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 HouseholdAnnual 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?
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.”
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 ]
Mark Carney and many other Canadians, from politicians to people in the street, insist that Canada will never become part of the US. But this is a reactive emotional response, without any real analysis. This is a pretty serious challenge and should not be brushed aside so lightly. There is a long history of American threats to Canada.
The 19th century saw the rise of “Manifest Destiny” in the United States, the belief that American expansion across North America was inevitable. This was used to justify the annexation of Texas in 1845 followed by the Mexican–American War which resulted in Mexico ceding to the U.S. all the land from Colorado and New Mexico to the Pacific.
Many U.S. politicians assumed that Canada would eventually fall to their Manifest Destiny and become part of America and some Canadians saw economic advantages in annexation.
The Canadian-American Reciprocity Treaty of 1854 provided duty-free trade in key goods, boosting Canada’s economy. However, the U.S. terminated the treaty in 1866, partly as retaliation for Britain’s tacit support for the Confederate States during the Civil War. This left Canada economically vulnerable and reinforced the need for a unified response to U.S. expansionist pressures.
The urgency of these fears led to Confederation in 1867. Here’s a cartoon from 1865 depicting Confederation as a wedding of two brothers (Upper and Lower Canada) with two young ladies (New Brunswick and Nova Scotia) along with a child (Prince Edward Island). Uncle Sam is skulking out in the hall, disgruntled that he was not invited.
In the aftermath of Confederation, border fortifications were improved and a large militia was created. The new Canadian government quickly moved to expand its borders westward, purchasing Rupert’s Land (most of western Canada and part of Nunavut) in 1870 from the Hudson’s Bay Company, preempting a U.S. claim to the region. Manitoba became a province in 1870, followed by British Columbia in 1871. In 1875 work began on the Canadian-Pacific Railway and continued until the last spike was driven in 1885.
President Ulysses S. Grant, in speeches and interviews during his presidency (1869–1877) speculated that Canada might seek union with the U.S. for economic and political advantages.
In 1911, a proposed Reciprocity Agreement between the two countries was the central issue in the Canadian federal election. Conservative leader Robert Borden campaigned against it, arguing that it would lead to political union with the U.S. His victory in that election was seen as a rejection of deeper economic integration in favor of maintaining Canada’s distinct status within the British Empire.
Trump may think that by hinting Canada should become part of the U.S. it will magically happen. He pulled it off with Panama. After repeated claims about taking back the Panama Canal, the giant American investment bank Black Rock ($11.6 trillion in assets under management) announced on March 4 that it had made a $22.8 billion deal to buy controlling stake in the Hong Kong company that operates the ports at each end of the canal. This effectively gave the U.S. control of the Panama Canal without firing a single shot.
But Canada is much bigger than the Panama Canal and even Elon Musk can not afford to buy it.
It is unlikely that Trump with all his bravado would engage the military to turn Canada into the 51st state. He in fact has said on several occasions that it would be done with economic force.
Trump’s main weapon for exerting economic force is tariffs. He loves tariffs for many reasons, as was covered in the recent blog Trump’s Tariff Tirade.
Trump’s current plan is a 25% tariff on all imports from Canada (paused until April 2). There is no doubt that erecting a wall of tariffs against Canada would exert a strong economic force. How strong would this force be?
The tariffs would make Canadian goods more expensive in the U.S. than from other suppliers. This would inevitably lead to lower sales by Canadian companies which would likely lead some businesses, especially those in industries hard hit by the tariffs, to reduce production and lay off workers.
Compounding this are the retaliatory tariffs planned by Canada which would raise the prices of many consumer goods for Canadians. As a result, consumer spending on everything from food to cars to housing supplies would slow, further weakening the economy.
The Bank of Canada has analyzed various tariff scenarios and their worst case has the Canadian GDP falling by 3%. This may not seem like very much, but it would probably put Canada into a recession.
The Bank typically fights a recession by reducing interest rates to stimulate the economy, making it easier for consumers to spend and for businesses to borrow to buy new equipment and hire more people.
