ClimateChangeFork

(Source: Unifying Quantum and Relativistic Theories)

The last few blogs have alternated between descriptions of AI—with its environmental impacts—and the upcoming elections. On a more abstract level, both issues represent key features of our reality: the elections are part of the short term and AI is longer term. My intention in this blog was to focus on the elections, with an emphasis on the frequent polling that all of us are bombarded with. But, as often happens, reality has its own priorities. This week was the announcement of the Nobel Prizes. The second and third Prizes, announced in the beginning of the week (October 8^th and 9^th) were for Physics and Chemistry. Below are the two citations:

Physics:

They used physics to find patterns in information

This year’s laureates used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning. John Hopfield created a structure that can store and reconstruct information. Geoffrey Hinton invented a method that can independently discover properties in data and which has become important for the large artificial neural networks now in use.

Chemistry:

They cracked the code for proteins’ amazing structures

The Nobel Prize in Chemistry 2024 is about proteins, life’s ingenious chemical tools. David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. Demis Hassabis and John Jumper have developed an AI model to solve a 50-year-old problem: predicting proteins’ complex structures. These discoveries hold enormous potential.

AI is mentioned in both Prizes. The surprise, to me and others, was that AI is not currently directly associated with Physics and certainly not with Chemistry, but the awards were given to scientists with backgrounds in the disciplines that allowed them to make key contributions to machine learning (Physics) and the use of AI in protein chemistry. The technique itself is associated with Computer Science. However, there is no Nobel in Computer Science (according to Wikipedia, “The top computer science award is the ACM Turing Award, generally regarded as the Nobel Prize equivalent for Computer Science.”)

The criteria to receive the Nobel Prizes in Physics and Chemistry are given below:

Who can receive the Prize?

According to Alfred Nobel’s will, the Nobel Prize in Physics is awarded to the person who made the most important discovery or invention in the field of physics and the Nobel Prize in Chemistry to the person who made the most important chemical discovery or improvement.

AI (through Google) tried to clarify the meaning of “most important discovery in the field” in the following way:

According to Alfred Nobel’s will, the key criteria for awarding a Nobel Prize is to recognize the person who has made “the most important discovery” within their field, which must have “conferred the greatest benefit to humankind,” meaning the discovery should be of significant impact and have a positive influence on society at large.

“Conferred the greatest benefit to humankind” might refer to having contributed to changing reality for the betterment of humankind. In an earlier blog titled “The Physics of Reality” (February 2, 2021), I used the Encyclopedia Britannica to define reality in the following way:

Physics, science that deals with the structure of matter and the interactions between the fundamental constituents of the observable universe. In the broadest sense, physics (from the Greek physikos) is concerned with all aspects of nature on both the macroscopic and submicroscopic levels. Its scope of study encompasses not only the behaviour of objects under the action of given forces but also the nature and origin of gravitational, electromagnetic, and nuclear force fields. Its ultimate objective is the formulation of a few comprehensive principles that bring together and explain all such disparate phenomena.

I was not the only skeptic asking whether, in its present form, AI qualifies ():

You might think that the Nobel Prize for Physics would go to a physicist. Not this, year though. As usual, the Prize was shared earlier this week, but both of the winners were computer scientists. It’s as if the Olympic gold for the 100-metre dash had gone to a cyclist.

It should be said that the two laureates, Geoffrey Hinton and John Hopfield, are highly distinguished in their field. However, that field is artificial intelligence (AI), which is not usually regarded as a branch of physics.

So quite a slap in the face for the physicists. But, then, the very next day, came a second slap —  this time for the chemists. The Nobel Prize for Chemistry was another shock win for computer science. One of the three winners, David Baker, has a background in biochemistry, but the other two — John Michael Jumper and Demis Hassabis — are leading AI experts.

The skepticism focused on the present concentration of the technology in the for-profit industry. Below are a few highlights from the NYT on the topic:

Google, thanks to the tens of billions of dollars it makes every year from its online search business, has long pursued giant research projects that could one day change the world.

On Wednesday, the Nobel Prize committee conferred considerable prestige to Google’s pursuit of big ideas. Demis Hassabis, the chief executive of Google’s primary artificial intelligence lab, and John Jumper, one of the lab’s scientists, were among a trio of researchers who received the Nobel Prize in Chemistry for their efforts to better understand the human body and fight disease through A.I.

The two Google scientists won their Nobels a day after Geoffrey Hinton, a former Google vice president and researcher, was one of two winners of the Nobel Prize in Physics for his pioneering work on artificial intelligence.

The Nobel wins were a demonstration of the growing role artificial intelligence is playing in areas far beyond the traditional world of the high-tech industry, and were a reminder of Silicon Valley’s influence in nearly every corner of science and the economy.

But the triumphant moment for Google was tempered by concerns that the commercial success that has allowed the company to pursue these long-term projects is under threat by antitrust regulators. The Nobel awards were also a reminder of worries that the tech industry isn’t paying enough attention to the implications of its open-throttled pursuit of building more powerful A.I. systems.

Dr. Hinton left Google, using his retirement as an opportunity to speak freely about his worry that the race toward A.I. could one day be catastrophic. He said on Tuesday that he hoped “having the Nobel Prize could mean that people will take me more seriously.”

Leading researchers such as Dr. Hassabis often describe artificial intelligence as a way to cure disease, battle climate change and solve other scientific mysteries that have long bedeviled the world’s researchers. The work that won a Nobel was a significant step in that direction.

Out-of-discipline Nobel Prizes are somewhat rare but they do happen. The recent one that comes to mind is the 2002 Prize in Economics, which went to Daniel Kahneman for his contributions to Behavioral Economics. I wrote three earlier blogs on the topics (November 21 – December 5, 2017) with “irrationality” as the common word in the three titles. We will continue to follow AI progress to see if it fits.

 In this blog, I will return to the issue of the environmental cost of AI (September 3^rd and 10^th blogs) that I wrote about in response to Sonya Landau’s question. The technology is changing fast and is being compared to some of the foundational documents of the United States. Below is an attempt to compare it to the Federalist Papers:

In the late 1780s, shortly after the Industrial Revolution had begun, Alexander Hamilton, James Madison and John Jay wrote a series of 85 spirited essays, collectively known as the Federalist Papers. They argued for ratification of the Constitution and an American system of checks and balances to keep power-hungry “factions” in check.

A new project, orchestrated by Stanford University and published on Tuesday, is inspired by the Federalist Papers and contends that today is a broadly similar historical moment of economic and political upheaval that calls for a rethinking of society’s institutional arrangements.

In an introduction to its collection of 12 essays, called the Digitalist Papers, the editors overseeing the project, including Erik Brynjolfsson, director of the Stanford Digital Economy Lab, and Condoleezza Rice, secretary of state in the George W. Bush administration and director of the Hoover Institution, identify their overarching concern.

“A powerful new technology, artificial intelligence,” they write, “explodes onto the scene and threatens to transform, for better or worse, all legacy social institutions.”

Of course, anything this big and important needs guidelines. NIST (National Institute of Standards and Technology), the US federal agency responsible for establishing standards, produced some:

Under the October 30, 2023, Presidential Executive Order, NIST developed a plan for global engagement on promoting and developing AI standards. The goal is to drive the development and implementation of AI-related consensus standards, cooperation and coordination, and information sharing.  Reflecting public and private sector input, on April 29, 2024, NIST released a draft plan. On July 26, 2024, after considering public comments on the draft, NIST released A Plan for Global Engagement on AI Standards (NIST AI 100-5). More information is available here.

