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Sunday, February 19, 2017

Is Being Average "Normal"?


     Many people are fascinated by surveys and tables of statistics. They can be fun -- and even useful, when interpreted by people who know what the data does, and does not, indicate. They can also be misused by presenting data in such a form that people will jump to conclusions that are not really supported by the data. This is done directly by many politicians and for political purposes and causes.
     I have mentioned before the quote popularized by Mark Twain -- "There are three kinds of lies: lies, damned lies, and statistics." I like statistics because I am fascinated by numbers. Their relationships and the ability to use various formula upon the data is a lot of fun. But I don't have sufficient training to use (or misuse) them properly.
     But this particular blog is not about such weighty matters. It is more about how people can take "everyday" data and make more of it than what they deserve. In particular, it is about averages and the definition of normal. An average is a fact when you are dealing with numbers. The average of two, five, and eleven is six.  The average height of a human male in the United States is approximately 69.7 inches (177 cm) -- but there are not that many men in the United States who are exactly 69.7 inches tall. In other words, few men are of "average" height.
     Averages only make sense for characteristics when you examine them within a distribution. A normal distribution curve looks like:



     Normal means, basically, that the curve has a uniform change throughout the graph -- there are just as many above the average as there are below the average and the percentages change consistently on both sides of the "bell curve". This ideal curve doesn't occur that often in nature. One problem that happens is that there are often assumptions that a distribution of data will meet a normal distribution without doing a sufficiently wide sampling of data to justify it. For example, the data for men and women's heights in the U.S. can look like:



     Note that the peak for men ends up around 70 inches, while the peak for women is at about 64.5 inches (164 cm). The peak is higher than for men and the curve is narrower. This indicates that women's heights don't vary as much as men and concentrate at the average. However, in both cases the curve is very "smooth" and symmetrical. For height, it seems that the data really does seem to support this -- with a longer "tail" at the tall end indicating a slightly greater number of very tall people versus the number of very short people.
     Any characteristic that can be measured can have an average. Skin color is based on the amount of pigmentation from different types of melanin in the skin. There could be an average value for this. The average weight for a U.S. male (in 2015) was 195.5 pounds (88.5 kg) -- an increase of 30 pounds (13.5 kg) since 1960. The number of hair follicles per square inch can be measured -- thus, there is an average value for "hairiness". Ear size can be measured from top to bottom (or front to back or distance out from the skull).
     How many people have an average height, weight, skin color, hairiness, and ear size? Probably only a half dozen or so within the United States. Are these six people the "normal" ones? No, not even from a statistical sense because there are going to be many other measurements that are potentially able to be done -- eye color, IQ, foot size, hand span, distance from bottom of nose to top of lip, and so forth. Everything that can be measured can have an average -- but none of them are "normal" because humans are not just one characteristic. They are combinations of many, many characteristics and there may be some that are genetically linked (if you have one then you also have the other) but most appear to be totally independent. Skin color is totally independent of IQ. Eye color appears to have little to do with height. Weight and height do have some correspondence but it is possible to have a tall thin person as well as a short heavy one.
     Since being average in every possible measurable area is highly unlikely,  it is certainly not normal to be average.

