We are now 55 years removed from the publication of Karl Popper’s Logic of Scientific Discovery (1959), in which Popper argued that falsification is an essential character of science, and it seems that very few of us in society actually embrace this notion to understand how and why they should be skeptical of ‘studies that show…’.

Popper’s argument goes something like this. Before the age of discovery, when explorers from Europe journeyed to the four corners of the globe, Europeans held the belief that all swans were white.  This seemed to be a natural conclusion.  After all, every swan that had been observed and reported had been white, and it was reasonable to assume that every swan that was, is, or will ever be, is white.  On January 10, 1697, the Dutch explorer Willem de Vlamingh found a habitat sporting a large number of black swans in and around the Swan River on the west coast of Australia.  His observation put an end to any validity of the claim that ‘all swans are white’.

In Popper’s logical structure, de Vlamingh’s observations falsified the hypothesis that an essential property of swans is their color. Using this example as a prototype, Popper then generalizes a guiding principle that no scientific theory can be proven, it can only be falsified.

Now, I expect that most educated people in society would reply, if asked, that they are aware that a scientific theory can never be proven, only disproved, and that they accept this as an essential facet of the scientific enterprise.  Some of the more knowledgeable may even point out the radical modifications required to Newtonian physics caused by the observations that eventually led to the birth of Quantum Mechanics and General Relativity.

And yet, these very same people seem to blindly believe any ‘fact’ that comes to light, as long as its sales pitch starts with ‘scientist have discovered’.  The media continually bombards us with stories of this kind, telling us about how a new study shows that eating this or that raises or lowers the risk of us contracting some horrible malady.  That kids who play video games or watch too much TV (however ‘too much’ is defined) are being driven to greater levels of violence or obesity or whatever.  Regardless of the subject matter, a vast component of our society is gullible and quite ready to believe as proven cause-and-effect any set of statistical correlations that a scientist may happen to discover in a set of data.

Before I am accused of being unfair and overly critical, I do want to state that I recognize that real life is never as simple as the idealized situation portrayed in the black swan anecdote. For example, imagine ourselves as contemporaries of de Vlamingh who stay at home in Holland.  After he arrives home, we happen to be at a meeting where he is presenting his black swan observations.  Why should we just give up on the ‘all swans are white’ hypothesis solely on his say so?  Perhaps the birds he observed are not actually swans but birds that look similar.  Perhaps they were actually white, and a recent fire had covered them with soot.  But suppose he came back with the body of a black swan and all our tests and examinations indicate that the bird is indeed black and a swan.  Does this mean that we have, with certainty, disproved the white swan hypothesis?   I think the answer is a qualified yes.  That is to say, we can no longer cling to the notion that all swans are white even if we later narrow the definition of ‘swan’ so that the hypothesis again becomes acceptable.

On the surface, it may seem that the preceding argument invalidates Popper’s approach, and that this whole enterprise is self-contradictory.  But some careful thought and identification of what is essential versus what is accidental in the scientific method assures us that we are on firm ground.

The essential aspects of the scientific enterprise is that we can believe that the world is understandable and that logic and the scientific method work as tools to reach this understanding.  These beliefs are meta-physical in that they rise above the accidents of any particular scientific hypothesis, theory, or test, and they are not ‘provable’ or ‘disprovable’.  We simply identify them as essential aspects of the world and how we interact with it. The accidental aspects are all that remains.

To try to illustrate this, let’s return for the final time to these annoying black swans and to de Vlamingh, who caused so much trouble.  The essential aspect in this historical narrative is that Europe held the belief in white swans based on a very large number of observations.  To hold a belief about the world is to tacitly assume that the world is understandable and that reason is a tool to understand it.  The accidentals of the narrative are that 1) prior to de Vlamingh’s observations all swans were white and 2) after his observations that belief could no longer be held unchallenged or unmodified.  The fact that we can argue whether the de Vlamingh’s birds are really swans or really black or whatever is only a discussion about the accidentals of the bird and not the essentials of understanding the world.