The World Economic Forum has the most complete description that I could find of the excessive energy needs of AI technology in its present state. It also shows proposals for how to address these needs in the near, intermediate, and long-term future:

AI and energy demand

Remarkably, the computational power required for sustaining AI’s rise is doubling roughly every 100 days. To achieve a tenfold improvement in AI model efficiency, the computational power demand could surge by up to 10,000 times. The energy required to run AI tasks is already accelerating with an annual growth rate between 26% and 36%. This means by 2028, AI could be using more power than the entire country of Iceland used in 2021.

The AI lifecycle impacts the environment in two key stages: the training phase and the inference phase. In the training phase, models learn and develop by digesting vast amounts of data. Once trained, they step into the inference phase, where they’re applied to solve real-world problems. At present, the environmental footprint is split, with training responsible for about 20% and inference taking up the lion’s share at 80%. As AI models gain traction across diverse sectors, the need for inference and its environmental footprint will escalate.

To align the rapid progress of AI with the imperative of environmental sustainability, a meticulously planned strategy is essential. This encompasses immediate and near-term actions while also laying the groundwork for long-term sustainability.

The long-term: AI and quantum computing

In the long term, fostering synergy between AI and burgeoning quantum technologies is a vital strategy for steering AI towards sustainable development. In contrast to traditional computing, where energy consumption escalates with increased computational demand, quantum computing exhibits a linear relationship between computational power and energy usage. Further, quantum technology holds the potential of transforming AI by making models more compact, enhancing their learning efficiency and improving their overall functionality — all without the substantial energy footprint that has become a concerning norm in the industry.

Realizing this potential necessitates a collective endeavor involving government support, industry investment, academic research and public engagement. By amalgamating these elements, it is possible to envisage and establish a future where advancement in AI proceeds in harmony with the preservation of the planet’s health.

As we stand at the intersection of technological innovation and environmental responsibility, the path forward is clear. It calls for a collective endeavor to embrace and drive the integration of sustainability into the heart of AI development. The future of our planet hinges on this pivotal alignment. We must act decisively and collaboratively.

Presently, the enormous power needs of AI, combined with the simultaneous need for an energy transition away from fossil fuels, is forcing companies to look to nuclear power. The most eye-catching move has been Microsoft’s plan to resurrect the Three Mile Island plant:

In a striking sign of renewed interest in nuclear power, Constellation Energy said on Friday that it plans to reopen the shuttered Three Mile Island nuclear plant in Pennsylvania, the site of the worst reactor accident in United States history.

Three Mile Island became shorthand for the risks posed by nuclear energy after one of the plant’s two reactors partly melted down in 1979. The other reactor kept operating safely for decades until finally closing, for economic reasons, five years ago.

Now a revival is at hand. Microsoft, which needs tremendous amounts of electricity for its growing fleet of data centers, has agreed to buy as much power as it can from the plant for 20 years. Constellation plans to spend $1.6 billion to refurbish the reactor that recently closed and restart it by 2028, pending regulatory approval.

To those of you too young to know what this turn signifies, the Wikipedia entry for Three Mile Island might help. I was never an enthusiast for making nuclear energy a strong component of the energy transition (see the November 11^th and 18^th posts from 2014 and the strong responses that these blogs provoked). My main reason was that global reliance on nuclear power would open the door to a much wider spread of nuclear power for military use. While they’re still unresolved, other objections—such as what to do with the radioactive waste produced—could be remediated with further research; the proximity to military applications could not. By all accounts, the primary objective of the energy transition is to convert electricity production to sustainable sources. So, even without resorting to nuclear energy, the amplified need for electricity to run AI is not an insurmountable problem.

However, the last paragraph of the World Economic Forum piece that discussed the long-term prospects of AI is the real key to the environmental impact. The technology for this scenario is quantum computing, which would generate the needed power to train and run the AI.

I have hardly talked about this technology before (one exception is the February 6, 2018 blog) but I will expand on it in future blogs. Those who want to know more right now can look at Amazon’s explanation. One short paragraph exposes us to the key concept of the qubits shown in Figure 1. The article defines them in the following way:

Quantum bits, or qubits, are represented by quantum particles. The manipulation of qubits by control devices is at the core of a quantum computer’s processing power. Qubits in quantum computers are analogous to bits in classical computers. At its core, a classical machine’s processor does all its work by manipulating bits. Similarly, the quantum processor does all its work by processing qubits.

Figure 1 – Progress of quantum computing over the last 20 years (Source: Aviva)

The graph in Figure 1 shows the universal preoccupation and the progress that has been made in this technology over the last 20 years. Estimates are that quantum computing will enter commercial applications toward the end of the decade but estimates can be wrong!

Based on today’s understanding of the technology, quantum computing will revolutionize the energy efficiency of computing:

Today, quantum computers’ electricity usage is orders of magnitude much less than any supercomputer, and this is counting all the different quantum architectures available. Let’s take for example superconducting qubits, the most expensive architecture, and these computers only consume about 25 kW. That amounts to 600 kWh daily, a thousand times less than the Frontier supercomputer. Much less is the consumption of neutral atoms quantum devices, such as PASQAL’s, which amount up to 7 kW.

Understanding this technology requires serious prerequisites. In future blogs, I will try to fill these knowledge voids.

Recently, Christine Lagarde, President of the European Central Bank, compared the present global economy to that of the 1920s. Below I cite some of the highlights:

The global economy is facing rifts comparable to the pressures that resulted in “economic nationalism” and a collapse in global trade in the 1920s and ultimately the Great Depression, the president of the European Central Bank has warned. “We have faced the worst pandemic since the 1920s, the worst conflict in Europe since the 1940s and the worst energy shock since the 1970s,” said Christine Lagarde on Friday, adding that these disruptions combined with factors such as supply chain problems had permanently changed global economic activity. In a speech at the IMF in Washington two days after the Federal Reserve cut interest rates by 50 basis points, pushing US equity markets to record highs, the ECB president argued that several parallels “between the “two twenties — the 1920s and 2020s — stand out”, pointing to “setbacks in global trade integration” and technological advances in both eras. While monetary policy in the 1920s made matters worse as adherence to the gold standard pushed leading economies into deflation and banking crises, “we are in a better position today to address these structural changes than our predecessors were”, stressed Lagarde. A century ago, she said, central bankers learnt the hard way that pegging the currency to gold and fixed exchange rates was “not robust in times of profound structural change” as it pushed the world into deflation, fuelling “economic malaise” and contributing to a “cycle of economic nationalism”. Today, central bankers’ tools for preserving price stability “have proved effective”, she said. Lagarde pointed to the quick fall in inflation once central banks started to raise rates in 2022. Consumer prices had shot up following a surge in post-pandemic demand, global supply chain disruptions and big rises in energy prices after Russia’s full-scale invasion of Ukraine. She described the episode as an “extreme stress test” for monetary policy.

President Lagarde’s focus, on the parallels of the 1920s and the 2020s, was mainly on Europe, although the Great Depression started in the US. The impact of the German hyperinflation that started in the 1920s had no less global impact. The “Roaring 1920s” gave rise to Nazi Germany and WWII, during which I, and many millions of others, lost most of our families. The impact of the present global unrest has yet to be seen. The focus of President Lagarde was the economy, but as she mentioned, political unrest has a major impact on the economy and vice-versa.