But a trade war will also increase inflation, triggered by increased costs of imported goods, shortages of critical materials brought on by a trade war and a drop in the value of the Canadian dollar due to less demand for Canadian products. Ultimately most of these costs will get passed through to the prices of goods and services in Canada, raising inflation.
Since Trump’s election, the Canadian dollar has dropped from .72 to .69 and some forecasts have it declining to .60.
To control inflation the Bank of Canada increases interest rates. This helps slow economic growth that pushes up prices. Higher interest rates make credit more expensive and reduces consumer and business spending.
But the bank can’t enact policies to counter a recession (reducing interest rates) and combat inflation (raising interest rates) at the same time.
On Feb 21, 2025, Tiff Macklem, Governor of the Bank of Canada, spoke about how a trade conflict would affect the Canadian economy and monetary policy. Here is his conclusion.
“Central banks can do little to mitigate the damage caused by a trade war. Our role will be to balance the upside risks to inflation from higher costs with the downside risks from weaker demand. Our focus will be to help smooth the painful adjustment to a lower path for the economy while preventing price increases from becoming higher ongoing inflation.” Bank of Canada guidance
For more detailed explanations of the economics of a trade war, watch Andrew Chang on About That.
There is a sharp increase in unemployment during recessions. Notice that only one recession in Canada since 1945 lasted (slightly) more than 2 years.
In a recent poll, 40% of Canadians said they were worried about losing their jobs. And this is even before the effects of any tariff has impacted the economy. Job worries
A recession brought on by a trade war could last as long as heavy tariffs depress the Canadian economy. What if the tariffs lasted throughout Trump’s term for 4 years?
Canadians have never lived through a recession much longer than 2 years. How many Canadians would be able to struggle through such a long recession with high unemployment for some and a reduced standard of living for all?
How many people in this situation might find the prospects of an economic union with the U.S. attractive in order to improve their financial prospects?
Kevin O’Leary is promoting the idea of an economic union with the U.S. (that many people feel is a stepping stone towards political union). He claims that there is already very strong interest among his 10 million followers on social media. Canada-U.S. Economic Union
What if the next U.S. administration decides to continue the tariff policies in 2028?
How many more people would feel desperate and ready to sacrifice some Canadian sovereignty for financial security as a solution to very dire circumstances?
Never underestimate the power of the dollar, especially if it’s an American dollar that could be worth $1.87 Canadian at that time.
Pneumonia is an infection of the air sacs (alveoli) in the lungs. There are more than 30 different causes of pneumonia. A pneumonia infection can be viral, bacterial or fungal. There are more than 90 different strains of Streptococcus pneumoniae, the most common bacterial pneumonia. It is often found in the upper respiratory tract of healthy people, especially children, where it is generally harmless or causes mild illnesses such as bronchitis, sinusitis, and ear infections. A severe form of pneumonia can develop when the bacteria moves from the upper respiratory tract to other parts of the body, leading to life-threatening diseases such as sepsis, meningitis and invasive pneumococcal disease.
Like other respiratory infections (colds, flu, RSV, bronchitis, Covid-19) most people get pneumonia after inhaling respiratory droplets from an infected person’s coughing, sneezing or talking. It can also be contracted by touching a contaminated surface. Fungal pneumonia is much less common than viral or bacterial pneumonia but you can get it from gardening if you breathe in spores from fungi in soil that has been contaminated such as from bird droppings.
It may be difficult to tell if you have pneumonia. It could be bronchitis which is inflammation of the pathways (bronchioles) that carry air to the lungs.
Like pneumonia, bronchitis is characterized by a cough, chest congestion and fever. Bronchitis is usually a viral infection so antibiotics are not effective. Contrary to what is said on many websites, the color of the mucus that is coughed up is not a reliable indicator as to whether the infection is viral or bacterial. Clear mucus turns yellow as the immune system begins to respond and becomes green as the immune response intensifies. This change in color can occur with both viral and bacterial infections.