So what I am criticizing in society is not that people can be confused about how to draw conclusions from scientific data, nor am I criticizing them for drawing conclusions I would not.  What I am criticizing is the acceptance of scientific conclusions without skepticism.  I am criticizing misplaced faith, which focuses on the accidental observations of a given study, and loses sight of the essential unprovable nature of the scientific method.  I worry about a society that uncritically accepts the term ‘Settled Science’ and turns its back on Black Swan Science.

Continuing on with the theme of language (natural and otherwise) and expanding on how we take for granted the ability of humans to shift contexts, usually on a moment’s notice, I submit a comical and whimsical topic.  ‘Yogi Berra Logic’.

For those unfamiliar with Yogi Berra, a brief biographical sketch is in order.  Berra is a retired baseball player and manager.  Born of Italian immigrants, Berra quit school after the eighth grade and, eventually got into major league baseball. He played for the New York Yankees from 1946-1965 as catcher and outfielder, and is generally regarded as one of the best catchers in the history of baseball.  He also seems to be both a genuinely good guy as well as an honorable man, as seen by his .

But Yogi Berra is perhaps best remembered these days for a collection of colorful and, at least on the surface, nonsensical sayings called Yogi-isms.  According to the article in Wikipedia, Yogi-isms are ‘…either an apparently obvious tautology or a paradoxical contradiction.’

For the moment, let’s not worry about whether this characterization is correct or whether the Yogi-isms described as  tautologies are rather examples of circular reasoning or of begging the question, or whether the ones described as paradoxes are, in fact, not true paradoxes but ironies.   It suffices to state that the idea the editor of Wikipedia was trying to convey is that Yogi-isms are pithy and witty but are devoid of much or all meaning. I would like to challenge that assessment.

As an example of a Yogi-ism consider the following quote, attributed to Yogi when he explained why he no longer went to Ruggeri’s restaurant in St Louis:

Nobody goes there anymore.  It’s too crowded.

Clearly this quote is funny (if you’re not laughing then stop reading, you don’t have a well-developed sense of humor and will only be harmed by the remainder of this post), but is it nonsensical?

Well, that depends on the context and meaning of the words ‘Nobody’ and ‘crowded’.  Returning to the ideas of essentials and accidentals, let us view the words ‘nobody’ and ‘crowd’ not as essential terms applying to all humanity (i.e., everybody, nobody, some, few, many) but as accidental names referring to two different subsets or categories of humanity.  Next try to match these accidental terms more accurately to the groups that they most likely represent.

Cool = the set of people who Yogi Berra regards as important to him.  Maybe they are friends and family.  Maybe they are baseball players.  Maybe they are authentic Italian people who eat at Ruggeri’s because they like the food.

Jerks = the set of people who Yogi Berra regards as useless or unimportant to him.  Maybe they are enemies.  Maybe they are pitchers (he is quoted as saying that ‘all pitchers are either liars or crybabies’).  Maybe they are image-conscious Yuppies who began eating at Ruggeri’s when it became known that he ate there and they thought it would be cool to be exposed to ethnic flavor.

Whatever the reasons Yogi had for dividing people up into these two groups, the Yogi-ism can now be translated into

Nobody from the Cool group goes there anymore.  It’s too crowded with Jerks.

Of course now the saying makes perfect sense (more precisely, it is intelligible), but it isn’t funny at all.  In addition, it makes clear a division of people into groups when, perhaps, the wise and polite thing is to not state this explicitly.

Is this what Yogi meant?  Am I attributing to much intelligence to a baseball player?  I don’t think so, and I am willing to bet that neither do you.  As discussed in earlier posts, the common ability of humans to speak and to learn language requires a fundamental capacity to tell essentials from accidentals.  Even a baseball player has this ability.  Furthermore, being humorous is a clear sign of intelligence, and Yogi is clearly funny.

In addition, I am also willing to bet that, before you read this analysis, you thought to yourself or muttered under your breath something to the effect of ‘Everyone knows what that means.  Why are you going on about it?’

But the reason, I am running on about it is that it is difficult to understand how a machine intelligence would be able to parse that Yogi-ism in such a fashion to draw the meaning.  How would such an AI determine context?  How would it resolve an obvious non sequitur by recognizing that ‘nobody’ refers to one group and ‘crowd’ refers to another?  How would it be able to draw from Berra’s background as a baseball player or proud Italian-American to offer reasonable inferences as to who would be in each group?