Saturday, February 11, 2017

Artificial Intelligence: Beyond the Turing test


     In 1950, the British mathematician Alan Turing gave an answer to the question -- how can you tell if a machine is intelligent? His (paraphrased) response was "if you cannot tell the difference between a human answering questions and a machine answering questions then it has achieved intelligence". This Turing Test is not universally accepted but it is probably the most widely used foundation of answering the question of what is Artificial Intelligence (AI).
     Alan Turing's test was based on the idea of an interviewer and a responder. Someone asks a question and someone answers a question. This led to a series of experiments in computer programs that simulated (or imitated) "normal" human interviewer/questioner situations. It might be between a therapist and patient or doctor and patient or a student and professor/teacher. Naturally, there had to be a way to make it impossible to physically tell whether it was a machine or not. It also had, built into the test, the requirement of equivalent skill in understanding and speaking/responding in a human language.
     In today's world, computer programs have advanced beyond simple questions and answers. We have computer programs beating humans in Chess, and Go, (and other games). The Turing Test might not be considered to apply to these situations but many people would consider this a form of AI. We have computer program/systems that make use of pattern recognition to identify potential suspects or targets of drones. So far, the final decision is still made by humans but stories/films such as The Minority Report indicate a possibility of the machines making final decisions even about what might happen.
     That is the "line in the sand" for people thinking about AI. Who makes the final decisions? Is it a human (with all of her, or his, faults and experience) or a machine (who, at heart, is still the results of a programmer's abilities and recognition of exceptions)? Isaac Asimov, in his Three Laws of Robotics, had the AI programming include self-restraints as to what the program/robot could do, or could not do, without undergoing self-destruction.
     Speed and safety. The primary reason for computer programs is NOT that they can do things that humans cannot do; the primary reason is that they do things much, much, faster (and reproducibly). So, if you design an AI that handles the coordination and operation of a nuclear reactor, you want the program to be able to respond very quickly. Putting a human into the decision path slows everything down. Who has the final responsibility?
     The same question exists within the possibility of self-driving automobiles and trucks. It is likely that AI programs can already drive as well as an average driver -- assuming that all of their sensors work properly (they can detect objects and highway lines and sounds and bouncing balls and the cars and buildings around them, ...). Certainly, in another five or ten years, AI self-driving programs will be able to control a vehicle much safer (and more rationally -- no road rage potential) than humans. But they would be making the final decision.
     If a self-driving AI makes a mistake or a necessary decision that costs lives, who has the responsibility? The programmer? The company that built the vehicle? The owner of the vehicle? What happens if the self-driving car is involved in an accident with a human-driven car? Is there presumption of innocence on the part of the self-driving car?
     In all these cases, the program and machine are taking the place of the human. If you keep them "behind the curtain" there may be no way to identify whether they are human or machine. They PASS the Turing Test. But, when the curtain is removed, what is the final verdict? Who/what has the responsibility? Who/what makes the final decision?

Thursday, January 26, 2017

Out-of-sync: When social evolution lags technical innovation


     Humans, as a whole, are very capable intellectually. They're "smart". They can figure out how to take something and improve it. They can take two or more, apparently unrelated, items and figure out something completely new that can be done by using all of them together in a way that has never been done before. This is called "technological advancement" or "technical innovation".
     This is good. It has allowed us to move from caves to weather-secure buildings with running water and toilets and to be able to go from point A to point B in hours rather than weeks or months. It has allowed us to be able to feed 100 people, for a year, from an acre of land rather than 20. In most areas of the planet, it has helped us to be able to spend some time each day doing something other than trying to survive.
     What humans, as a whole, are NOT very good at -- is using technology consistently for constructive purposes. We invent ways to initiate, and control, fire and some individuals start burning down forests, or houses, or even tying alternative health specialists to stakes and burning them. We figure out ways to communicate at the speed of light across the planet and use it to spread false information more quickly. We work on methods to cure disease and help those who have incurable problems to live more easily and efficiently -- then we use those same methods to create diseases that kill millions. These are examples of "social evolution" (or lack thereof 😢).
     This is an example of being "out-of-sync" (or out of synchronization). Technology becomes capable of certain actions before people have matured sufficiently to consistently make use of the technology for good. Another way of putting it is that technology advances faster than people's ability to use it properly -- and the spread between these two appears to continue to widen.
     In order to curtail this problem, of course, one of two solutions can be applied. The first would be to slow down technical innovation. This is unpopular and unenforceable. As stated at the beginning -- humans, as a whole, are very capable intellectually. If one government, or group of people, agreed to slow down technological development in one area then another group of people will be completely willing, and able, to proceed on the development path on their own. A similar method could be done on an individual basis (since usually there is someone who is first with a technical innovation) with that person deciding to slow down, or withhold, research or results. This might work for a while but it disrupts many social policies of reward and competition.
     The other thing to do would be to accelerate social development. This sounds great but it also has problems. One problem is there no way to accelerate social development universally. In other words, if you have a nuclear bomb it only takes ONE finger to "push the button". And, within current economic patterns, there is no incentive to devote the resources (time, people, energy, "money") to help people to develop socially; there is no direct, short-term, "profit" from a more mature, socially capable, human.
     So, we have a problem. The problem doesn't appear to have a solution. They why talk about it? Because awareness of the problem is, in itself, an approach to a solution. If most scientists and engineers and tinkerers keep this problem in mind then technology can be presented in such ways that constructive ideas are promoted before destructive ones. This doesn't stop abuse but it does reduce it and slow it down. Such awareness can also avoid the social problem of technical abandonment -- "I just invented it -- it's not my responsibility how it is used". A true, but very lazy, excuse.
     Do you have any suggestions as to how to keep technical innovation and social responsibility at the same point?