A few days after her speech, The United Nations assembled for its annual meeting in NYC; many world leaders were in attendance. Some meetings took place trying to alleviate deadly conflicts but they had very little impact. The United Nations was first assembled on October 24, 1945, in San Francisco, at the conclusion of WWII, with the objective preventing a re-occurrence of the conditions of the 1920s and the disasters they led to. It now finds itself in a crisis:

The United Nations itself has had a turbulent year. A record number of its staff, 220 in total, have been killed in the war in Gaza. Its humanitarian resources, a crucial backbone of the global relief effort, are overstretched and underfunded as needs multiply rapidly because of wars, climate change and natural disasters. At the same time, its leadership struggles to play a meaningful role in conflict mediation.

The wars that are taking place now have the potential to expand into global nuclear conflicts. President Putin, and some other Russian leaders, include this existential threat at every opportunity. Up to now, as far as we know, we are the only planet in the vast universe on which life exists. Humanity’s existence is the exception, not the rule, meaning that a nuclear WWIII could decimate the only spark of life in the universe!

On the political side, in many democracies, the meaning of right and left in terms of the relative role of the state in our political life has lost its meaning. The substitute has become a version of the “replacement theory” (see my previous blog “Immigration and Politics,” March 5, 2024). The “replacements” vary: Black for white, Muslims for non-believers, immigrants for natives, and Jews for non-Jews. In the 1920s it was Jews for Aryans. Today, the people who are hated, feared, and blamed include a broader variety of non-natives, including many without advanced economic qualifications. Whatever their characteristics, immigrants(and people of color)have been the scapegoats for every societal ill—both in the 1920s and today. In many democracies, they became tickets to election victories and power. After the 1920s, Hitler and the Nazi party converted the Weimar republic into a deadly autocracy. Today, Ex-President Trump is threatening to follow suit. I will end this blog with an outline of the process that took place in the pre-WWII period.

The 1920s started the process that put Hitler in power. One of his most important tools was his call to “replace” the Jews of Eastern Europe with the “pure-race” of Aryan Germans:

Hitler did not invent the hatred of Jews. He capitalised on antisemitic ideas that had been around for a long time.

Hitler was born in Austria in 1889. He developed his political ideas in Vienna, a city with a large Jewish community, where he lived from 1907 to 1913. In those days, Vienna had a mayor who was very anti-Jewish, and hatred of Jews was very common in the city.

During the First World War (1914-1918), Hitler was a soldier in the German army. At the end of the war he, and many other German soldiers like him, could not get over the defeat of the German Empire. The German army command spread the myth that the army had not lost the war on the battlefield, but because they had been betrayed. By a ‘stab in the back’, as it was called at the time. Hitler bought into the myth: Jews and communists had betrayed the country and brought a left-wing government to power that had wanted to throw in the towel.

The United States entered WWI against Germany on April 6, 1917. It was a decisive turning point:

The entry of the United States was the turning point of the war, because it made the eventual defeat of Germany possible. It had been foreseen in 1916 that if the United States went to war, the Allies’ military effort against Germany would be upheld by U.S. supplies and by enormous extensions of credit. These expectations were amply and decisively fulfilled.

After the war and the resulting Treaty of Versailles was signed on June 28, 1919, The German people needed a scapegoat; Hitler and the new Nazi party offered the Jews.

The US serves as a model of Nazi Germany on several different levels. One important level was the US territorial expansion since independence, as shown in Figure 1. The vast territory was occupied by immigrating European at the expense of a native population. For Germany to do the same in Europe they needed to vacate important sections of Europe and populate them with “pure” Aryans. Jews were the best candidates to be replaced.

The Holocaust Encyclopedia says this of the

in Europe at the beginning of the 1930s:

Jews have lived in Europe for more than two thousand years. The American Jewish Yearbook placed the total Jewish population of Europe at about 9.5 million in 1933. This number represented more than 60 percent of the world’s Jewish population, which was estimated at 15.3 million. Most European Jews resided in eastern Europe, with about 5 1/2 million Jews living in Poland and the Soviet Union. Before the Nazi takeover of power in 1933, Europe had a dynamic and highly developed Jewish culture. In little more than a decade, most of Europe would be conquered, occupied, or annexed by Nazi Germany and most European Jews—two out of every three—would be dead.

The replacement plan got the name of Lebenstraum (“living space”):

Following Adolf Hitler’s rise to power, Lebensraum became an ideological principle of Nazism and provided justification for the German territorial expansion into Central and Eastern Europe.^[5] The Nazi policy Generalplan Ost (lit. ’Master Plan for the East’) was based on its tenets. It stipulated that Germany required a Lebensraum necessary for its survival and that most of the populations of Central and Eastern Europe would have to be removed permanently (either through mass deportation to Siberia, extermination, or enslavement), including Polish, Ukrainian, Russian, Czech, and other Slavic nations considered non-Aryan. The Nazi government aimed at repopulating these lands with Germanic colonists in the name of Lebensraum during and following World War II.^[6][7][8][9] Entire populations were ravaged by starvation; any agricultural surplus was used to feed Germany.^[6] The Jewish population was to be exterminated outright.

Hitler’s strategic program for Greater Germany was based on the belief in the power of Lebensraum, especially when pursued by a racially superior society.^[7] People deemed to be part of non-Aryan races, within the territory of Lebensraum expansion, were subjected to expulsion or destruction.^[7] The eugenics of Lebensraum assumed it to be the right of the German Aryan master race (Herrenvolk) to remove the indigenous people in the name of their own living space. They took inspiration for this concept from outside Germany.^[7] Hitler and Nazi officials took a particular interest in manifest destiny, and attempted to replicate it in occupied Europe.^[9] Nazi Germany also supported other Axis Powers‘ expansionist ideologies such as Fascist Italy‘s spazio vitale and Imperial Japan‘s hakkō ichiu.^[10]

The result was the genocide of 6 million Jews.

“Replacement theory” has never made anyone or any country “Great.” We don’t need to repeat it in the 2020s.

(Source: ResearchGate)

 The IPAT identity is a central feature of every sustainability discussion. Just put the acronym into the search box to see this blog’s coverage of the topic. Three previous blogs stand out. The post from November 26, 2012, “Tackling Environmental Justice: a Global Perspective,” provides the background description:

There is a useful identity that correlates the environmental impacts (greenhouse gases, in Governor Romney’s statement) with the other indicators. The equation is known as the IPAT equation (or I=PAT), which stands for Impact Population Affluence Technology. The equation was proposed independently by two research teams; one consists of Paul R. Ehrlich and John Holdren (now President Obama’s Science Adviser), while the other is led by Barry Commoner (P.R. Ehrlich and J.P. Holdren; Bulletin of Atmospheric Science 28:16 (1972). B. Commoner; Bulletin of Atmospheric Science 28:42 (1972).)

The identity takes the following form:

1. Impact = Population x Affluence x Technology

Almost all of the future scenarios for climate change make separate estimates of the indicators in this equation. The difference factor of 15 in GDP/Person (measure of affluence), between the average Chinese and average American makes it clear that the Chinese and the rest of the developing world will do everything they can to try to “even the score” with the developed world. The global challenge is how to do this while at the same time minimizing the environmental impact.

