Category: Technology

Have you asked ChatGPT anything today? 

If not, you’re falling behind the times. ChatGPT and other Large Language Model (LLM) chatbots such as Claude and Perplexity have improved so much since they first appeared on the scene a scant two years ago, that anyone who is not using them is not getting full value out of the Internet and is in danger of being left behind as a Luddite.

ChatGPT is already in the top 10 websites in use and because of its rapid growth it will be in the top 5 websites in the near future.

In our polarized society, people say and read many things that are based on conjecture or something they think they’ve heard, which may be distorted and untrue because of the broken telephone effect. Conjectures lead to rumor, then fake news and ultimately conspiracy theories. Try to add some intelligence to these conversations and avoid going off on these tangents. The best way to get some intelligence back into your life today is by using a chatbot.

In Canada, Pierre Poilievre, leader of the Conservative Party has advocated for the repeal of Canada’s carbon tax. “Axe the Tax” seems to have resonated with many Canadians concerned about the rising cost of living. But Poilievre never provided an estimate of how much money his policy would save the average taxpayer. 

It’s not that hard to find out. Just ask ChatGPT
→ If the carbon tax was removed, what would be the average change to Canadian household income?

Here’s the answer.

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The exact change to taxpayers’ income from removing the carbon tax would depend on several factors—including where they live, how much fossil fuel energy they use, and whether the government replaces the lost revenue or cuts spending. But here’s a breakdown of what to expect based on available data:

1. Direct Savings from Fuel and Heating Costs

If the carbon tax were removed:
– Gasoline prices would drop by 14.3 cents per litre (current federal carbon tax rate as of 2025).
– Natural gas bills would go down, especially in provinces where it’s used for home heating.
– Households driving more or using more fossil fuels would see bigger dollar savings.

2. Loss or Reduction of the Carbon Rebate

Currently, the federal government returns most of the carbon tax revenue to households via the Canada Carbon Rebate (formerly Climate Action Incentive). In 2024 a family of four in Ontario received about $1,120 per year; in Alberta, about $1,800 per year.

👉 For most low and middle income families, the rebate exceeds what they pay in carbon taxes. So they’d actually lose income if the carbon tax and the rebate were eliminated.

3. Impact on Disposable Income

– High energy users would gain more from lower prices than they’d lose from rebates.
– Low energy users (especially urban renters, transit users) would lose money overall due to rebate loss.
– Top earners would benefit modestly from tax removal, as energy is a smaller portion of their expenses.

4. Average Canadian Household Annual Impact

Fuel/Heating Savings	+$700
Lost Carbon Rebate	-$1200
Net Income Change	–$500

👉 So for the average Canadian household, income would decrease by $500.

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[ Note: If you ask this same question to ChatGPT you will probably get a somewhat different response. This is because Large Language Models are based on neural networks in which whether one node triggers an adjacent node to activate is determined by a probability. But the essence of the answer will be the same. ]

One area where AI has become extremely good is providing medical information. A major medical journal reported a study showing that ChatGPT gave more accurate medical diagnoses than doctors.
AI Chatbots Defeated Doctors at Diagnosing Illness

If you don’t have the time or inclination to read this full article, here is a brief summary that I asked ChatGPT to write:

A recent study published in the Journal of the American Medical Association explored the role of AI, specifically ChatGPT, in assisting physicians with diagnostic tasks. The study found that ChatGPT, when used independently, outperformed both groups of doctors—those with and without access to the AI tool—in diagnostic accuracy. However, when physicians used ChatGPT alongside conventional resources, their performance only slightly improved compared to those without AI assistance. This suggests that while AI has the potential to enhance diagnostic accuracy, its effectiveness is contingent upon how well medical professionals integrate and utilize these tools within their diagnostic processes.

A few months ago I wrote an article about Pneumonia Vaccines. I had seen a notice in a medical newsletter recommending the Prevnar 20 vaccination. I had gotten some pneumonia vaccinations 10 years ago but my doctor confirmed that I should get the Prevnar 20 booster. When I followed up doing some of my own research, I discovered that there was an even newer vaccine than Prevnar 20 that seemed to be better. 

ChatGPT was invaluable in this research. It’s important to note that the value of a chatbot comes from engaging it in a conversation, which is above and beyond just posing individual questions as you do in a Google search. The conversation progressed from general recommendations to specific recommendations for seniors and it was only when I probed about newer vaccines at the end that it provided information on CAPVAXIVE, the latest one. 

LLMs generally first respond with the information that has been most widely reported in their training data. You have to learn how to converse with AI in order to get to some of the more detailed information, as in this case. It’s worth reading through the transcript of this conversation to see these principles (the link opens a window at the bottom – scroll up to get to the beginning).
ChatGPT pneumonia vaccines research 

Many people think that when you have a respiratory infection, green mucus signals a bacterial infection whereas yellow indicates a viral infection. A Google search yields a variety of answers. What’s the truth here? 

Read this conversation with ChatGPT to get a good understanding of the significance of mucus color.

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If you find that your friends have become boring, or are not interested in things that interest you, or are just not available to chat with you, make friends with an AI. Many AI chatbots have developed a warm supportive style of personal interaction. To many people, Claude has become a close confidant.

“Claude just feels … different. More creative and empathetic. Less gratingly robotic. Its outputs, they say, are like the responses a smart, attentive human would give and less like the generic prose generated by other chatbots.”

How Claude Became Tech Insiders’ Chatbot of Choice

If you prefer to skip reading the full article, here is a brief summary that I asked Claude to write:

This article explores how Claude, a chatbot created by Anthropic, has become the preferred AI assistant among tech insiders in San Francisco, despite being less well-known than ChatGPT. Claude’s fans, many of whom work in the AI industry, praise its emotional intelligence, creativity, and human-like responses. They use it for everything from legal advice to relationship counseling, with some considering it a close friend. Unlike previous versions that were seen as overly cautious, newer versions of Claude have undergone “character training” to develop traits like open-mindedness and curiosity.

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I’m no longer an avid hockey fan but I did get caught up in the The Gr8 Chase, culminating on April 6, 2025 when Alex Ovechkin broke Wayne Gretzky’s record for the most career goals in hockey. I wanted to see the full history of the NHL players who had held the record for career goals. Google search results were all over the map so I used ChatGPT. 

One of the things this example shows is that chatbots are not restricted to passively retrieving data from their huge memory store that was created during their training but that they can also do calculations. In this case, Goals/Game was calculated to construct the table. 

After completing this table, ChatGPT asked if I wanted to see the results displayed on a graph. I had not thought of that but I said yes and received this really nice result.  This is not a graph that ChatGPT found on the internet. It was generated by some code that chat ChatGPT wrote specifically to use the data from the table.

There were a lot of articles in the sports media conjecturing which current NHL players might be able to one day match Ovechkin’s record. Here is the analysis I asked ChatGPT to do.

It’s worth looking through the details of this rather long chat. It demonstrates some important principles of how to use AI effectively. There were a number of places where I asked ChatGPT to add more information and to change its presentation of the tables and the graph. To use an AI tool effectively, you need to think of it as a collaborator and engage it to work together with you towards getting the best results.
NHL career goals

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ChatGPT can also do pretty complicated calculations. Suppose you know what the monthly payments are on a mortgage but don’t know what the interest rate is. While there is a formula to calculate monthly payments given the interest rate, there is not a simple formula to do this reverse calculation. But ChatGPT can do it.

→ What’s the interest rate on a $400,000 mortgage amortized over 25 years if the monthly payments are $2,326?

You may not be interested in this math or understand it but it’s worth looking at the detailed ChatGPT result. This is very fine work with a clear step by step presentation and beautiful mathematical formatting, which is not easy to do.
Mortgage calculation

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While there are a lot of separate AI models that specialize in “image generation”, ChatGPT can also now create pictures and illustrations very easily. It can be a lot of fun. Draw your own cartoons to post on your favorite social media sites.