Treatments for bronchitis include various home remedies such as honey, hot tea and other liquids. Using a vaporizer to keep the air moist may make it easier to breathe. There are also many over the counter medicines. Particularly effective are expectorant cough medicines with guaifenesin such as Robitussin, Mucinex and Buckley’s. They dilute the mucus, making it easier to cough up and clear the air pathways for breathing.
Colds, flu and bronchitis can lead to pneumonia. Your immune system and general health may be weakened from fighting the viral infection making it easier for the virus and other bacteria to infect your lungs, causing pneumonia. Distinct signs of pneumonia are a high fever (over 39° C/102° F), specs of blood in mucus, shortness of breath and difficulty breathing.
A doctor may be able to diagnose pneumonia from your symptom history and by listening to your breathing. A chest X-ray provides a more definitive diagnosis. Blood tests cannot diagnose pneumonia but they can help detect whether you have an infection and its severity. A laboratory analysis of a mucus specimen is also possible but this is somewhat unreliable due to the risk of contamination of the specimen and it takes several days. Rather than waiting, if pneumonia is suspected a doctor will generally prescribe an antibiotic. But there are many different antibiotics for the different strains of pneumonia and it is somewhat of a guess as to which one will work. There are a few antiviral medicines for pneumonia but several of them are not very effective unless they are administered within 48 hours of the onset of symptoms.
If your condition worsens, you need to go to the hospital. Signs of severe pneumonia infection are rapid breathing, severe shortness of breath, chest pain, coughing up large amounts of blood, bluish color on fingernails, lips or skin and high fever (above 40°C/104°F).
In hospital you are given a tube that clips on your nose to provide oxygen. This will make it easier to breathe as the supplemental oxygen compensates for the oxygen that you can’t get from your congested lungs. You will be started on an intravenous antibiotic. This delivers more antibiotics more quickly than by using pills. They will start with a combination of several antibiotics. If you don’t improve in a couple of days, they will change to another antibiotic cocktail. It is possible that none of the antibiotics administered in the hospital help you recover if your pneumonia is a viral infection. It may be largely up to your immune system to bring about your recovery.
Pneumonia is a serious illness. The fatality rate of people aged 65 or more who are admitted to hospital for pneumonia is up to 15%.
Fortunately there are vaccines to help protect you against pneumonia.
Pneumonia Vaccines
Prior to 2021, adults were recommended to get two vaccines: Prevnar 13 followed by Pneumovax 23 six months later.
Prevnar 13 helps protect against 13 types of pneumonia bacteria (approved in 2010-2013).
Pneumovax 23 targets 23 types of pneumonia bacteria (approved in 1983).
Many different types of pneumonia bacteria circulate in the population. The most common types that cause disease can vary across different populations and tend to change over time, leading to the development of newer vaccines over the years.
Prevnar 20 (Pneu-C-20) was approved by the FDA and Health Canada in 2021/2022. It protects against 20 types of bacteria, including all those covered by Prevnar 13 plus 7 additional strains that are responsible for a significant portion of pneumonia cases.
Pneu-C-21 (CAPVAXIVE) was approved in 2024. It only protects against one more type of bacteria than Prevnar 20 but about half of the bacteria types it targets are different from those targeted by Prevnar 20. The drug company claims that overall, Pneu-C-21 protects against bacteria responsible for 84% of pneumonia cases in adults compared to 52% by Prevnar 20. Their analysis is based on CDC data from 10 states (California, Colorado, Connecticut, Georgia, Maryland, Minnesota, New Mexico, New York, Oregon, Tennessee). The prevalent pneumonia bacteria strains where you live could be somewhat different. Merck Analysis
Recommendations
The Centers for Disease Control and Prevention recommends pneumococcal vaccination for children younger than 5 years and adults 50 years or older and for children and adults of other ages who are at increased risk. CDC Recommendations
The National Advisory Committee on Immunization in Canada (NACI) recommends either Prevnar 20 or Pneu-C-21 for adults aged 65 or older or younger adults with certain health risks, even though Pneu-C-21 seems to offer distinctly better protection than Prevnar 20. NACI recommendations
For those who haven’t received any pneumonia vaccine, a single dose of Prevnar 20 or Pneu-C-21 is sufficient to provide comprehensive protection.