So, at the end of the day, I remain as skeptical of AI taking over as always.  I understand that there are theoretical analyses of high sophistication in the science of Artificial Intelligence, but I am reminded of another Yogi-ism:

In theory there is no difference between theory and practice.  In practice, there is.

In this post, I would like to explore some of the nuances of language and communication, especially as they arise in everyday speaking.  Particular focus is on context, tone, and non-verbal cues that add additional complexity to an already complex situation.  I close with some thoughts about how we can ever teach a computer to navigate through this highly complex landscape in hopes of someday having software mimic this behavior.

Learning language was the focus of the last post, where I argued that every human shares in a capacity to distinguish and classify object properties, and that capacity is what allows humans to learn to speak. In the situations examined in that earlier post, the teacher of the new language (be it the traveler’s foreign friend or the newborn’s friends and family) genuinely wanted the student to learn the new language.  That is a special context that is rarely available in common social interactions.

Most communication events take place casually, where both communicants expect roughly the same level of maturity and expertise of the other.  The nuances then arise from either an assumption made by one party that is only partially, if at all, shared by the other, or from a conscious effort by one party to layer on additional meaning or adapt the plain meaning to something else.

Into the first category fall all the little innocent miscommunications that fill our day-to-day lives.  Into the second fall things like half truths, jokes, double entendres, left-handed compliments, sarcasms, hints, lies, and cons. In other words, this category contains all the spicier and more interesting aspects of language.  We’ll deal with each category in turn.

First consider the innocent miscommunication.  It is best exemplified by an anecdote.  Last Friday I was meeting a friend for lunch.  He had arranged to pick me up, and most of our communication, after the initial phone call, was done by texting.  At 11:42 am he issued the following text:

On my way in 5 min.

I promptly got up and went to wait for him.  At 11:56 am, I called him asking where he was, to which he responded “I’ll be there in about 10 minutes”.  Needless to say, I was confused. When he arrived, we talked it out.  What he meant by ‘on my way’ was that he was leaving in 5 minutes whereas I interpreted it to mean that he would be arriving at my location 5 minutes after the text was sent.   Clearly the text meant different things to each of us

Sender:    ‘On my way in 5 min’ = ‘I am leaving my location in 5 minutes’

Receiver:  ‘On my way in 5 min’ = ‘I will arrive at your location in 5 minutes’

A simple foul-up really, but one that resulted from context and assumption.

Next, consider the much more interesting realm of the intentional addition of meaning to add information or flavor, or to deceive.  I don’t have the space or the inclination to cover all aspects, but I will touch upon 3 of the above-mentioned items: hints, left-handed compliments, and sarcasm.

The hint category is, of course, a staple in murder mysteries, and finds frequent expression in the works of Agatha Christie.  She actually goes into this point with the words from one her most famous sleuths, Miss Marple.  In the ‘Thumbmark of Saint Peter’, one of Miss Marple’s nieces calls for help when her entire village begins to ostracize her, believing her responsible for the sudden death of her husband.  In the process of solving the mystery for her niece, Miss Marple resolves to find meaning to the seemingly feverish last words of the dying man. As a preamble to her explanation, she starts by discussing context.  To quote:

“Has it ever occurred to you,” the old lady went on, “how much we go by what is called, I believe, the context?  There is a place on Dartmoor called Grey Wethers.  If you were talking to a farmer there and mentioned Grey Wethers, he would probably conclude that you were speaking of these stone circles, yet it is possible that you might be speaking of the atmosphere; and in the same way, if you were meaning the stone circles, an outsider, hearing a fragment of the conversation, might think you meant the weather. So when we repeat a conversation, we don’t, as a rule, repeat the actual words; we put in some other words that seem to us to mean exactly the same thing.”

This “as a rule” substitution is part and parcel of how we think and express and describe events in the world around us, and it is entirely contextual.  It’s behind every hint ever given for a riddle or a problem.  The plain meaning of the words in the hint are augmented by the context into which the words belong.  It isn’t clear how to describe it, you either get the hint or you don’t.