Thursday, December 15, 2016

Gambling: a matter of risk versus reward


    When you hear the word "gambling" you start thinking about casinos, and roulette wheels or maybe hands of poker. But, in real life, gambling is a matter of risk and reward. Crossing the street involves risk and the reward is getting to your destination across the street. Asking someone out for a date involves risk (emotional and, occasionally, physical) in the hopes of rewards of reciprocity of affection or friendship.
    However, gambling still is usually classified internally depending on whether one considers the risk to be voluntary or involuntary -- and reasonable depending on whether the chance of reward is sufficient to justify the amount of risk. If we think it is a high risk and does not require to be done then, and only then, do we usually call it gambling. So, games of chance are considered to be gambling but crossing the street is not.
    This isn't true of everyone. Jack Nicholson, in "As Good As It Gets", portrays someone who is all-too-aware of the everyday risks of life. He continually strives to eliminate risk by isolating himself and entering into rigid routines and being hyper-careful of hygiene and exposure. The rewards of everyday life are not enough for him to take these risks. He is "fortunate" to be able to cater to these attempted avoidances of risk because he has a lucrative occupation that allows him to do this. It is only when he sees a reward that is large enough that he increases his willingness to take more risk.
    Nicholson' s character is seen as abnormal because his awareness of risks is much greater than his recognition of potential rewards. The risks exist -- but so do the rewards that he cannot grasp. There is not a single, appropriate, balance even though there are certainly, within a given society, an expectation of being able to make "reasonable" judgements on such.
    For someone living in a war zone, the risks of doing anything rise and the potential rewards narrow. For someone with a dependable environment and financial basis, the risks seem smaller because the downside of failure is much less even if they don't achieve the hoped-for rewards.
    There are various phobias -- more specialized than those that Nicholson's character revealed -- that are still an out-of-balance reflection of the risk versus the reward. To Chicken Little, the sky may fall upon him if he goes out -- or to the agoraphobic. One person may be willing to work in high construction, balancing themselves on girders while another person may have difficulty getting onto a balcony.
    Life is a gamble. Being able to weigh the risks and rewards are more computable with games of chance than they are within everyday activities. But the risks must be taken to get to the rewards. It is part of life's journey to learn to make those decisions based on known risks and benefits.
    What risks do you see that outweigh the possible benefits?

Saturday, October 29, 2016

The Luddite effect -- when the new does not transition the old


    In the eighteenth and nineteenth centuries in London, as part of the "Industrial Revolution", a group of workers in the textile industry started gathering together to fight against technical replacements for their labor. Their fear was based in reality. The textile industry in England was a large one within which a considerable portion of the workers earned their living. A mechanized loom might replace the manual efforts of dozens of women and men.
    Similar to the situations that often exist today, these people were hard-working and had developed their skills over their lifetimes and, sometimes literally overnight, there was no longer any market for those skills. The response -- a losing battle -- was to destroy machines, make threats to those who were instigating the changes, and disrupt the ability for the new factories to produce. Some historical accounts indicate that the leaders of the workers recognized that there was no way to defeat the change but wanted better leverage to provide retraining and support of the unemployed.
    Government response was primarily organized around protecting the new factories, their owners, and products. Severe laws were passed and a number of "show trials" were held with death or penal transportation/exile as potential penalties. These laws, in effect, did succeed in breaking the movement.
    Other areas of skilled labor were also displaced within the context of the Industrial Revolution. Although history books usually focus on the improved ability to manufacture goods (and decrease of prices for the average consumer), they do not often indicate the huge labor displacement which was a direct effect of the change.
    The Luddites provide a practical history lesson. Change is difficult for societies to adopt and it is particularly hard on those who have invested much time and effort on the old. If change is to happen (and it is difficult to avoid it) then the process of moving away from the old must be kept in mind.
    There are a number of changes currently going on in current times. One is semi-involuntary, one is semi-voluntary, and another is fully voluntary.
    Climate change is semi-involuntary. This is because it was probably avoidable but made difficult to avoid because of inertia of old methods of business. Although there is still the chance to make the change less severe, it has already made significant changes to the world. The Great Barrier Reef is close-to-death largely because of the increase in global water temperature. The glaciers continue to shrink around the world -- this is especially important in the Asian subcontinent where winter storage of water in snowpacks and glaciers provide water to billions of people. "100-year-floods" and "100-year-storms" are occurring more often as the water temperature rises.
    A semi-voluntary area of change is the shift from non-renewable energy sources. Since the change has to be encouraged, and pushed for, it falls into the voluntary category. It is reaching the tipping point where it is almost easier to use new, renewable, energy sources than to keep using the old ones. However, just as happened in the textile industry, it is very important to recognize, and assist, the people and families dedicated to the old energy systems. Solar panel factories located at old coal mines to allow easier transitions?
    A full voluntary area of change is the strong push towards greater and greater independent automation. Phones get smaller and more powerful. Robots can take over more manual labor in a programmable fashion (as opposed to dedicated design such as in the textile mills). Innovation and extensive education becomes more and more necessary for general job positions.
    Whether voluntary, involuntary, or a mixture of such, change requires preparation and assistance in moving from the old. The is a necessity for the change and, when it is forgotten, much suffering can occur as well as rebellion (isolated or global).
    What happens to the old when the new comes? This is an age old question but, with more rapid change comes the need to actively address the needs for migration, retraining, and restructuring.

