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GUEST APPEARANCE by Paul Bassett: Cosmologist, internationally-known AI specialist, keynote speaker and published author. His third contribution follows, speaking on Our Universe.

It gives me tremendous pleasure (again) in introducing my long-time friend and colleague, Paul Bassett. Paul has written a blog contribution below, which I know you will find extremely thought-provoking. Your responses are of course, solicited.

Paul Bassett photo Paul Bassett is a retired software engineer, author, entrepreneur, and inventor. His invention of Frame Technology (used around the world to automate software development) won him CIP’s Technology Innovation Award. He’s published numerous papers and a book Framing Software Reuse. Paul was a member of IEEE’s Distinguished Visitor Program, and has given keynote addresses, taught computer science at York University, and co-founded several businesses, including two successful software engineering companies. His MSc in artificial intelligence (U. of Toronto) imbued him with a life-long passion for divining the role and future life in the universe.


What is the Name of Our Universe?

“Our universe” means different things in cultures with different creation myths. In my culture, “our universe” usually means the observable universe, which is a sphere with the Earth at its centre; it is the largest volume of matter that can ever affect us. Its radius is 46.6 billion light-years (1 light-year = 9.46 billion km.) and growing at one light-year per year. But the universe created at the “Big Bang” (13.8 billion years ago) surrounds “our universe”, and is unimaginably larger still. Virtually all the matter in the “Big Bang universe” is moving away from us faster than the speed of light, so can never affect us.

In “our universe”, we can see galaxies that can never see each other because any pair of galaxies that are more than 13.8 billion light-years apart have not had enough time since the Big Bang for light to travel from one to the other. So one could say that those galaxies are outside each other’s universes.

Finally, there is the notion of a ‘multiverse’, a universe some cosmologists speculate is spawning universes all the time, just as it spawned our “Big Bang universe”. With so many universes, there is no name for any of them! That said, “our universe” is the de facto name for the one and only universe that matters to us.


Is artificial intelligence intelligent? or is it just machine learning?

There are many ways to define intelligence. Almost all of them involve problem solving proficiency. Problem-solving in turn, is deeply connected to the notion of algorithm, a method for converting inputs to outputs, or in mathematics, computing a function. Every computable function* has a countably infinite number of algorithms that can compute it, each varying greatly in its proficiency – the time and memory it requires to compute its outputs.

All brains and computers work by performing algorithms*. Brains have algorithms whose outputs are algorithms. Normally, brains invent/improve algorithms that computers use, as is. But ever since computers were invented, a goal has been to enable computers to invent/improve their own algorithms, what is commonly referred to as machine learning.

Human intelligence correlates with how quickly one can learn, with the vastness of one’s knowledge, expertise, wisdom, creativity,…This somewhat vague list of attributes all boil down, as I said, to the proficiency of various algorithms. After decades of frustratingly small advances, algorithms have recently been devised that allow simulated, multi-layered neural networks to learn to become much better than any human at quite a few impressive problem domains: from playing games such as checkers, chess, backgammon, poker and go, to medical diagnoses, to language translation, to facial recognition, to driving cars, to big-data pattern recognition, and so on. These machines are said to employ deep learning (“deep” means many layers of simulated neurons, each learning a different aspect of how to solve an overall problem).

Are these machines intelligent? In their domains of expertise, YES. Do they exhibit general intelligence? NO, because they still lack many key algorithms. In particular, no deep learning system today can give reasons for its choices (e.g., why it makes particular chess moves); nor do we know how to enable a machine to be an expert in multiple domains (e.g., chess and medicine). Billions of dollars are being spent on achieving general-purpose AI. And recent rapid progress leaves less and less room for skepticism*.

What is clear now is this: Like humans do, AIs will acquire their intelligence, not from human programmers, but by learning from experience, aided and unaided by teachers. Programmers may give them their initial learning algorithms, but what they learn, including learning to learn better, will emerge from an AI’s interactions with its environments.

*For those who still believe brains can think in ways that machines never can: Almost a century ago computer science pioneer Alan Turing and mathematician Alonzo Church, conjectured that a certain well-defined set contained all and only the functions that matter and energy can ever compute. (This countably infinite set is infinitesimal compared to the uncountably infinite set of all functions.) Since then, many have tried to refute it and failed. More recently, physicist David Deutsch finally proved the conjecture, assuming only that matter and energy obey the laws of quantum mechanics. Thus both brains and (quantum) computers are confined to thinking using algorithms in that set.