Most of the previous examples in this blog have focused on the emissions of carbon dioxide and the resulting anthropogenic climate change. One good example can be seen in the May 31, 2022 blog, “Electric Utilities Through the Lens of the IPAT Identity,” which uses the IPAT form as:

2. CO₂ = Population x (GDP/Capita) x (energy/GDP) x (Fossil/Energy) x (CO₂/Fossil)

Equations 1 and 2 consider CO2 as the impact and GDP/Capita as the affluence, while the next three terms in Equation 2 represent the technology. It was shown before that if the proper units are used in both equations, they become an identity: something that is always true, regardless of the values plugged in.

The May 31, 2022 blog analyzed the impact of electric utilities on

||the identity. Meanwhile, the June 29, 2016 blog analyzed how the ongoing rise in CO₂ emissions and decline in global fertility impact the identity.

The reference and the figure at the top of this blog expand the relationship to almost everything. The price that we are paying for this expansion is the loss of the identity nature of the equation and the conversion of the identity from an objective, irrefutable truth to a subjective opinion.

Recent press reports have started to pay more attention to the declining global fertility rate, which means that we are approaching a global population decline. I have often heard and read the opinion that since population is an indicator on the right side of the identity, population decline will be followed by an emissions decline, resulting in a decrease in the impact of climate change. The opinion generally was that such a change is “good” for the world. Such an opinion does not take into account that the decline in population required for such changes in the global demographic are not taken into account in the identity. This is just one example of the prevailing opinion that the IPAT identity is not without its controversies. A summary of these is given by AI (through Google):

Critisism of IPAT and analysis of terms in terms of good or bad.

The IPAT equation (environmental impact = population * affluence * technology) has been criticized for several reasons, including:

• Simplicity

The equation is too simplistic to address complex problems.

• Interdependencies

The equation assumes that the three factors operate independently, but they may interact with each other.

• Averaging

Averaging the operands of the equation can destroy critical information and lead to information loss.

• Ecological fallacy

Averaging affluence and technology assumes that all members of the population operate identically.

• Technology

Technology cannot be properly expressed in a unit, and the value of the ratio depends on other factors.

• Differences between rich and poor

The equation doesn’t account for the vast differences between rich and poor.

• Individualist and consumerist approach

The equation predisposes the formula to an individualist and consumerist approach to solving environmental impact.

The main advantage of the wide use of the identity is that if it is run as an identity and not as an “opinion,” it can be quantified and can serve as a starting point for global trends.

The purpose of this blog is to extend the IPAT to the new global trends that were discussed in previous blogs this year (April 16^th, August 13^th and August 20^th) that started in my lifetime and will continue to dominate the reality of our children and grandchildren. These trends were explored in previous blogs in terms of their impacts on the 10 most populated countries that together constitute more than 50% of the global population. Of these, the only developed country is the USA. Table 1 represents all these trends as generational (defined approximately as 25 years) changes in post WWII trends. The data for the global trends were taken directly from Google searches.

Table 1 –  Generational changes in global, post-WWII trends

As I mentioned before, the impact of the carbon emissions and the decline in fertility were analyzed through IPAT in previous blogs. Aside from population and affluence, the other three trends from this acronym will be quantified in future blogs.

From my perspective, the top photograph encapsulates the way we vote. The picture is not AI generated or even edited; I took it with my iPhone. It shows a mirror in my apartment that faces my terrace, which looks out onto my city. It is a mixture of the “me,” “us,” and “them” that constitute the general trajectory of every political election. The collective weight that we put on each component determines the outcome. In the US we are now facing the presidential election, which has already started in some states and will be concluded on Election Day (November 5^th).

A month and a half ago (July 30^th), I wrote about the binary aspects of this election and about the global consequences that are derived from the extensive power that the American constitution grants the president, along with the central role that the US holds in the global well-being. It is true that not all countries and people are happy with the central role that the US plays in global affairs, and some are experimenting with steps that they can take to reduce this imbalance in power. However, very few are denying the magnitude of the US president’s role.

The July 30^th  blog was partially focused on the recent major change that took place in the coming US presidential election: the withdrawal of President Biden from the candidacy for a second term and his replacement by VP Kamala Harris. As was mentioned there, the replacement completely changed the dynamics of the election. A week ago, the first, and most likely last debate (ex-president Trump just announced that he will not be part of a second debate) between the two candidates, took place. The last statistics that I saw reported a TV audience of 67 million people that watched either all or part of the debate. I was one of them. I didn’t find any direct references about the number of people outside the US that watched, but there was plenty of coverage from foreign responses. The NYT got a few insiders’ opinions about various aspects of the debate. I am cherry-picking one of them below:

Binyamin Appelbaum Trump kept describing the United States as a failing nation. His candidacy remains the best evidence for that claim. The Republican candidate for president of the United States baldly asserted on national television that doctors are executing babies after birth. He said that immigrants are stealing and eating Americans’ pet dogs and cats. He defended the rioters who attacked the Capitol on Jan. 6. Even if he loses the election, this debate was a reminder — though, frankly, one we didn’t need — that our democracy has big problems.

The first sentence of Appelbaum’s response was a derivative of President Reagan’s famous line from his 1980 presidential debate: “Are you better off today than you were four years ago?” VP Harris went around the question, trying to circumvent any conflicts with President Biden, instead describing her middle-class background. Appelbaum, on the other hand, is citing Trump’s statement that the US is currently a failing nation—as opposed to how it was when he was president (he was elected in November 2016 and failed to be reelected for a second term). This contradicts a recent report, which ranked the US as the third best country in the world, following Switzerland and Japan based on a variety of attributes, divided into the categories of Adventure, Agility, Cultural Influence, Entrepreneurship, Heritage, Movers, Open for Business, Power, Quality of Life, and Social Purpose.(Best Countries in the World | U.S. News). VP Harris returned to this issue of a supposedly failing nation later in the debate by mentioning that he was fired by 81 million people (not reelected).

The wars between Russia and Ukraine and between Hamas and Israel were mentioned, with Trump making the claim that if he had been the president, these wars would have never started because he would have made deals with both sides. This is obviously a claim that cannot be tested.

Real world present events such as hurricane Francine were threatening the Gulf Coast at the time of the debate but the candidates were never asked how they would treat such extreme weather events, which are projected to amplify over the next four years. Climate change was an explicit question that they were asked about, but both avoided it completely and shifted their answers to speak instead about the car industry. One global threat that was mentioned a few times by President Trump in connection with the Russia-Ukraine war, was the threat of WWIII. I wrote about it in two previous blogs (December 29, 2020 and March 22, 2022). But the situation has become dearer now because Ukraine has started to counter the non-stop Russian bombardments of its country by bombing Russian territories across its border. One recent target was the city of Belgorod:

The Russian authorities said on Saturday that a Ukrainian attack on the city of Belgorod had killed at least 22 people and injured nearly 110 others, in what would be the deadliest single assault against a Russian city since the start of the war nearly two years ago. Russia’s Defense Ministry said in a statement that Ukraine had hit Belgorod — a regional center of around 330,000 residents about 25 miles north of the Ukrainian border — with two missiles and several rockets, adding that the strike was “indiscriminate.”

I wrote about the city of Belgorod in my 2020 and 2022 blogs that I previously mentioned. To most Americans, it is a city like any other; not to Russians. As was mentioned in the 2020 blog, this city was formerly named Stalingrad. The history of WWII cannot be told without mentioning this name, which marked the start of a reversal of the Nazi Germany military successes—at the price of hundreds of thousands of Russian and German casualties. Bombing this city means targeting a source of deep-rooted Russian pride. Meanwhile, Russia is the country most heavily equipped with nuclear weapons, as I described in the 2022 blog. The nuclear risk that this entails is described on Wikipedia.