Next is the idea of a left-handed compliment.  When I was in high school, one guy stands out as the king of left-handed compliments.  Like most high-schoolers, originality and variety were not high on his list.  His standard stock in trade was to walk up to someone in the hall and say, in a fairly high voice “That shirt looks good…on you!”  To pull this highly dazzling witticism off, he would exaggerate the pause and then say the last part “on you!” with a clownish emphasis reminiscent of Steve Martin’s “well excuse me!”

Of course, he was versatile enough to change “shirt” to “hair cut” or “pants” or whatever part of the victim’s appearance was most deserving of derision.

Finally consider the related category of sarcasm.  Often a standard joke when dealing with smart yet social-awkward robots (I’m thinking Red Dwarf here), there is something altogether funny and yet nearly indescribable in the situation where a statement meant sarcastically is interpreted seriously.   I really can’t improve on the following clip from the Big Bang theory – so enjoy

Anyway, I hope that after that short tour of the contextual modes of language it is clear that there is far more to meaning than what is found on the written page or in the dry monotone speaking.  Artificial intelligence practitioners have yet to capture even the basic capacity that allows a newborn to learn its own mother tongue, let alone allow a person (Sheldon not withstanding) to be able to understand hints and humor and sarcasm.

And as far as the dangers of the AI demon escaping the pentagram, well, let me say a couple of things.  First, if only AI were that capable, my tech support experiences would be a whole lot better.  Second, until that day comes, I’ll keep worrying about those human scam artists, snake-oil salesman, and con artists who are a lot less book-learned and a whole lot smarter than the average PhD in computer science.

There has been a lot of commentary floating around about the recent ‘summoning a demon’ remarks of by Elon Musk on the dangers of artificial intelligence.

Mr. Musk may be an excellent entrepreneur but his philosophical arts are clearly lacking. Human intelligence has an intrinsic capacity that AI practitioners have yet to capture. This intrinsic capacity is best demonstrated by considering how humans learn their language.

Consider the following images of four fairly common objects: 1) an ordinary magnifying glass, 2) an empty water glass, 3) a water bottle with a magenta filter, and 4) a magenta USB drive.

Now imagine that you are about to travel to another country where you don’t speak the native language. You’ve made arrangements to travel on Monday with your interpreter arriving a day later. You pack your bag, making sure to include the four objects shown above (not as strange as it sounds – I routinely travel with three of them).

Due to a strike that happens a few hours after you arrive you become stranded in the country without your interpreter and with no immediate way home. Having nothing better to do you try your hand at learning the language.

You open your bag and pull out the four objects shown above. After a brief pantomime where you open and close your jaw and make sounds in English, one of the native speakers gets the notion that you want to learn some of their mother tongue. Now the fun begins.

The native points at the bottle and says “zerk”. What does this mean? There are many possibilities. He might be saying their word for “container”, or “clear”, or “plastic”, or “water”, or “magenta” or etc. You hope he means “container” and you point at the glass and say “zerk”. He then shakes his head and says “quig”. What does that mean? Maybe it means “empty”, or “glass”, or “clear”, or maybe even “container “.

You open the water bottle and pour all of the water into the glass. Your ad hoc teacher now points at the glass and says “zerk”. Inspired, you then pour some water into your hand, look at him expectantly, and then say “zerk”. Happy that you are now getting the idea, he nods his head vigorously, smiles broadly and says “zerk!” Congratulations you’ve now learned your first word in his language.

Let’s step back for a minute and discuss how you likely put together the concept of water with the sound “zerk”. It was unlikely that for the first attempt at teaching you his language, that your friend would say the word for an accidental associated with the bottle and would focus on the essentials instead. The problem is identifying which properties were essential and which were accidental and then determining which of the hopefully smaller set of essential properties was meant by “zerk”. Somehow you recognized or assumed (implicitly) that an essential property of your interaction with him would be to focus on the essential properties of the objects in question. You also assume that both of you possess a similar way of perceiving the world and culturally categorizing it. Basically, you have an abundance of clues to help you put things together. You also have a capacity to separate out essential properties from accidental ones, so that if the color of the liquid in the bottle were different, or the filter were missing, the bottle would remain a bottle conceptually (even if it looks different).