“Leaders are born – not made”

Many people believe this to be the case, that leadership qualities are inherent and will surface given the opportunity in the working world. However, more evidence leans in favour of the view that any normal person can be a leader, depending on time, place and circumstance. A usually quiet, shy individual in one group setting may be aggressive and loud in another group setting.

How and when a person shows leadership qualities depends on many factors, not the least of which are: his/her self-image, knowledge of a particular subject, peer support, his/her level of commitment to group goals or philosophy, age, past experiences with groups, community status, and so forth. It is entirely possible (witness the armed forces) to ‘train’ or teach others in the skills of leadership, but whether they use this added knowledge in a leadership role rests on the person’s willingness to try. There is no better instructor than practice.

Leaders develop styles over time and in the position they are given or inherit. Many of you are aware already what these styles are, but here is a refresher for those who forget or don’t know.

Types of leadership style

  1. the ‘front man’ – a group member who has skills at dealing with outsiders
  2. the ‘idea man’ – regularly suggests alternative routes to take
  3. the ‘inspirational figure’ – attempts to judge group functions morally
  4. the ‘wisdom purveyor’ – cites previous cases  of conflict and solutions
  5. the ‘expediter’ – an efficiency expert, concerned with time and process
  6. the ‘game leader’ – tries different ways to lift spirits with jokes, etc.
  7. the ‘master of technique’ – a systems expert, oriented around agenda

Some leadership functions associated with style

  1. Organization – structures his/her work and that of others
  2. Integration – manages, resolves conflict, creates a positive atmosphere
  3. Internal data management – helps information exchange and feedback
  4. Membership – makes sure he/she (and others) remains a member of the group
  5. Initiation – leader encourages new ideas and practices
  6. Gatekeeping – filters data entering and leaving the group
  7. Production – responsible for task accomplishment
  8. Reward – evaluates members’ behaviour and fosters a positive attitude
  9. Representation – defends the group from external threats; spokesman

There are many models of group development leaders work with, and you are probably familiar with some of these. They grew out of many management training manuals and leadership theories from the 1960s and 1970s, but still apply today. Growth direction is to the right >.

Model A. Inclusion > Control > Affection > Intimacy

Model B. Forming (testing and independence; attempts to identify task) > Storming (development of intragroup conflicts; emotional responses to task demands) > Norming (development of group cohesion, expression of opinions) > Performing (functional role-relatedness, emergence of solutions)

Model C. “Gimme” > “Gripe” > “Grope” > “Grasp”

In effect, what leaders in newly formed and continuous groups do, is: a) set the climate for discussion, b) develop structure for planning together, c) identify needs, d) state the objectives, goals or mission, e) design the methods to achieve these,  f) do it, as a group or team, and g) evaluate what you have done.

Finally, leaders who are experienced and successful, must deal with channels or networks of communication within the group (or organization) which are discernible but also which change over time. Networks have less to do with physical location of members than with manifest or latent opportunities to communicate. From formal meetings that are set up, to at the water-cooler gossip sessions, networks can influence more or less, the quality of decisions made, member satisfaction, and overall group or corporate efficiency.

More next time about conflict resolution caused by communication breakdowns.






“The more things change, the more they remain the same”

“Plus ca change, plus la meme chose.”

I often hear this aphorism spoken in social situations. But does the speaker really know its significance?

It refers to how social conditions change over time but leave an identifiable common thread behind. This legacy can be in the form of traditional mores, problem-solving methods, folkways, artifacts, and so forth.

The saying can also imply that as knowledge expands, ignorance reduces, but is never gone. Ignorance then is not only squelched by what is changed, it is recreated due to the knowledge residuals that replace and reinstate it. This is why technology always precedes cultural change. New observable, technical values have to be placed within the existing social ‘homeostasis’ and juggled about by consumers, standards bureaucrats and detractors for a while, and either rejected outright (like failed patent applications), or slowly incorporated (like the electric razor), or adopted instantly (like cellphones). This process of values synthesis and adoption (‘homeogenesis’) to produce a new social structure takes longer to achieve or be recognized when we’re talking about ‘social’ as distinct from ‘technical’ values. That is because new social values such as promoted through fashion, buying trends, fads, minority group behaviour, social movements, require an experimental period before adoption (or rejection) occurs. Legalizing marijuana in some states and provinces has taken decades to achieve. Similarly with women’s right (like men’s) to go topless in public in Canada, the legalization of abortion clinics, the appointment of women as Supreme Court Justices, new rights for gays and lesbians, the abolishing of capital punishment, laws for driving with an iPhone, and so forth, all required lengthy public or private debate before acceptance.