Sunday, October 9, 2016

Jazz and Laughter: participation makes the difference


    Have you ever been in a room, talking with someone in a quiet corner, when a sudden noise comes from another section of the room? There is another group of people over there and they are very animated and the noise bursts out again. It is someone laughing -- and others in the group are joining them and obviously having an enjoyable time. Yet that noise, even once it is recognized as someone laughing, may not sound that pleasant. Some laughs are called "lilting" or other pleasant references but others are sometimes compared to sounds of other animals or objects in collision.

    Whether the sound, at a distance, is pleasant or unpleasant, it can still be considered very appropriate within a group who are all participating in the interaction together. It is this participation that lowers the guards, and criteria, and allows everyone to relax into a mutual experience. If you hear it from a distance -- not as part of the group -- it is a noise. If you are part of the experience it melds into the overall situation.

    It is not always necessary to be with the group in order to participate. In the above case, you are unlikely to be able to hear and see what is going on without being among the others. In the case of music, it is often a degree of intensity. In order to immerse in the music, it is necessary to be able to hear it properly. That may mean being in a quiet room, with other quiet people, so that all of the sound can be noticed, listened to, examined, and felt.

    On the other hand, it is just as valid of an experience of music to be in the midst of an explosion of sound and people and participate in the emotions of the fellow concertgoers and the movements of the band -- in spite of the fact that it may be so loud that the notes can no longer be distinguished from each other. In the one case, it is the music that is experienced and, in the other, it is the experience that is set to music.

    Jazz is an interesting juxtaposition of music and experience. A jazz piece, even when played by the same group of musicians, is not expected to sound the same twice in a row. The variation expands further when it is played by a different gathering. Although a spectator may not be directly singing or playing an instrument they have to be an active participant to fully take part. There are factors of anticipation -- what will happen next -- and surprise -- not expecting what did happen. The music will flex according to the weather and the internal needs of the players and the audience.

    There has been much speculation about whether robots could ever "replace" a human. Alan Turing presented what is called the "Turing Test" which says that if you are separated from a computer that can give responses -- so you have no direct knowledge of whether it is a computer or not -- and a human cannot tell whether or not it is a computer giving responses then it "passes". It is truly Artificial Intelligence.

    I would submit that an even better test would require the AI to be able to participate within a set of people and know when to laugh.

    What activities come to mind when you think of a need to be an active participant?
   

