distribution shape (nothing similar to a smoothed normal distribution). That would be an incorrect interpretation of our probability analogy. For those readers who think that, instead picture the entire (sub)distribution partitioned into 20 or 100 equal sized spokes. And now each one of those is used to complete the dartboard design, of same number of spokes. The monkey would then blindly aim for the dartboard (the board could be spun along its center if that's a concern).
Of course we know the subdistributions of the population (e.g., each colored segment above) is unequal-weighted to begin with. This is just to be analytically complete in describing the random process the computer goes through. The computer does not simply, randomly guess and model off of the entire distribution. This is similar to say FICO credit scores, where the computer seeks first to isolate the user into (legally it must be very rudimentary) demographic buckets first, and then more finely guess the parametric characteristics for each group and how they vary versus the overall population. In the end, we hope the entire "model" works, but the proof is only seen in better and more consistent output, and without Microsoft providing an actual confidence, we on our own here collect and demonstrate their flawed out-of-sample results that give users a false sense of accuracy.
The errors caused by this tool -in failing to have an unbiased guess as to one's age- shows faults right away as we seen it applied above to a single person. For appropriate people, this can also be shown by looking at his or her own pictures from say two decades ago, and noticing how Microsoft's guessed age overestimates then towards a sticky value of just less than 30. As if pivoting about one of the final segment ages above (e.g., see the sampled ones above). For even the three other self-portraits further above, we can see how contortions to the picture, altering the face shape, provide a narrow age bias that is also further in the wrong direction.
This of course is not an issue only for the author, but across the entire human population, where even a correct answer could have been a false negative (e.g., a lucky guess). Let's look at other important contortions, on different types of people. We'll look at Hollywood actresses, a group that is selection biased towards a segment whose very career survival depends on "being young". Bing's product, if anything, would always want to err on the side of looking younger when guessing at this cohorts. But there too, it instead spectacularly at times (nor can it account for pervasive cosmetic surgery and other artificial deformities.)
But look at this Andy Warhol impression of his most illustrious muse, Marilyn Monroe. What would be your age guess on this?
HowOldRobot doesn't even recognize a face in the upper left. But then working clock-wise, it concludes these ages for the other quadruplets: 53, 66, 72. Ouch, though also a reflection of the high and tight, upward bias we see above that is possible for women. All of these guesses are horrible, wrong in the same direction, and the worst offender was a guess at 72 (twice the age at which she died.)
We noticed earlier that the conditional gender probability is not equal. Notice the two upper distributions in the four-some further above. Notice the blue colored male distribution and how for a main segment it must often compete in the middle age categories, with the larger female populations (in red and pink). This only means that on the margins, the "advanced" robot can confuse a middle-age man with a female. Despite the current cultural focus on make-up and beauty, we can see from the information supplied above, that female faces ages faster over their lengthier life-spans. And the false-positives exist in both directions.
Let's return now to the popular news above, concerning the wrong-doings of James Franco. How did Microsoft's age-guessing application even work on him?
Actually, do you mean her? That's right, blame it all on a sloppy trumpeting of "machine learning", but Microsoft pegs James Franco at 8 years older than reality (which is relatively a lot) and also has James guessed to be one hirsute woman. This should not fit nicely into their disclaimer about "not getting the age and gender quite right".
To see what the mathematics is of what should be going on, see this blue image below from a scientific capture done on the author's face. Notice that the statistical characteristics assumed to go into a complicated, high-resolution face (shown in our self-portrait at the top of this article) really only boil down to a small number of independent factors we literally summarize below. And for age it might be a little less since we are only taking billions of unique people and asking to boil that down to just more than 100 discrete integer ages, not identify unique people in -say- a photomontage. 
Actually, do you mean her? That's right, blame it all on a sloppy trumpeting of "machine learning", but Microsoft pegs James Franco at 8 years older than reality (which is relatively a lot) and also has James guessed to be one hirsute woman. This should not fit nicely into their disclaimer about "not getting the age and gender quite right".
Some may know that this is similar to a popular idea to what was created by a 20th-century Harvard statistician's, self-named Chernoff's faces. Unlike changing markers or lines on a graph, the complex face can capture and communicate cues that serve as feature variables. A human might see Chernoff faces in any physician's office, where only a single dimension is used to express happy through sad/pain. By simply altering the value of the mouth and eyes, along the illustrations, and the patient pointing to the facial expression they most relate to. We can see below that an adjustment can also be made in a small number of other ways, say to the length and width of the face, as another measure to communicate. Note that touting the transformation of a high-dimension, color imagery that exists today is overboard when the Microsoft age-guessing errors are this bad.