Now instead of a traveler to an antique land, you are a newborn. Someone is saying “doht” and is pointing at the USB key. What do they mean? How many repetitions and cross-references do you go through in order to figure out what it means? As a newborn, you don’t have the contextual and logical clues that helped you as they did in the foreign travel scenario. But you do have an innate capacity to apprehend the world. Whether it takes a hundred or a thousand attempts, after enough pointing back and forth between the USB key and the bottle, it suddenly hits that “doht” means magenta.

It probably takes longer to realize that a common property shared by the magnifying glass, the drinking glass and the bottle is the property of “clear”. Is this property an essential or accidental property? Most of us would answer it is essential for a magnifying glass to be clear but an accident if the bottle and the drinking glass are clear. So how can any of us learn what clear means when sometimes it falls into one category and sometimes into the other?

I don’t know. I do know that somehow we all can do it. I can’t explain it and I don’t quite know how to describe it any more than I daresay anyone else does, but I know it exists because I witness it.

Now let’s return to the comments of Mr. Musk. It is a sweepingly generous statement to say that our AI efforts have been even primitive successes. As a science, we do not understand this world-apprehending capacity that each of us is equipped with, in firm-ware, as we emerge from the womb. We know we have it but we have yet to codify it let alone translate it into something a computer can emulate. And even if we could, how do we give the machine all the contextual cues and clues that we take for granted in our interactions with our fellow humans. Sometimes I doubt we ever will succeed but for sake of argument I won’t press the point.

What I do know is that we are decades or centuries away from having this capability shared by the silicon and plastic companions that accompany us in our modern life. No Matrix, or Skynet, or Demon Seed is just around the corner, ready to burst out of the pentagram to which we attempt to confine it. No man-made machine with sinister or sublime intelligence is ready to sway our world – except for the man-made machines we manufacture through the tried–and-true method of making babies.

One last word is in order. Print out the four pictures featured here in the post, make up some novel sounds for things like “clear”, “small”, “shiny”, and the like, and see how long it takes for a friend to guess. Then give them a turn. It’s actually fun.

In the last post, I spoke a little about essentials and accidentals, but I didn’t try to define these terms.  In this post, I touch upon their traditional definitions in philosophy, and then turn to the application of these ideas to object-oriented programming.

At the heart of the philosophical concepts of essentials and accidentals is the classification or categorization of the property of an object as being needed (essential) or as being possible (accidental).  The use of the words ‘must’ and ‘could’ work nicely in this regard.  Consider the following two sentences:

A mammal must have warm blood.

A mammal could have fur.

The first captures an essential property of a mammal (namely that it regulates its internal temperature) while the second describes an accidental (it may have fur like a dog or have no fur like a dolphin).

All this sounds simple enough in principle, but there are always problems in the application.  The most common problem is that two people considering the same object won’t necessarily list the same number or type of its properties or classify them in the same way essential or accidental.  These differences reflect differences in perception, perspective, and the context and relative importance that each person places on the object.  For example, a mechanic may view a car’s essential properties as including weight, acceleration, torque, and engine displacement.  A city planner may view a car’s essential properties to include how often it is driven, its average speed, and its fuel mileage.

To illustrate this idea in more detail let’s start by examining something as simple as an ideal geometric point in a two-dimensional plane, a favorite prop in Euclidean geometry. An essential property most people would agree upon would be that an ideal point actually takes up no space, that is to say that it is truly zero-dimensional.   Others may expand the list of essential properties to include the notion that the point must have a location within the plane.  The list of accidental properties is much harder to nail down (precisely because they are not ‘musts’) but could include things like the color of the point as drawn or imagined, the distance of the point from an arbitrary origin, and the coordinate system and values within it for the position of the point.  Thus this simple object from high school geometry is not as sweet and innocent as it seems.  Scratch below the surface and it reveals a surprising degree of complexity and a tenacious resistance in being precisely defined.  The situation grows more complex as we turn our attention to composite objects – objects that are made of parts that we could characterize as objects in their own right.