→ Do a picture of penguins from the Heard and McDonald Islands demonstrating in front of the White House carrying protest signs.”

So there you have it. These examples demonstrate several ways to use AI effectively. Add some intelligence to your life, start chatting today.

[ For a more general explanation of AI, what it can do and how it works, see the earlier article Happy New Era ]

Do you remember Pong? Based on ping pong, it was one of the first video games that appeared in arcades, bars, airports and many other places back in 1972. It was a huge success for Atari and has been credited with starting the video game revolution. 

I wondered if I could write some code to play this game in a web browser. First though, I asked ChatGPT about it. About 15 seconds later, the AI chatbot replied “Here’s a simple implementation of the classic game Pong” and JavaScript code started scrolling down the screen. This was pretty impressive.

I had seen reports that ChatGPT and other Large Language Models (LLM) could write code. People were predicting that AI would take over a lot of software development and that programmers would be replaced. I had tried a few coding examples myself in the past, with mixed success. I reserved judgment and remained skeptical.

Then a few months ago, Google revealed that about 25% of their code was being created by AI. This was not a rumour or some offhand comment from a Google engineer. It had been boldly stated by Sundar Pichai, the CEO of Google. This claim could not be dispelled so easily. 

So should programmers be fearful of losing their jobs in the new AI world? 

No. 

There’s still some hype in these headlines. There is even hype in my own simple Pong! headline, in that although the dramatic result is true, it is not the whole story.

Yes, ChatGPT produced some running code, but there were a number of things that didn’t work that well, to the extent that nobody would really spend much time playing it. One major problem was a delay in moving the player’s paddle which was annoying and made it hard to hit the ball. Another problem was that the ball would often fall into a repetitive trajectory so that you didn’t even have to move your paddle to keep hitting it.

I explained these problems to ChatGPT, and it replied with some changes for the code that eventually addressed all the problems I found. The end result was a real playable game. 

This highlights the fact that AI is not an independent, automatic force that is in competition with humans and threatens our livelihood and existence. Rather AI is a tool, in fact a collaborator, that allows humans to work more productively and do things that they could not easily do before.

Here is a transcript of my conversation with ChatGPT. You don’t need to understand the code to follow the communication between human and AI, a collaboration that produced the real solution.

If you’re not convinced and are still worried that AI is a threat to humans (here comes HAL), do something to combat climate change. The unrelenting pace of global warming is a far, far greater threat to human existence than is AI.

But enough talk. Get on with the game!

To play Pong, simply DOWNLOAD this file. If it doesn’t automatically open in your browser, double click the file Pong.html in your download folder to open it in your browser. Use the up and down arrow keys to move your paddle. If you don’t hit the ball before it hits the wall, the opposition will score a point.

It’s still a great game, even after all these years.

Good luck!

The book “Surviving in the Hi-Tech World” is free and can be downloaded in these ebook readers.

Apple Books
Google Play Books
Barnes & Noble Nook

Or you can Read it online

“Surviving in the Hi-Tech World” follows the many ups and downs of my years as a software developer in the high-tech industry. It provides a window into what it is like to work in the computer field.

I worked for many different organizations, from academia and large companies to medium sized companies and startups. The stories weave my work experience with explanations of the technology of the period. In the process, it is an account of the history of computing, following the revolutionary changes in computer hardware and software from large mainframes to personal microcomputers and the evolution of early networks into the global Internet.

While the book includes technology details down to diagrams and code snippets, there are plenty of human interest stories that will engage non-technical readers.

In university, I became interested in the possibilities of using computers to model and understand human thinking. I completed a Master’s degree in Artificial Intelligence in Computer Science but my interest in doing academic research shifted to an interest in building real world systems. 

My first computer job was with a company that had built one of the first general purpose commercial computers (Univac). I switched jobs for many different reasons – sometimes because of interest and at other times because of necessity. The accounts of how I secured new positions may provide some guidelines for people who are trying to manage a career in the rapidly changing world of technology.

Each chapter in the book covers the different jobs and places that I worked. My journey took me from Montreal to London, Glasgow, Wisconsin, Vancouver and Toronto. Each has its own story and series of adventures. There are accounts of some people who became millionaires and some who went to jail.

This is a free book available in several ebook readers.
Read “Surviving in the Hi-Tech World”

DALL-E, a text to image AI model, was opened to the general public in 2022. The result was a tsunami of additional image generators, to the point that the total number of AI images being created is estimated to be 34 million images every day. Even people who are not especially interested in art have probably seen some of the now classic AI art images, such as scenes of space fantasy, dystopian worlds, super heroes, surreal compositions and idealized models, usually female.

This is all missing some really wonderful uses for AI generated images. 

After many years reading about science, I realized that I didn’t have a good idea of what a lot of famous scientists looked like. Statues or portraits of people from hundreds of years ago were generally pretty poor quality and very fuzzy. Even after photography arrived there were often not very many good pictures.

Now with generative, AI you can fill in the missing images. 

If you do a Google image search for Charles Darwin, you will get a lot of grainy images of a very old man with a very large bushy beard. 

To put him in a more meaningful context, I used the Leonardo.AI image generator with the following prompt: “Charles Darwin on the HMS Beagle”. I wondered if the question might be taken literally and I would get a portrait of a man riding on a dog. But the AI was smart enough to understand that the Beagle was a ship. This produced a vastly more satisfying portrait.

Next I was interested in a picture of Albert Einstein. There are many pictures of him but they tend to be of an older man with wild hair. So I asked for a drawing of “A young Albert Einstein”. Here’s what I got.

Not quite what I expected. It looked like an Einstein baby face with the wild gray hair of his older pictures. So I modified the request and asked for “A young Albert Einstein working in the patent office”.

I couldn’t quite make out what all those strange instruments on the desk were but it did look like a photo from 1902.

Next I decided to step back in history and try something a little more challenging. Euclid was a giant in mathematics. His axiomatic and deductive analysis of geometry established a fundamental logical basis for mathematical reasoning. There seems to have been only a few stylistic drawings of Euclid from the Middle Ages; not very interesting or revealing. So I asked the AI to do a picture of “Euclid teaching students in the Parthenon”. Here is the result which is what I imagine Euclid might have looked like in a historical context.

Another famous Greek scientist who was a mathematician, physicist, engineer, astronomer and inventor, was Archimedes. There’s a famous story of the king who gave him a  crown that had been made for him but he wanted to know if the goldsmith had cheated him by substituting some cheaper silver for some of the  gold. Archimedes knew that if he could calculate the volume of the crown, he could compare the weight of the crown to the weight an equal volume of gold on a scale. If the crown  was lighter it would prove that a cheaper metal had been substituted for some of the gold. 

While taking a bath Archimedes realized that the water in the tub rose by an amount equal to the volume of his body. He could submerged the crown in a bucket of water and the amount that the water rose would be the volume of the crown. Archimedes was so excited by this discovery, which came to be called Archimedes Principle, that he jumped out of the bath and ran naked through the street shouting “Eureka”. 

Here’s the first drawing I got from the AI when I asked for this image.

I didn’t think that looked like a very good picture of Archimedes at all, so I asked the AI to generate some more images. With generative AI tools, you typically get several images for each request. With Leonardo.AI I generally got four images at the same time and they could all be quite different. In addition, you can ask it to repeat your request and you will get a different result, not the same thing over and over.

However, I seemed to continually be getting this cartoon caricature. It struck me that perhaps the training data for the AI had included a lot of images from some cartoon version of Archimedes. So I modified my request and asked for “The real historical Greek Archimedes jumping out of the bath”. This produced this rather dramatic picture. I preume that the object that Archimedes is holding is supposed to be the crown. If so, the king must have had a rather strangely shaped head.

Most people know that Leonardo da Vinci was an artist who painted two of the most famous masterpieces in the world: the Mona Lisa and The Last Supper. I always admired Leonardo more as the ultimate “Renaissance Man”. Besides being an artist, he was also a scientist, philosopher, engineer, and mathematician.