For those who have already received Prevnar 13/Pneumovax 23, it is advised to get either Prevnar 20 or Pneu-C-21 five years later.
You may have to pay for a Prevnar 20 or a Pneu-C-21 vaccination depending on where you live and your health plan. Some drug stores are not aware that Pneu-C-21 is the latest pneumonia vaccine. If you get your doctor to send in a prescription for CAPVAXIVE they can order it.
Trump’s announcement that he would levy a 25% tariff on all Canadian goods because of the large number of illegal immigrants and fentanyl that was coming across the Canadian border didn’t make a lot of sense. Compared to many other countries, the number of illegal immigrants and fentanyl arriving in the U.S. from Canada is really quite small. So what’s the real reason for this tariff war?
There is very little analysis at this point about what Trump’s real motives are. From a few articles that I have been able to uncover, here is a scenario that provides some perspective.
Since Trump’s inauguration, there has been a bewildering onslaught of changes brought on through executive orders. One thing to realize about this is that although Trump is basically a black and white thinker who shoots from the hip, the strategy being used is much more sophisticated. It’s called “flood the zone with chaos” and was devised by Steve Bannan. The effect is to keep the opposition off guard and give the impression that Trump is in full power, even though many of his executive orders are blocked by the courts. This is explained in a podcast by the widely followed New York Times podcaster Ezra Klein. Summary of Flood the Zone
While tariffs are typically used as a lever for negotiation, for Trump they are the point. They are a source of revenue to pursue his vision of a Gilded Age: “Instead of taxing our citizens to enrich other countries, we will tariff and tax foreign countries to enrich our citizens.” Trump Tariffs Are Not a Means but an End
One of the things that’s near and dear to Trump is reducing taxes, as he did in his first term. Trump promised tax cuts numerous times on the campaign trail and said that tariffs would cover the cost. In his thinking, “tariff is the most beautiful word there is”. Trump’s Tax Break Promises
If you look at U.S. imports by country, it’s clear that to raise a lot of revenue Trump would levy a tariff on Mexico, China and Canada.
Top U.S. import trading partners 2023 However, imposing a tariff as a means to increase revenue does not sound good. Trump was probably aware that there is a long history of strong support for free trade by economists, ever since David Ricardo’s theory of comparative advantage in 1817 (for those who remember Economics 100).
In addition, the tariffs that Trump enacted in his first term had an overall negative impact on the economy. American firms and consumers paid the vast majority of the cost of the tariffs. There may have been some job increases in some of the targeted sectors such as steel, but sectors that relied on imported components and those in export industries faced retaliation from trade partners and were harmed. Brookings Institution Report on Tariffs 2020
But Trump had an issue that was a winner. He had received a lot of support for his tough stand on illegal immigration and drug traffic crossing the Mexican border. So this became the reason that he was going to levy a stiff tariff on Mexico. It was a simple extension to apply the same rationale to Canada.
At the end of the video, there is the suggestion that Trump’s real plan is to put economic pressure on Canada to become the 51st state.
The U.S. has a history of annexing adjacent territories. In 1845 Texas was annexed from Mexico. This was followed by the 1848 Mexican-American War in which the U.S. took the rest of the land from Texas to California.
And in 1866 a bill was introduced in Congress to annex Canada.
However, the U.S. was just recovering from the Civil War and there was no real appetite for further adventure. The bill was never passed or acted on. 1866 Bill to Annex Canada
Trump has in fact stated numerous times that Canada should become the 51st state. He hasn’t gotten much support for this idea from Canada or from the U.S., except for Canadian shark Kevin O’Leary who probably thinks there’s a great opportunity for him for a deal and he has been promoting the idea of an economic union between the U.S. and Canada. More recently Trump has admitted that although he would “love to see” Canada join the union, he seemed to acknowledge it “would be a long shot.”