Saturday, August 27, 2016

GIGO: Garbage In and Garbage Out


     In the world of computer science, where a lot of acronyms were created (and still are), there was a term GIGO. GIGO is an acronym for "Garbage In, Garbage Out". This is a shortened version of saying that if what goes into a system (a computer, as one example) is not legitimate then it cannot be processed into an appropriate output. For example, a cruise control system takes into account current gear ratio, axle speed, tachometer reading, and so forth. If the cruise control believes that the car is moving at 100 miles per hour (60 kph) then it will do inappropriate things to the brakes and accelerator if, in reality, the wheels are spinning freely on a patch of ice and the car is not making any forward progress.
     If a data entry person (who are almost always touch typists -- not looking at the keys) had their hands at the wrong point on the keyboard as they entered data into a system, then things are not going to go well. You may get a $1,000 refund or you may get a bill for $10,000. In the first example, there is an assumption -- that the speed of the axles is a reliable indication of the speed of the car. In the second example, there is a lack of verification that the correct data are being entered.
     Data can also be wrong if the input instrument fails. If your thermostat breaks, then it cannot give proper information back to the processor that controls the oven temperature. You find out when the turkey comes out raw from the oven or the bread starts burning and catching on fire.
     Unfortunately, we are not always able to know, or immediately be able to be certain, that a problem has occurred. We may come up with a result that is completely false -- but we do not know that. This is why it is so important to have secondary, or backup, systems and studies to make sure that results are consistent. In the case of scientific studies, one study may be interesting and have results that may entice OTHER researchers to try to duplicate the study or try a different approach, but the results of the one study cannot, and should not, be relied upon on their own.
    In the past decade, the term GIGO has expanded a bit. It now is sometimes used for a situation where any type of inappropriate incoming conditions results in bad outgoing conditions. Eating "junk foods" is an example of this in the area of nutrition. If you eat foods that do not supply the appropriate "building blocks" (see my earlier blogs on nutrition) then it is difficult to build, and maintain, a healthy body. Another situation might be a building that is created with bad materials (inferior steel, poorly mixed concrete, lack of specified reinforcements, ...) and it collapses when a problem (perhaps an earthquake for which it was SUPPOSED to have been adequate) occurs.
     Another situation is more human-directed. That is falsification of data. In other words, people can (and do) sometimes lie. This may be for many different reasons -- they want different results to be true or they think that other results will bring them more attention (and funding) or they trust that no one will bother to cross-check the results.Two instances (I'm sure there have been more) of this have happened in the past decade -- one dealing with autism and vaccines and the other having to do with climate change. While it is difficult to be certain of the motivations, the results were considerable -- a loss of trust in vaccinations (and a rise in preventable illnesses and deaths) and possible delays in addressing environmental problems.
      In the area of politics, of course, this happens all of the time. A politician will say something and reactions and decisions are made based on what they say. If what they say is truthful, then those reactions and decisions have a better chance to be good ones. If what they say is not truthful, then it is unlikely the results will be good ones. Unfortunately, the rules of scientific studies are rarely followed in politics -- the facts may be researched but the results of those fact-verifications are either not seen, ignored, or misbelieved based on what the recipients want to have happen.
     A situation that lies between the two is the matter of data gathering for political purposes. Polls are used to reflect, and to influence, people's attitudes. However, a poll may gather data that is not representative and, thus, the results are not accurate. For example, if a poll only calls people on "landline" phones then the people who have only cell (mobile) phones will be excluded. It turns out that certain groups of people are more likely to have only cell phones -- thus the poll will be skewed away from the cell phone group and not be accurate. Or the poll could call people only in the evening hours -- and certain groups not present during the evening hours will not be represented. Or some people will filter out calls based on incoming numbers and so only those who do NOT filter their calls are represented in the poll results. It is more difficult than ever to get an appropriate poll base.
     Finally, a situation that combines computer science, politics, polling data, and cross-checking -- voting. There are two types of fraud that can exist in elections. One is voting fraud -- where someone who is not entitled to vote (or who is entitled to vote once but votes more than once) is able to vote. This situation sounds scary but actually does not happen a lot. The other is election fraud. This is where people who should be able to vote are prevented from voting, or their vote is changed such that the person/cause for which they are voting does not get credit (possibly giving it to the opposing situation). This happens much more often than voter fraud and appears to be increasing in volume. Both voter fraud and election fraud are instances of GIGO in the political arena. They are addressed in the same ways -- simplification (fewer systems or people between the voter and the recording of the vote) and cross-checking (paper trails for electronic voting systems, receipts for voting records, duplication of systems and verifying that both results are the same, ...). Unfortunately, people often decide to make things "easy" and "fast" which tends to increase the opportunities for election fraud.
     In what areas of life do you see the principle of GIGO operating? How do you, or would you, make sure that the information, on which you make your decisions, is correct?