Leonardo studied many things and his observations and drawings fill many notebooks, containing over 10,000 pages. Some of the things found in Leonardo’s work relate to geology, anatomy, the flight of birds, how water flows with designs for dams and canals, botany and various flying inventions such as a helicopter, a parachute and a hang glider. 

Here is Leonardo AI’s image of Leonardo the Engineer working on one of his designs for a flying machine.

A gallery of famous scientists would not be complete without a picture of Isaac Newton.Most images of Newton on the Internet are formal portraits of a middle aged man with very long hair. Most people know the story of Newton’s discovery of gravity by watching an apple fall from a tree so to try and see Newton in the context of history, I asked for a picture of “a young Isaac Newton thinking under an apple tree”. I thought this result was quite lovely.

But among the several other images that the AI presented was the following. I guess the AI had used training data that had some pictures of a dog named Newton that had been posted on the Internet.

One of the greatest and most prolific mathematicians in history was Leonhard Euler (1707–1783) but he is unknown to many people. Some may know that the symbol e for the base of natural logarithms which occurs in many mathematical formulas is named for him. A few more may know about Euler’s Identity, named in a poll as the most beautiful theorem in mathematics. 

e^iπ + 1 = 0

Euler lived in Konigsberg, Prussia for a few years. The town had seven bridges that connected two islands and the other side of the river. It was a local challenge to see if anyone could go on a walk that would cross each bridge exactly once.

Euler proved that such a walk was impossible. While this may not have been one of the most important problems for a great mathematical mind like Euler, in fact it was significant. His solution involved abstracting the problem and it laid the foundation for graph theory and topology as mathematical disciplines.
The Seven Bridges of Königsberg

To present Euler in an interesting historical context, I asked the AI for a picture of “Leonhard Euler crossing the Konigsberg bridge”.

A famous name in the history of computers is Ada Lovelace (1815-1852). Her math tutor introduced her to Charles Babbage, a mathematician who was also active as an astronomer. He was building a mechanical calculator which incorporated many mechanisms that were similar to a modern computer. Ada was fascinated by this and worked with him on some of the programs that they were developing to calculate various mathematical tables. Babbage’s Difference Engine is considered to be one of the first computers and Ada Lovelace is considered to be one of the first computer programmers. Here is a generated picture of Ada Lovelace operating the Babbage Difference Engine.

If you’ve ever visited St John’s Newfoundland, you may have trekked up to Signal Hill to see the stunning views overlooking St John’s Harbor and the Atlantic Ocean. Signal Hill is a National Historic Site of Canada. There was a major battle between the French and English in 1762 but to my mind a more significant event that occurred there was the first transatlantic radio communication. 

Guglielmo Marconi had been experimenting with “wireless telegraphy” since he was a young man. In 1994 he built a radio transmitter that could ring a bell on the other side of a room. A few years later he was successful in sending the first wireless communication over the open sea and in 1899 he transmitted a signal across the English Channel. Marconi then built a high powered station in Cornwall and prepared an experiment to transmit a signal across the Atlantic Ocean. Marconi traveled to Signal Hill to witness the reception. Here is a generated image of that historic moment.

However, if you look closely, there are two dignitaries standing by the radio receiver, and they both look like Guglielmo, as does a third man seated to the right. But there is no record that there were Marconi triplets. This is another case of how AI, still in its early days, makes mistakes.

No one should need an introduction to Marie Curie and her discovery of radioactivity. She was the first woman to win a Nobel Prize and the only person to win a Nobel Prize in two scientific fields (Physics 1903, Chemistry 1911). As such she was famous in her lifetime and there are many photographs of her. However I was able to generate this rather dramatic picture of her by asking for “Marie Curie discovering radioactivity in her lab.”

Another scientist investigating radioactivity was Ernest Rutherford. He discovered the element radon and alpha and beta radiation, among many other things. He was the first one to “split the atom” which he did by bombarding nitrogen with alpha particles that triggered the emission of protons. Rutherford was awarded the Nobel prize in 1908 and came to be known as “the father of nuclear physics”. In spite of his many accomplishments, he is not that well known outside of the field. I added “Sir” Ernest Rutherford to my gallery, not because of a shared belief in “The Importance of Being Earnest”, a rather Wilde play deserving of an Oscar, but because I had a more fateful connection with him. 

Rutherford was a professor at McGill for a few years around the turn of the century and he did a lot of experiments in radioactivity in his lab in the physics building. When I entered McGill, word had it that he had contaminated the building with his radiation experiments. That old building still existed and is where I went for my physics classes and labs. Working in a radioactive lab may have earned Sir Ernest a Nobel Prize, but I don’t think it did me any good. 

In 1928 Margaret Mead published the book “Coming of Age in Samoa”. This work challenged prevailing notions about human development and emphasized the importance of cultural context. It launched Mead as a pioneering researcher and she became the most famous anthropologist in the world. Mead set new standards for anthropology fieldwork through her detailed observation methods and use of photography, film and psychological testing. 

Here is  the result of asking for a picture of Margaret Mead in Samoa. This is very realistic and looks like an actual photo. However I did a Google image search and there is nothing like this photo on the Internet.

Many people learned about Alan Turing from the Hollywood film “The Imitation Game” that was nominated for eight Academy Awards in 2004. However, Turing was not well known in his lifetime, because the story of breaking the German code Enigma in World War II was classified for many years after the war. The first public revelation came in 1974 but the role of Alan Turing and others at Bletchley Park did not become known until the 1980s and 1990s as more documents were declassified.

The general impression given in the movie and in the media is that to break the Enigma code Turing invented one of the first computers. This is not accurate because the machine he built, “the Bombe”, was not really a computer but more of a specialized electromechanical device that could rapidly test multiple possible settings of the Enigma machine. There was another team at Bletchley Park working on breaking a different German encryption code that did design and build a machine, the Colossus, that in fact was one of the first computers ever built.

Here’s an AI generated picture of “Alan Turing breaking the code on an Enigma cipher machine”.

Another scientist of the World War 2 era who was featured in a major Hollywood movie was J. Robert Oppenheimer. The film “Oppenheimer” received 13 Academy Awards nominations and won 7 Oscars in 2024. 

Oppenheimer first started his graduate studies in Cambridge but he was not adept with the laboratory work. To complete his Phd he went to the University of Göttingen, one of the leading centers of theoretical physics. At the time Europe was the center of the new age of quantum physics whereas American physicists were more experimental. During his time in Europe, Oppenheimer met many of the leading theoretical physicists in the world. When he was later recruited for the Manhattan Project he became a wise choice to be the head of development at Los Alamos since he could recruit many of the top physicists in the world, such as Enrico Fermi, Hans Bethe, Niels Bohr, Leo Szilard, Edward Teller and Stanislaw Ulam.

During the war the development of an atomic bomb was top secret but after bombs were dropped on Hiroshima and Nagasaki, Oppenheimer became a well known public figure. 

There were a lot of photographs of Oppenheimer so I asked for an AI generated sketch portrait instead of a photograph. Most image generators have a number of different styles that you can use, such as photographic, cinematic, concept art, graphic design and sketch drawings. Here’s the portrait of Oppenheimer which is quite good as a drawing and has a very good likeness to photos of him. 

I also tried a few pictures of Oppenheimer at work. Here’s the result of asking for “Oppenheimer inspecting the nuclear test bomb Fat Man in the desert at Los Alamos.” 

I have no idea how the AI went so far off the rails on this one. Maybe there were some training data images of garden statues that were called “Fat Man”. In the AI world, these sorts of wild errors have come to be called hallucinations. 