On February 3 after meeting with Trudeau, Trump agreed to delay tariffs for 30 days after Trudeau agreed to appoint a fentanyl ‘czar’ and list Mexican cartels as terrorists. Trump Pauses Tariffs
So what happens in 30 days?
Trump is very erratic and unpredictable but he can’t stand being seen as a loser (Do you remember the 2020 election? Trump never admitted that he lost). He is unlikely to completely back off from imposing tariffs. He’s already raising a number of other issues where he thinks Canada is taking advantage of the U.S. that he wants to rectify. Trump’s Laundry List
I expect Trump will back off on tariffs that would have a serious impact on the American economy, such as the automotive sector and energy imports, and instead target specific industries, such as Canadian agricultural products and lumber.
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.
“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.
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.
eiπ + 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.
In a matter of only a few weeks the Covid subvariant JN.1 has become the dominant Covid strain in Canada and the U.S. Although the WHO declared it a variant of interest it is not thought to pose a new high risk. However it does seem to be spreading quickly. JN.1 Here’s what to know
There is waning immunity to Covid in the population but only about 15% of people have gotten one of the updated XBB.1.5 vaccines that became available in September 2023. Fewer and fewer people are taking personal precautions to minimize contact. I have been amazed at the number of people, especially seniors, who no longer wear masks in enclosed spaces like stores, theaters and public transit.
In Canada an increase in hospitalizations has not shown up in the December statistics but a year ago a large spike in hospitalizations occurred in January/February. Canada Covid-19 epidemiology update
In the U.S. there has been a 50% increase in hospitalizations among people age 70+ since November. New York Times Covid-19 Tracker
Anybody who is 65 or older or has underlying health conditions is at risk for severe Covid. Many of these people know about Paxlovid, an antiviral drug that can be taken to combat Covid if you test positive. However only 10% of these people at risk take Paxlovid.
Paxlovid does not kill the Covid virus. It prevents the virus from replicating, thereby reducing the severity of the infection. To be effective it must be started very early before the Covid virus has replicated widely and is causing serious illness.
Paxlovid received FDA emergency use authorization in December 2021 based on clinical trial data that significantly reduced hospitalization and death. In a very large study in the U.S. there was a 26% reduction in hospitalization and 73% reduction in death for patients who took Paxlovid. The study used an extremely large sample of one million cases from the National Covid database, so the results are statistically very significant. If half of the patients eligible for Paxlovid had taken it during the period of the study 135,000 hospitalizations and 48,000 deaths could have been prevented. Paxlovid effectiveness against hospitalization and death
The procedure to get Paxlovid varies by location. In Ontario it couldn’t be easier. If you test positive for Covid and it is within five days of your first symptoms, all you need to do is call your pharmacy. The pharmacist will ask you a short screening questionnaire over the phone. If you meet all the conditions and are not taking a medication that is contraindicated, she will issue a prescription that you can probably get in an hour.
Why did so many people choose not to use Paxlovid?
There seem to be a number of reasons that people who tested positive for Covid did not opt for Paxlovid.
– Some people delayed because they thought their Covid was mild. Then when Covid really came on in the second week, it was too late to use Paxlovid. – Some people were wary of a “Paxlovid rebound” – initially the medicine worked but later on there was a resurgence of Covid. This was an early observation but it has not been borne out by more recent studies. – Many people do not consider that they are at high risk for severe Covid even though they are 65+ or have a health condition like asthma, diabetes or obesity. – Other people are worried about the side effects such as an unpleasant aftertaste, stomach upset or muscle aches. None of the side effects are very serious and the Paxlovid treatment is only 5 days. People Who Need Paxlovid Are Not Taking It
Altogether these don’t seem to be very good arguments for not using Paxlovid compared to the very significant reduction of risk of hospitalization and death. But then we are living in an age where science is not trusted and people don’t make rational decisions.
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.
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
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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)