In the debate, President Trump didn’t try to address how he would handle this explosive situation. He just mentioned the threat to indicate that it wouldn’t have existed if he had won the 2020 election.

Going back to the opening photograph, the personal part of the election will be different for each of us. For those of us who live from paycheck to paycheck or are out of work, the main criteria for choosing a candidate might be the help that they promise a given group or a specific state. For those of us who are better off, the projected taxation rates will be the key issue. However, the view of the NYC Harbor in the photograph should remind us that threats such as climate change and nuclear war are threats to all of us. We will all be better off if we elect a president not only based on the promises that they make but on our perception of how they will navigate the ship of the Union through the unpredictable storming seas—both figurative and literal—that will threaten all of us.

 (Source: Designhubz.com)

Last week, I promised to use this week’s blog to show the results of an initial exploration of the role that AI presently plays in sustainability. Sonya Landau raised the question with me, so I used Copilot (Microsoft AI) to look into three important, sustainability-related themes: global human survival and sustainability, energy cost, and sustainable reality. As I said last week:

Meanwhile, the Sustainability program/course is excellent preparation for the students’ post-undergraduate lives. By the time present students graduate, it is probable that they will be able to generate content for a sustainability course through AI. The challenges will be to motivate the students to go beyond this and to give them the tools to critique the generated content.

Below are the results of my three searches:

“Global human survival and sustainability”

Copilot

Sent by Copilot:

Ah, the grand challenge of our time: ensuring the survival of both our species and the beautiful blue marble we call home. 🌍 Let’s dive into this, shall we?

1. Planetary Boundaries: A Wake-Up CallScientists have been busy with their cosmic stethoscopes, and they’ve diagnosed Earth with a few health issues. You see, our planet has these nine primary “planetary boundaries” that act as vital signs. Think of them as Earth’s version of cholesterol levels or blood pressure. And guess what? We’ve been a bit reckless.

• Climate Change: Yep, we’ve blown past this one. Our carbon emissions are like a fever that won’t break.
• Biosphere Integrity: That’s a fancy way of saying biodiversity. And guess what? We’re not doing so hot. The Amazon rainforest side-eyeing us right now.
• Freshwater Availability: Imagine a water cooler at a global office. Well, it’s running dry. We’re guzzling more than we should.
• Land Use: We’ve been redecorating the planet, but not in a good way. Urban sprawl, deforestation, and highways—oh my!
• Nutrient Pollution: Picture phosphorus and nitrogen as the party crashers in Earth’s aquatic ecosystems. They’re causing algal blooms and ocean dead zones. Not cool, phosphorus. Not cool.
• Novel Entities: These are like the uninvited guests at the planetary party—microplastics, radioactive waste, and other human-made pollutants. They’re littering the place.

But wait, there’s hope! Ocean acidification, air pollution, and ozone depletion are still within the safe zone. So, it’s not all doom and gloom. These boundaries aren’t “tipping points” of no return; they’re more like flashing warning signs. Like when your blood pressure spikes—you know you need to cut back on the fries and take a walk. Earth’s giving us side-eye too, saying, “Fix this, humans!”

2. Sustainable Population: How Many Is Too Many?Now, let’s talk numbers. How many of us can comfortably share this cosmic apartment? Well, estimates vary, but recent science suggests we might be overbooked. Like a crowded elevator during rush hour.

• Some say the sweet spot is between 2 and 4 billion people. Yep, that’s quite a range. It’s like saying, “Party’s full; no more RSVPs!”
• Paul R. Ehrlich, a population guru, thinks the optimum population is even lower—around 1.5 to 2 billion. That’s like having a cozy dinner party with close friends.

So, what’s the prescription? Well, we need to be mindful roommates. Sustainable living, conservation, and responsible resource use are our best bets. Imagine Earth as a co-op—everyone pitches in to keep the place tidy. No leaving dirty dishes in the sink (looking at you, plastic waste).

3. Human Sustainability: It’s PersonalBut here’s the twist: Our survival isn’t just about Earth’s health. It’s also about our well-being. When we’re stressed, burned out, and running on fumes, we’re not great roommates. So, self-care matters too. Think of it as maintaining your own little corner of the planet.

Remember, we’re all in this together. Earth’s our shared home, and we’ve got to be better tenants. So, let’s turn off the lights when we leave a room (literally and metaphorically), recycle, hug a tree (figuratively, unless you’re a koala), and maybe—just maybe—consider carpooling to the cosmic block party. 🌱🌎

And hey, if you ever need a pep talk about planetary boundaries or sustainable living, I’m here. Just like a cosmic life coach, minus the crystals and incense. 

The main references, based on which this AI gave this response, are shown below:

Smithsonian: Humans Have Exceeded Six of the Nine Boundaries Keeping Earth Habitable

Wikipedia: Sustainable population

Thrive Global: Introducing Our Human Sustainability Hub

Springer Link: Creation, humanity, science and sustainability for human survival

My own descriptions have been a bit more modest. Table 1 in the August 20^th blog shows the global changes in five key areas that took place over my lifetime in the 10 most populated countries. These changes directly impact our global environment, with some of them having a direct impact on global survivability. This theme repeats in number of blogs.

“Energy cost of AI”:

Ah, the energy appetite of artificial intelligence—quite the hot topic these days! 🌍🔌

So, let’s dive into it. AI, especially the more powerful and resource-hungry models, can indeed be quite the energy hog. Imagine a model generating an image—just one image—using something like the Stable Diffusion XL. That seemingly innocent image creation process can gobble up as much energy as charging your trusty smartphone. 📱💡

But wait, there’s more! If you were to churn out a thousand of those images, you’d be emitting as much carbon dioxide as driving just over four miles in a gas-powered car. 🚗💨 Now, I don’t know about you, but that’s a lot of virtual road trips for some pixels!

However, not all AI tasks are equally voracious when it comes to electricity. Creating images? Energy-intensive. Generating text? Not so much. It’s like comparing a marathon runner to someone casually strolling through a park. 🏃‍♂️🌳

And here’s the twist: using smaller, task-specific AI models can be dozens of times more efficient than summoning the colossal, all-purpose ones. It’s like opting for a compact car instead of a gas-guzzling SUV. 🚗🌿

Now, let’s zoom out a bit. The big picture: electricity consumption from data centers, AI, and cryptocurrency combined could double by 2026, according to projections. Yep, that’s right—AI, data centers, and crypto are collectively adding “at least one Sweden or at most one Germany” to global electricity demand. 🌐🔌

So, while AI is flexing its computational muscles, it’s also doing a little dance with our power grids. But hey, it’s not all doom and gloom. Researchers are working on making AI more energy-efficient, like teaching it to sip electrons instead of chugging them. 🤖🍵

Remember, though, these numbers aren’t just about AI; they’re a mix of data centers, crypto mining, and AI. It’s like trying to figure out which ingredient makes your grandma’s secret sauce so addictive—tricky business! 🤔🔍

In the end, balance is key. We want AI to be smart, but not at the expense of our planet. So, let’s keep innovating, optimizing, and maybe throw in a sprinkle of solar panels for good measure. ☀️🌎

And hey, if you ever need a virtual energy-saving tip, just holler—I’m here! 😊👋

Again, the main three references, based on which this AI gave its response, are shown below:

MIT Technology Review: AI is an energy hog. This is what it means for climate change.