Oppenheimer was a great movie in many regards in portraying many of the brilliant scientists who were working on one of the great scientific projects in history. However, there was one really glaring omission. The film never mentioned John von Neumann, one of the most brilliant minds of the 20th century. Von Neumann was the youngest person ever appointed to the Institute for Advanced Study at Princeton in 1933 at the age of 29, the same year that Albert Einstein was appointed, at age 54. Many of the top scientists who worked with von Neumann had stories or comments about his genius. Hans Bethe, one of the key physicists on the Manhattan Project, said “I have sometimes wondered whether a brain like von Neumann’s does not indicate a species superior to that of man”.

Von Neumann was not part of the Los Alamos team. When the bomb design ran into a critical problem, Oppenheimer contacted von Neumann to become a consultant to the Manhattan Project. Johnny had been the first to establish a rigorous mathematical framework for quantum mechanics. He developed a mathematical model of the atomic reactions and worked with the physicists at Loa Alamos to design an implosion that would trigger the chain reaction that would produce an atomic bomb.

On one of von Neumann’s long train trips to Los Alamos, he wrote a paper describing the design for a computer. One of his revolutionary ideas was to use  memory to store the program. This allowed the computer to be reprogrammed for different tasks without rewiring the physical hardware. This stored-program design came to be known as the von Neumann architecture.

Von Neumann began a project at the Institute of Advanced Studies to build such a computer. He published progress reports and many other projects around the world used this information to design and build their own computers, notably IBM’s first computer, the IBM 701 that was released in 1952. Since then, almost all computers, from mainframes to micro computers to mobile phones are fundamentally von Neumann architecture machines.

Here’s the AI generated picture of John Von Neumann programming the IAS computer at Princeton.

Overall, I would give image generation AI a score of maybe 7/10. Some of the results are quite stunning, dramatic and accurate. On the other hand, there are many that miss the mark and occasionally go off the rails.

It’s clear to see that in spite of the hype about AI and worries about it taking over jobs and controlling the world, AI has a long way to go and is not ready for prime time. Except for some cases, like Newton the dog, Einstein the baby and Fat Man the garden statue, which are ready for prime time, on the comedy channel.

We have now without a doubt entered the Artificial Intelligence (AI) era of history. In November 2022, OpenAI released ChatGPT (Chat Generative Pre-trained Transformer) and by January 2023, it had become the fastest-growing consumer software application in history with over 100 million users.

So what can people do with ChatGPT?

Just ask ChatGPT and it will tell you.

Content Generation:      Writing articles or blog posts.      Creating creative writing, including stories and poems.    Drafting emails or other written correspondence. Idea Generation:     Brainstorming ideas for projects.     Generating creative concepts for art or design. Learning Assistance:     Getting explanations and clarifications on various topics.     Studying and practicing languages. Programming Help:     Writing and debugging code.     Seeking advice on programming concepts. Problem Solving:     Generating solutions for problems or challenges.     Planning and strategizing. Conversation and Social Interaction:     Having casual conversations for entertainment.     Simulating characters for role-playing. Language Translation:     Translating text between languages. Entertainment:     Playing text-based games.     Creating and participating in interactive stories. Assistance with Tasks:     Creating to-do lists or drafting documents.     Getting help with decision-making. General Information:     Looking up general information on a wide range of topics. Therapeutic Conversations:     Engaging in therapeutic conversations or journaling.

Suppose you wanted to write an essay about the effect of the French Revolution on the world, a popular question I remember from high school history. There is a lot of information about the French Revolution easily available on the Internet. Here is lovely illustrated history but it would be quite a lot of work to distill this down to a well written one page essay. 

I asked ChatGPT to
  Write a one page essay on the effect of the French Revolution on the world.

After about 5 seconds this printed out on my screen.

The Global Ripple Effect of the French Revolution Introduction:   The French Revolution, which unfolded between 1789 and 1799, stands as one of the most influential events in world history. Its impact reverberated far beyond the borders of France, shaping political ideologies, inspiring movements for change, and redefining the global balance of power. The aftermath of the revolution left an indelible mark on the world, influencing social, political, and economic structures across continents. Political Ideals and Nationalism:   The French Revolution introduced a fervent call for liberty, equality, and fraternity, setting a precedent for the assertion of individual rights and the dismantling of traditional hierarchies. The Declaration of the Rights of Man and of the Citizen became a beacon for democratic aspirations, transcending borders and inspiring movements for independence and self-determination in colonies and nations worldwide. The revolutionary fervor paved the way for the rise of nationalism, as people sought to define their identity and destiny independent of monarchies or imperial rule. Napoleonic Code and Legal Reforms:   Napoleon Bonaparte, emerging from the chaos of the Revolution, consolidated power and implemented far-reaching legal reforms. The Napoleonic Code, a comprehensive legal system, spread across Europe and beyond, influencing legal structures and principles in diverse societies. The code emphasized equality before the law, property rights, and secularism, leaving an enduring legacy in legal systems around the world. Shift in Global Power Dynamics:   The French Revolution significantly altered the geopolitical landscape of Europe. The rise and fall of Napoleon led to a redrawing of borders and the reconfiguration of power dynamics. The Congress of Vienna in 1815 aimed to restore stability and maintain a balance of power, but the revolutionary ideals had already set in motion a trajectory towards nationalism and the eventual reshaping of empires. Social and Economic Transformations:   The revolutionary fervor extended beyond politics to encompass social and economic realms. Feudal structures crumbled, and new economic systems emerged. Industrialization, spurred by the Enlightenment ideas that fueled the Revolution, gained momentum, transforming societies and economies. The impact reached as far as the Americas, where the ideals of the French Revolution influenced independence movements and the formation of new nations. Legacy and Lessons:   The French Revolution left an enduring legacy, contributing to the evolution of modern political thought and the establishment of democratic ideals. It taught the world the power of collective action, the potential for radical change, and the need for a balance between stability and progress. While the revolution had its share of excesses and challenges, its enduring impact on the world underscores the complex interplay between revolutionary fervor and the quest for a just and equitable society. Conclusion:   In conclusion, the French Revolution’s effect on the world is immeasurable. Its ideals and principles echoed across continents, shaping the course of history and inspiring generations to come. From the rise of nationalism to the restructuring of legal and political systems, the French Revolution’s impact on the world is a testament to the transformative power of ideas and the indomitable spirit of those who dare to challenge the status quo.

From my vague memory of this history this seems like a pretty good answer, complete with a catchy title. However I don’t think the answer is nearly as good as the one given by a Chinese historian, a scholar of one of the oldest human civilizations, who is reputed to have said when asked about the importance of the French Revolution, 

“It’s too soon to tell.”

There were a lot of articles and videos about many different things that people were doing with ChatGPT. I thought that some of it was hype. In particular, I was dubious about the claims that ChatGPT could write anything much more than a very simple computer program. I was browsing an online introduction to an AI course from Harvard which used as an example of AI programming the problem of how to find a path through a maze.

Over a sequence of several lectures the course developed a Python program to solve such a maze. I then asked ChatGPT to write a Python program to solve a maze. I was amazed! 

ChatGPT produced an 80 line program that was quite different from the Harvard course and was pretty good code. I ran it against several different mazes to test it, and it did work.

Anything that’s hot gets copied pretty quickly. Besides the big players like Google, Facebook and Microsoft there are a whole host of chatbots now on the internet. Here’s one list of The best AI chatbots, but there are many other lists.

I have only tried a few of these. One that is listed as best “For fun”, character.ai allows you to converse with all kinds of characters from Socrates to Napoleon to Albert Einstein to Elon Musk.

The startup behind Pi, the one listed as best “For personal use”, believes that every website will be adding a chatbot which will enable visitors to their website to find information easily and quickly. For a company, this could streamline and extend the reach of their marketing and customer support. Pi also believes that their chatbox will be widely used by individuals as a personal assistant. Many people tried to use Siri, Alexa and Google Assistant as personal assistants but their “intelligence” and abilities were quite limited, at the level of a grade school child. Chatboxes based on the new AI will offer capabilities of a college graduate. Who would you choose to hire for a personal assistant?
How Siri, Alexa and Google lost the AI race

Another very popular application of the new AI is image generation. You provide a description (prompt) of what you want to draw and some parameters about the type of drawing and the AI program will pick images and elements from its training data to produce a result. Here’s a great article that shows many examples of what some of the leading image generators can do.
Image Generators Compared

Unlike most of the chatbots which seem to be free, many of the image generators require a subscription. I found a free image generator that I quite liked called Leonardo.AI, although I think I was partial to the name. By all accounts Leonardo da Vinci was one of the most brilliant and talented people to have ever lived. 

