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You can only blow bigger bubble-gum balloons for so long, until they finally burst in your face.

The International Monetary Fund warns that global debt—recently hitting a whopping $164 trillion—has reached its highest (and most worrying) level since the 2008 financial crisis.

via Global debt reached dangerous highs at $164 trillion — Quartz


Pollution Crisis: Clothes Shedding – Shedding Clothes. Not so preposterous?

Many articles have been published recently (BBC, CBC, Guardian, etc.) about the damage being done to our environment by wearing and washing clothes. Synthetics are the worst for shedding fibres that don’t degrade naturally, wools and cottons being better because they are bio-degradable. But all clothes shed fibres that get into the water, soil, air and food-chains, fibres which are ingested by fish, animals, birds and humans. Clothes also contain dyes and repellents that can be harmful over time as clothes shed.

Research in several British universities has shown that the parts-per-million of noxious fibres found in oceans, lakes and rivers around the world, has dramatically increased in the past 30 years, to the point now that clothing manufacturers are being targeted to take action. Some recent developments in new washing-machine ‘filters’ show promise. Reducing or eliminating polyester, terylene, viscose, acrylic and nylon content, and favouring cotton, wool and silk in manufacturing processes, would go a long way in reducing environmental pollution.

Another way, more controversial perhaps, is to also wear less clothing or no clothes at all in your homes, at work, or on the beach. And for longer periods befor buying more clothes. This would no doubt require drastic social change in terms of local, regional or national by-laws. But, a primordial leap ‘backwards’ in time, may do the trick in reducing contaminants. Nudists/Naturists around the world have been saving us from pollutants for 100 years. And there are about 33 million of them around the world, and growing in numbers. So that’s a good thing.Freedom at Last!

Women and Climate Change in the context of sexual inequality. A very good summary here, with obvious lessons embedded in it.

Identified by changes in temperature, precipitation, winds, and other indicators, climate change is often referred to as a long-term shift in weather conditions (Government of Canada, 2015). According to the Government of Canada (2015), climate change can involve both changes in average conditions, as well as, changes in variability such as extreme events. While existing […]


And we all stand with you!! Great example by Paul McCartney.

Paul McCartney stood in solidarity with the thousands protesting gun violence across the nation Saturday at the March for Our Lives in New York. “One of my best friends was killed by gun violence right around here, so it’s important to me not just to march today but to take action tomorrow and to have these people…

via Paul McCartney Joins March for Our Lives in New York City — Variety

How can anyone help but show empathy? This magnificent and necessary event should remind us all about the power of community, and the great need at this time for truly responsible politics.

Chris Matthews was brought to tears during MSNBC’s coverage of the March for Our Lives rally on Saturday in Washington D.C. After one of the Parkland survivors who was shot in last month’s deadly shooting threw up on stage, before returning with a smile to sing “Happy Birthday” to one of the victims, Matthews was…

via Chris Matthews Brought to Tears After Parkland Survivor’s Emotional Speech at March for Our Lives (Watch) — Variety

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.


Putting us on the map, at Lakehead University!

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