Nature: Generative AI’s environmental costs are soaring — and mostly secret

Penn Today: The hidden costs of AI: Impending energy and resource strain

Here is another recent opinion on this: MSN: The AI boom is spurring an energy crisis: is there a sustainable way out?

Essentially, there are a lot of cases where AI is being used unnecessarily. Separating the tasks that need AI from those that don’t will cut back on energy use. Also, once electricity generation is fully carbon-free and mostly solar (see the June 25, 2024 blog), AI’s energy hunger will be considerably less threatening.

“Sustainable Reality”:

“Sustainable Reality” often refers to the concept of integrating sustainability into various aspects of life, including investing, real estate, and business practices. One notable example is the Morgan Stanley Sustainable Reality report, which highlights how sustainable funds have outperformed traditional funds, especially during volatile periods like the COVID-19 pandemic¹². This demonstrates that incorporating environmental, social, and governance (ESG) criteria can potentially provide better financial returns and reduce investment risks. 

This AI description reads more as advertisement for Morgan Stanley than a productive summary of the issue.

Ideally, I would have liked to change the title of the required, college-wide category of the ASU General Education course that was described last week’s blog. I would have re-named it “sustainable reality,” similar to the title of Roger Penrose’s book, “The Road to Reality,” with the obvious difference that Penrose focused on Einsteinian Cosmology, while my focus is confined to our human reality.

Admittedly, all three AI descriptions that I am including in this blog are initial responses and represent me opening a conversation with the AI site. I have asked students in the past to start such conversations in my cosmology course, starting with large abstract questions (see “How to Explain Reality,” January 26, 2022).

I will return to the role of AI in future blogs to follow the progress of the technology. Even the best AI will not be very helpful with navigating our present reality. The simple reason is that the all-important training of our computers that run the AI is based on present and past realities that are in a constant state of change.

We are approaching the end of summer (“officially,” it ends on September 22^nd, not on Labor Day). Watching from my terrace in NYC, I can see that the sun rises a bit later and sets a bit earlier every day – days are getting shorter and nights longer. Fall semesters are starting and presidential elections are visible on the horizon. It’s time to think about the future.

About two weeks ago, I got an email from Sonya Landau, my editor and friend who lives in Arizona and is studying at the University of Arizona:

I think that your new focus on technology, specifically AI, is really interesting but I also think it’s important to talk about the environmental impacts of that technology. I don’t have time to write a guest blog about it and I’m not asking you to drop everything and write about this but maybe you could cover it soon.

I assumed that the request came as a partial response to the Global Digitalization and Algorithmic Decision Making (August 13, 2024) blog that she had just finished editing. But it’s very likely that her focus was also on the huge price in energy use that these technologies extract. Some of you might remember her previous guest blogs on October 9, 2018; June 22, 2021; and June 11, 2023, and wish that she would change her mind and write a guest blog on the topic. Please use the comment area to let both of us know.

I promised her that I would have a look.

The first thing I did was to check what I had already written on the topic. Just put AI into the Search box and scan a few of the entries. I followed with a literature check of the topic. There is already a rich array of literature on the topic that I will refer to in future blogs. A Nature reference can serve as an example: The role of artificial intelligence in achieving the Sustainable Development Goals.

I followed this with a Copilot (Microsoft AI) exploration of “Global human survival and sustainability” and the “energy cost of AI,” which will be discussed next week.

The final thing that I did, trying to respond to Sonya’s request, was to search for interesting recent contributions that her home state, Arizona, is now offering. The best example that I could come up with was a recent change that Arizona State University has made to its General Education Program.

I will start with the statements that the university’s website issues about the objectives of its undergraduate General Studies (General Education) classes (according to ASU’s website):

In addition to preparing students for careers and advanced study, a baccalaureate education should prepare students for satisfying personal, social and civic lives. Students should both acquire a depth of knowledge in a particular academic or professional discipline and also be broadly educated, with knowledge of interdisciplinary and transdisciplinary approaches to address an array of questions. They should develop the general intellectual skills required to continue learning throughout their lives. The ASU general studies requirements complement the undergraduate major by developing critical learning skills, investigating the traditional branches of knowledge, and introducing students to approaches applicable to addressing contemporary challenges.

The structure of the new General Studies program is shown in Figure 1

Figure 1 – Structure of the undergraduate General Studies program at ASU (Source: University undergraduate General Studies requirements | Academic Catalog)

The main innovation here is the last contribution, which introduces “Sustainability” as a required category rather than just a course (even though it is still in the form of a single 3-credit course), with the following learning objectives:

Upon completion of a course in Sustainability, students will be able to do the following:

1. demonstrate an understanding of the earth and its ecosphere, including the measures that indicate their capacities and limits

2. trace historical impacts of a range of socio-economic, political or cultural choices on integrated human-environmental well-being

3. envision pathways toward futures characterized by integrated human-environmental well-being

4. articulate an approach to addressing contemporary questions or challenges that employs concepts or practices of sustainability.

According to the site:

The learning objectives emphasize systems thinking, where human and non-human systems are understood as intimately connected, with human actions affecting all life on a planet with limits and boundaries.

Here is how the course works:

All students, regardless of major, will fulfill a three-credit course that address sustainable development, socio-ecological systems and how they relate to global challenges and opportunities.

The last thing that I explored was the meaning of sustainability. I searched Google for synonyms and I got the following list: green, imperishable, livable, renewable, supportable, unending, and worthwhile.

Two of the four learning objectives of the Sustainability category are anchored on timing (trace historical impacts and envision pathways toward futures). All of them are globally targeted. Only one of the 7 synonyms directly refers to timing (unending). Renewability can be interpreted as a mitigation for unsustainability.

Most people interpret sustainability as “green,” as in environmental. The synonyms of environmental (also Google) are: ecological, conservationist, environment-friendly, eco-friendly, ozone-friendly, sustainable, and recyclable. Again, of these terms, only recyclable can be related to timing, and even then, only indirectly. On the other hand, if you ask for the opposite of environmental sustainability you will get environmental degradation, which definitely involves time.

I haven’t decided whether I’d like to try to enroll at ASU as a student (there’s no age limit) or join as an adjunct professor to teach the Sustainability course. However, I have one strong piece of advice for these students: I recommend taking this course toward the end of their degree; all the other 32 General Studies courses serve as excellent prerequisites. Meanwhile, the Sustainability program/course is excellent preparation for the students’ post-undergraduate lives. By the time present students graduate, it is probable that they will be able to generate content for a sustainability course through AI. The challenges will be to motivate the students to go beyond this and to give them the tools to critique the generated content.

😄

Last week I tried to analyze the recent Paris Olympics by using a similar methodology to that I used to analyze global trends such as climate change, fertility decline, digitization, global penetration of electricity, and the use of nuclear energy. These trends were quantified and analyzed in the 10 most populated countries, which together represent more than 50% of both the global population and the global GDP. Similarly, the Paris Olympics were analyzed through 10 countries from which athletes, collectively, won 61% of the gold medals and 59% of the total medals. These 10 countries were analyzed in terms of their collective population (28% of present global population) and collective GDP (63% of global GDP). This analysis shed light on the issue that the Paris Olympics winners mostly came from the countries with the top GDPs. A similar analysis of future Olympics (starting with the Los Angeles Olympics in 2028) will show us if we are making progress in making the Olympics more inclusive by serving a higher percentage of the global population.