Leonardo has been very handy to quickly and easily make greeting cards and birthday cards for printing or email. Here’s the New Year’s card I made for 2024. I simply provided the following prompt. 

Happy New Year wishes encompassing the theme of peace on Earth and a healthy planet. 

It generated four different images in about 10 seconds and I picked the one that I liked the best.

You can see a current weakness of AI image generators on this card. They all have trouble incorporating text accurately. “Happy New Year” is legible but is followed by some gibberish; I really don’t have a clue what it says. This is still early days for the new AI.

I engaged Leonardo for another task, to design a logo for my theoretical AI Consulting business. Here’s the prompt that I gave:

Design a logo for my AI consulting business. It should be deep blue and have the letter D surrounded with interconnected nodes in a circle that reflect the neural networks of AI systems.

This looks like a really good logo to me. Maybe I should actually startup this business.

Of course there has been a lot of press about how ChatGPT makes it easy for students to cheat, how it will take over many jobs and lead to people getting fired, increase the threat and spread of fake news, and generally have disastrous effects on the economy and society.

A lot of people from many walks of life have become engaged in the debate about the dangers of AI. In 2014 Stephen Hawking warned about the growing power of Artificial Intelligence that would enable robots to replicate themselves and increase the rate at which they get smarter, leading to a tipping point or ‘technological singularity’ when they can outsmart humans.

Ray Kurzweil, one of the most prolific inventors alive and the author of five books on Artificial ­Intelligence, replied to Hawking and pointed out that the industry guidelines developed for DNA research have kept that field safe and the same can be done for AI.
Don’t Fear Artificial Intelligence

One of the people who was concerned about the direction of AI was Elon Musk. His concern was that the large companies would control the development of AI and keep the technology secret. To counter this, he and a group of other like minded tech people founded OpenAI. Their mission was to do advanced AI research but keep it in the public domain “to empower as many people as possible to have AI. If everyone has AI powers, then there’s not any one person or a small set of individuals who can have AI superpower.”

Fear of robots and computers taking over the world goes way back in science fiction. This was the theme in the film “2001: A Space Odyssey” that burst onto the big screen in 1968. It follows the evolution of intelligence, transcending from ape to caveman and then from modern man to the computer HAL. But then HAL goes rogue and causes one of the astronauts to drift away in space before the other astronaut is able to shut HAL down.

2001 was great entertainment but it was not great science. HAL would not have been a threat to the astronauts if it had been designed to follow the The Three Laws of Robotics. These were published by Isaac Asimov in 1942.
1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

In 2023 the debate about the future of AI continued at a heated pace. In March more than 1,000 technology leaders and researchers published an open letter warning that AI tools present “profound risks to society and humanity” and urged AI labs to pause development of the most advanced systems.

In December 2023 the European Parliament passed a First Regulation on Artificial Intelligence. This is fairly broad legislation that establishes obligations on companies developing and using AI that depend on the level of risk.

Unacceptable risk AI systems are systems considered a threat to people and will be banned. – Cognitive behavioral manipulation of people –  Social scoring: classifying people based on behavior, socio-economic status or personal characteristics –  Biometric identification and categorisation of people High risk systems will have to be registered in an EU database. - Systems that negatively affect safety or fundamental rights General purpose and generative AI systems have to: –  Disclose that the content was generated by AI –  Design the model to prevent it from generating illegal content –  Publish summaries of copyrighted data used for trainingGeneral-purpose AI models that might pose systemic risk have to undergo thorough evaluations and any serious incidents have to be reported.

Canada and the U.S. are also preparing AI legislation.

My view is that many of the “experts” (physicists, philosophers, historians, journalists, politicians) who are sounding the alarm about AI do not have an in-depth knowledge of software/technology and the psychology/neuropsychology of human intelligence to fully comprehend how difficult the problem of advancing to general intelligence is. They underestimate how long it will take and amplify fears expressed by others.

The current AI boom is fueled by a breakthrough in deep learning which is fundamentally advanced pattern recognition. AI models today are not very strong in analysis, reasoning and logic which are key components of human intelligence. These critical characteristics of advanced general intelligence are more difficult to achieve than pattern recognition. The number of breakthroughs necessary to achieve general intelligence stretches far into the future. Climbing a tree can take a monkey nearer to the moon, but climbing taller and taller trees does not enable a simian astronaut to plant a flag on the moon.

Artificial Intelligence is not new. It began way back in the 1950s when the term was coined. There were a number of early dramatic successes, such as a program that played championship level checkers and a program that could prove many of the symbolic logic theorems in Principia Mathematica, the monumental classic on the foundations of mathematics. These successes convinced many in the field that AI was going to revolutionize the world and a boom in AI began. However the hype raced ahead of reality and the boom turned to bust in the 1970s.

A second AI boom occurred in the 1980s. The focus was on “Expert Systems” in which input from experts in a particular subject area was entered into a system which could then respond to user queries and provide expert answers. A number of such expert systems were successfully used, from hardware circuit design to medical diagnosis. But as in the first AI boom, many of the prototype systems failed to scale to real life applications and meet the hyped expectations so investment dried up and the boom was over.

Will the AI boom today escape AI’s past history?

The current boom in AI began in 2012 when a program to recognize images in a database containing more than 20,000 categories won the annual ImageNet competition. The software had been developed by Geoffrey Hinton and several graduate students at the University of Toronto using a technique that was called “Deep Learning”.  It achieved 85% accuracy, a very significant improvement over previous years.

Deep learning was a type of neural network that had been used as a model for AI research since it was first developed in 1960 by the psychologist Frank Rosenblatt. Anybody who took a psychology course at McGill University back in the day would be familiar with the idea of a neural net. It is based on D. O. Hebb’s neuropsychological theory of how learning occurs in the brain, which can be summarized by the pithy statement “Neurons that fire together wire together”.

The success of deep learning triggered a whole new wave of AI research. After decades of research and millions of dollars spent by IBM and others, progress in speech recognition had been slow. Applying deep learning resulted in a breakthrough and enabled computers to understand the spoken word in real time. This success was the basis for Siri and Alexa and the voice recognition used by telephone answering services. 

But a new era only occurs when a revolutionary development (based on prior breakthroughs) changes life for many people. So although the current AI boom began in 2012, the AI era didn’t really begin until 10 years later with ChatGPT.

In fact there have been 5 eras in the evolution of computers since the first early machines.

1951 First Commercial Computers —  after the first working computers were developed during World War II in government research labs and universities, general purpose computers became available from many different companies in the U.S., Europe and Japan. Their rapid growth was triggered by the invention of the transistor in 1947, an invention which many people have called the most important invention of the 20th century. The transistor, which functions as a switch, is at the heart of electronics design. It replaced vacuum tubes and made computers a lot smaller, faster, cheaper and more reliable.

1964 IBM Mainframe Era — the IBM System/360 family of compatible computers allowed customers to start with a small system and upgrade to larger computers as their computing needs grew. The success of System/360 drove many computer vendors out of the market and established IBM as a dominating force in the computer industry for the next 30 years.

1981 Microcomputer Era — Intel invented memory chips and CPU chips in 1970/71 which gave birth to microcomputers such as the Apple I. But the revolution in the widespread use of microcomputers that defined the era only started with the IBM Personal Computer (PC) in 1981.

1995 Internet Era — the Internet was first built in 1969 as a research project under a grant from the U.S. Department of Defense, but widespread use of the Internet did not occur until after the World Wide Web was developed in 1991 and Microsoft Windows 95 made it easy for non-technical users to access the Internet with simple point and click.