Returning now to the above scientific computer capture of the author, in blue, similar to a screen the government may capture behind the scenes in a public venue. The properties here could have the potential of a binary indicator value at each point along the image, reducing all of the easy to understand face information to quick security print in the magnitude of ~2(40*50) combinations. Unfortunately this is still too much information and too costly to store and retrieve. So instead the science migrates to a lower magnitude, excluding a portion of the eyeglass portion of the picture above, of roughly ~2100. And the compacted number of eigen principals behind this author's face (or any other) is even lower still, which is why contortions to the self-portrait above don't change the age guess much as so little weight is being placed on most of the dozen or so variables that are truly minor in the end. One of the weaknesses of the machine learning approach is that it assumes that every data set has the capability to be addressed meaningfully through it's process. Sometimes the products are still far off from being an important tool, one that the underlying science and math hasn't been well recognized for, and won't be unless further technological and mathematical advancements can be made.
Imagine the whimsical and brute force nature of mixing together Play-Doh, which comes with the individual colors (standing in for ages) segregated into different containers. Anyone can mathematically present a color model at he time of purchase, where each container equals separates and explains a single color. Put a few colors side-by-side, and a traditional linear model makes sense still (similar to a magician using parallel and linear blades to saw through a volunteer laying in a box). Now we will think about things more complicated representative of real world data. Say the elegance of the Chinese philosophical symbol 太極圖 (known as yin and yang in modern culture). Intermediate mathematicians can still come up with a more sophisticated mathematical expression to bisect the two opposing colors there.
But imagine a more complicated, and somewhat spurious, kneading of the different Play-Doh colors. One can then have a final assembly of a product that is merely too complicated to model the colors from the amalgamation, even as the compact exterior mold looks seductively benign.
Big data algorithms don't care as much about mathematical accuracy as they do with their core strength of display and summary. At times you might assume every museum exhibit, concerning something similar with Play-Doh, would look similar. It takes a fine problem to have a mathematical logic and meaning to it. Put differently, their interpretation of the artistic and multi-dimensional model above is that every color is in a certain place for a reason, similar to the fundamental, ex ante logic we had for the colors in the population distribution manufactured earlier in this article.
Even as Microsoft's model aims (and successfully does to a partial though insufficient degree) to reduce the variability in age guessing, from a universal set of the population, we showed here that the conditional volatility allows for heterogeneous errors in large, pre-defined segments of the population at any point in time. This makes to any reasonable quantitative person, the Microsoft product fail versus how it is advertised. It also completely fails in different ways as could have been generally been predicted, and presents a definite, permanent setback in moving to fast forward in innovation roll-outs. The product failed unaccountably (by Microsoft anyway) with this author. And with Andy Warhol and Marilyn Monroe. And with James Franco. Social media is populated with other cases of breakdown.
In the final analysis, it will always be wiser counsel for companies esteemed as Google and Apple and Microsoft, to operate and promote within their confines of what's possible, given the technical and resource gaps that still exist. Given the large-stakes risks, gaffes and secret data solicitation should be avoidable, in order to more secure the public's trust. There are many accomplishments already and human improvements to not give false product launches.
We noticed as well, both here and generally in life, that faces are extraordinarily beautiful and complex. As an artist they are highly difficult to even draw and explain. Clearly probability and statistics have a place in cracking the riddle behind how they work. One day we might wrest control -to a robot- of quick and life-threatening decisions anywhere in the world. These errors will no longer be a source of pleasure, but rather imply real lives were continuously sacrificed. Right now you wouldn't want a monkey as a TSA agent, on guard to check airline passengers ID and highlight suspicion. We could all appreciate the nuisance and aggravation caused by repeated errors and loop-holes in code. Statistical false-positives (people routinely being inconvenienced) and false-negatives (threats that always go undetected) are both frequent, costly, and will lead to hazardous vulnerabilities. Unfortunately, we operate in a commercial world where Watson and Deep Blue are forced only through brute force (not through something advanced and clever) to stay ahead of humans, but then are advertised as proof that technology companies today can easily solve everything important. Such as strangely (and quickly) stating illogical things that no human alive could: falsely concluding with all of Microsoft's technical muscle, that James Franco is actually an older woman.
Brilliant one read on this topic. Also on related to this in today's English news daily the Hindu on the missing of big picture in Big Data.
ReplyDeleteThanks much gomu. This article was shared fairly widely on social media as well. Hope you are well and please share this article with your friends!
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