An interesting example of such progress can come from a different global trend: gender equity. Table 1 shows the share of males and females in the recent 10 Olympic games, compared to the first modern game that took place in Athens in 1896. We started with all-male games and ended in the “promised land”: full equity in athletic participation in the Paris Olympics.

Table 1 – Trends in share of athletes of each gender in the last 10 Olympic Games and the first modern Games in Athens (Source: Statista)

In terms of medal winners, we are very far from equity (defined as fully scaled with population –  equal global chance of producing a medal winner, independent of geography). However, in terms of performance, life is more complicated.

One important reason is that in order to be an Olympic medal winner, you have to be both very gifted and extensively trained. The two are obviously connected. Superb training enhances achievements but the ability to pay for the training is concentrated in rich countries. Since winning medals gain prestige, rich countries are happy to spend the money to attract promising athletes from emerging countries. To partially counter these trends, the Olympic committee limits the number of athletes from each country in most sports and allows people with dual citizenship to choose the country that they will represent. Rich countries may facilitate citizenship applications from gifted athletes from poor countries and thus encourage emigration similar to the “brain drain” that was described before in a different context (See “Back to the Energy and Population Transitions: Electrification and Brain Drain,” February 1, 2022). On the other hand, gifted athletes from rich countries who either didn’t make the team or didn’t want to represent their original rich country, have an option to represent more needy emerging countries.

American colleges play a central element in that dynamic:

At the 2024 Paris Olympics, 272 former, current and incoming NCAA student-athletes combined to earn 330 medals for 26 countries. The medalists competed in 21 Olympic sports and represented 90 schools and 22 conferences.

Of the medals earned by athletes with NCAA ties, 127 were gold, 95 were silver and 108 were bronze. Women accounted for 58% of all NCAA medalists and 80, or 63%, of the 127 gold medals.

The United States included the most NCAA medalists of any country, with 184 medalists.

Details about the dominant American colleges in the medal distribution can be found in (NCAA’s comprehensive Olympic qualifier dashboard):

Top five NCAA (National Collegiate Athletic Association) schools, by number of medals 

1. Stanford: 34 medals won by 22 medalists — 12 gold, 11 silver, and 11 bronze
2. California: 17 medals won by 13 medalists — 4 gold, 6 silver, and 7 bronze
3. Texas: 16 medals won by 13 medalists — 7 gold, 7 silver, and 3 bronze
4. Virginia: 15 medals won by 8 medalists — 7 gold, 5 silver, and 3 bronze
5. Southern California: 13 medals won by 12 medalists — 6 gold, 2 silver, and 5 bronze

Top teams/countries based on NCAA athletes are listed below:

1. United States: 385 athletes from 138 schools, 45 conferences, and 19 NCAA sports
2. Canada: 132 athletes from 69 schools, 19 conferences, and 13 NCAA sports
3. Australia: 44 athletes from 32 schools, 13 conferences, and 7 NCAA sports
4. Nigeria: 38 athletes from 35 schools, 10 conferences, and 3 NCAA sports
5. Jamaica: 34 athletes from 25 schools, 11 conferences, and 2 NCAA sports
   
6. Germany: 34 athletes from 33 schools, 14 conferences, and 7 NCAA sports
   

Colleges and universities in the US are willing to spend large sums of money to acquire the best training facilities and best trainers to attract the best athletes from all over the world. The athletes benefit by getting a good education as well as celebrity that could help them with post-college opportunities.

However, these dynamics might slow down as we move to the next Olympics in LA. As was discussed in previous blogs, many colleges now face declining enrollment and schools need to economize—and in some cases close—to accommodate these changes.

The issue was discussed in the October 10^th and October 31, 2023 blogs. These blogs also discussed some of the steps that colleges are taking to counter this trend. Figure 1, taken from the October 10^th blog, shows the trend.

Figure 1 – Historical US college enrollment from 1970-2020 (Source: Education Data Initiative)

A major factor in the drop in enrollment comes from the drop in fertility that was discussed in earlier blogs. The first impact of a drop in fertility is a drop in the age population of present and future college students. Most college students are Generation Z (born from 1995 – 2012). Per definition, the present drop in fertility directly impacts generation Alpha (born from 2013 – 2025). By the time of the LA Olympics, most college students will be generations Alpha and Z. The drop in enrollment will probably continue as colleges continue to economize. College administrators will have to decide how and where to balance tight budgets. Athletic fields will not escape those cuts entirely, and American colleges will likely become somewhat less attractive for athletes.

Throughout the more than 12 years that I have been writing this blog, my emphasis has been on trying to identify and analyze what I see as evolving global trends that can help both students and others navigate through changing realities. I have not always been consistent in my way of presenting these realities. In almost all cases, I have tried to quantify the trends and present the results either in terms of a figure and/or using individual sovereign countries to illustrate the changes. Two recent examples make these points: last week (August 13^th), the emphasis was on global digitization (access to computers), to illustrate global technology penetration. I assembled a table similar to Table 1 below. Earlier this year (January 30^th), the emphasis was on global changes in the fertility rate and I included many more developed countries than Table 1 shows. The main difference was that my objectives in compiling these data were different. Showing computer access, my objective was to represent global trends with an emphasis on emerging countries, while the table that shows the decline of fertility was meant to show important issues that are more abundant in developed countries. Both tables included the population and recent GDP/capita of the selected countries.

In this blog, I am including Table 1, based on a compilation of recent population and GDP/capita. I showed last week that the 10 listed countries simultaneously account for more than 50% of the global population and more than 50% of GDP and thus fairly represent our world. In this table, I have included 5 recent trends that were discussed in previous blogs (put them in the search box to scan through the examples). The 5 trends include computer access, electricity access, fertility, carbon emissions, and estimated number of nuclear warheads. All five trends are anthropogenic (generated by us humans). All five have a major impact on our lives and all of them started in my lifetime. Each trend has a set of complex impacts, with both destructive and positive potential. The impact they have on our lives is projected to increase.

Table 1 – Recent trends in 10 large countries that exceed 50% of the global population (4.5 billion people) and 50% of the global GDP (55 trillion $US). The approximate global population is taken as 8.2 billion and the approximate global GDP is approaching 110 trillion US$.
(The data for the nuclear warheads were taken from the March 22, 2022 blog and the data for the carbon emissions per capita were taken from the carbon offsets website COTAP.org.

Using a similar format, I have compiled a similar table of the 10 countries that won the most medals (both gold and total) in the recent Paris Olympic Games that just finished. The objective was the same: to explore what a concentrated list of the 10 top medal winners can teach us about the world at large.

Table 2 – The 10 countries from which athletes collected the largest number of medals in the 2024 Paris Olympic Games

The Olympics had a total number of 329 medal events in 32 sports. There were three medals: gold, silver and bronze. Taking the recent global population to be 8.2 billion and the recent global GDP as approaching 110 trillion $US, the 10 countries listed in Table 2 account for 28% of the global population and 63% of the global GDP. Combined, these 10 countries won 61% of the gold medals and 59% of all the medals.This closely matches the 10 countries’ share of the GDP  (63% of the population to 61% and 59% of the medals) but lags considerably behind their combined share of the global population.