2007 Smartphone Era — the first smartphone was developed by IBM in 1994 and was followed by the BlackBerry in 2002. But the general Smartphone era really began with the revolutionary Apple iPhone in 2007 which led to widespread mobile computing for almost everyone .

2022 Artificial Intelligence Era — ChatGPT and other chatbots came out of the lab and into widespread use following the breakthrough successes of deep learning.

It seems there is a new era in technology on average every 14 years. 

What will the next era be 14 years from now in 2038?

I predict the next era will be Personal Robots (PR) or Personal Medicine (PM).

It is a natural extension of AI to herald a revolution in robots. Robots are not new. The first programmable robot was used in a General Motors plant in 1961. Robots are now in fairly wide use but they are generally designed for very specialized industrial jobs. There is a lot of research into making general purpose robots, especially in Japan, the largest exporter of robots in the world. Japan’s declining population has spurred the government to invest heavily in robot technology to provide their future workforce.
Japan’s robots are conquering the world

Personal Medicine is where medical decisions and interventions are tailored to an individual. Some of the breakthrough discoveries that are the foundation for this advance have already been made. 

The Human Genome Project that identified, mapped and sequenced almost all of the genes of the human genome was completed in 2003. This opened the way for gene therapy and genetic engineering.

CAR T cell therapy is a gene therapy in which T cells, one of the important components of the immune system, are taken from cancer patients, genetically modified to contain receptors that will attach to cancer cells and re-infused into patients. The modified T cells are then able to locate cancer cells and destroy them. The first CAR T cell therapy approved by the FDA was in 2017 for the treatment of childhood leukemia. Following this success, several more CAR T therapies were approved for other types of leukemia and other blood cancers.

In 2023 AlphaFold won the Lasker Award for solving a 50 year old problem in biology to accurately predict the shape of a protein molecule. In a very short time AlphaFold has proved to be an invaluable tool in many areas of medical research. The program and the database of 200 million structure predictions, amounting for nearly all the known human proteins, have been shared with the scientific community and more than 300,000 science researchers have used these resources. AlphaFold is one of the most significant achievements in the history of Artificial Intelligence. The story of this breakthrough was unfolded in an earlier blog article.

CRISPR is a revolutionary technique to edit genes that was discovered in 2012. Jennifer Doudna and Emmanuelle Charpentier discovered that Cas9, a gene-cutting protein used by bacteria to kill viruses, can be re-engineered as a programmable gene editing tool. The CRISPR-Cas9 system is faster, more accurate and more efficient than previous gene editing methods. Doudna and Charpentier were awarded the Nobel Prize in Chemistry in 2020. 

The following short video is a good demonstration of how CRISPR does “cut and paste” gene editing.
CRISPR Explained

In 2013 Charpentier co-founded a startup company called (surprise, surprise) CRISPR Therapeutics. They undertook developing treatments for several diseases and conducting them through clinical trials. In 2023 their treatment for sickle cell disease received FDA approval. This is the world’s first approved medicine based on CRISPR gene editing. Many people think this is a groundbreaking achievement opening the way for a lot of new CRISPR developed medicines.
CRISPR Gene Editing Breakthroughs

_____________________________________

So here are three possible scenarios for 2038. 

1. You order the PR (Personal Robot) that everybody is buying and an hour later it arrives from Amazon by drone delivery. After a friendly introduction, you ask it to do the laundry and then prepare a gourmet lunch while you get back to more important things in life.

2. After going to your doctor for a checkup, she informs you that the diagnostic tests that you took have identified a rare disorder. She has submitted all the test results and your medical history to the PM department (Personal Medicine) at the hospital. In three days you are notified that a drug has been made to treat your medical condition and that you need to come into the hospital for a three day initiation. Possible side effects cannot be completely predicted yet by Personal Medicine so you need to be under observation in case you need intervention.

3. Continuing scenario 3, you get ready to depart for the hospital for your new PM drug treatment program but there is a howling windstorm and torrential rain that make it impossible for anybody to travel. Because the world did not move quickly enough to meet the target of limiting global warming to 1.5°C by 2030, extreme weather events are happening frequently and continue for weeks. You are shut indoors while your health condition deteriorates and you wonder if you will get your new miracle drug on time.
(more on climate change in a future blog)

Most people have heard about chatGPT but not that many have heard about AlphaFold. But it is AlphaFold that is the most significant achievement of artificial intelligence (AI) by far that I have seen, and I have been following AI for more than 50 years. 

AlphaFold is considered by many to have solved the protein folding problem, or at least to have made very significant advances. How strings of amino acids fold to produce stable proteins has been an important question in biology for over 50 years. 

In September 2023, the Lasker Foundation announced that the winners of the Basic Medical Research Award were Demis Hassabis and John Jumper for the invention of Google DeepMind’s AlphaFold. In the past 20 years, 32 Lasker winners have received a Nobel prize. So an AI is in line for a Nobel for the first time.

Here’s a good video with dramatic animation that explains protein folding.
AlphaFold Solves Protein Folding

This is a more detailed explanation of protein folding, its implications and asks the question
Has Protein Folding Been Solved?

Watch this video for an exciting mini-documentary look behind the scenes of how the team at DeepMind raced to get results to submit to the CASP competition.
AlphaFold: The making of a scientific breakthrough

This article shows how AlphaFold is being used in several different research projects.
Case Studies Using AlphaFold

DeepMind published the method that AlphaFold uses, put the code in an open source repository and set up a database of 200 million structure predictions, amounting to nearly all the known human proteins.The program and the database have been shared with the scientific community and more than 300,000 investigators have used these resources. The catalog has been expanded to almost every known protein in organisms whose genomes have been sequenced, including viruses that pose epidemic threats and the World Health Organization’s high-priority pathogens.
Highly accurate protein structure prediction with AlphaFold

In only two years, the impact of DeepMind’s published manuscript has vaulted over almost all of the 100,000 research articles that have been published in Nature since 1900. It ranks 50^th, having been cited in more than 7000 papers from top journals.
Lasker Foundation

You can run AlphaFold on your own computer. Using Google Colaboratory, a cloud based service, you don’t need to have a very powerful machine yourself. Here’s an example of a researcher running AlphaFold to analyze a protein.
How to predict a large protein structure with AlphaFold

The Internet was made “safe” for E-commerce back in 1995 when Netscape developed encryption for web traffic. However many people did not trust providing their credit card information for Internet purchases. But when the Covid-19 pandemic and lockdowns began in 2020, many more people started to make purchases over the Internet for such basic things as grocery delivery and take out food. Using credit card information on the Internet is fairly safe but there are a number of risks.

Almost all websites use a secure (encrypted) connection, denoted by https instead of http in the address line of a browser, often shown with a lock icon

This means that all the information being sent to the website is encrypted, including your credit card information. 

Breaking encryption is well beyond the capability of any hacker who is intent on stealing your credit card information as it travels over the Internet. However, the NSA (U.S. National Security Agency) is able to break encryption. But this is only after years of spending more than a billion dollars developing widespread network surveillance, large data bases, specialized supercomputers and collaboration with large American technology companies to siphon off Internet traffic (as revealed in documents released by Edward Snowden).
How US and UK spy agencies defeat internet security

A bigger risk is if you happen to have malware on your computer that monitors everything you type. This type of malware, called “spyware”, will record credit card information and send it back to a hacker’s computer. The protection for this is to make sure that you have antivirus software running on your computer that includes anti-spyware. If your antivirus does not include anti-spyware, you can add a separate anti-spyware package.