Social movements, cultural deviance, war – can change existing values in any society, as well as value change introduced normally through new technologies and day-to-day social interaction of individuals at work or at play. So social change can occur from many sources. Nonetheless, the point is that in spite of external symbolizations and material products of actual change, human behaviour at its core does not. Human behaviour collectively remains glued to basic inherent features.

Pinker (2011) in his book The Better Angels of Our Nature: Why Violence Has Declined, supports this view in his assumption “…supported by evidence for the psychic unity of humankind – that people in every society have all the basic human faculties such as language, causal reasoning, intuitive psychology, sexual jealousy, fear, anger, love, and disgust, and that the recent mixing of human populations had revealed no qualitative innate differences among them.” (p. 613) The constancy of behavioural traits across cultures persists in spite of supra-social changes. This also takes into account the influence of endogenous experiences which may influence the genome, thus leading to behavioural changes based on what humans do. “Behavioural genetics confirms [for example] that aggressive tendencies can be inherited” (p. 617) Regardless, Pinker comes out swinging in favour of how the faculty of reason can “interact with the moral sense”, thus leading to a reduction in aggressive tendencies.

The pull-back from collective destruction as a species is optimistically shared by Pinker (and others), and rests on “the escalator of reason”, powered by “literacy, cosmopolitanism, and education.” He has faith that reason will prevail in its work to produce less violence in this world. Social change into a new homeostasis is possible, in spite of our negative traits.

I am not quite so optimistic as Pinker, although I would like to be. As we change the world around us, through physical technology, rational-technical problem-solving, and new human agreements based on empathy and compassion, there is hope for humankind only if these change mechanisms come to predominate the world theater. ‘Lead by example’ hopefully will become the “plus la meme chose”, as the constant legacy we leave behind amidst positive change of the human condition.

September 23, 2015
T. Hill

The pessimist c…

The pessimist complains about the wind.
The optimist expects it to change.
The realist adjusts the sails.
– William A. Ward

Life can make pessimists out of us;

Religion can make optimists out of us;

Science can make realists out of us.

But of course, all three statements are a function of time, place, and circumstance. It is entirely possible that each of these can be interchangeable, such that religion for example, can make pessimists out of us, or science can make us optimistic, or life can make us realists. Ward was summarizing tendencies in populist thinking as a result of living, having faith, or rational thought based on evidence. 

Atheists may be optimistic; a suicidal person may feel that his/her actions may be altruistic; and a priest may favour logical thought over dogma. There are always exceptions to the rule, and although common jingles may make common sense, one can always find people who do not fit the mold, or who are at either end of the bell-curve, if you will. Such is the make up of society. To the extent sayings like the above attempt to orient us in a ‘better’ direction to our behaviour, they have civic utility, e.g., it is better to be a realist than a pessimist. The consequences will be better, for us and the people around us. Or so it goes.

Behavioural generalizations, from psychology or sociology or any social science, serve the broad purpose of providing guidance to social policy. Forcing a ‘modes of reasoning’ course into the core curriculum of a university undergraduate program may have the ultimate net effect of producing a graduate with realistic thinking skills in preparation for the real world. And this is a good thing. As Desiderata so wisely indicates, the world can be “full of trickery”, and one should “go placidly amid the noise and haste”, and “be on good terms with all persons.” 

All norm-setting (behavioural standards) attempts, such as the Desiderata (google it), ostensibly derive from real life experiences. Behaviours that become ‘realistic’ and normative therefore, can be of two kinds: the empirical realism type (this is the case, or exists, independent of what I think about it) and the transcendental realism type (reality is the result of the imposition of our minds upon it or the situation). Both kinds of behavioural realism can co-exist without contradiction.

Ward’s dictum reminds us all of patterns of thought out there in the real world, patterns which he believes should be emulated. Each of us has such patterns, known to us or unconscious, that guide our thoughts and actions on a daily basis. This the stuff of which ‘morality’ is made, with tolerance limits implied. Change agents in society, who seek to alter the status quo, face arguments regularly from the masses about not being “realistic”, even though their platforms may in fact be more realistic than prevailing views of the world. Embracing change has risks; that is why ‘winners’ may be seen as heroes, and ‘losers’ as misfits or fools – irrespective of the integrity and rational strength of their arguments. Social realism can be chameleon-like, capricious or mutable, as it so needs, to get people to see alternative ways of thinking and doing. Adjusting the sails may be difficult, but with evidence and a clear mind and conscience, it can be done and done to everyone’s satisfaction.

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