The recent summer Olympic Games are the fourth Games that I have briefly covered, starting with the ones in 2012 (see August 27, 2012; September 8, 2016 and August 17, 2021 blogs for previous Games). Each event came  with different perspectives.

Unlike the 5 trends that I have compiled in Table 1, the Olympic Games didn’t start in my lifetime. A short history is described in Wikipedia with the following two paragraphs:

The modern Olympic Games (OG; or Olympics; French: Jeux olympiques, JO)^[a][1] are the world’s leading international sporting events. They feature summer and winter sports competitions in which thousands of athletes from around the world participate in a variety of competitions. The Olympic Games are considered the world’s foremost sports competition, with more than 200 teams, representing sovereign states and territories, participating. By default, the Games generally substitute for any world championships during the year in which they take place (however, each class usually maintains its own records).^[2] The Olympic Games are held every four years. Since 1994, they have alternated between the Summer and Winter Olympics every two years during the four-year Olympiad.^[3][4]

Their creation was inspired by the ancient Olympic Games, held in Olympia, Greece from the 8th century BC to the 4th century AD. Baron Pierre de Coubertin founded the International Olympic Committee (IOC) in 1894, leading to the first modern Games in Athens in 1896. The IOC is the governing body of the Olympic Movement, which encompasses all entities and individuals involved in the Olympic Games. The Olympic Charter defines their structure and authority.

I observed (through TV, I couldn’t get there) these Olympics as much as I could. I thought that France did a magnificent job organizing this event. I was delighted that despite the unrest that France experienced before the Olympics, there were no signs of unrest during the event. Others share my opinion: NYT: Merci, Paris: We needed these Olympics:

Every venue, every day, every event. Full, alive, loud and proud. From the opening ceremony on, the French filled the cup till it runneth over, delivering an Olympic Games that mixed art with sport, and history with future, and national flag-waving with a worldwide welcoming.

For those who watched, and especially those who attended, these were the Games we needed. The Pandemic Olympics were still so vivid at the start of Paris 2024. The 2020 Games were played in 2021, and while broadcast all over the world, were seen live by almost no one. It was the anti-Olympics. As American rower Nick Mead put it: “Part of the Olympic experience is meeting all these people from all over the world, who are doing what you’re doing, maybe in a different sport or a different country, to have that same lifestyle.” The Games bring them together and introduce them all to the world.

And MSN: Why Paris 2024 Was the Most Important Olympics in Recent History:

The moment Zaho de Sagazan hit the opening note of Sous le Ciel de Paris, a great weight was lifted from the sporting world’s shoulders. Nineteen days of fierce competition, outrageous showmanship, and world record-breaking efforts officially complete, the 2024 Paris Olympic Games had finally drawn to a triumphant close. For the athletes gathered in the Stade de France for the closing ceremony on Sunday night, the sound of 70,000 cheering fans was enough to make the lifetime of training, sacrifice and dedication feel worth it, but for the city itself, the deafening roars of approval may as well have been sighs of relief. Amid all the speculation and anticipation, it had been a Games that went off largely without a hitch.

The next Olympics is scheduled for the summer of 2028 in LA, well within the next presidential tenure of the winner of the November election. I hope that both ex-president Trump and VP Harris will tell all of us, before the November elections, how they intend to help LA to run it as well as Paris did. The next blog will focus on the role that college campuses play in the success of Olympic Games.

A recent description of a decision made by Spanish police caught my attention (An Algorithm Told Police She Was Safe. Then Her Husband Killed Her. – The New York Times):

Ms. Hemid’s husband of more than a decade, Bouthaer el Banaisati, regularly punched and kicked her, she later told the police. He also called her a “whore,” “disgusting” and “worthless,” according to the police report.

Before Ms. Hemid left the station that night, the police had to determine if she was in danger of being attacked again and needed support. A police officer clicked through 35 yes or no questions — Was a weapon used? Were there economic problems? Has the aggressor shown controlling behaviors? — to feed into an algorithm called VioGén that would help generate an answer.

VioGén produced a score:
low risk
Lobna Hemid
2022 Madrid

The police accepted the software’s judgment and Ms. Hemid went home with no further protection. Mr. el Banaisati, who was imprisoned that night, was released the next day. Seven weeks later, he fatally stabbed Ms. Hemid several times in the chest and abdomen before killing himself. She was 32 years old.

This is a disastrous single-case example. Let’s go to the basics to examine if this case foreshadows the global future.

What is algorithmic decision-making? Below is Google’s AI answer:

Algorithmic decision-making (ADM) is the use of computational methods to allow machines to automatically make decisions or complete tasks. ADM systems, also known as algorithmic decision systems (ADS), analyze large amounts of data to find correlations or other information that can be used to make decisions. The data can come from a variety of sources, including databases, text, social media, images, sensors, or speech.

A more detailed description can be found on Wikipedia, starting with the following paragraph:

Automated decision-making (ADM) involves the use of data, machines and algorithms to make decisions in a range of contexts, including public administration, business, health, education, law, employment, transport, media and entertainment, with varying degrees of human oversight or intervention. ADM involves large-scale data from a range of sources, such as databases, text, social media, sensors, images or speech, that is processed using various technologies including computer software, algorithms, machine learning, natural language processing, artificial intelligence, augmented intelligence and robotics. The increasing use of automated decision-making systems (ADMS) across a range of contexts presents many benefits and challenges to human society requiring consideration of the technical, legal, ethical, societal, educational, economic and health consequences.^[1][2][3]

I have recently started using AI in blogs and occasionally I also used AI in class to give assignments to my students to explore some big existential questions. The present shortcomings of relying on AI for decision-making are being widely discussed and I am sure that conversation will continue. The example that starts this blog, of using AI for police work, is an extreme example with a deadly consequence. However, progress in AI and ADM is rapid and one can see benefits in many computerized decision-making situations. They could shift government regulations and law-and-order decisions, moving away from individual subjectivities and political orientations to apply laws uniformly based on the most detailed information possible. Going back to the Spanish example that started this blog, the main problem that I see is that the police interview was not detailed enough, and the statistical background on which the ADM should have been based, not extensive enough. Work is being done by companies that are employing tremendous resources to try to improve the system. Not surprisingly, these efforts are concentrated in the rich countries.

Converting society to ADM requires extensive global digitization. The question is, are we ready?

A subway ride in my city (NY) can convince most that in developed countries, almost every household owns and uses a computer of one sort or another (yes, an iPhone counts as a computer). The exact number in the US is given in Table 1. In developing countries, the technological penetration is a bit slower. Unsurprisingly, computers have a hard time penetrating households that don’t have electricity. One of my blogs earlier this year (April 16, 2024) discussed electricity penetration in the 10 most populated countries. Table 1 is taken from that blog and modified to include the recent share of households with computers. Data for this column were taken from the World Bank.

The overall picture in developing countries is shown in Figure 1.

Figure 1 – Percentage of households with home computers in developing countries (Source: Statista)

The recent developments of AI and robotics all depend on the penetration of digitization into almost all aspects of life. The beginning of this global shift can be traced to the invention of the transistor in 1948. All of this took place in one decent human lifetime (I was born in 1939).

All signs point to this being just the beginning of the digital transition. A recent article in the New York Times describes some of the changes that digitization produced in India through the eyes of a truck driver. It points out that “The gap between India’s state of infrastructure and that of other large Asian economies remains significant” but looks at some of the progress that is underway.

I will return to this issue in future blogs.