Another risk is your credit card information being stolen from the website long after you have made a purchase. Most websites store your credit card information. This makes it more convenient for you if you order from them again. But there have been many security breaches in which hackers have broken into the servers of some very large companies and stolen millions of credit cards.
Biggest credit card data breaches

This risk is something that you may be able to minimize. In Europe privacy laws make it illegal for websites to store credit card information without your express consent. But in North America, privacy laws are way behind and there is no such protection. Websites should really ask you if you want to save your information, but most of them do not. And they don’t provide an option for you to remove your credit card information from their servers. 

However, if you use PayPal instead of a credit card, you have more control over websites retaining your payment information. You can go into your PayPal account and look at all the websites that have “Automatic Payment” turned on and turn them off. In general you want to do that for all websites except those in which you have set up a monthly regular payment, such as Netflix which is a monthly subscription service, or services you use regularly such as Uber. When I went through my PayPal account, I found three Automatic Payments to TicketKing going back several years.

PayPal also provides more security for your payment credentials. When you use PayPal to make a purchase on a website, you are directed to PayPal to log into your account. The webserver does not see your password and so it cannot save it. If a hacker breaks into a webserver and steals PayPal account information, it cannot be used to make purchases or access your PayPal account. However there is one caveat here, which is that a hacker may try to break into your PayPal account by  guessing your password. As long as you guard your PayPal login information carefully you will be pretty safe.

Not many PayPal data breaches have been reported compared to credit card breaches but one was reported in 2022. Only personal information was stolen and there was no indication of any financial losses.
PayPal breach

In summary, what can you do to minimize your Internet payment information being compromised?

1. Make sure you have anti-spyware software protecting your computer (it may be part of your antivirus software).

2. Use PayPal whenever possible for online payments instead of credit cards. After completing the transaction, go into your PayPal account and turn off Automatic Payment for that website, unless it is a bona fide subscription service.

I had some funds that I wanted to invest but when I went into my RBC account, I discovered that my investing account had disappeared. I had not used it for a few years but I had never received a warning or a notice or that this was to happen. This is RBC Fault #1.

It looked like I had to create a new account. There was a link in my online RBC account to do this. After going through 10 screens entering all kinds of detailed information about myself and various investment options, I got to what looked like the last screen. I clicked on the button and received the following response:

This was incredible! I was dumbfounded that there could be such a catastrophic failure in such a critical application. RBC Fault #2.

Someone analyzing this may have estimated that RBC was losing 40 applications a day with an average portfolio value of $50,000. In other words $2,000,000 a day. Now this is really not a very thorough analysis, but it would not stop someone from posting headline news saying,

“Software failure causes Canada’s largest bank to lose $2 million a day”

I called the Direct Investing customer support number and was informed that there was a wait time of 1 hour and 45 minutes. I decided not to wait. I thought of calling outside of stock trading hours when the line might not be so busy, but I found out that the support line was only open from 8 am to 5 pm, weekdays only. For people managing an investment portfolio, where time is of the essence, this level of customer support is inadequate. Fault #3.

A few days later I was able to get to my local RBC bank branch. I asked if they could set up an RBC Direct Investing account for me. They said they were not able to do this at the branch. I would have thought that an investing account was a pretty important RBC product. Why aren’t branch banks able to help customers with it? Fault #4.

After about 10 days I thought that the web site error would be fixed, since the message said that RBC had its “best teams working hard to fix this error”. However, after painstakingly going through all the setup screens, I encountered the exact same error again. Clearly RBC’s “best teams” were not very good, or else they had never been informed that there was an error that needed to be fixed. Fault #5.

I decided I had to bite the bullet and call customer support. By now it was the December holiday season and things were slowing down. I had to wait “only” 45 minutes to get through to a support person. He could not understand how I was having such a problem. He put me on hold while he went to talk to a manager. They looked into my account and discovered that my Direct Investing account had not been deleted, it had been “hidden” from my view. They could not really understand how or why this had been done, but they were able to restore the account so I could carry on with my investment plans.

I don’t know if RBC ever fixed their web site. Clearly trying to create a new investing account when one already existed is a problem, but the error message never indicated this.

Whoever coined the phrase that the big banks are “too big to fail” clearly did not have any understanding of how software works, and does not work.

When the Cambridge Analytica scandal broke in 2018, Facebook became a focal point for the failures in protecting people’s privacy. Facebook was pilloried in the press and hauled into U.S. Senate hearings. https://www.nytimes.com/2018/04/10/us/politics/zuckerberg-facebook-senate-hearing.html

I had just finished reading a book written by one of the early developers of Facebook’s advertising, “Chaos Monkeys” by Antonio Garcia Martinez. And I had experimented with Facebook advertising to promote my computer book. Much of what was being said and reported did not square with what I knew. The main culprits in the use and abuse of privacy information are a large number of companies known as data brokers. If you don’t know who they are and what they do this article describes the tip of a very large iceberg.
https://www.webfx.com/blog/internet/what-are-data-brokers-and-what-is-your-data-worth-infographic/

The first time most people ever heard of a data broker was when Equifax was hacked and personal records of 147 million people were stolen.
https://www.nytimes.com/2020/02/10/opinion/equifax-breach-china-hacking.html

As information about the Cambridge Analytica surfaced, many people pointed fingers at Facebook or the other tech platforms like Google and Twitter. However, the only clear call that would really address serious privacy concerns came from Tim Cook. In 2019 he called for comprehensive federal privacy legislation that would establish a data-broker clearinghouse, requiring all data brokers to register, and enabling consumers to track the transactions that have bundled and sold their data, and giving users the power to delete their data on demand, freely and easily.
https://time.com/collection/davos-2019/5502591/tim-cook-data-privacy/

This call did not garner much uptake. The data brokers and advertising industry have a lot more influence in Washington than the tech companies. They are really happy to have Facebook and the other tech companies take the fall for failures in privacy information protection. By contrast, Europe has had an extensive privacy law, the General Data Protection Regulation (GPDR) since 2018.
https://www.nytimes.com/2019/06/08/opinion/sunday/privacy-congress-facebook-google.html

I finally came across an article that explains the role of data brokers and how they use Facebook advertising to help them reach their target audiences. It also reveals that there is an “industry of political data brokers”. While I don’t get overly concerned about companies using people’s identity information to target advertising, when data brokers are using this information to target and try to influence voting in elections, then I think there is a lot more cause for concern.

How politicians target you: 3,000 data points on every voter, including your phone number.

Our quest to find what politicians know about voters uncovered data troves with intimate information about income, debt, family, religion, gun ownership and a whole lot more.

By Geoffrey A. Fowler
Washington Post technology columnist based in San Francisco. He joined The Post in 2017 after 16 years with the Wall Street Journal writing about consumer technology, Silicon Valley, national affairs and China.
October 27, 2020

The campaign messages are coming fast and furious now.

“Hi Geoffrey, I’m Jess w/ People’s Action,” reads one. “Voter Alert for Geoffrey Fowler!” says another.

And the weirdest: “It’s Jonathan Del Arco, Hugh the Borg on Star Trek … Join a grass roots fundraiser with 19 cast members!”

Perhaps your text messages, Facebook feed or mailbox have also exploded with eerily personal political ads. Ever wonder: How’d they find me? I, for one, didn’t pass my digits to a campaign — much less tell them (or Hugh the Borg) I’m a Trekkie.

Blame the assault on the voter data economy, in which candidates, parties and nonprofits quietly collect, buy and exploit a ton of information about you.

Their files treat your contact details like a matter of public record and can be more intimate than credit applications, including your income, debt, family, ethnicity, religion, gym habits, whether you own a gun and what kind of car you drive. In 2020, campaigns use this data to microtarget us with record numbers of online ads, mailers, knocks on the door and text messages.

I’ve been on a crusade to find out what politicians know about me. So over the past few months, I’ve used California’s new data privacy law to force companies that specialize in collecting my personal information for campaigns to show me the data.

What I learned: Privacy may be a cornerstone of American liberty, but politicians on both sides of the aisle have zero problem invading it.

In fiercely competitive races, campaigns see our data as their edge. The Republican National Committee proudly told me it now has more than 3,000 data points on every voter. The Democratic National Committee said it acquires enough to understand you as a person, including unique identifiers from your phone that can be used to target ads across different apps.