The idea of assigning essential and accidental properties to an object becomes even more interesting when the object is not in the real world but is in cyberspace.  In this context, we are not simply the natural philosopher trying to characterize the objects we find or create in our world in the hope of inferring something true about all objects of that type. As programmers, we become the first cause in the microcosm of our program, and our choices in understanding how and why we choose a particular definition reveal things both profound and interesting about ourselves as thinking beings.

The creation or definition of an object, or more precisely an object class, is a way of mapping a mental conception of the mode of being of the object into computer instructions for handling data about those modes.  Defining what these terms mean precisely is a difficult endeavor but let me start by at least posing some questions to consider in order to help in fleshing these ideas out.

• What do the philosophical terms ‘essential’ and ‘accidental’ mean when we are the creators of the form of an object (i.e., the class)?
• Do the object properties represent an essential or an accidental property of an object?
• Is it true that the value of member data is always an accidental?

Again concrete illustrations will be much more useful.  Consider an OOP representation of the ideal point, which is frequently presented in discussions about graphics primitives.  A typical construction might look like (in a language-agnostic form)

Note that I am only dealing with the object’s properties and not how they are assigned or observed, so all the machinery that allows the object to interact with the outside environment (‘getters’ and ‘setters’) will be suppressed.

Now that we have defined our (computational) object let’s see if we can classify which parts of our object definition are essential and which are accidental.  A curious point is now apparent: there is no obvious place where we specify that the agreed-upon essential property of an ideal geometric point is that it is zero-dimensional.  Clearly all objects that are instantiations of the class ‘Point in a plane’ possess this essential property but they don’t know it and they can’t share it with the rest of the world.

It may seem that the only entity that knows this truth is the one who defined the object class in the first place.  But a small amount reflection on how the programmer programs delivers a more likely explanation: that the creator doesn’t really need to identify or even be aware of the essential properties of the object.  The creator simply employs the very capacity he uses to learn and interact with the world when he defines the class.  He doesn’t know how he does it any more than a bird knows how it flies; he simply does.  The programmer then depends on this capacity to know how many and which kinds of objects get instantiated to perform whatever computational task is desired.

What, then, to make of the object properties like ‘x_component’, ‘y_component’, and ‘color’?  Are these essentials or accidentals?  In some sense they are both.  From the definition of the object class, these data are essential because they are required for all objects of type ‘Point in a plane’.  But from the point-of-view of the programmer’s mental conception of what the object represents they are usually accidentals since their actual values are clearly not important.  Changing the color of a point or moving it around in the plane doesn’t stop the object from being a point.  So it is essential that the object has a memory location allocated to hold a value but it is completely accidental what value is inserted.

I like to imagine what would happen if Aristotle were to be transported from ancient Greece to modern times.  I assume that after he adjusted to the changes and settled in, he would reflect on all of the marvels of modern life. I believe he would be pleased with the state of physics, although he would probably bristle at the criticism leveled against his writings.  After all, he didn’t have the benefit of all of the technology that Newton and Einstein had.  I think he would look with fascination and delight at the state of biology and medicine – subjects that occupied much of his efforts.  But I suspect that no aspect of our current times would interest him more than computer science.

Aristotle is, without a doubt, one of the most prolific and influential thinkers of all time.  And perhaps, his crowning achievement is his work on logic and on the characterization of being.  He introduced the systematic study of logic and made it a central part of his philosophy. His imprint is found everywhere in Western thought.

All that said, Aristotle is not an easy read.  Whether that is a consequence of his thought or some facet of how is translated remains a mystery to me.  But the examples he employs in his work on logic in his Organon are hard to crack. Since I think he genuinely wanted his students to understand what he was driving at, if he were here today he would rework the Organon within the context of object-oriented programming.

Defining classes and providing them with attributes (member data and functions) would be a natural laboratory to demonstrate a large number of the philosophical concepts Aristotle birthed into Western thought.  The concepts of categories and the study of being and the ideas of essentials and accidentals have very clean distinctions in object-oriented programming.

It is my intention to explore these connections in the coming posts.  For now I will simply sign-off with the modest observation that every time someone plays a video game they are learning a little about how Aristotle thought.