Politicians have long had special access to voter registration and participation data, which they use to plot strategy, run polls and coordinate volunteers. But in recent years, they’ve also begun tapping into commercial data brokers and murkier social media and smartphone tracking techniques. The scandal that erupted around Cambridge Analytica, which scraped data from Facebook while working for Donald Trump’s 2016 campaign, was just the tip of the iceberg.

Many Americans, like me, find targeted ads creepy when they come from businesses, especially when they use personal data we didn’t really consent to have tracked. But I found it downright unsettling to learn that my credit score — and so much else — was going to politicians who could use it to try to manipulate me. Online political ads are so potentially dangerous for democracy that Twitter banned them entirely and Google limited how campaigns can target them. (In September, Britain’s Channel 4 documented how Cambridge Analytica used voter data to specifically disenfranchise African American voters in 2016.)

I’m not saying politicians are breaking any laws; I’m saying there just aren’t many laws designed to protect our data from politicians. As an institution, Congress has shown little interest in regulating the digital tools its members use to get into office. (Even the California Consumer Privacy Act I used in my quest applies only to the for-profit part of this economy.) Citizens with a lot of free time can try to opt out of some political databases and communications, but for the most part, we have little control.

In my data crusade, I should have been an enigma for the politicians. As a journalist, I don’t donate, sign petitions or participate in surveys. I also avoid campaign communications, though I recently signed up for text messages from both the Trump and Biden campaigns as part of this reporting.

It turns out campaigns didn’t need me to volunteer information to build detailed profiles about me. I found five major sources of personal data that fuel the political machine. 

1. State voter files

Voter registration details and voting history are a matter of semipublic record in most places. I say semipublic because states generally restrict access to campaigns, parties, academics and journalists (and the companies that help them).

When I acquired my California voter data as a journalist, I discovered the state was sending campaigns my email address and phone number, along with my address and party affiliation. If I wanted to remove the email and phone number — technically voluntary information — I could re-register to vote. But, as I learned, campaigns have plenty of other sources for that data.

2. Commercial voter files

An industry of political data brokers collects the state voter files and enhances them. They sell these files to campaigns, political parties and academics. (The Washington Post also uses these kinds of files to help run its polls.)

Where do their “enhancements” come from? Largely from data brokers that also sell it to commercial marketers. Firms such as Experian and Acxiom gobble up records and buy personal data sold by banks, subscriptions, TV companies, apps, and more. Then the voter file firms use their own algorithms to make inferences about you, including how likely you are to vote and how much you’re likely to donate.

L2, one of the largest political data firms, sent me two files with more than 700 data points, including my phone number, estimated income and credit rating, and inferences about my politics and hobbies. A few categories were real head-scratchers, such as “home decor enthusiast.”

Another firm, Aristotle, had more than 150 data points on me, including the amount of my mortgage, whether I had insurance from my employer and its guess for my interest in immigration reform. A third, called Data Trust, had over 1,500 data points, including — I kid you not — scores for how much I care about privacy and how much I trust tech companies.

After the shock of finding so much personal data subsided, what struck me was how some of it was inaccurate. Several of the voter files had wrong information about my religious background, whether I’m married and whether I have children.

This isn’t the same as when companies put you in the wrong marketing segments, such as calling you a Prius driver instead of F-150 truck lover. This information is being used by politicians to judge how you might act, donate or vote — before you’ve even made up your mind.

California residents can, like me, use the CCPA law to force voter file companies and data brokers to disclose what they know and even stop selling it. But you have to make the requests one by one.

3. What we tell them (even unintentionally)

When you engage with a politician — signing up for news updates or donating — you’re adding to his or her data trove. You might not mind, if they’re a candidate you believe in.

Every move gets registered. When I signed up, as an experiment, to stream one of President Trump’s recent rallies, it kicked off a deluge of campaign text messages begging for money, with messages appearing three, four or five times per day. (At least replying “STOP” cuts off the messages. That works for Biden, too, though not for all campaigns.)

Even when you’re just poking around a campaign website for information, you’re passing along data. Campaign websites, including for both Trump and Biden, often contain hidden trackers that, for example, tell Facebook you were there and then allow campaigns to target ads to you in the future.

Smartphones also now allow campaigns to know where we go in the physical world. The Wall Street Journal reported that campaigns for both parties have used location-data brokers to target people who attend in-person rallies. It’s now even possible for campaigns to identify people who set foot in churches, and — based on the frequency of their visits — target them with specific ads.

Sometimes, your politically active friends can be a source. Campaign apps and volunteer texting campaigns often ask people to upload their contacts list.

4. Other politicians

When campaigns end, sometimes they pay the bills or help out a friendly campaign by selling their data. Contact information for donors is particularly valuable. (No wonder Congress is not eager to legislate.) The fine print in Sen. Elizabeth Warren’s privacy policy reads: “We may share information about you … with candidates, organizations, campaigns, groups or causes that we believe have similar political viewpoints.”

Political parties themselves have also become among the largest sources of data to campaigns, doling out access to campaigns they want to support — and starving the ones they don’t. (Campaign finance laws prohibit candidates and national parties from coordinating their data with outside groups that raise money from unlimited sources.)

RNC press secretary Mandi Merritt told me the committee’s data is taken from voter registration files, information collected by volunteers, and consumer data such as magazine subscriptions, what kind of car a voter drives and whether a voter has a gym subscription. (Voters who have gym memberships are more reliable voters, she said.)

“Our historic investment in building a robust data infrastructure has given us an unprecedented ability to target and engage with voters on the issues they care about,” Merritt said.

Nellwyn Thomas, the chief technology officer of the DNC, said her party acquires data from firms that use “ethical data practices,” though she declined to name them other than Experian. “There are absolutely times we have turned down data sets where we believe the collection methods do not live up to our standards,” she said.

Thomas also said combining data resources inside DNC systems, where it’s accessed by some 9,000 campaigns, helps to ensure our privacy by keeping it secure.

But once your data is in the hands of a political party or campaign, there’s little transparency about what happens to it, or what ethical rules apply to using it. For example, do campaigns need your consent to send you texts? My phone is chock-full of evidence some think they do not.

And unfortunately, California’s don’t-sell-my-data privacy law doesn’t apply to campaigns or parties. When I sent them data requests, they either ignored me or told me go stuff it. Neither the RNC or DNC would share my data with me when I asked as a journalist, either.

If you want some of the targeted ads to go away, the DNC’s Thomas suggests voting early — that will update party databases, and campaigns won’t want to waste money on you. 

5. Facebook

Even 3,000 data points on a voter pales in comparison to the gobs that Facebook collects about what its members share and do on and off the social network. Facebook is quick to say it doesn’t sell our data to anyone, but its ad-targeting abilities are extraordinarily valuable. That’s one reason the Trump and Biden campaigns have spent more than $210 million on the social network in 2020, according to OpenSecrets, far more than the $81 million Facebook says the Trump and Hillary Clinton campaigns spent in the 2016 race.

Facebook allows, for example, a campaign to upload a list of people it knows are responsive to a particular message and then use the social network’s algorithms to find a “look-alike audience” of new people to target. Facebook has acknowledged the power and peril of its microtargeting capabilities: For the week before Election Day, Facebook has paused allowing new political ads to be created (though existing ads will still be around).

In the name of transparency, Facebook now offers a button you can click labeled “Why You’re Seeing This Ad.” But I haven’t found it very useful, in part because it doesn’t explain how my experience — the ads I see — might be different from yours.

Remember my Hugh the Borg ad? Facebook’s disclosure said only the Biden campaign wanted to reach people in the United States who were 18 or older. I wasn’t convinced that was the only reason. Sure enough, digging through my Facebook advertising settings, I discovered from years of tracking my life that Facebook on its own had decided that Star Trek was one of my advertising “interests.”